{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using Multi-layer Perceptron and Convolutional Neural Networks for Satellite image classification - 2025\n", "\n", "Antonio Fonseca" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Packages to be installed:\n", "\n", "```\n", "conda install -c conda-forge umap-learn\n", "pip install phate\n", "conda install -c conda-forge imageio\n", "pip install wandb\n", "```" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/gpfs/gibbs/project/dijk/ahf38/conda_envs/geo_comp2/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n", "/gpfs/gibbs/project/dijk/ahf38/conda_envs/geo_comp2/lib/python3.8/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: /gpfs/gibbs/project/dijk/ahf38/conda_envs/geo_comp2/lib/python3.8/site-packages/torchvision/image.so: undefined symbol: _ZN5torch3jit17parseSchemaOrNameERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE\n", " warn(f\"Failed to load image Python extension: {e}\")\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "cuda\n" ] } ], "source": [ "import numpy as np\n", "import codecs\n", "import copy\n", "import json\n", "import scipy.io\n", "from scipy.spatial.distance import cdist, pdist, squareform\n", "from scipy.linalg import eigh\n", "import matplotlib.pyplot as plt\n", "from sklearn.cluster import KMeans\n", "import random\n", "from sklearn import manifold\n", "import os\n", "# import phate\n", "# import umap\n", "import pandas as pd \n", "# import scprep\n", "from torch.nn import functional as F\n", "\n", "\n", "import pandas as pd\n", "from sklearn.metrics import r2_score\n", "from sklearn.preprocessing import MinMaxScaler\n", "# import seaborn as sns\n", "\n", "import torch\n", "from torch.utils.data import Dataset, DataLoader\n", "from torch.utils.data.sampler import SubsetRandomSampler,RandomSampler\n", "from torchvision import datasets, transforms\n", "from torch.nn.functional import softmax\n", "from torch import optim, nn\n", "import torchvision\n", "import torchvision.transforms as transforms\n", "import torchvision.datasets as datasets\n", "import time\n", "\n", "import wandb\n", "import datetime\n", "\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "print(device)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have an autoencoder working on MNIST, let's use this model to visualize some geodata. For the next section we will use the SAT-6 (https://csc.lsu.edu/~saikat/deepsat/)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "SAT-6 consists of a total of 405,000 image patches each of size 28x28 and covering 6 landcover classes - barren land, trees, grassland, roads, buildings and water bodies. 324,000 images (comprising of four-fifths of the total dataset) were chosen as the training dataset and 81,000 (one fifths) were chosen as the testing dataset. Similar to SAT-4, the training and test sets were selected from disjoint NAIP tiles. Once generated, the images in the dataset were randomized in the same way as that for SAT-4. The specifications for the various landcover classes of SAT-4 and SAT-6 were adopted from those used in the National Land Cover Data (NLCD) algorithm. \n", "\n", "The datasets are encoded as MATLAB .mat files that can be read using the standard load command in MATLAB. Each sample image is 28x28 pixels and consists of 4 bands - red, green, blue and near infrared . The training and test labels are 1x4 and 1x6 vectors for SAT-4 and SAT-6 respectively having a single 1 indexing a particular class from 0 through 4 or 6 and 0 values at all other indices.\n", "\n", "The MAT file for the SAT-6 dataset contains the following variables:\n", "\n", "- train_x\t28x28x4x324000 uint8 (containing 324000 training samples of 28x28 images each with 4 channels)\n", "- train_y\t324000x6 uint8 (containing 6x1 vectors having labels for the 324000 training samples)\n", "- test_x\t28x28x4x81000 uint8 (containing 81000 test samples of 28x28 images each with 4 channels)\n", "- test_y\t81000x6 uint8 (containing 6x1 vectors having labels for the 81000 test samples)\n", "\n", "Labels:\n", "- Building = 0\n", "- Barren_land = 1\n", "- Tree=2\n", "- Grassland=3\n", "- Road = 4\n", "- Water = 5\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [] }, "outputs": [], "source": [ "# Using the satelite images dataset\n", "###############################################################################\n", "#load the data\n", "data = scipy.io.loadmat(\"./SAT-4_and_SAT-6_datasets/sat-6-full.mat\")\n", "train_images = data['train_x']\n", "train_labels = data['train_y']\n", "\n", "test_images = data['test_x']\n", "test_labels = data['test_y']" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training data shape : (28, 28, 4, 324000) (6, 324000)\n", "Testing data shape : (28, 28, 4, 81000) (6, 81000)\n" ] } ], "source": [ "####################################################################\n", "#Checkout the data\n", "print('Training data shape : ', train_images.shape, train_labels.shape)\n", "print('Testing data shape : ', test_images.shape, test_labels.shape)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training data shape : (324000, 28, 28, 4) (324000, 6)\n" ] } ], "source": [ "#Change the dimension to fit into the model\n", "x_train = train_images.transpose(3,0,1,2)\n", "t_train = train_labels.transpose()\n", "\n", "# x_test = test_images.transpose(3,0,1,2)\n", "# t_test = test_labels.transpose()\n", "print('Training data shape : ', x_train.shape, t_train.shape)\n", "# print('Testing data shape : ', x_test.shape, t_test.shape)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "count, t_train[count,:]: 0, [0 0 1 0 0 0]\n", "count, t_train[count,:]: 1, [0 1 0 0 0 0]\n", "count, t_train[count,:]: 2, [0 0 0 0 0 1]\n", "count, t_train[count,:]: 3, [0 0 0 0 0 1]\n", "count, t_train[count,:]: 4, [0 0 0 0 0 1]\n", "count, t_train[count,:]: 5, [1 0 0 0 0 0]\n", "count, t_train[count,:]: 6, [1 0 0 0 0 0]\n", "count, t_train[count,:]: 7, [0 0 0 0 0 1]\n", "count, t_train[count,:]: 8, [0 1 0 0 0 0]\n", "count, t_train[count,:]: 9, [0 0 1 0 0 0]\n", "count, t_train[count,:]: 10, [0 0 0 0 0 1]\n", "count, t_train[count,:]: 11, [0 1 0 0 0 0]\n", "count, t_train[count,:]: 12, [0 1 0 0 0 0]\n", "count, t_train[count,:]: 13, [0 0 0 0 1 0]\n", "count, t_train[count,:]: 14, [0 0 0 0 0 1]\n", "count, t_train[count,:]: 15, [0 0 1 0 0 0]\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Check what is in each channel\n", "fig,ax = plt.subplots(4,4, figsize=(10,10))\n", "ax = ax.ravel()\n", "list_idx = np.linspace(0,100,num=16,dtype=np.int64)\n", "for count, idx in enumerate(list_idx):\n", "# print(idx)\n", " print('count, t_train[count,:]: {}, {}'.format(count, t_train[count,:]))\n", "# print(x_train[idx,:,:,0:3])\n", " ax[count].imshow(x_train[count,:,:,0:3])\n", " ax[count].set_title(str(np.argmax(t_train[count,:])))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x_train.shape: (324000, 28, 28, 4)\n", "data.dtype: uint8\n", "target.dtype: int64\n", "dataset_size: 50000\n", "split: 10000\n" ] } ], "source": [ "# split in training and testing\n", "from torch.utils.data import Dataset, DataLoader\n", "from torch.utils.data.sampler import SubsetRandomSampler\n", "import torchvision.transforms as transforms\n", "from scipy.ndimage import zoom\n", "\n", "\n", "class MyDataset(Dataset):\n", " def __init__(self, data, target):\n", " print('data.dtype: {}'.format(data.dtype))\n", " print('target.dtype: {}'.format(target.dtype))\n", " self.data = torch.from_numpy(data).float()\n", " self.target = torch.from_numpy(target).float()\n", " \n", " \n", " def __getitem__(self, index):\n", " x = self.data[index]\n", " y = self.target[index]\n", " return x, y\n", " \n", " def __len__(self):\n", " return len(self.data)\n", "\n", "print('x_train.shape: {}'.format(x_train.shape))\n", "n_samples = 50000\n", "dataset = MyDataset(x_train[:n_samples,:,:,:], np.argmax(t_train[:n_samples],axis=1))\n", "del x_train, t_train\n", "dataset_size = len(dataset)\n", "print('dataset_size: {}'.format(dataset_size))\n", "test_split=0.2\n", "\n", "batch_size=1024 \n", "\n", "# -- split dataset\n", "indices = list(range(dataset_size))\n", "split = int(np.floor(test_split*dataset_size))\n", "print('split: {}'.format(split))\n", "# np.random.shuffle(indices) # Randomizing the indices is not a good idea if you want to model the sequence\n", "train_indices, val_indices = indices[split:], indices[:split]\n", "\n", "# -- create dataloaders\n", "# #Original\n", "train_sampler = SubsetRandomSampler(train_indices)\n", "valid_sampler = SubsetRandomSampler(val_indices)\n", "\n", "dataloaders = {\n", " 'train': torch.utils.data.DataLoader(dataset, batch_size=batch_size, sampler=train_sampler),\n", " 'test': torch.utils.data.DataLoader(dataset, batch_size=batch_size, sampler=valid_sampler),\n", " 'all': torch.utils.data.DataLoader(dataset, batch_size=5000, shuffle=False),\n", " }" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "tags": [] }, "outputs": [], "source": [ "class FFnet(nn.Module):\n", " '''\n", " Linear activation in the middle (instead of an activation function)\n", " '''\n", " def __init__(self):\n", " super(FFnet, self).__init__()\n", " self.enc_lin1 = nn.Linear(3136, 1000) # 28 x 28 x 4\n", " self.enc_lin2 = nn.Linear(1000, 500)\n", " self.enc_lin3 = nn.Linear(500, 250)\n", " self.enc_lin4 = nn.Linear(250, 6)\n", " \n", " self.relu = nn.ReLU()\n", " self.tanh = nn.Tanh()\n", "\n", " def encode(self, x):\n", " x = self.enc_lin1(x)\n", " x = self.relu(x)\n", " x = self.enc_lin2(x)\n", " x = self.relu(x)\n", " x = self.enc_lin3(x)\n", " x = self.relu(x)\n", " output = self.enc_lin4(x)\n", " return output\n", "\n", " def forward(self, x):\n", " z = self.encode(x)\n", " return z" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 15, "metadata": { "tags": [] }, "outputs": [], "source": [ "## Second routine for training and evaluation (using the )\n", "# Training and Evaluation routines\n", "import time\n", "def train(model,loss_fn, optimizer, train_loader, test_loader, num_epochs=None, verbose=False):\n", " \"\"\"\n", " This is a standard training loop, which leaves some parts to be filled in.\n", " INPUT:\n", " :param model: an untrained pytorch model\n", " :param loss_fn: e.g. Cross Entropy loss or Mean Squared Error.\n", " :param optimizer: the model optimizer, initialized with a learning rate.\n", " :param training_set: The training data, in a dataloader for easy iteration.\n", " :param test_loader: The testing data, in a dataloader for easy iteration.\n", " \"\"\"\n", " print('optimizer: {}'.format(optimizer))\n", " if num_epochs is None:\n", " num_epochs = 100 \n", " print('n. of epochs: {}'.format(num_epochs))\n", " for epoch in range(num_epochs+1):\n", " start = time.time()\n", " # loop through each data point in the training set\n", " for data, targets in train_loader:\n", " # run the model on the data\n", " model_input = data.view(data.size(0),-1).to(device)# TODO: Turn the 28 by 28 image tensors into a 784 dimensional tensor.\n", " if verbose: print('model_input.shape: {}'.format(model_input.shape))\n", " \n", " # Clear gradients w.r.t. parameters\n", " optimizer.zero_grad()\n", " \n", " out = model(model_input) # The second output is the latent representation\n", " if verbose:\n", " print('targets.shape: {}'.format(targets.shape))\n", " print('targets: {}'.format(targets))\n", " print('out.shape: {}'.format(out.shape))\n", " print('out: {}'.format(out))\n", "\n", " # Calculate the loss\n", " targets = targets.type(torch.LongTensor).to(device) # add an extra dimension to keep CrossEntropy happy.\n", " if verbose: print('targets.shape: {}'.format(targets.shape))\n", " loss = loss_fn(out,targets)\n", " if verbose: print('loss: {}'.format(loss))\n", " \n", " # Find the gradients of our loss via backpropogation\n", " loss.backward()\n", "\n", " # Adjust accordingly with the optimizer\n", " optimizer.step()\n", "\n", " # Give status reports every 100 epochs\n", " loss_train, acc_train = evaluate(model,train_loader,verbose)\n", " loss_test, acc_test = evaluate(model,test_loader,verbose)\n", " if epoch % 10==0:\n", " print(f\" EPOCH {epoch}. Progress: {epoch/num_epochs*100}%. \")\n", " print(\" Train loss: {:.4f}. Train Acc: {:.4f}, Test loss: {:.4f}. Test Acc: {:.4f}. Time/epoch: {:.4f}\".format(loss_train, acc_train, loss_test, acc_test, (time.time() - start))) #TODO: implement the evaluate function to provide performance statistics during training.\n", "\n", " wandb.log({\n", " \"Loss/train\": loss_train,\n", " \"Loss/val\": loss_test,\n", " \"Accuracy/train\": acc_train,\n", " \"Accuracy/val\": acc_test,\n", " \"epoch\": epoch\n", " }, step=epoch)\n", "\n", "def evaluate(model, evaluation_set, verbose=False):\n", " \"\"\"\n", " Evaluates the given model on the given dataset.\n", " Returns the percentage of correct classifications out of total classifications.\n", " \"\"\"\n", " with torch.no_grad(): # this disables backpropogation, which makes the model run much more quickly.\n", " correct = 0\n", " total = 0\n", " loss_all=0\n", " \n", " for data, targets in evaluation_set:\n", " targets= targets.to(device)\n", " # run the model on the data\n", " model_input = data.view(data.size(0),-1).to(device)# TODO: Turn the 28 by 28 image tensors into a 784 dimensional tensor.\n", " if verbose:\n", " print('model_input.shape: {}'.format(model_input.shape))\n", " print('targets.shape: {}'.format(targets.shape))\n", " out = model(model_input)\n", " targets = targets.type(torch.LongTensor).to(device)\n", " loss = loss_fn(out,targets)\n", " \n", " if verbose: print('out[:5]: {}'.format(out[:5]))\n", " loss_all+=loss.item()\n", " \n", " # the class with the highest energy is what we choose as prediction\n", " _, predicted = torch.max(out.data, 1)\n", " total += targets.size(0)\n", " correct += (predicted == targets).sum().item()\n", " acc = 100 * correct / total \n", " loss = loss_all/total\n", " return loss, acc\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Deleting previous model\n" ] }, { "data": { "text/html": [ "Finishing last run (ID:tk5smslm) before initializing another..." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Waiting for W&B process to finish... 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Run summary:


Accuracy/train36.825
Accuracy/val37.54
Loss/train0.00154
Loss/val0.00153
epoch58

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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/GeoComp_Matera2023" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/GeoComp_Matera2023/runs/bdbijsdw" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: Adam (\n", "Parameter Group 0\n", " amsgrad: False\n", " betas: (0.9, 0.999)\n", " capturable: False\n", " eps: 1e-08\n", " foreach: None\n", " lr: 0.01\n", " maximize: False\n", " weight_decay: 0\n", ")\n", "n. of epochs: 100\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0008. Train Acc: 66.3475, Test loss: 0.0008. Test Acc: 66.8600. Time/epoch: 4.3759\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0004. Train Acc: 81.9250, Test loss: 0.0004. Test Acc: 81.8900. Time/epoch: 3.9911\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0002. Train Acc: 89.1425, Test loss: 0.0003. Test Acc: 88.6500. Time/epoch: 3.9714\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0002. Train Acc: 93.1425, Test loss: 0.0002. Test Acc: 92.5700. Time/epoch: 3.7580\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0015. Train Acc: 36.8250, Test loss: 0.0015. Test Acc: 37.5400. Time/epoch: 3.8162\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0015. Train Acc: 36.8250, Test loss: 0.0015. Test Acc: 37.5400. Time/epoch: 3.9956\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0015. Train Acc: 36.8250, Test loss: 0.0015. Test Acc: 37.5400. Time/epoch: 3.7411\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0015. Train Acc: 36.8250, Test loss: 0.0015. Test Acc: 37.5400. Time/epoch: 4.1185\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0015. Train Acc: 36.8250, Test loss: 0.0015. Test Acc: 37.5400. Time/epoch: 3.8451\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0015. Train Acc: 36.8250, Test loss: 0.0015. Test Acc: 37.5400. Time/epoch: 4.0117\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0015. Train Acc: 36.8250, Test loss: 0.0015. Test Acc: 37.5400. Time/epoch: 3.9103\n", "Deleting previous model\n" ] }, { "data": { "text/html": [ "Finishing last run (ID:bdbijsdw) before initializing another..." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train36.825
Accuracy/val37.54
Loss/train0.00154
Loss/val0.00153
epoch100

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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/GeoComp_Matera2023" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/GeoComp_Matera2023/runs/0dxs47tg" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: Adam (\n", "Parameter Group 0\n", " amsgrad: False\n", " betas: (0.9, 0.999)\n", " capturable: False\n", " eps: 1e-08\n", " foreach: None\n", " lr: 0.005\n", " maximize: False\n", " weight_decay: 0\n", ")\n", "n. of epochs: 100\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0005. Train Acc: 75.6150, Test loss: 0.0005. Test Acc: 75.5200. Time/epoch: 4.0944\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0003. Train Acc: 84.7850, Test loss: 0.0003. Test Acc: 84.7500. Time/epoch: 3.9586\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0002. Train Acc: 92.3075, Test loss: 0.0002. Test Acc: 91.8300. Time/epoch: 3.8414\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0002. Train Acc: 93.0650, Test loss: 0.0002. Test Acc: 92.7300. Time/epoch: 4.0199\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0002. Train Acc: 89.9625, Test loss: 0.0002. Test Acc: 89.8600. Time/epoch: 4.3224\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0002. Train Acc: 90.8100, Test loss: 0.0002. Test Acc: 90.1700. Time/epoch: 4.3020\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0005. Train Acc: 77.6900, Test loss: 0.0005. Test Acc: 77.7400. Time/epoch: 3.9768\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0003. Train Acc: 87.5625, Test loss: 0.0003. Test Acc: 87.5900. Time/epoch: 3.8873\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0003. Train Acc: 87.9400, Test loss: 0.0003. Test Acc: 87.7400. Time/epoch: 4.0226\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0002. Train Acc: 93.2150, Test loss: 0.0002. Test Acc: 92.6300. Time/epoch: 4.4531\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0002. Train Acc: 92.7275, Test loss: 0.0002. Test Acc: 92.2400. Time/epoch: 3.9699\n", "Deleting previous model\n" ] }, { "data": { "text/html": [ "Finishing last run (ID:0dxs47tg) before initializing another..." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train92.7275
Accuracy/val92.24
Loss/train0.00016
Loss/val0.00017
epoch100

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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/GeoComp_Matera2023" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/GeoComp_Matera2023/runs/stjo6gi9" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: Adam (\n", "Parameter Group 0\n", " amsgrad: False\n", " betas: (0.9, 0.999)\n", " capturable: False\n", " eps: 1e-08\n", " foreach: None\n", " lr: 0.001\n", " maximize: False\n", " weight_decay: 0\n", ")\n", "n. of epochs: 100\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0005. Train Acc: 78.5275, Test loss: 0.0005. Test Acc: 78.4300. Time/epoch: 4.0875\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0003. Train Acc: 88.7000, Test loss: 0.0003. Test Acc: 88.5600. Time/epoch: 3.8260\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0002. Train Acc: 92.8550, Test loss: 0.0002. Test Acc: 92.3100. Time/epoch: 3.9107\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0003. Train Acc: 85.9700, Test loss: 0.0003. Test Acc: 85.9500. Time/epoch: 3.7582\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0002. Train Acc: 92.8800, Test loss: 0.0002. Test Acc: 92.4900. Time/epoch: 3.9415\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0001. Train Acc: 94.0175, Test loss: 0.0002. Test Acc: 93.3500. Time/epoch: 3.8053\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0002. Train Acc: 92.9325, Test loss: 0.0002. Test Acc: 92.3500. Time/epoch: 4.0517\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0002. Train Acc: 92.4325, Test loss: 0.0002. Test Acc: 91.9600. Time/epoch: 4.0073\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0002. Train Acc: 92.7200, Test loss: 0.0002. Test Acc: 92.0100. Time/epoch: 3.7292\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0001. Train Acc: 96.1825, Test loss: 0.0001. Test Acc: 95.4600. Time/epoch: 4.0354\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0001. Train Acc: 93.8075, Test loss: 0.0002. Test Acc: 92.5900. Time/epoch: 4.2393\n", "Deleting previous model\n" ] }, { "data": { "text/html": [ "Finishing last run (ID:stjo6gi9) before initializing another..." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train93.8075
Accuracy/val92.59
Loss/train0.00015
Loss/val0.00018
epoch100

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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/GeoComp_Matera2023" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/GeoComp_Matera2023/runs/rhx17fj2" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: Adam (\n", "Parameter Group 0\n", " amsgrad: False\n", " betas: (0.9, 0.999)\n", " capturable: False\n", " eps: 1e-08\n", " foreach: None\n", " lr: 0.0001\n", " maximize: False\n", " weight_decay: 0\n", ")\n", "n. of epochs: 100\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0004. Train Acc: 81.5800, Test loss: 0.0004. Test Acc: 81.4000. Time/epoch: 4.6343\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0002. Train Acc: 91.4275, Test loss: 0.0002. Test Acc: 91.2600. Time/epoch: 3.9659\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0003. Train Acc: 86.6925, Test loss: 0.0003. Test Acc: 86.5700. Time/epoch: 4.0900\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0002. Train Acc: 93.3275, Test loss: 0.0002. Test Acc: 93.0000. Time/epoch: 3.7836\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0003. Train Acc: 86.7875, Test loss: 0.0003. Test Acc: 86.6300. Time/epoch: 3.9666\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0003. Train Acc: 86.1525, Test loss: 0.0003. Test Acc: 86.0300. Time/epoch: 4.4136\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0003. Train Acc: 89.4050, Test loss: 0.0003. Test Acc: 88.9200. Time/epoch: 4.2230\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0001. Train Acc: 95.5700, Test loss: 0.0001. Test Acc: 94.6200. Time/epoch: 4.1061\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0001. Train Acc: 96.5025, Test loss: 0.0001. Test Acc: 95.6900. Time/epoch: 4.3656\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0002. Train Acc: 92.6275, Test loss: 0.0002. Test Acc: 91.9200. Time/epoch: 4.0278\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0001. Train Acc: 97.1175, Test loss: 0.0001. Test Acc: 96.3000. Time/epoch: 3.7401\n" ] } ], "source": [ "lr_range = [0.01,0.005,0.001, 0.0001]\n", "\n", "for lr in lr_range:\n", " if 'model' in globals():\n", " print('Deleting previous model')\n", " del model, loss_fn, optimizer\n", " model = FFnet().to(device)\n", " DATETIME = datetime.datetime.now().strftime('%Y-%m-%d-%H_%M_%S')\n", " wandb.init(project=\"GeoComp_Matera2023\", entity=\"ahof1704\", name=\"CNN_sat_FFnet_{}\".format(DATETIME))\n", " wandb.watch(model, log=\"all\", log_freq=1)\n", " optimizer = torch.optim.Adam(model.parameters(), lr = lr)\n", "\n", " loss_fn = nn.CrossEntropyLoss().to(device)\n", " train(model,loss_fn, optimizer, dataloaders['train'], dataloaders['test'],verbose=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Using CNNs for a image dataset " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "tags": [] }, "outputs": [], "source": [ "class CNNet(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.conv1 = nn.Conv2d(4, 6, 5)\n", " self.pool = nn.MaxPool2d(2, 2)\n", " self.conv2 = nn.Conv2d(6, 16, 5)\n", " self.fc1 = nn.Linear(16 * 4 * 4, 120)\n", " self.fc2 = nn.Linear(120, 84)\n", " self.fc3 = nn.Linear(84, 6)\n", "\n", " def forward(self, x):\n", " x = self.pool(F.relu(self.conv1(x)))\n", " x = self.pool(F.relu(self.conv2(x)))\n", " x = torch.flatten(x, 1) # flatten all dimensions except batch\n", " x = F.relu(self.fc1(x))\n", " x = F.relu(self.fc2(x))\n", " x = self.fc3(x)\n", " return x\n", "\n", "\n", "model = CNNet()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "tags": [] }, "outputs": [], "source": [ "# Training and Evaluation routines\n", "import time\n", "def train(model,loss_fn, optimizer, train_loader, test_loader, num_epochs=None, verbose=False):\n", " \"\"\"\n", " This is a standard training loop, which leaves some parts to be filled in.\n", " INPUT:\n", " :param model: an untrained pytorch model\n", " :param loss_fn: e.g. Cross Entropy loss of Mean Squared Error.\n", " :param optimizer: the model optimizer, initialized with a learning rate.\n", " :param training_set: The training data, in a dataloader for easy iteration.\n", " :param test_loader: The testing data, in a dataloader for easy iteration.\n", " \"\"\"\n", " \n", " print('optimizer: {}'.format(optimizer))\n", " if num_epochs is None:\n", " num_epochs = 100\n", " print('n. of epochs: {}'.format(num_epochs))\n", " for epoch in range(num_epochs+1):\n", " start = time.time()\n", " # loop through each data point in the training set\n", " for data, targets in train_loader:\n", " \n", " # run the model on the data\n", " model_input = data.permute(0, 3, 2, 1).to(device)\n", " if verbose: print('model_input.shape: {}'.format(model_input.shape))\n", " \n", " # Clear gradients w.r.t. parameters\n", " optimizer.zero_grad()\n", " \n", " out = model(model_input) # The second output is the latent representation\n", " if verbose:\n", " print('targets.shape: {}'.format(targets.shape))\n", " print('targets: {}'.format(targets))\n", " print('out.shape: {}'.format(out.shape))\n", " print('out: {}'.format(out))\n", "\n", " # Calculate the loss\n", " targets = targets.type(torch.LongTensor).to(device) # add an extra dimension to keep CrossEntropy happy.\n", " if verbose: print('targets.shape: {}'.format(targets.shape))\n", " loss = loss_fn(out,targets)\n", " if verbose: print('loss: {}'.format(loss))\n", " \n", " # Find the gradients of our loss via backpropogation\n", " loss.backward()\n", "\n", " # Adjust accordingly with the optimizer\n", " optimizer.step()\n", "\n", " # Give status reports every 100 epochs\n", " if epoch % 10==0:\n", " print(f\" EPOCH {epoch}. Progress: {epoch/num_epochs*100}%. \")\n", " loss_train, acc_train = evaluate(model,train_loader,verbose)\n", " loss_test, acc_test = evaluate(model,test_loader,verbose)\n", " print(\" Train loss: {:.4f}. Train Acc: {:.4f}, Test loss: {:.4f}. Test Acc: {:.4f}. Time/epoch: {:.4f}\".format(loss_train, acc_train, loss_test, acc_test, (time.time() - start))) #TODO: implement the evaluate function to provide performance statistics during training.\n", " \n", "\n", "def evaluate(model, evaluation_set, verbose=False):\n", " \"\"\"\n", " Evaluates the given model on the given dataset.\n", " Returns the percentage of correct classifications out of total classifications.\n", " \"\"\"\n", " with torch.no_grad(): # this disables backpropogation, which makes the model run much more quickly.\n", " correct = 0\n", " total = 0\n", " loss_all=0\n", " \n", " for data, targets in evaluation_set:\n", "\n", " # run the model on the data\n", " model_input = data.permute(0, 3, 2, 1).to(device)\n", " if verbose:\n", " print('model_input.shape: {}'.format(model_input.shape))\n", " print('targets.shape: {}'.format(targets.shape))\n", " out = model(model_input)\n", " targets = targets.type(torch.LongTensor)\n", " loss = loss_fn(out,targets)\n", " \n", " if verbose: print('out[:5]: {}'.format(out[:5]))\n", " loss_all+=loss.item()\n", " \n", " # the class with the highest energy is what we choose as prediction\n", " _, predicted = torch.max(out.data, 1)\n", " total += targets.size(0)\n", " correct += (predicted == targets).sum().item()\n", " acc = 100 * correct / total \n", " loss = loss_all/total\n", " return loss, acc\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Deleting previous model\n" ] }, { "ename": "NameError", "evalue": "name 'loss_fn' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[14], line 6\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mglobals\u001b[39m():\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDeleting previous model\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m model, loss_fn, optimizer\n\u001b[1;32m 7\u001b[0m model \u001b[38;5;241m=\u001b[39m CNNet()\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m 8\u001b[0m optimizer \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39moptim\u001b[38;5;241m.\u001b[39mAdam(model\u001b[38;5;241m.\u001b[39mparameters(), lr \u001b[38;5;241m=\u001b[39m lr) \n", "\u001b[0;31mNameError\u001b[0m: name 'loss_fn' is not defined" ] } ], "source": [ "lr_range = [0.01,0.005,0.001]\n", "\n", "for lr in lr_range:\n", " if 'model' in globals():\n", " print('Deleting previous model')\n", " del model, loss_fn, optimizer\n", " model = CNNet().to(device)\n", " optimizer = torch.optim.Adam(model.parameters(), lr = lr) \n", "\n", " loss_fn = nn.CrossEntropyLoss().to(device)\n", " train(model,loss_fn, optimizer, dataloaders['train'], dataloaders['test'],verbose=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define Your Neural Network\n", "Before we can run the sweep, let's define a function that creates and trains our neural network.\n", "\n", "In the function below, we define a simple CNN in Pytorch, and add the following lines of code to log models metrics, visualize performance and output and track our experiments easily:\n", "\n", "- wandb.init() – Initialize a new W&B run. Each run is single execution of the training script.\n", "- wandb.config – Save all your hyperparameters in a config object. This lets you use our app to sort and compare your runs by hyperparameter values.\n", "- callbacks=[WandbCallback()] – Fetch all layer dimensions, model parameters and log them automatically to your W&B dashboard.\n", "- wandb.log() – Logs custom objects – these can be images, videos, audio files, HTML, plots, point clouds etc. Here we use wandb.log to log images of Simpson characters overlaid with actual and predicted labels." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Sweeping with WandB" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "tags": [] }, "outputs": [], "source": [ "# Configure the sweep – specify the parameters to search through, the search strategy, the optimization metric et all.\n", "sweep_config = {\n", " 'method': 'random', #grid, random, bayes\n", " 'metric': {\n", " 'name': 'Accuracy/val',\n", " 'goal': 'maximize' \n", " },\n", " 'parameters': {\n", " 'epochs': {\n", " 'values': [10, 20, 50]\n", " },\n", " 'batch_size': {\n", " 'values': [32,64,128]\n", " },\n", " 'weight_decay': {\n", " 'values': [0.0005, 0.005, 0.05]\n", " },\n", " 'learning_rate': {\n", " 'values': [1e-2, 1e-3, 1e-4, 3e-4, 3e-5, 1e-5]\n", " },\n", " 'optimizer': {\n", " 'values': ['adam', 'sgd', 'rmsprop']\n", " }\n", " }\n", "}\n" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Create sweep with ID: 4bmop0um\n", "Sweep URL: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um\n" ] } ], "source": [ "# Initialize a new sweep\n", "# Arguments:\n", "# – sweep_config: the sweep config dictionary defined above\n", "# – entity: Set the username for the sweep\n", "# – project: Set the project name for the sweep\n", "sweep_id = wandb.sweep(sweep_config, entity=\"ahof1704\", project=\"CNN_Sat_sweep\")" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "tags": [] }, "outputs": [], "source": [ "class CNNet(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.conv1 = nn.Conv2d(4, 6, 5)\n", " self.pool = nn.MaxPool2d(2, 2)\n", " self.conv2 = nn.Conv2d(6, 16, 5)\n", " self.fc1 = nn.Linear(16 * 4 * 4, 120)\n", " self.fc2 = nn.Linear(120, 84)\n", " self.fc3 = nn.Linear(84, 6)\n", "\n", " def forward(self, x):\n", " x = self.pool(F.relu(self.conv1(x)))\n", " x = self.pool(F.relu(self.conv2(x)))\n", " x = torch.flatten(x, 1) # flatten all dimensions except batch\n", " x = F.relu(self.fc1(x))\n", " x = F.relu(self.fc2(x))\n", " x = self.fc3(x)\n", " return x\n", "\n", "\n", "model = CNNet()" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "tags": [] }, "outputs": [], "source": [ "# Training and Evaluation routines for Sweeping \n", "import time\n", "\n", "loss_fn = nn.CrossEntropyLoss().to(device)\n", "\n", "def train(config=None):\n", " \"\"\"\n", " This is a standard training loop, which leaves some parts to be filled in.\n", " INPUT:\n", " :param model: an untrained pytorch model\n", " :param loss_fn: e.g. Cross Entropy loss of Mean Squared Error.\n", " :param optimizer: the model optimizer, initialized with a learning rate.\n", " :param training_set: The training data, in a dataloader for easy iteration.\n", " :param test_loader: The testing data, in a dataloader for easy iteration.\n", " \"\"\"\n", " \n", " with wandb.init(config=config):\n", " verbose=False\n", " model = CNNet().to(device)\n", " model.train()\n", "\n", " # Config is a variable that holds and saves hyperparameters and inputs\n", " config = wandb.config\n", "\n", " print('optimizer: {}'.format(config.optimizer))\n", " # Define the optimizer\n", " if config.optimizer=='sgd':\n", " optimizer = torch.optim.SGD(model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay, momentum=0.9, nesterov=True)\n", " elif config.optimizer=='rmsprop':\n", " optimizer = torch.optim.RMSprop(model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay)\n", " elif config.optimizer=='adam':\n", " optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate, betas=(0.9, 0.999))\n", "\n", " # -- create dataloaders\n", " train_sampler = SubsetRandomSampler(train_indices)\n", " valid_sampler = SubsetRandomSampler(val_indices)\n", "\n", " dataloaders = {\n", " 'train': torch.utils.data.DataLoader(dataset, batch_size=config.batch_size, sampler=train_sampler),\n", " 'test': torch.utils.data.DataLoader(dataset, batch_size=config.batch_size, sampler=valid_sampler),\n", " 'all': torch.utils.data.DataLoader(dataset, batch_size=5000, shuffle=False),\n", " }\n", " train_loader = dataloaders['train']\n", " test_loader = dataloaders['test']\n", "\n", " for epoch in range(config.epochs+1):\n", " start = time.time()\n", " # loop through each data point in the training set\n", " for data, targets in train_loader:\n", "\n", " # run the model on the data\n", " model_input = data.permute(0, 3, 2, 1).to(device)\n", " if verbose: print('model_input.shape: {}'.format(model_input.shape))\n", "\n", " # Clear gradients w.r.t. parameters\n", " optimizer.zero_grad()\n", "\n", " out = model(model_input) # The second output is the latent representation\n", " if verbose:\n", " print('targets.shape: {}'.format(targets.shape))\n", " print('targets: {}'.format(targets))\n", " print('out.shape: {}'.format(out.shape))\n", " print('out: {}'.format(out))\n", "\n", " # Calculate the loss\n", " targets = targets.type(torch.LongTensor).to(device) # add an extra dimension to keep CrossEntropy happy.\n", " if verbose: print('targets.shape: {}'.format(targets.shape))\n", " loss = loss_fn(out,targets)\n", " if verbose: print('loss: {}'.format(loss))\n", "\n", " # Find the gradients of our loss via backpropogation\n", " loss.backward()\n", "\n", " # Adjust accordingly with the optimizer\n", " optimizer.step()\n", "\n", " loss_train, acc_train = evaluate(model,train_loader,verbose)\n", " loss_test, acc_test = evaluate(model,test_loader,verbose) \n", " \n", " # Give status reports every 100 epochs\n", " if epoch % 10==0:\n", " print(f\" EPOCH {epoch}. Progress: {epoch/config.epochs*100}%. \")\n", " print(\" Train loss: {:.4f}. Train Acc: {:.4f}, Test loss: {:.4f}. Test Acc: {:.4f}. Time/epoch: {:.4f}\".format(loss_train, acc_train, loss_test, acc_test, (time.time() - start))) #TODO: implement the evaluate function to provide performance statistics during training.\n", "\n", " wandb.log({\n", " \"Loss/train\": loss_train,\n", " \"Loss/val\": loss_test,\n", " \"Accuracy/train\": acc_train,\n", " \"Accuracy/val\": acc_test,\n", " \"epoch\": epoch\n", " }, step=epoch)\n", "\n", "def evaluate(model, evaluation_set, verbose=False):\n", " \"\"\"\n", " Evaluates the given model on the given dataset.\n", " Returns the percentage of correct classifications out of total classifications.\n", " \"\"\"\n", " model.eval()\n", " with torch.no_grad(): # this disables backpropogation, which makes the model run much more quickly.\n", " correct = 0\n", " total = 0\n", " loss_all=0\n", "\n", " for data, targets in evaluation_set:\n", "\n", " # run the model on the data\n", " model_input = data.permute(0, 3, 2, 1).to(device)\n", " if verbose:\n", " print('model_input.shape: {}'.format(model_input.shape))\n", " print('targets.shape: {}'.format(targets.shape))\n", " out = model(model_input)\n", " targets = targets.type(torch.LongTensor).to(device)\n", " loss = loss_fn(out,targets)\n", "\n", " if verbose: print('out[:5]: {}'.format(out[:5]))\n", " loss_all+=loss.item()\n", "\n", " # the class with the highest energy is what we choose as prediction\n", " _, predicted = torch.max(out.data, 1)\n", " total += targets.size(0)\n", " correct += (predicted == targets).sum().item()\n", " acc = 100 * correct / total \n", " loss = loss_all/total\n", " return loss, acc\n" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: zzam8458 with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 64\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1e-05\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.05\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230517_235613-zzam8458" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run dutiful-sweep-1 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/zzam8458" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0088. Train Acc: 83.1675, Test loss: 0.0088. Test Acc: 83.3300. Time/epoch: 3.3380\n", " EPOCH 10. Progress: 100.0%. \n", " Train loss: 0.0028. Train Acc: 92.3525, Test loss: 0.0029. Test Acc: 92.4100. Time/epoch: 3.2960\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train92.3525
Accuracy/val92.41
Loss/train0.00284
Loss/val0.00293
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run dutiful-sweep-1 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/zzam8458
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/oft3pjvq" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0099. Train Acc: 87.1850, Test loss: 0.0103. Test Acc: 86.9400. Time/epoch: 4.9065\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0049. Train Acc: 94.0975, Test loss: 0.0054. Test Acc: 93.6300. Time/epoch: 4.8879\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0035. Train Acc: 95.4450, Test loss: 0.0042. Test Acc: 94.4000. Time/epoch: 4.8633\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0038. Train Acc: 95.4750, Test loss: 0.0047. Test Acc: 94.5100. Time/epoch: 4.6823\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0052. Train Acc: 93.3300, Test loss: 0.0058. Test Acc: 92.8400. Time/epoch: 4.6711\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0033. Train Acc: 96.1875, Test loss: 0.0042. Test Acc: 95.2100. Time/epoch: 4.8509\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train96.1875
Accuracy/val95.21
Loss/train0.00333
Loss/val0.00418
epoch50

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Accuracy/train95.9025
Accuracy/val95.92
Loss/train0.00333
Loss/val0.00346
epoch50

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Accuracy/train36.825
Accuracy/val37.54
Loss/train0.04804
Loss/val0.04794
epoch10

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Accuracy/train36.825
Accuracy/val37.54
Loss/train0.0481
Loss/val0.04798
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run vague-sweep-5 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/jc5xf3r3
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Accuracy/train90.5125
Accuracy/val90.44
Loss/train0.00192
Loss/val0.00196
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run glowing-sweep-6 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/i7nmk94y
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Accuracy/train94.735
Accuracy/val94.49
Loss/train0.00217
Loss/val0.0023
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run deep-sweep-7 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/8e2wk0j2
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Accuracy/train95.33
Accuracy/val95.57
Loss/train0.00357
Loss/val0.00366
epoch50

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Run summary:


Accuracy/train97.645
Accuracy/val97.16
Loss/train0.00049
Loss/val0.00063
epoch50

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Accuracy/train95.0375
Accuracy/val94.89
Loss/train0.0041
Loss/val0.00427
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run lucky-sweep-10 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/3qr8irec
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/t4osxxhb" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0016. Train Acc: 91.7150, Test loss: 0.0016. Test Acc: 91.6600. Time/epoch: 2.3046\n", " EPOCH 10. Progress: 50.0%. \n", " Train loss: 0.0007. Train Acc: 96.3875, Test loss: 0.0008. Test Acc: 95.9900. Time/epoch: 2.4241\n", " EPOCH 20. Progress: 100.0%. \n", " Train loss: 0.0004. Train Acc: 97.8550, Test loss: 0.0006. Test Acc: 97.0100. Time/epoch: 2.4088\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train97.855
Accuracy/val97.01
Loss/train0.00044
Loss/val0.00063
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run scarlet-sweep-11 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/t4osxxhb
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Accuracy/train36.825
Accuracy/val37.54
Loss/train0.04805
Loss/val0.04793
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run proud-sweep-12 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/3d41qa4k
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Accuracy/train95.7625
Accuracy/val95.75
Loss/train0.00353
Loss/val0.0036
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run atomic-sweep-13 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/xvc9j71t
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Accuracy/train95.3175
Accuracy/val95.46
Loss/train0.0009
Loss/val0.00093
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run lilac-sweep-14 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/w3ttjtb4
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/6kgluzmq" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0046. Train Acc: 93.9450, Test loss: 0.0048. Test Acc: 93.8200. Time/epoch: 4.6201\n", " EPOCH 10. Progress: 100.0%. \n", " Train loss: 0.0035. Train Acc: 95.2925, Test loss: 0.0039. Test Acc: 95.0600. Time/epoch: 4.7475\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train95.2925
Accuracy/val95.06
Loss/train0.0035
Loss/val0.00385
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run pleasant-sweep-15 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/6kgluzmq
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/vn8tnx3k" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0042. Train Acc: 76.6925, Test loss: 0.0043. Test Acc: 76.9400. Time/epoch: 2.2380\n", " EPOCH 10. Progress: 100.0%. \n", " Train loss: 0.0169. Train Acc: 58.1875, Test loss: 0.0170. Test Acc: 58.5100. Time/epoch: 2.3528\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train58.1875
Accuracy/val58.51
Loss/train0.0169
Loss/val0.01697
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run kind-sweep-16 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/vn8tnx3k
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Accuracy/train36.825
Accuracy/val37.54
Loss/train0.02402
Loss/val0.02403
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run astral-sweep-17 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/7m5xr9i9
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Accuracy/train97.6825
Accuracy/val97.25
Loss/train0.00051
Loss/val0.00062
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run robust-sweep-18 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/do2ct7u1
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/uchsrn45" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0176. Train Acc: 55.3525, Test loss: 0.0178. Test Acc: 55.5500. Time/epoch: 3.2013\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0240. Train Acc: 36.8250, Test loss: 0.0241. Test Acc: 37.5400. Time/epoch: 3.1492\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0240. Train Acc: 36.8250, Test loss: 0.0241. Test Acc: 37.5400. Time/epoch: 3.1419\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0240. Train Acc: 36.8250, Test loss: 0.0241. Test Acc: 37.5400. Time/epoch: 3.0273\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0241. Train Acc: 36.8250, Test loss: 0.0241. Test Acc: 37.5400. Time/epoch: 3.1642\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0240. Train Acc: 36.8250, Test loss: 0.0241. Test Acc: 37.5400. Time/epoch: 3.1493\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train36.825
Accuracy/val37.54
Loss/train0.02405
Loss/val0.02409
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run clear-sweep-19 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/uchsrn45
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/oc0l1pdk" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0018. Train Acc: 88.6375, Test loss: 0.0018. Test Acc: 88.6900. Time/epoch: 2.2337\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0008. Train Acc: 96.1925, Test loss: 0.0008. Test Acc: 96.0400. Time/epoch: 2.3739\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0005. Train Acc: 97.7425, Test loss: 0.0006. Test Acc: 97.5600. Time/epoch: 2.3606\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0004. Train Acc: 98.0750, Test loss: 0.0006. Test Acc: 97.5400. Time/epoch: 2.3336\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0003. Train Acc: 98.4550, Test loss: 0.0005. Test Acc: 97.6800. Time/epoch: 2.2281\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0003. Train Acc: 98.6000, Test loss: 0.0006. Test Acc: 97.7000. Time/epoch: 2.3394\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train98.6
Accuracy/val97.7
Loss/train0.0003
Loss/val0.00055
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run unique-sweep-20 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/oc0l1pdk
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/xdwakrae" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0104. Train Acc: 70.3125, Test loss: 0.0103. Test Acc: 70.9600. Time/epoch: 3.0324\n", " EPOCH 10. Progress: 50.0%. \n", " Train loss: 0.0019. Train Acc: 95.6000, Test loss: 0.0021. Test Acc: 95.4500. Time/epoch: 3.1860\n", " EPOCH 20. Progress: 100.0%. \n", " Train loss: 0.0016. Train Acc: 95.8350, Test loss: 0.0021. Test Acc: 95.1400. Time/epoch: 3.1535\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train95.835
Accuracy/val95.14
Loss/train0.00164
Loss/val0.00205
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run deep-sweep-21 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/xdwakrae
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Accuracy/train97.0225
Accuracy/val96.52
Loss/train0.00246
Loss/val0.00275
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run wandering-sweep-22 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/cr02yzq9
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Run summary:


Accuracy/train98.8875
Accuracy/val97.92
Loss/train0.00047
Loss/val0.001
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run devout-sweep-23 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/cmkt7j2h
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Accuracy/train96.075
Accuracy/val95.97
Loss/train0.00077
Loss/val0.00085
epoch10

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Accuracy/train94.41
Accuracy/val94.74
Loss/train0.00108
Loss/val0.0011
epoch50

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Accuracy/train92.945
Accuracy/val92.39
Loss/train0.00312
Loss/val0.00339
epoch10

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Accuracy/train96.3
Accuracy/val96.22
Loss/train0.0032
Loss/val0.00339
epoch10

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Accuracy/train96.1425
Accuracy/val94.87
Loss/train0.00307
Loss/val0.00423
epoch10

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Accuracy/train94.0325
Accuracy/val94.1
Loss/train0.00116
Loss/val0.00121
epoch20

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Run summary:


Accuracy/train96.85
Accuracy/val96.66
Loss/train0.00132
Loss/val0.00147
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run bumbling-sweep-30 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/pjh7m7v6
Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_005204-pjh7m7v6/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: k19vq8g3 with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 128\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 50\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 3e-05\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: sgd\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.05\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_005326-k19vq8g3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run winter-sweep-31 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/k19vq8g3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0031. Train Acc: 87.0525, Test loss: 0.0031. Test Acc: 86.9800. Time/epoch: 2.3651\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0012. Train Acc: 93.9275, Test loss: 0.0013. Test Acc: 94.1100. Time/epoch: 2.3194\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0011. Train Acc: 94.6950, Test loss: 0.0011. Test Acc: 94.6200. Time/epoch: 2.3236\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0011. Train Acc: 93.9350, Test loss: 0.0012. Test Acc: 94.1100. Time/epoch: 2.1896\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0009. Train Acc: 95.4750, Test loss: 0.0010. Test Acc: 95.4700. Time/epoch: 2.3240\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0009. Train Acc: 95.7675, Test loss: 0.0010. Test Acc: 95.7500. Time/epoch: 2.3105\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/val▁▃▄▅▆▆▆▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇█▇███████████████
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Run summary:


Accuracy/train95.7675
Accuracy/val95.75
Loss/train0.00091
Loss/val0.00097
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run winter-sweep-31 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/k19vq8g3
Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_005326-k19vq8g3/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Sweep Agent: Waiting for job.\n", "\u001b[34m\u001b[1mwandb\u001b[0m: Job received.\n", "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 41ow5vvs with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 128\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 50\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.001\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.005\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_005543-41ow5vvs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run mild-sweep-32 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/41ow5vvs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0013. Train Acc: 92.6025, Test loss: 0.0014. Test Acc: 92.5400. Time/epoch: 2.4629\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0008. Train Acc: 96.0200, Test loss: 0.0010. Test Acc: 95.3400. Time/epoch: 2.4240\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0005. Train Acc: 97.5950, Test loss: 0.0009. Test Acc: 96.1700. Time/epoch: 2.3089\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0004. Train Acc: 97.9350, Test loss: 0.0010. Test Acc: 96.0000. Time/epoch: 2.2992\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0004. Train Acc: 98.0250, Test loss: 0.0011. Test Acc: 95.8700. Time/epoch: 2.4359\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0007. Train Acc: 96.9100, Test loss: 0.0016. Test Acc: 94.9100. Time/epoch: 2.4494\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train96.91
Accuracy/val94.91
Loss/train0.0007
Loss/val0.00165
epoch50

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Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_005543-41ow5vvs/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Sweep Agent: Waiting for job.\n", "\u001b[34m\u001b[1mwandb\u001b[0m: Job received.\n", "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: rma15prl with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 128\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 50\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.0001\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: rmsprop\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.0005\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_005805-rma15prl" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run eager-sweep-33 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/rma15prl" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0021. Train Acc: 88.6875, Test loss: 0.0021. Test Acc: 88.5300. Time/epoch: 2.2395\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0010. Train Acc: 94.8100, Test loss: 0.0011. Test Acc: 94.8900. Time/epoch: 2.3652\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0006. Train Acc: 97.1975, Test loss: 0.0007. Test Acc: 96.8800. Time/epoch: 2.3607\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0005. Train Acc: 97.9025, Test loss: 0.0006. Test Acc: 97.4800. Time/epoch: 2.3467\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0004. Train Acc: 97.8625, Test loss: 0.0006. Test Acc: 97.2900. Time/epoch: 2.2306\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0015. Train Acc: 92.9100, Test loss: 0.0017. Test Acc: 92.7400. Time/epoch: 2.2342\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train92.91
Accuracy/val92.74
Loss/train0.00147
Loss/val0.00167
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run eager-sweep-33 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/rma15prl
Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_005805-rma15prl/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 9g0xstpx with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 64\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 50\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.001\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.05\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_010017-9g0xstpx" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run cosmic-sweep-34 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/9g0xstpx" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0024. Train Acc: 93.3225, Test loss: 0.0025. Test Acc: 93.3200. Time/epoch: 3.3641\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0016. Train Acc: 95.8700, Test loss: 0.0019. Test Acc: 95.5100. Time/epoch: 3.1685\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0013. Train Acc: 96.5725, Test loss: 0.0018. Test Acc: 96.1900. Time/epoch: 3.1581\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0014. Train Acc: 96.4300, Test loss: 0.0021. Test Acc: 95.7500. Time/epoch: 3.3234\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0011. Train Acc: 96.9625, Test loss: 0.0018. Test Acc: 96.0000. Time/epoch: 3.3414\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0009. Train Acc: 98.0200, Test loss: 0.0022. Test Acc: 96.9100. Time/epoch: 3.3105\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train98.02
Accuracy/val96.91
Loss/train0.00087
Loss/val0.00223
epoch50

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Accuracy/train98.8025
Accuracy/val97.27
Loss/train0.00024
Loss/val0.00066
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run whole-sweep-35 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/hljjsi11
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Accuracy/train93.3725
Accuracy/val93.34
Loss/train0.00515
Loss/val0.00545
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run apricot-sweep-36 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/nt3b11pr
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Accuracy/train36.825
Accuracy/val37.54
Loss/train0.02439
Loss/val0.02443
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run soft-sweep-37 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/zvbsrho9
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Accuracy/train97.1975
Accuracy/val96.95
Loss/train0.00116
Loss/val0.00137
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run silvery-sweep-38 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/js3qtjb0
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Accuracy/train96.5675
Accuracy/val96.05
Loss/train0.00269
Loss/val0.00296
epoch10

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Accuracy/train97.8525
Accuracy/val97.54
Loss/train0.00087
Loss/val0.0011
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run golden-sweep-40 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/pcr7q62f
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Accuracy/train79.135
Accuracy/val79.7
Loss/train0.01414
Loss/val0.01411
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run vibrant-sweep-41 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/tq2yb5rq
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Accuracy/train36.825
Accuracy/val37.54
Loss/train0.02403
Loss/val0.02408
epoch20

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Run summary:


Accuracy/train36.825
Accuracy/val37.54
Loss/train0.02402
Loss/val0.02406
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run olive-sweep-43 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/oibs8tj5
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Accuracy/train93.975
Accuracy/val94.09
Loss/train0.00114
Loss/val0.0012
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run dauntless-sweep-44 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/td3idvxx
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/cet2jps7" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0066. Train Acc: 91.0700, Test loss: 0.0067. Test Acc: 91.1100. Time/epoch: 5.0446\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0029. Train Acc: 96.4875, Test loss: 0.0034. Test Acc: 96.0800. Time/epoch: 5.1598\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0022. Train Acc: 97.2775, Test loss: 0.0034. Test Acc: 96.4800. Time/epoch: 5.1723\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0009. Train Acc: 99.0950, Test loss: 0.0022. Test Acc: 97.9000. Time/epoch: 5.1286\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0011. Train Acc: 98.7550, Test loss: 0.0031. Test Acc: 97.5600. Time/epoch: 5.1539\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0007. Train Acc: 99.1825, Test loss: 0.0029. Test Acc: 97.8000. Time/epoch: 5.0007\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train99.1825
Accuracy/val97.8
Loss/train0.00071
Loss/val0.00285
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run charmed-sweep-45 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/cet2jps7
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/wgxc0r89" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0057. Train Acc: 88.1100, Test loss: 0.0058. Test Acc: 87.8200. Time/epoch: 3.1096\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0025. Train Acc: 93.7300, Test loss: 0.0026. Test Acc: 93.8200. Time/epoch: 2.9596\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0022. Train Acc: 94.6750, Test loss: 0.0023. Test Acc: 94.5100. Time/epoch: 2.9497\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0020. Train Acc: 95.2350, Test loss: 0.0021. Test Acc: 95.1200. Time/epoch: 3.0862\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0019. Train Acc: 95.4075, Test loss: 0.0020. Test Acc: 95.2700. Time/epoch: 3.0906\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0018. Train Acc: 95.4550, Test loss: 0.0019. Test Acc: 95.4600. Time/epoch: 2.9645\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train95.455
Accuracy/val95.46
Loss/train0.0018
Loss/val0.00191
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run stilted-sweep-46 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/wgxc0r89
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Run summary:


Accuracy/train36.825
Accuracy/val37.54
Loss/train0.02403
Loss/val0.02405
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run visionary-sweep-47 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/wpego9ua
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Run summary:


Accuracy/train95.5025
Accuracy/val95.56
Loss/train0.0009
Loss/val0.00096
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run bright-sweep-48 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ntymp54i
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Run summary:


Accuracy/train95.5525
Accuracy/val95.25
Loss/train0.00197
Loss/val0.00217
epoch50

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/rvcbs4dg" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0038. Train Acc: 82.9650, Test loss: 0.0038. Test Acc: 82.7400. Time/epoch: 2.1988\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0013. Train Acc: 93.0050, Test loss: 0.0013. Test Acc: 93.0800. Time/epoch: 2.3553\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0010. Train Acc: 95.3475, Test loss: 0.0010. Test Acc: 95.1500. Time/epoch: 2.3193\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0010. Train Acc: 94.9400, Test loss: 0.0010. Test Acc: 94.9600. Time/epoch: 2.3394\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0009. Train Acc: 95.6050, Test loss: 0.0009. Test Acc: 95.5400. Time/epoch: 2.1788\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0010. Train Acc: 95.0950, Test loss: 0.0010. Test Acc: 95.0300. Time/epoch: 2.1963\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train95.095
Accuracy/val95.03
Loss/train0.00097
Loss/val0.001
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run peach-sweep-50 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/rvcbs4dg
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Accuracy/train97.645
Accuracy/val96.57
Loss/train0.00049
Loss/val0.00079
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run different-sweep-51 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ykrhvl2b
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Run summary:


Accuracy/train95.8775
Accuracy/val94.9
Loss/train0.00158
Loss/val0.00222
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run polished-sweep-52 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ru1sh0ei
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/pxhxw92y" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0042. Train Acc: 87.8425, Test loss: 0.0043. Test Acc: 87.7200. Time/epoch: 3.0504\n", " EPOCH 10. Progress: 100.0%. \n", " Train loss: 0.0023. Train Acc: 94.3600, Test loss: 0.0024. Test Acc: 94.1600. Time/epoch: 3.1346\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train94.36
Accuracy/val94.16
Loss/train0.00226
Loss/val0.00243
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run pretty-sweep-53 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/pxhxw92y
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Accuracy/train93.5025
Accuracy/val93.51
Loss/train0.00244
Loss/val0.00249
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run robust-sweep-54 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/um2tdmbb
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/hcn68694" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0060. Train Acc: 91.3650, Test loss: 0.0064. Test Acc: 91.2400. Time/epoch: 4.5928\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0039. Train Acc: 95.1425, Test loss: 0.0040. Test Acc: 95.1500. Time/epoch: 4.7674\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0026. Train Acc: 96.7850, Test loss: 0.0028. Test Acc: 96.5700. Time/epoch: 4.7427\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0034. Train Acc: 95.6125, Test loss: 0.0036. Test Acc: 95.4800. Time/epoch: 4.5831\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0026. Train Acc: 96.7900, Test loss: 0.0031. Test Acc: 96.3800. Time/epoch: 4.5847\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0019. Train Acc: 97.6125, Test loss: 0.0024. Test Acc: 97.1300. Time/epoch: 4.5635\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train97.6125
Accuracy/val97.13
Loss/train0.00193
Loss/val0.00244
epoch50

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Accuracy/train97.5575
Accuracy/val97.21
Loss/train0.00052
Loss/val0.0006
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run driven-sweep-56 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/1j8th984
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Run summary:


Accuracy/train99.16
Accuracy/val97.59
Loss/train0.00038
Loss/val0.00129
epoch50

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/a1co11pt" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0025. Train Acc: 87.2775, Test loss: 0.0025. Test Acc: 87.2400. Time/epoch: 2.4782\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0011. Train Acc: 94.2025, Test loss: 0.0012. Test Acc: 93.7700. Time/epoch: 2.4222\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0010. Train Acc: 94.8650, Test loss: 0.0011. Test Acc: 94.3300. Time/epoch: 2.4377\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0008. Train Acc: 96.0625, Test loss: 0.0009. Test Acc: 95.4800. Time/epoch: 2.3026\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0007. Train Acc: 96.4650, Test loss: 0.0008. Test Acc: 95.9900. Time/epoch: 2.4351\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0006. Train Acc: 97.0775, Test loss: 0.0008. Test Acc: 96.6400. Time/epoch: 2.4276\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train97.0775
Accuracy/val96.64
Loss/train0.00062
Loss/val0.00075
epoch50

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Run summary:


Accuracy/train36.825
Accuracy/val37.54
Loss/train0.04806
Loss/val0.04796
epoch10

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Accuracy/train95.4
Accuracy/val94.77
Loss/train0.00427
Loss/val0.00538
epoch20

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Accuracy/train96.1525
Accuracy/val95.73
Loss/train0.00157
Loss/val0.00169
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run smart-sweep-61 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/24buqjcp
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Accuracy/train94.9975
Accuracy/val95.08
Loss/train0.00388
Loss/val0.00399
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run dandy-sweep-62 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/t7ub61uh
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Accuracy/train93.8025
Accuracy/val93.97
Loss/train0.00112
Loss/val0.00113
epoch10

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Accuracy/train97.015
Accuracy/val96.82
Loss/train0.00253
Loss/val0.0028
epoch50

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Accuracy/train36.825
Accuracy/val37.54
Loss/train0.01216
Loss/val0.01224
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run glorious-sweep-65 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/9605oqnw
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Accuracy/train36.825
Accuracy/val37.54
Loss/train0.02403
Loss/val0.02406
epoch50

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Accuracy/train96.245
Accuracy/val96.18
Loss/train0.00073
Loss/val0.00079
epoch50

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Accuracy/train36.825
Accuracy/val37.54
Loss/train0.04808
Loss/val0.04795
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run fine-sweep-68 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/7fx470wl
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Accuracy/train96.5075
Accuracy/val96.0
Loss/train0.00069
Loss/val0.00085
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run robust-sweep-69 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/5wjouk0d
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ualodyxv" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0121. Train Acc: 36.8250, Test loss: 0.0121. Test Acc: 37.5400. Time/epoch: 2.4980\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0120. Train Acc: 36.8250, Test loss: 0.0121. Test Acc: 37.5400. Time/epoch: 2.4229\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0120. Train Acc: 36.8250, Test loss: 0.0121. Test Acc: 37.5400. Time/epoch: 2.4428\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0120. Train Acc: 36.8250, Test loss: 0.0121. Test Acc: 37.5400. Time/epoch: 2.3018\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0120. Train Acc: 36.8250, Test loss: 0.0121. Test Acc: 37.5400. Time/epoch: 2.2865\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0120. Train Acc: 36.8250, Test loss: 0.0121. Test Acc: 37.5400. Time/epoch: 2.2946\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
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Loss/train█▄▂▄▃▅▂▅▃▂▃▁▃▇▂▁▂▄▂▂▂▂▂▂▂▁▂▂▂▂▂▂▁▁▃▂▁▂▁▁
Loss/val▅▄▃▃▃▆▄▄▄█▄▃▅▄▄▂▃▅▃▃▆▂▂▅▄▄▄▃▃▄▃▃▄▃▃▂▆▂▂▁
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Run summary:


Accuracy/train36.825
Accuracy/val37.54
Loss/train0.01203
Loss/val0.01208
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run worthy-sweep-70 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ualodyxv
Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_021315-ualodyxv/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: aica9whg with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 128\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 50\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1e-05\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: rmsprop\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.05\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_021530-aica9whg" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run stoic-sweep-71 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/aica9whg" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0047. Train Acc: 78.7500, Test loss: 0.0047. Test Acc: 78.8200. Time/epoch: 2.3694\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0017. Train Acc: 89.8825, Test loss: 0.0018. Test Acc: 89.6000. Time/epoch: 2.2194\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0014. Train Acc: 92.1825, Test loss: 0.0015. Test Acc: 91.9300. Time/epoch: 2.3605\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0013. Train Acc: 93.3900, Test loss: 0.0013. Test Acc: 93.2300. Time/epoch: 2.3704\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0012. Train Acc: 93.8375, Test loss: 0.0012. Test Acc: 93.8600. Time/epoch: 2.2167\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0011. Train Acc: 94.0700, Test loss: 0.0012. Test Acc: 94.1000. Time/epoch: 2.2159\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train▁▄▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇▇▇▇▇▇▇████████████████
Accuracy/val▁▄▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇▇▇▇▇▇▇▇███████████████
Loss/train█▅▄▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
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Run summary:


Accuracy/train94.07
Accuracy/val94.1
Loss/train0.00113
Loss/val0.00118
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run stoic-sweep-71 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/aica9whg
Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_021530-aica9whg/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: ye109ifx with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 64\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 50\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.0001\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: sgd\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.0005\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_021743-ye109ifx" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run kind-sweep-72 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ye109ifx" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0037. Train Acc: 89.5775, Test loss: 0.0039. Test Acc: 89.3200. Time/epoch: 3.1174\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0017. Train Acc: 95.9275, Test loss: 0.0018. Test Acc: 95.7400. Time/epoch: 3.0752\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0014. Train Acc: 96.5225, Test loss: 0.0016. Test Acc: 96.3300. Time/epoch: 2.9782\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0013. Train Acc: 96.7325, Test loss: 0.0015. Test Acc: 96.3300. Time/epoch: 3.1177\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0012. Train Acc: 96.8750, Test loss: 0.0014. Test Acc: 96.6700. Time/epoch: 3.0941\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0009. Train Acc: 97.8125, Test loss: 0.0011. Test Acc: 97.4300. Time/epoch: 3.0942\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train▁▄▅▅▆▆▆▆▆▆▆▆▆▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇█▇██▇█████
Accuracy/val▁▄▅▆▆▆▆▆▇▆▆▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇██▇██▇█████
Loss/train█▆▅▄▄▄▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▁▂▁▁▂▁▁▁▁▁
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epoch▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇███

Run summary:


Accuracy/train97.8125
Accuracy/val97.43
Loss/train0.00092
Loss/val0.00108
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run kind-sweep-72 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ye109ifx
Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_021743-ye109ifx/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: k6x3u2n4 with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 32\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 50\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1e-05\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: sgd\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.05\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_022031-k6x3u2n4" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run bumbling-sweep-73 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/k6x3u2n4" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0143. Train Acc: 80.5825, Test loss: 0.0144. Test Acc: 80.4000. Time/epoch: 4.7606\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0045. Train Acc: 94.2575, Test loss: 0.0047. Test Acc: 94.1900. Time/epoch: 4.7643\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0041. Train Acc: 94.9200, Test loss: 0.0043. Test Acc: 94.8200. Time/epoch: 4.7542\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0038. Train Acc: 95.3125, Test loss: 0.0039. Test Acc: 95.0100. Time/epoch: 4.6119\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0038. Train Acc: 95.4925, Test loss: 0.0039. Test Acc: 95.3200. Time/epoch: 4.6420\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0035. Train Acc: 95.5825, Test loss: 0.0037. Test Acc: 95.3700. Time/epoch: 4.6310\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

Run history:


Accuracy/train▁▅▆▆▇▇▇▇▇▇▇█▇█▇▇█████▇█▇████████████████
Accuracy/val▁▅▆▆▇▇▇▇▇▇▇███▇▇█████▇█▇██████▇█████████
Loss/train█▅▄▃▂▂▂▂▂▂▂▁▁▁▁▂▁▁▁▁▁▁▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
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Run summary:


Accuracy/train95.5825
Accuracy/val95.37
Loss/train0.00352
Loss/val0.00369
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run bumbling-sweep-73 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/k6x3u2n4
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Accuracy/train98.605
Accuracy/val97.02
Loss/train0.00118
Loss/val0.00372
epoch50

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Accuracy/train93.1525
Accuracy/val93.24
Loss/train0.00253
Loss/val0.00261
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run curious-sweep-75 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/yjbe5i0g
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/6048t5bg" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0020. Train Acc: 90.9550, Test loss: 0.0020. Test Acc: 91.2400. Time/epoch: 2.4864\n", " EPOCH 10. Progress: 100.0%. \n", " Train loss: 0.0010. Train Acc: 95.1750, Test loss: 0.0010. Test Acc: 95.1600. Time/epoch: 2.4245\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train95.175
Accuracy/val95.16
Loss/train0.00097
Loss/val0.00101
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run clean-sweep-76 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/6048t5bg
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Accuracy/train95.0275
Accuracy/val95.1
Loss/train0.00194
Loss/val0.00204
epoch50

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Accuracy/train93.64
Accuracy/val94.03
Loss/train0.00121
Loss/val0.00124
epoch10

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Accuracy/train36.825
Accuracy/val37.54
Loss/train0.02402
Loss/val0.02407
epoch10

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Accuracy/train95.9375
Accuracy/val95.66
Loss/train0.00159
Loss/val0.00169
epoch50

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Accuracy/train36.825
Accuracy/val37.54
Loss/train0.04805
Loss/val0.04794
epoch10

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Accuracy/train92.8875
Accuracy/val92.78
Loss/train0.00131
Loss/val0.00139
epoch10

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Accuracy/train95.695
Accuracy/val95.62
Loss/train0.00349
Loss/val0.00367
epoch20

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Loss/val█▄▄▄▃▃▄▁▄▄▃
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Run summary:


Accuracy/train93.59
Accuracy/val93.7
Loss/train0.00235
Loss/val0.00236
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run magic-sweep-84 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/lpnx987r
Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_024151-lpnx987r/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 1sytqfqj with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 128\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 50\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.0001\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: sgd\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.0005\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_024239-1sytqfqj" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run eager-sweep-85 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/1sytqfqj" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0019. Train Acc: 91.2225, Test loss: 0.0019. Test Acc: 91.1300. Time/epoch: 2.3752\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0010. Train Acc: 95.2700, Test loss: 0.0010. Test Acc: 95.0900. Time/epoch: 2.1876\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0008. Train Acc: 95.9575, Test loss: 0.0008. Test Acc: 95.9300. Time/epoch: 2.3217\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0008. Train Acc: 95.8325, Test loss: 0.0009. Test Acc: 95.5600. Time/epoch: 2.3351\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0007. Train Acc: 96.5900, Test loss: 0.0007. Test Acc: 96.3200. Time/epoch: 2.3392\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0007. Train Acc: 96.2000, Test loss: 0.0008. Test Acc: 95.9700. Time/epoch: 2.1825\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train96.2
Accuracy/val95.97
Loss/train0.00074
Loss/val0.0008
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run eager-sweep-85 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/1sytqfqj
Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_024239-1sytqfqj/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: b6eyrf08 with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 64\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 50\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.0001\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: rmsprop\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.0005\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_024450-b6eyrf08" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run radiant-sweep-86 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/b6eyrf08" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0047. Train Acc: 86.9175, Test loss: 0.0047. Test Acc: 86.9200. Time/epoch: 3.2241\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0015. Train Acc: 96.1675, Test loss: 0.0016. Test Acc: 96.0600. Time/epoch: 3.0156\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0010. Train Acc: 97.7150, Test loss: 0.0012. Test Acc: 97.2600. Time/epoch: 3.0039\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0007. Train Acc: 98.4500, Test loss: 0.0009. Test Acc: 97.9400. Time/epoch: 3.1780\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0005. Train Acc: 98.8300, Test loss: 0.0009. Test Acc: 98.2600. Time/epoch: 3.1700\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0006. Train Acc: 98.6625, Test loss: 0.0009. Test Acc: 97.7700. Time/epoch: 3.1696\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train98.6625
Accuracy/val97.77
Loss/train0.00057
Loss/val0.00095
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run radiant-sweep-86 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/b6eyrf08
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/w3vdhzz8" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0121. Train Acc: 36.8250, Test loss: 0.0121. Test Acc: 37.5400. Time/epoch: 2.4030\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0121. Train Acc: 36.8250, Test loss: 0.0121. Test Acc: 37.5400. Time/epoch: 2.3531\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0121. Train Acc: 36.8250, Test loss: 0.0122. Test Acc: 37.5400. Time/epoch: 2.2054\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0121. Train Acc: 36.8250, Test loss: 0.0122. Test Acc: 37.5400. Time/epoch: 2.2200\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0121. Train Acc: 36.8250, Test loss: 0.0122. Test Acc: 37.5400. Time/epoch: 2.3810\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0120. Train Acc: 36.8250, Test loss: 0.0121. Test Acc: 37.5400. Time/epoch: 2.3692\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train36.825
Accuracy/val37.54
Loss/train0.01205
Loss/val0.01212
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run prime-sweep-87 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/w3vdhzz8
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/eu9lai07" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0044. Train Acc: 75.5350, Test loss: 0.0044. Test Acc: 76.2800. Time/epoch: 2.2035\n", " EPOCH 10. Progress: 100.0%. \n", " Train loss: 0.0013. Train Acc: 93.4050, Test loss: 0.0014. Test Acc: 93.2300. Time/epoch: 2.3293\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

Run history:


Accuracy/train▁▆▇▇█▇█████
Accuracy/val▁▆▇▇█▇█████
Loss/train█▃▂▂▁▂▁▁▁▁▁
Loss/val█▃▂▂▁▂▁▁▁▁▁
epoch▁▂▂▃▄▅▅▆▇▇█

Run summary:


Accuracy/train93.405
Accuracy/val93.23
Loss/train0.00134
Loss/val0.00142
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run gentle-sweep-88 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/eu9lai07
Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_024954-eu9lai07/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Sweep Agent: Waiting for job.\n", "\u001b[34m\u001b[1mwandb\u001b[0m: Job received.\n", "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: u7xrakak with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 128\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 50\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.0003\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: sgd\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.0005\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_025041-u7xrakak" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run efficient-sweep-89 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/u7xrakak" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0033. Train Acc: 81.8575, Test loss: 0.0033. Test Acc: 81.8200. Time/epoch: 2.2008\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0013. Train Acc: 93.2875, Test loss: 0.0013. Test Acc: 93.1900. Time/epoch: 2.1925\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0013. Train Acc: 93.7175, Test loss: 0.0013. Test Acc: 93.5500. Time/epoch: 2.3314\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0009. Train Acc: 95.6000, Test loss: 0.0010. Test Acc: 95.1500. Time/epoch: 2.3217\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0008. Train Acc: 96.1400, Test loss: 0.0009. Test Acc: 95.5400. Time/epoch: 2.2083\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0007. Train Acc: 96.5650, Test loss: 0.0009. Test Acc: 95.7400. Time/epoch: 2.2010\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train96.565
Accuracy/val95.74
Loss/train0.0007
Loss/val0.00088
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run efficient-sweep-89 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/u7xrakak
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Run summary:


Accuracy/train36.825
Accuracy/val37.54
Loss/train0.01203
Loss/val0.01213
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run zesty-sweep-90 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/p4whnt5d
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/9mzw55p6" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0041. Train Acc: 89.8300, Test loss: 0.0042. Test Acc: 89.6100. Time/epoch: 3.1495\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0020. Train Acc: 94.8225, Test loss: 0.0021. Test Acc: 94.7100. Time/epoch: 3.1173\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0017. Train Acc: 95.6725, Test loss: 0.0019. Test Acc: 95.5400. Time/epoch: 2.9793\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0017. Train Acc: 95.6575, Test loss: 0.0019. Test Acc: 95.4800. Time/epoch: 3.1003\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0022. Train Acc: 94.2775, Test loss: 0.0023. Test Acc: 94.3300. Time/epoch: 3.0977\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0015. Train Acc: 96.1125, Test loss: 0.0017. Test Acc: 95.9600. Time/epoch: 3.1133\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train96.1125
Accuracy/val95.96
Loss/train0.00151
Loss/val0.00168
epoch50

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Run summary:


Accuracy/train36.825
Accuracy/val37.54
Loss/train0.02402
Loss/val0.02407
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run pleasant-sweep-92 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/migt86gf
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Accuracy/train92.3425
Accuracy/val92.1
Loss/train0.00576
Loss/val0.00596
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run vague-sweep-93 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/3zun05t5
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Accuracy/train36.825
Accuracy/val37.54
Loss/train0.01203
Loss/val0.01212
epoch10

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Accuracy/train92.3075
Accuracy/val90.65
Loss/train0.00355
Loss/val0.00455
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run bright-sweep-95 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/7xfwvxrr
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Accuracy/train92.965
Accuracy/val92.87
Loss/train0.00135
Loss/val0.00138
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run young-sweep-96 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/agga0njv
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Accuracy/train95.0225
Accuracy/val94.75
Loss/train0.00385
Loss/val0.00431
epoch10

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Accuracy/train36.825
Accuracy/val37.54
Loss/train0.04808
Loss/val0.04796
epoch20

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Accuracy/train94.4275
Accuracy/val94.48
Loss/train0.00474
Loss/val0.00494
epoch10

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Run summary:


Accuracy/train96.025
Accuracy/val95.71
Loss/train0.00076
Loss/val0.00084
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run clear-sweep-100 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/uiatywb3
Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_030704-uiatywb3/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: zovc4bqy with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 64\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 20\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.0003\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.05\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_030915-zovc4bqy" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run proud-sweep-101 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/zovc4bqy" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0035. Train Acc: 89.5725, Test loss: 0.0036. Test Acc: 89.5100. Time/epoch: 3.3588\n", " EPOCH 10. Progress: 50.0%. \n", " Train loss: 0.0017. Train Acc: 95.7350, Test loss: 0.0019. Test Acc: 95.2800. Time/epoch: 3.1717\n", " EPOCH 20. Progress: 100.0%. \n", " Train loss: 0.0009. Train Acc: 97.8425, Test loss: 0.0014. Test Acc: 96.8700. Time/epoch: 3.1696\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/val▁▅▅▅▆▆▁▇▇█▆▇▇▆█▇▅▇███
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Run summary:


Accuracy/train97.8425
Accuracy/val96.87
Loss/train0.00089
Loss/val0.00137
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run proud-sweep-101 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/zovc4bqy
Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_030915-zovc4bqy/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: z5r2tlkg with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 128\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 50\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1e-05\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: rmsprop\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.05\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_031041-z5r2tlkg" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run sweet-sweep-102 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/z5r2tlkg" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0026. Train Acc: 89.0050, Test loss: 0.0026. Test Acc: 88.8600. Time/epoch: 2.3906\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0011. Train Acc: 94.3475, Test loss: 0.0012. Test Acc: 94.2700. Time/epoch: 2.2251\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0010. Train Acc: 95.2050, Test loss: 0.0011. Test Acc: 94.9500. Time/epoch: 2.2297\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0009. Train Acc: 95.3600, Test loss: 0.0010. Test Acc: 95.2900. Time/epoch: 2.3617\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0009. Train Acc: 95.7775, Test loss: 0.0009. Test Acc: 95.5500. Time/epoch: 2.3671\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0009. Train Acc: 95.9625, Test loss: 0.0009. Test Acc: 95.7900. Time/epoch: 2.2033\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train95.9625
Accuracy/val95.79
Loss/train0.00088
Loss/val0.00092
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run sweet-sweep-102 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/z5r2tlkg
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/9ogv0d1d" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0013. Train Acc: 93.2350, Test loss: 0.0014. Test Acc: 93.1000. Time/epoch: 2.3610\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0009. Train Acc: 95.3750, Test loss: 0.0010. Test Acc: 95.3200. Time/epoch: 2.3356\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0006. Train Acc: 97.2500, Test loss: 0.0006. Test Acc: 97.0500. Time/epoch: 2.1837\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0006. Train Acc: 96.9925, Test loss: 0.0007. Test Acc: 96.6600. Time/epoch: 2.3134\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0006. Train Acc: 96.8000, Test loss: 0.0007. Test Acc: 96.4400. Time/epoch: 2.3188\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0005. Train Acc: 97.5625, Test loss: 0.0006. Test Acc: 97.2300. Time/epoch: 2.3414\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train97.5625
Accuracy/val97.23
Loss/train0.0005
Loss/val0.00062
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run wobbly-sweep-103 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/9ogv0d1d
Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_031254-9ogv0d1d/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: vhvf0jcd with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 128\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.01\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: rmsprop\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.0005\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_031505-vhvf0jcd" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run mild-sweep-104 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/vhvf0jcd" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0047. Train Acc: 77.7925, Test loss: 0.0046. Test Acc: 78.7000. Time/epoch: 2.4174\n", " EPOCH 10. Progress: 100.0%. \n", " Train loss: 0.0120. Train Acc: 36.8250, Test loss: 0.0121. Test Acc: 37.5400. Time/epoch: 2.3413\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train36.825
Accuracy/val37.54
Loss/train0.01203
Loss/val0.01214
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run mild-sweep-104 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/vhvf0jcd
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/5jzm4hd6" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0195. Train Acc: 78.7525, Test loss: 0.0194. Test Acc: 79.0700. Time/epoch: 4.9323\n", " EPOCH 10. Progress: 50.0%. \n", " Train loss: 0.0050. Train Acc: 94.3575, Test loss: 0.0053. Test Acc: 93.9700. Time/epoch: 4.8345\n", " EPOCH 20. Progress: 100.0%. \n", " Train loss: 0.0047. Train Acc: 94.2750, Test loss: 0.0050. Test Acc: 93.8500. Time/epoch: 4.8189\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train94.275
Accuracy/val93.85
Loss/train0.0047
Loss/val0.005
epoch20

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/x5wzmb9m" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0136. Train Acc: 81.9050, Test loss: 0.0138. Test Acc: 81.6700. Time/epoch: 4.7866\n", " EPOCH 10. Progress: 50.0%. \n", " Train loss: 0.0047. Train Acc: 93.9850, Test loss: 0.0049. Test Acc: 94.1600. Time/epoch: 4.7301\n", " EPOCH 20. Progress: 100.0%. \n", " Train loss: 0.0041. Train Acc: 94.7500, Test loss: 0.0042. Test Acc: 95.0900. Time/epoch: 4.7042\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train94.75
Accuracy/val95.09
Loss/train0.00407
Loss/val0.00423
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run vocal-sweep-106 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/x5wzmb9m
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/jd4zr49s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0078. Train Acc: 89.4400, Test loss: 0.0081. Test Acc: 89.3800. Time/epoch: 5.2359\n", " EPOCH 10. Progress: 50.0%. \n", " Train loss: 0.0036. Train Acc: 95.1000, Test loss: 0.0038. Test Acc: 95.2400. Time/epoch: 5.1533\n", " EPOCH 20. Progress: 100.0%. \n", " Train loss: 0.0028. Train Acc: 96.6600, Test loss: 0.0032. Test Acc: 96.2400. Time/epoch: 5.1585\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train96.66
Accuracy/val96.24
Loss/train0.00278
Loss/val0.00316
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run vocal-sweep-107 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/jd4zr49s
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/p9k6cxx7" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0018. Train Acc: 88.2000, Test loss: 0.0018. Test Acc: 88.5900. Time/epoch: 2.3159\n", " EPOCH 10. Progress: 50.0%. \n", " Train loss: 0.0007. Train Acc: 96.4925, Test loss: 0.0008. Test Acc: 96.2200. Time/epoch: 2.4209\n", " EPOCH 20. Progress: 100.0%. \n", " Train loss: 0.0005. Train Acc: 97.6750, Test loss: 0.0006. Test Acc: 97.1800. Time/epoch: 2.4273\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train97.675
Accuracy/val97.18
Loss/train0.00048
Loss/val0.00061
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run worthy-sweep-108 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/p9k6cxx7
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Accuracy/train98.7525
Accuracy/val97.61
Loss/train0.00054
Loss/val0.0012
epoch50

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/zyau70g3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0241. Train Acc: 36.8250, Test loss: 0.0241. Test Acc: 37.5400. Time/epoch: 3.0341\n", " EPOCH 10. Progress: 50.0%. \n", " Train loss: 0.0240. Train Acc: 36.8250, Test loss: 0.0240. Test Acc: 37.5400. Time/epoch: 3.0451\n", " EPOCH 20. Progress: 100.0%. \n", " Train loss: 0.0240. Train Acc: 36.8250, Test loss: 0.0241. Test Acc: 37.5400. Time/epoch: 3.0080\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train36.825
Accuracy/val37.54
Loss/train0.02402
Loss/val0.02406
epoch20

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Accuracy/train95.44
Accuracy/val95.32
Loss/train0.00189
Loss/val0.00202
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run radiant-sweep-111 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/2luvylas
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Accuracy/train93.76
Accuracy/val93.97
Loss/train0.00261
Loss/val0.00265
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run hopeful-sweep-112 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/tvzq27e3
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Accuracy/train94.905
Accuracy/val94.71
Loss/train0.00194
Loss/val0.00209
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run fluent-sweep-113 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/p67cfog1
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Accuracy/train98.5625
Accuracy/val98.28
Loss/train0.00128
Loss/val0.00174
epoch50

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Accuracy/train97.0625
Accuracy/val96.83
Loss/train0.00061
Loss/val0.00069
epoch50

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Accuracy/train98.95
Accuracy/val97.94
Loss/train0.00024
Loss/val0.0005
epoch50

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Accuracy/train95.7625
Accuracy/val95.83
Loss/train0.00164
Loss/val0.00169
epoch20

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Accuracy/train95.695
Accuracy/val95.49
Loss/train0.00085
Loss/val0.00091
epoch10

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Accuracy/train95.365
Accuracy/val95.1
Loss/train0.00186
Loss/val0.00194
epoch10

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Accuracy/train93.735
Accuracy/val93.37
Loss/train0.0025
Loss/val0.00266
epoch20

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/s9ih4ev0" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0038. Train Acc: 91.0400, Test loss: 0.0039. Test Acc: 90.8100. Time/epoch: 3.3814\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0024. Train Acc: 93.5725, Test loss: 0.0026. Test Acc: 93.3100. Time/epoch: 3.2949\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0009. Train Acc: 98.0000, Test loss: 0.0013. Test Acc: 97.1200. Time/epoch: 3.2832\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0010. Train Acc: 97.7750, Test loss: 0.0016. Test Acc: 96.7800. Time/epoch: 3.1826\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0006. Train Acc: 98.6025, Test loss: 0.0017. Test Acc: 96.9600. Time/epoch: 3.2852\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0013. Train Acc: 96.8175, Test loss: 0.0028. Test Acc: 95.3000. Time/epoch: 3.3088\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train96.8175
Accuracy/val95.3
Loss/train0.0013
Loss/val0.00278
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run major-sweep-121 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/s9ih4ev0
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/mfega71i" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0121. Train Acc: 37.4350, Test loss: 0.0122. Test Acc: 38.0400. Time/epoch: 2.2479\n", " EPOCH 10. Progress: 50.0%. \n", " Train loss: 0.0122. Train Acc: 36.8250, Test loss: 0.0123. Test Acc: 37.5400. Time/epoch: 2.1978\n", " EPOCH 20. Progress: 100.0%. \n", " Train loss: 0.0122. Train Acc: 36.8250, Test loss: 0.0122. Test Acc: 37.5400. Time/epoch: 2.3595\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train36.825
Accuracy/val37.54
Loss/train0.01216
Loss/val0.01224
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run autumn-sweep-122 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/mfega71i
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Accuracy/train36.825
Accuracy/val37.54
Loss/train0.01203
Loss/val0.01209
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run eager-sweep-123 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/sagxbth3
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Accuracy/train36.825
Accuracy/val37.54
Loss/train0.04805
Loss/val0.04797
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run stellar-sweep-124 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/eko31h9c
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Accuracy/train98.9125
Accuracy/val97.4
Loss/train0.00089
Loss/val0.00354
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run floral-sweep-125 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/rc4w0al7
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/3hue7myw" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0017. Train Acc: 90.8700, Test loss: 0.0018. Test Acc: 90.8500. Time/epoch: 2.4444\n", " EPOCH 10. Progress: 50.0%. \n", " Train loss: 0.0007. Train Acc: 96.5750, Test loss: 0.0008. Test Acc: 96.2600. Time/epoch: 2.4537\n", " EPOCH 20. Progress: 100.0%. \n", " Train loss: 0.0006. Train Acc: 97.2350, Test loss: 0.0007. Test Acc: 96.7600. Time/epoch: 2.3120\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train97.235
Accuracy/val96.76
Loss/train0.00056
Loss/val0.00067
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run rare-sweep-126 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/3hue7myw
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Accuracy/train36.825
Accuracy/val37.54
Loss/train0.01203
Loss/val0.0121
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run drawn-sweep-127 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/g2qv1hr9
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Accuracy/train96.0875
Accuracy/val95.71
Loss/train0.00317
Loss/val0.00357
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run wild-sweep-128 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/14i7qqb3
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Accuracy/train98.125
Accuracy/val97.56
Loss/train0.00153
Loss/val0.00214
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run ancient-sweep-129 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ikirp9xn
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Accuracy/train98.6475
Accuracy/val97.94
Loss/train0.00053
Loss/val0.00104
epoch50

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/bms9a5ow" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0086. Train Acc: 83.8575, Test loss: 0.0087. Test Acc: 83.9500. Time/epoch: 2.9495\n", " EPOCH 10. Progress: 50.0%. \n", " Train loss: 0.0029. Train Acc: 92.1350, Test loss: 0.0031. Test Acc: 91.9500. Time/epoch: 3.0603\n", " EPOCH 20. Progress: 100.0%. \n", " Train loss: 0.0026. Train Acc: 93.2900, Test loss: 0.0027. Test Acc: 93.1500. Time/epoch: 3.0438\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train93.29
Accuracy/val93.15
Loss/train0.00255
Loss/val0.00269
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run effortless-sweep-131 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/bms9a5ow
Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_040201-bms9a5ow/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: ihr6pabg with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 128\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 50\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.0003\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.005\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_040317-ihr6pabg" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run glowing-sweep-132 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ihr6pabg" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0016. Train Acc: 90.3525, Test loss: 0.0017. Test Acc: 90.4900. Time/epoch: 2.2637\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0007. Train Acc: 96.8375, Test loss: 0.0007. Test Acc: 96.4900. Time/epoch: 2.3961\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0007. Train Acc: 96.5400, Test loss: 0.0008. Test Acc: 95.9700. Time/epoch: 2.4040\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0004. Train Acc: 98.0225, Test loss: 0.0006. Test Acc: 97.0300. Time/epoch: 2.3816\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0004. Train Acc: 97.9450, Test loss: 0.0007. Test Acc: 96.8300. Time/epoch: 2.2781\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0003. Train Acc: 98.3350, Test loss: 0.0007. Test Acc: 97.1100. Time/epoch: 2.2493\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train98.335
Accuracy/val97.11
Loss/train0.00035
Loss/val0.00074
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run glowing-sweep-132 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ihr6pabg
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/4ecbpj83" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0304. Train Acc: 83.0275, Test loss: 0.0309. Test Acc: 83.3500. Time/epoch: 4.7452\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0061. Train Acc: 92.8800, Test loss: 0.0062. Test Acc: 93.0900. Time/epoch: 4.5664\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0056. Train Acc: 93.4800, Test loss: 0.0057. Test Acc: 93.3600. Time/epoch: 4.5281\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0054. Train Acc: 93.1425, Test loss: 0.0054. Test Acc: 93.2300. Time/epoch: 4.7129\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0052. Train Acc: 93.7025, Test loss: 0.0053. Test Acc: 93.6900. Time/epoch: 4.6930\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0052. Train Acc: 93.7325, Test loss: 0.0052. Test Acc: 93.8400. Time/epoch: 4.7197\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train93.7325
Accuracy/val93.84
Loss/train0.00516
Loss/val0.00522
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run tough-sweep-133 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/4ecbpj83
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Run summary:


Accuracy/train95.72
Accuracy/val95.75
Loss/train0.00085
Loss/val0.00092
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run dulcet-sweep-134 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ddvk082j
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ccaoqtld" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0053. Train Acc: 63.4450, Test loss: 0.0053. Test Acc: 64.0900. Time/epoch: 2.3517\n", " EPOCH 10. Progress: 100.0%. \n", " Train loss: 0.0026. Train Acc: 86.4825, Test loss: 0.0026. Test Acc: 86.9600. Time/epoch: 2.1717\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train86.4825
Accuracy/val86.96
Loss/train0.00261
Loss/val0.00265
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run good-sweep-135 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ccaoqtld
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/tv6lmibh" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0482. Train Acc: 36.8250, Test loss: 0.0481. Test Acc: 37.5400. Time/epoch: 4.8126\n", " EPOCH 10. Progress: 50.0%. \n", " Train loss: 0.0480. Train Acc: 36.8250, Test loss: 0.0480. Test Acc: 37.5400. Time/epoch: 4.8172\n", " EPOCH 20. Progress: 100.0%. \n", " Train loss: 0.0480. Train Acc: 36.8250, Test loss: 0.0480. Test Acc: 37.5400. Time/epoch: 4.7995\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train36.825
Accuracy/val37.54
Loss/train0.04804
Loss/val0.04795
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run radiant-sweep-136 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/tv6lmibh
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/w39caajm" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0036. Train Acc: 91.1700, Test loss: 0.0037. Test Acc: 91.2600. Time/epoch: 3.3021\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0017. Train Acc: 95.7500, Test loss: 0.0018. Test Acc: 95.4500. Time/epoch: 3.2997\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0014. Train Acc: 96.6900, Test loss: 0.0015. Test Acc: 96.2900. Time/epoch: 3.1159\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0013. Train Acc: 96.6850, Test loss: 0.0015. Test Acc: 96.2900. Time/epoch: 3.3020\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0010. Train Acc: 97.5825, Test loss: 0.0012. Test Acc: 97.1500. Time/epoch: 3.2864\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0010. Train Acc: 97.5925, Test loss: 0.0012. Test Acc: 97.1400. Time/epoch: 3.2681\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train97.5925
Accuracy/val97.14
Loss/train0.00099
Loss/val0.0012
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run clear-sweep-137 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/w39caajm
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Run summary:


Accuracy/train94.94
Accuracy/val94.87
Loss/train0.00096
Loss/val0.00104
epoch10

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/rmk7xwg2" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0036. Train Acc: 77.4625, Test loss: 0.0036. Test Acc: 77.7900. Time/epoch: 2.3357\n", " EPOCH 10. Progress: 50.0%. \n", " Train loss: 0.0038. Train Acc: 78.8350, Test loss: 0.0038. Test Acc: 79.0700. Time/epoch: 2.3126\n", " EPOCH 20. Progress: 100.0%. \n", " Train loss: 0.0032. Train Acc: 81.2675, Test loss: 0.0032. Test Acc: 81.9000. Time/epoch: 2.3034\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train81.2675
Accuracy/val81.9
Loss/train0.00318
Loss/val0.00321
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run mild-sweep-139 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/rmk7xwg2
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/1wevhc82" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0024. Train Acc: 93.7875, Test loss: 0.0025. Test Acc: 93.8200. Time/epoch: 3.3504\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0012. Train Acc: 96.7600, Test loss: 0.0015. Test Acc: 96.1400. Time/epoch: 3.2640\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0007. Train Acc: 98.2800, Test loss: 0.0011. Test Acc: 97.5600. Time/epoch: 3.3123\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0007. Train Acc: 98.5250, Test loss: 0.0013. Test Acc: 97.4900. Time/epoch: 3.3069\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0005. Train Acc: 98.8725, Test loss: 0.0014. Test Acc: 97.7500. Time/epoch: 3.1509\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0006. Train Acc: 98.5400, Test loss: 0.0014. Test Acc: 97.1700. Time/epoch: 3.1518\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train98.54
Accuracy/val97.17
Loss/train0.00055
Loss/val0.00143
epoch50

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/xhucx4ro" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0141. Train Acc: 68.0175, Test loss: 0.0140. Test Acc: 68.3800. Time/epoch: 3.1224\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0032. Train Acc: 91.7075, Test loss: 0.0033. Test Acc: 92.0200. Time/epoch: 2.9470\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0027. Train Acc: 93.5925, Test loss: 0.0029. Test Acc: 93.3000. Time/epoch: 2.9227\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0024. Train Acc: 94.1850, Test loss: 0.0025. Test Acc: 93.8900. Time/epoch: 2.9237\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0022. Train Acc: 94.2875, Test loss: 0.0023. Test Acc: 94.2000. Time/epoch: 3.0559\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0019. Train Acc: 95.2075, Test loss: 0.0020. Test Acc: 95.2100. Time/epoch: 3.0550\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/val▁▅▆▆▇▇▇▇▇▇▇▇▇▇███▇██████████████████████
Loss/train█▄▃▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
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Run summary:


Accuracy/train95.2075
Accuracy/val95.21
Loss/train0.00193
Loss/val0.00204
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run sweet-sweep-141 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/xhucx4ro
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/iv54em4c" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0480. Train Acc: 36.8250, Test loss: 0.0479. Test Acc: 37.5400. Time/epoch: 4.5864\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0481. Train Acc: 36.8250, Test loss: 0.0479. Test Acc: 37.5400. Time/epoch: 4.5563\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0481. Train Acc: 36.8250, Test loss: 0.0480. Test Acc: 37.5400. Time/epoch: 4.6828\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0481. Train Acc: 36.8250, Test loss: 0.0480. Test Acc: 37.5400. Time/epoch: 4.7069\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0481. Train Acc: 36.8250, Test loss: 0.0480. Test Acc: 37.5400. Time/epoch: 4.7404\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0482. Train Acc: 36.8250, Test loss: 0.0481. Test Acc: 37.5400. Time/epoch: 4.6482\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train36.825
Accuracy/val37.54
Loss/train0.04817
Loss/val0.04809
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run trim-sweep-142 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/iv54em4c
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ma6pe9c9" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0153. Train Acc: 80.8250, Test loss: 0.0152. Test Acc: 80.6200. Time/epoch: 4.7573\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0045. Train Acc: 94.4025, Test loss: 0.0046. Test Acc: 94.7300. Time/epoch: 4.7037\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0041. Train Acc: 94.7325, Test loss: 0.0043. Test Acc: 94.8600. Time/epoch: 4.6952\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0035. Train Acc: 95.7775, Test loss: 0.0037. Test Acc: 95.6700. Time/epoch: 4.6677\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0035. Train Acc: 95.6200, Test loss: 0.0036. Test Acc: 95.5900. Time/epoch: 4.5579\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0030. Train Acc: 96.2000, Test loss: 0.0033. Test Acc: 96.2100. Time/epoch: 4.5332\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train▁▃▄▅▆▆▇▇▇▇▇▇▇▇▇▇▇█▇█▇███████████████████
Accuracy/val▁▃▄▅▆▆▇▇▇▇▇▇▇▇▇▇▇█▇█████████████████████
Loss/train█▆▅▄▃▃▂▂▂▂▂▂▂▂▂▂▂▁▁▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
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Run summary:


Accuracy/train96.2
Accuracy/val96.21
Loss/train0.00305
Loss/val0.00328
epoch50

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/23nqvcc2" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0048. Train Acc: 73.7925, Test loss: 0.0049. Test Acc: 74.1700. Time/epoch: 2.3877\n", " EPOCH 10. Progress: 100.0%. \n", " Train loss: 0.0081. Train Acc: 57.1475, Test loss: 0.0081. Test Acc: 57.7900. Time/epoch: 2.2014\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train57.1475
Accuracy/val57.79
Loss/train0.00814
Loss/val0.00813
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run resilient-sweep-144 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/23nqvcc2
Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_043309-23nqvcc2/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 8xl53zs7 with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 64\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 20\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1e-05\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: rmsprop\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.05\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_043349-8xl53zs7" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run breezy-sweep-145 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/8xl53zs7" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0059. Train Acc: 84.4050, Test loss: 0.0059. Test Acc: 84.3900. Time/epoch: 3.1628\n", " EPOCH 10. Progress: 50.0%. \n", " Train loss: 0.0026. Train Acc: 93.5075, Test loss: 0.0027. Test Acc: 93.4200. Time/epoch: 3.1191\n", " EPOCH 20. Progress: 100.0%. \n", " Train loss: 0.0023. Train Acc: 94.1400, Test loss: 0.0023. Test Acc: 94.0900. Time/epoch: 3.1040\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train94.14
Accuracy/val94.09
Loss/train0.00227
Loss/val0.00234
epoch20

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Accuracy/train93.2875
Accuracy/val93.2
Loss/train0.00258
Loss/val0.00269
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run wandering-sweep-146 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/fzw96h8a
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Accuracy/train95.6375
Accuracy/val95.69
Loss/train0.00184
Loss/val0.00193
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run revived-sweep-147 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/zyqvr0bs
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Accuracy/train93.685
Accuracy/val93.61
Loss/train0.00114
Loss/val0.00117
epoch10

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Accuracy/train94.015
Accuracy/val93.94
Loss/train0.0023
Loss/val0.00237
epoch10

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Accuracy/train83.5575
Accuracy/val83.89
Loss/train0.00291
Loss/val0.00293
epoch10

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Accuracy/train96.485
Accuracy/val95.93
Loss/train0.00074
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epoch20

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Accuracy/train95.385
Accuracy/val95.49
Loss/train0.00356
Loss/val0.00364
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run hearty-sweep-152 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ie0howoj
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Accuracy/train93.5375
Accuracy/val93.73
Loss/train0.00468
Loss/val0.00474
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run worthy-sweep-153 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/y8m14jm2
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Accuracy/train96.7925
Accuracy/val96.63
Loss/train0.00065
Loss/val0.0007
epoch20

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Accuracy/train94.6425
Accuracy/val94.8
Loss/train0.0041
Loss/val0.0042
epoch10

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Accuracy/train95.94
Accuracy/val95.8
Loss/train0.00332
Loss/val0.00352
epoch20

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Accuracy/train93.615
Accuracy/val93.54
Loss/train0.00121
Loss/val0.00125
epoch20

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/d67tqps8" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0030. Train Acc: 91.3375, Test loss: 0.0031. Test Acc: 91.4300. Time/epoch: 3.3121\n", " EPOCH 10. Progress: 100.0%. \n", " Train loss: 0.0027. Train Acc: 92.4125, Test loss: 0.0029. Test Acc: 92.1500. Time/epoch: 3.1438\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train92.4125
Accuracy/val92.15
Loss/train0.00272
Loss/val0.00291
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run polar-sweep-158 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/d67tqps8
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/qrx1ovtv" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0076. Train Acc: 89.5575, Test loss: 0.0079. Test Acc: 89.4100. Time/epoch: 4.7665\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0033. Train Acc: 95.7075, Test loss: 0.0035. Test Acc: 95.4600. Time/epoch: 4.5480\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0032. Train Acc: 95.9850, Test loss: 0.0034. Test Acc: 95.7300. Time/epoch: 4.5604\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0022. Train Acc: 97.3125, Test loss: 0.0025. Test Acc: 96.7700. Time/epoch: 4.6915\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0025. Train Acc: 96.8350, Test loss: 0.0029. Test Acc: 96.3100. Time/epoch: 4.6664\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0020. Train Acc: 97.3450, Test loss: 0.0026. Test Acc: 96.6500. Time/epoch: 4.7361\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train97.345
Accuracy/val96.65
Loss/train0.00202
Loss/val0.00257
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run smooth-sweep-159 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/qrx1ovtv
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/r0tcydda" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0065. Train Acc: 91.3400, Test loss: 0.0066. Test Acc: 91.3500. Time/epoch: 4.7255\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0027. Train Acc: 96.7075, Test loss: 0.0029. Test Acc: 96.6900. Time/epoch: 4.7231\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0021. Train Acc: 97.3500, Test loss: 0.0025. Test Acc: 97.1000. Time/epoch: 4.6715\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0023. Train Acc: 97.2850, Test loss: 0.0029. Test Acc: 96.6300. Time/epoch: 4.5619\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0011. Train Acc: 98.8325, Test loss: 0.0019. Test Acc: 97.8100. Time/epoch: 4.6892\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0010. Train Acc: 98.7925, Test loss: 0.0021. Test Acc: 97.6700. Time/epoch: 4.7450\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train98.7925
Accuracy/val97.67
Loss/train0.00099
Loss/val0.00214
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run expert-sweep-160 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/r0tcydda
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Run summary:


Accuracy/train36.825
Accuracy/val37.54
Loss/train0.04807
Loss/val0.04799
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run frosty-sweep-161 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/c8zovo8a
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/v3panwv7" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0075. Train Acc: 90.4825, Test loss: 0.0077. Test Acc: 90.5300. Time/epoch: 5.1299\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0039. Train Acc: 94.7775, Test loss: 0.0041. Test Acc: 94.7800. Time/epoch: 4.9728\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0028. Train Acc: 96.6000, Test loss: 0.0030. Test Acc: 96.3700. Time/epoch: 4.9783\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0024. Train Acc: 97.0150, Test loss: 0.0027. Test Acc: 96.6700. Time/epoch: 5.1013\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0023. Train Acc: 97.0275, Test loss: 0.0027. Test Acc: 96.8100. Time/epoch: 5.1476\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0019. Train Acc: 97.7550, Test loss: 0.0024. Test Acc: 97.1600. Time/epoch: 5.1112\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train97.755
Accuracy/val97.16
Loss/train0.00187
Loss/val0.0024
epoch50

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Accuracy/train97.1825
Accuracy/val96.74
Loss/train0.00059
Loss/val0.00069
epoch50

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Accuracy/train95.49
Accuracy/val95.28
Loss/train0.00172
Loss/val0.00192
epoch10

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Accuracy/train96.4925
Accuracy/val96.23
Loss/train0.0014
Loss/val0.00158
epoch10

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Accuracy/train93.97
Accuracy/val93.91
Loss/train0.00113
Loss/val0.0012
epoch10

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Accuracy/train91.3125
Accuracy/val91.18
Loss/train0.00159
Loss/val0.00164
epoch10

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Run summary:


Accuracy/train98.265
Accuracy/val94.76
Loss/train0.00077
Loss/val0.00307
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run dashing-sweep-168 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/5u6w994s
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/85n9g74f" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0024. Train Acc: 93.3700, Test loss: 0.0025. Test Acc: 93.2400. Time/epoch: 3.3243\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0017. Train Acc: 96.1225, Test loss: 0.0018. Test Acc: 95.9500. Time/epoch: 3.1756\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0012. Train Acc: 97.3975, Test loss: 0.0015. Test Acc: 96.5800. Time/epoch: 3.1586\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0010. Train Acc: 97.4225, Test loss: 0.0014. Test Acc: 96.7000. Time/epoch: 3.1708\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0012. Train Acc: 97.2975, Test loss: 0.0017. Test Acc: 96.5400. Time/epoch: 3.2793\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0008. Train Acc: 98.0125, Test loss: 0.0013. Test Acc: 97.2000. Time/epoch: 3.3170\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/val▃▂▃▁▆▅▅▅▆▇▆▅▆▇▅▅▇▆▇▇█▇▆▇▇▆█▇▅█▇▇██▆█▇███
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Run summary:


Accuracy/train98.0125
Accuracy/val97.2
Loss/train0.00083
Loss/val0.00129
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run leafy-sweep-169 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/85n9g74f
Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_051144-85n9g74f/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: ujqcuip6 with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 32\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 50\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 3e-05\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.05\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_051442-ujqcuip6" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run super-sweep-170 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ujqcuip6" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0067. Train Acc: 91.5300, Test loss: 0.0069. Test Acc: 91.5500. Time/epoch: 5.0092\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0035. Train Acc: 95.7025, Test loss: 0.0039. Test Acc: 95.3100. Time/epoch: 5.0090\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0030. Train Acc: 96.2850, Test loss: 0.0035. Test Acc: 95.8500. Time/epoch: 5.1127\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0023. Train Acc: 97.2225, Test loss: 0.0028. Test Acc: 96.6000. Time/epoch: 5.1614\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0022. Train Acc: 97.3725, Test loss: 0.0028. Test Acc: 96.6000. Time/epoch: 5.1437\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0020. Train Acc: 97.5775, Test loss: 0.0027. Test Acc: 96.8700. Time/epoch: 5.1046\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train97.5775
Accuracy/val96.87
Loss/train0.00201
Loss/val0.00273
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run super-sweep-170 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ujqcuip6
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/u69utldz" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0482. Train Acc: 36.8250, Test loss: 0.0480. Test Acc: 37.5400. Time/epoch: 5.1869\n", " EPOCH 10. Progress: 100.0%. \n", " Train loss: 0.0481. Train Acc: 36.8250, Test loss: 0.0480. Test Acc: 37.5400. Time/epoch: 5.1436\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train36.825
Accuracy/val37.54
Loss/train0.04806
Loss/val0.04797
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run divine-sweep-171 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/u69utldz
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/xcl9in94" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0084. Train Acc: 88.6625, Test loss: 0.0084. Test Acc: 88.4500. Time/epoch: 4.7097\n", " EPOCH 10. Progress: 50.0%. \n", " Train loss: 0.0040. Train Acc: 94.9450, Test loss: 0.0042. Test Acc: 95.1500. Time/epoch: 4.6628\n", " EPOCH 20. Progress: 100.0%. \n", " Train loss: 0.0033. Train Acc: 95.9900, Test loss: 0.0036. Test Acc: 96.0000. Time/epoch: 4.8030\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train95.99
Accuracy/val96.0
Loss/train0.00332
Loss/val0.00358
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run toasty-sweep-172 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/xcl9in94
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/7cxdc1z4" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0056. Train Acc: 92.7550, Test loss: 0.0057. Test Acc: 92.7800. Time/epoch: 4.7130\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0043. Train Acc: 94.2050, Test loss: 0.0045. Test Acc: 94.2900. Time/epoch: 4.6583\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0035. Train Acc: 95.8050, Test loss: 0.0036. Test Acc: 95.5500. Time/epoch: 4.6534\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0035. Train Acc: 95.9175, Test loss: 0.0037. Test Acc: 95.7400. Time/epoch: 4.8396\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0033. Train Acc: 95.9150, Test loss: 0.0034. Test Acc: 95.9900. Time/epoch: 4.8309\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0033. Train Acc: 96.0400, Test loss: 0.0034. Test Acc: 95.9800. Time/epoch: 4.8290\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Loss/train█▇▇▄▄▄▆▄▄▃▃▄▃▅▃▂▂▃▂▂▂▂▁▂▂▂▂▃▂▁▂▂▁▂▁▁▄▁▁▂
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Run summary:


Accuracy/train96.04
Accuracy/val95.98
Loss/train0.00333
Loss/val0.00345
epoch50

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Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_052224-7cxdc1z4/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: ne93obuu with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 32\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.001\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: sgd\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.05\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_052640-ne93obuu" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run super-sweep-174 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ne93obuu" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0489. Train Acc: 36.8250, Test loss: 0.0488. Test Acc: 37.5400. Time/epoch: 4.6089\n", " EPOCH 10. Progress: 100.0%. \n", " Train loss: 0.0485. Train Acc: 36.8250, Test loss: 0.0484. Test Acc: 37.5400. Time/epoch: 4.6947\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train36.825
Accuracy/val37.54
Loss/train0.04855
Loss/val0.04845
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run super-sweep-174 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ne93obuu
Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_052640-ne93obuu/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: y5v8up57 with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 32\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 50\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1e-05\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: rmsprop\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.005\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_052746-y5v8up57" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run robust-sweep-175 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/y5v8up57" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0089. Train Acc: 88.8375, Test loss: 0.0090. Test Acc: 88.7100. Time/epoch: 4.7166\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0046. Train Acc: 94.0175, Test loss: 0.0049. Test Acc: 93.9500. Time/epoch: 4.6910\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0039. Train Acc: 95.2975, Test loss: 0.0042. Test Acc: 95.0700. Time/epoch: 4.6789\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0033. Train Acc: 96.0875, Test loss: 0.0037. Test Acc: 95.6800. Time/epoch: 4.8283\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0031. Train Acc: 96.1325, Test loss: 0.0036. Test Acc: 95.7300. Time/epoch: 4.8624\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0030. Train Acc: 96.2850, Test loss: 0.0034. Test Acc: 95.9300. Time/epoch: 4.7943\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train96.285
Accuracy/val95.93
Loss/train0.00299
Loss/val0.00341
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run robust-sweep-175 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/y5v8up57
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/vg8m4yn1" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0014. Train Acc: 92.4675, Test loss: 0.0015. Test Acc: 92.3100. Time/epoch: 2.2125\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0008. Train Acc: 95.9925, Test loss: 0.0008. Test Acc: 95.8100. Time/epoch: 2.3310\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0005. Train Acc: 97.6750, Test loss: 0.0006. Test Acc: 97.1600. Time/epoch: 2.3246\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0009. Train Acc: 94.9925, Test loss: 0.0011. Test Acc: 94.3800. Time/epoch: 2.3237\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0003. Train Acc: 98.5775, Test loss: 0.0005. Test Acc: 97.5500. Time/epoch: 2.3323\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0003. Train Acc: 98.4100, Test loss: 0.0006. Test Acc: 97.1000. Time/epoch: 2.1881\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train98.41
Accuracy/val97.1
Loss/train0.00035
Loss/val0.00064
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run dauntless-sweep-176 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/vg8m4yn1
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/de5trgo5" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0029. Train Acc: 92.6575, Test loss: 0.0031. Test Acc: 92.4200. Time/epoch: 3.1186\n", " EPOCH 10. Progress: 50.0%. \n", " Train loss: 0.0019. Train Acc: 95.2225, Test loss: 0.0020. Test Acc: 95.1000. Time/epoch: 3.0661\n", " EPOCH 20. Progress: 100.0%. \n", " Train loss: 0.0021. Train Acc: 95.0450, Test loss: 0.0021. Test Acc: 94.9100. Time/epoch: 2.9048\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/val▁▂▄▅▄▆▆▅▇▆▇▅▅▆▇▇█▇█▆▆
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Run summary:


Accuracy/train95.045
Accuracy/val94.91
Loss/train0.00205
Loss/val0.00214
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run lunar-sweep-177 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/de5trgo5
Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_053413-de5trgo5/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: g3gifqn2 with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 64\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 20\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.0001\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: rmsprop\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.05\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_053528-g3gifqn2" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run dulcet-sweep-178 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/g3gifqn2" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0030. Train Acc: 92.4275, Test loss: 0.0031. Test Acc: 92.4600. Time/epoch: 3.1681\n", " EPOCH 10. Progress: 50.0%. \n", " Train loss: 0.0024. Train Acc: 93.8675, Test loss: 0.0024. Test Acc: 93.7700. Time/epoch: 3.1402\n", " EPOCH 20. Progress: 100.0%. \n", " Train loss: 0.0025. Train Acc: 93.5575, Test loss: 0.0025. Test Acc: 93.3100. Time/epoch: 3.1162\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train93.5575
Accuracy/val93.31
Loss/train0.00248
Loss/val0.00254
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run dulcet-sweep-178 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/g3gifqn2
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/gmswq9kf" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0051. Train Acc: 76.9100, Test loss: 0.0051. Test Acc: 77.2800. Time/epoch: 2.3900\n", " EPOCH 10. Progress: 50.0%. \n", " Train loss: 0.0013. Train Acc: 93.1200, Test loss: 0.0014. Test Acc: 93.0600. Time/epoch: 2.3309\n", " EPOCH 20. Progress: 100.0%. \n", " Train loss: 0.0012. Train Acc: 93.5450, Test loss: 0.0013. Test Acc: 93.5600. Time/epoch: 2.3350\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train93.545
Accuracy/val93.56
Loss/train0.00124
Loss/val0.00127
epoch20

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Accuracy/train70.56
Accuracy/val71.14
Loss/train0.00724
Loss/val0.00728
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run icy-sweep-180 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/jb2q9yd4
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Accuracy/train36.825
Accuracy/val37.54
Loss/train0.04805
Loss/val0.04795
epoch20

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Accuracy/train95.0675
Accuracy/val94.77
Loss/train0.00392
Loss/val0.00405
epoch10

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Accuracy/train97.3625
Accuracy/val97.17
Loss/train0.00117
Loss/val0.00129
epoch10

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Run summary:


Accuracy/train98.4775
Accuracy/val97.19
Loss/train0.00033
Loss/val0.00071
epoch50

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/z8gmtpb5" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0047. Train Acc: 75.2350, Test loss: 0.0047. Test Acc: 75.3900. Time/epoch: 2.1857\n", " EPOCH 10. Progress: 100.0%. \n", " Train loss: 0.0019. Train Acc: 89.2050, Test loss: 0.0020. Test Acc: 89.4500. Time/epoch: 2.2985\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train89.205
Accuracy/val89.45
Loss/train0.00194
Loss/val0.002
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run drawn-sweep-185 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/z8gmtpb5
Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_054447-z8gmtpb5/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: rrka9xc6 with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 128\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 50\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.001\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.05\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_054528-rrka9xc6" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run absurd-sweep-186 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/rrka9xc6" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0016. Train Acc: 90.9850, Test loss: 0.0017. Test Acc: 90.8500. Time/epoch: 2.2980\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0008. Train Acc: 95.9450, Test loss: 0.0009. Test Acc: 95.8100. Time/epoch: 2.4216\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0005. Train Acc: 97.3225, Test loss: 0.0007. Test Acc: 96.8300. Time/epoch: 2.4150\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0009. Train Acc: 95.5575, Test loss: 0.0012. Test Acc: 95.2200. Time/epoch: 2.3986\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0005. Train Acc: 97.7025, Test loss: 0.0007. Test Acc: 96.8500. Time/epoch: 2.2496\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0010. Train Acc: 94.8900, Test loss: 0.0014. Test Acc: 94.0500. Time/epoch: 2.2725\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train94.89
Accuracy/val94.05
Loss/train0.00099
Loss/val0.00142
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run absurd-sweep-186 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/rrka9xc6
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/c90l9ve2" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0055. Train Acc: 86.3700, Test loss: 0.0055. Test Acc: 86.4100. Time/epoch: 3.1368\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0025. Train Acc: 93.6250, Test loss: 0.0026. Test Acc: 93.4500. Time/epoch: 3.0174\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0023. Train Acc: 94.3850, Test loss: 0.0024. Test Acc: 94.2100. Time/epoch: 2.9791\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0021. Train Acc: 94.6550, Test loss: 0.0022. Test Acc: 94.7100. Time/epoch: 3.1562\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0019. Train Acc: 95.2050, Test loss: 0.0021. Test Acc: 95.1700. Time/epoch: 3.1645\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0019. Train Acc: 95.3175, Test loss: 0.0020. Test Acc: 95.1600. Time/epoch: 3.1532\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train95.3175
Accuracy/val95.16
Loss/train0.00193
Loss/val0.00204
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run ethereal-sweep-187 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/c90l9ve2
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/zad7mg7r" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0031. Train Acc: 91.4550, Test loss: 0.0032. Test Acc: 91.5800. Time/epoch: 3.1675\n", " EPOCH 10. Progress: 50.0%. \n", " Train loss: 0.0014. Train Acc: 96.3650, Test loss: 0.0016. Test Acc: 95.9700. Time/epoch: 3.1474\n", " EPOCH 20. Progress: 100.0%. \n", " Train loss: 0.0010. Train Acc: 97.5000, Test loss: 0.0012. Test Acc: 97.0200. Time/epoch: 3.1174\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train97.5
Accuracy/val97.02
Loss/train0.00104
Loss/val0.00122
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run effortless-sweep-188 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/zad7mg7r
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/rg05su6p" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0146. Train Acc: 78.8775, Test loss: 0.0147. Test Acc: 79.2400. Time/epoch: 4.8435\n", " EPOCH 10. Progress: 50.0%. \n", " Train loss: 0.0060. Train Acc: 93.6275, Test loss: 0.0063. Test Acc: 93.2900. Time/epoch: 4.8304\n", " EPOCH 20. Progress: 100.0%. \n", " Train loss: 0.0093. Train Acc: 91.3300, Test loss: 0.0099. Test Acc: 91.2600. Time/epoch: 4.7901\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train91.33
Accuracy/val91.26
Loss/train0.00934
Loss/val0.00989
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run usual-sweep-189 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/rg05su6p
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Accuracy/train95.175
Accuracy/val95.1
Loss/train0.00365
Loss/val0.00384
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run valiant-sweep-190 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/shvybvl5
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Accuracy/train93.8375
Accuracy/val94.14
Loss/train0.00459
Loss/val0.0047
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run autumn-sweep-191 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/mdyofjir
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Accuracy/train97.155
Accuracy/val96.75
Loss/train0.00243
Loss/val0.00274
epoch10

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Accuracy/train99.075
Accuracy/val97.32
Loss/train0.0008
Loss/val0.0028
epoch50

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Accuracy/train97.9275
Accuracy/val97.25
Loss/train0.0009
Loss/val0.00121
epoch20

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Accuracy/train95.3275
Accuracy/val95.22
Loss/train0.00382
Loss/val0.00399
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run noble-sweep-195 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/kyg2yqo0
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Accuracy/train36.825
Accuracy/val37.54
Loss/train0.02402
Loss/val0.02404
epoch50

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Accuracy/train95.2325
Accuracy/val95.33
Loss/train0.00178
Loss/val0.00183
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run classic-sweep-197 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/02r18sv0
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Accuracy/train96.8
Accuracy/val96.34
Loss/train0.00133
Loss/val0.00152
epoch10

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Accuracy/train36.825
Accuracy/val37.54
Loss/train0.04867
Loss/val0.04863
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run light-sweep-199 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/z882e1y9
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Run summary:


Accuracy/train96.7025
Accuracy/val96.37
Loss/train0.00139
Loss/val0.0015
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run royal-sweep-200 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/csip2xoo
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/8l0v5gap" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0020. Train Acc: 89.7825, Test loss: 0.0021. Test Acc: 89.6000. Time/epoch: 2.3561\n", " EPOCH 10. Progress: 50.0%. \n", " Train loss: 0.0013. Train Acc: 93.0475, Test loss: 0.0014. Test Acc: 92.9100. Time/epoch: 2.1859\n", " EPOCH 20. Progress: 100.0%. \n", " Train loss: 0.0012. Train Acc: 93.8225, Test loss: 0.0012. Test Acc: 93.7300. Time/epoch: 2.1930\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train93.8225
Accuracy/val93.73
Loss/train0.00116
Loss/val0.00121
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run balmy-sweep-201 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/8l0v5gap
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/y27npk0v" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0046. Train Acc: 78.8800, Test loss: 0.0046. Test Acc: 78.6800. Time/epoch: 2.3478\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0014. Train Acc: 93.1050, Test loss: 0.0014. Test Acc: 93.1500. Time/epoch: 2.1986\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0012. Train Acc: 93.8950, Test loss: 0.0012. Test Acc: 93.8100. Time/epoch: 2.1873\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0011. Train Acc: 94.6275, Test loss: 0.0011. Test Acc: 94.8700. Time/epoch: 2.3376\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0010. Train Acc: 94.8450, Test loss: 0.0011. Test Acc: 95.0900. Time/epoch: 2.3416\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0011. Train Acc: 94.4350, Test loss: 0.0011. Test Acc: 94.5800. Time/epoch: 2.2144\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train94.435
Accuracy/val94.58
Loss/train0.00107
Loss/val0.00112
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run eternal-sweep-202 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/y27npk0v
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Run summary:


Accuracy/train90.8825
Accuracy/val90.69
Loss/train0.00855
Loss/val0.00864
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run cerulean-sweep-203 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/t5gq6iyz
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Run summary:


Accuracy/train97.28
Accuracy/val96.76
Loss/train0.0021
Loss/val0.00255
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run decent-sweep-204 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/04lbmtm5
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Accuracy/train92.4275
Accuracy/val91.16
Loss/train0.00669
Loss/val0.00812
epoch20

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/1557bxks" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0055. Train Acc: 83.4050, Test loss: 0.0056. Test Acc: 83.7600. Time/epoch: 3.0134\n", " EPOCH 10. Progress: 100.0%. \n", " Train loss: 0.0032. Train Acc: 91.2975, Test loss: 0.0035. Test Acc: 90.7600. Time/epoch: 2.9857\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train91.2975
Accuracy/val90.76
Loss/train0.00322
Loss/val0.00353
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run twilight-sweep-206 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/1557bxks
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/itxvvi4e" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0074. Train Acc: 90.0900, Test loss: 0.0076. Test Acc: 90.0700. Time/epoch: 4.7231\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0041. Train Acc: 94.9225, Test loss: 0.0041. Test Acc: 94.8500. Time/epoch: 4.5452\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0037. Train Acc: 95.1475, Test loss: 0.0038. Test Acc: 95.2200. Time/epoch: 4.5440\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0036. Train Acc: 95.5275, Test loss: 0.0037. Test Acc: 95.4800. Time/epoch: 4.5302\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0033. Train Acc: 96.1000, Test loss: 0.0034. Test Acc: 95.9400. Time/epoch: 4.6989\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0034. Train Acc: 95.6400, Test loss: 0.0034. Test Acc: 95.7700. Time/epoch: 4.6846\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train95.64
Accuracy/val95.77
Loss/train0.00336
Loss/val0.00342
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run smart-sweep-207 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/itxvvi4e
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Accuracy/train92.755
Accuracy/val92.41
Loss/train0.00276
Loss/val0.00297
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run elated-sweep-208 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/42wuc4gz
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Run summary:


Accuracy/train97.0225
Accuracy/val96.89
Loss/train0.00247
Loss/val0.00266
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run noble-sweep-209 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/35gvosc0
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/j4pup8og" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0014. Train Acc: 93.0450, Test loss: 0.0015. Test Acc: 93.0200. Time/epoch: 2.4545\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0008. Train Acc: 95.6450, Test loss: 0.0009. Test Acc: 95.6600. Time/epoch: 2.2921\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0006. Train Acc: 97.1450, Test loss: 0.0007. Test Acc: 96.8900. Time/epoch: 2.2876\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0006. Train Acc: 97.1675, Test loss: 0.0007. Test Acc: 96.8500. Time/epoch: 2.3911\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0005. Train Acc: 97.6325, Test loss: 0.0006. Test Acc: 97.1200. Time/epoch: 2.3969\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0004. Train Acc: 98.0375, Test loss: 0.0006. Test Acc: 97.5100. Time/epoch: 2.2635\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train98.0375
Accuracy/val97.51
Loss/train0.00042
Loss/val0.0006
epoch50

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Accuracy/train97.755
Accuracy/val97.18
Loss/train0.00048
Loss/val0.00067
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run honest-sweep-211 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/prgzf0iw
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Accuracy/train95.185
Accuracy/val94.98
Loss/train0.00091
Loss/val0.00095
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run visionary-sweep-212 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/r82ztge9
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Accuracy/train85.035
Accuracy/val85.0
Loss/train0.00256
Loss/val0.0026
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run soft-sweep-213 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/annf7jys
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Accuracy/train95.185
Accuracy/val95.06
Loss/train0.00097
Loss/val0.00104
epoch10

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Accuracy/train98.785
Accuracy/val97.25
Loss/train0.00048
Loss/val0.00144
epoch50

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Run summary:


Accuracy/train36.825
Accuracy/val37.54
Loss/train0.02412
Loss/val0.02417
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run warm-sweep-216 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/cqfn8ipr
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/x90fff0g" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0481. Train Acc: 36.8250, Test loss: 0.0480. Test Acc: 37.5400. Time/epoch: 4.9988\n", " EPOCH 10. Progress: 100.0%. \n", " Train loss: 0.0481. Train Acc: 36.8250, Test loss: 0.0480. Test Acc: 37.5400. Time/epoch: 5.1742\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train36.825
Accuracy/val37.54
Loss/train0.04807
Loss/val0.04796
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run swept-sweep-217 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/x90fff0g
Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_064449-x90fff0g/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: awxs0f2q with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 128\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1e-05\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: sgd\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.0005\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_064600-awxs0f2q" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run cool-sweep-218 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/awxs0f2q" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0058. Train Acc: 73.0175, Test loss: 0.0058. Test Acc: 73.4500. Time/epoch: 2.2151\n", " EPOCH 10. Progress: 100.0%. \n", " Train loss: 0.0021. Train Acc: 89.6050, Test loss: 0.0022. Test Acc: 89.4400. Time/epoch: 2.1574\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train89.605
Accuracy/val89.44
Loss/train0.00213
Loss/val0.00218
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run cool-sweep-218 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/awxs0f2q
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/i8lgbjvl" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0112. Train Acc: 87.1725, Test loss: 0.0114. Test Acc: 87.3900. Time/epoch: 4.7143\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0060. Train Acc: 91.7525, Test loss: 0.0062. Test Acc: 91.9000. Time/epoch: 4.5731\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0050. Train Acc: 93.5525, Test loss: 0.0052. Test Acc: 93.4300. Time/epoch: 4.5421\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0048. Train Acc: 93.5650, Test loss: 0.0050. Test Acc: 93.5300. Time/epoch: 4.7165\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0042. Train Acc: 94.9375, Test loss: 0.0045. Test Acc: 94.6100. Time/epoch: 4.6815\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0040. Train Acc: 95.0375, Test loss: 0.0043. Test Acc: 94.8200. Time/epoch: 4.6573\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train95.0375
Accuracy/val94.82
Loss/train0.00404
Loss/val0.00426
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run summer-sweep-219 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/i8lgbjvl
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Run summary:


Accuracy/train98.97
Accuracy/val97.58
Loss/train0.00082
Loss/val0.0022
epoch50

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/59l6xn7o" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0042. Train Acc: 80.7750, Test loss: 0.0043. Test Acc: 80.6300. Time/epoch: 2.3286\n", " EPOCH 10. Progress: 50.0%. \n", " Train loss: 0.0011. Train Acc: 94.6550, Test loss: 0.0011. Test Acc: 94.9500. Time/epoch: 2.1682\n", " EPOCH 20. Progress: 100.0%. \n", " Train loss: 0.0010. Train Acc: 95.3900, Test loss: 0.0010. Test Acc: 95.5600. Time/epoch: 2.3293\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train95.39
Accuracy/val95.56
Loss/train0.00095
Loss/val0.00099
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run sunny-sweep-221 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/59l6xn7o
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/w1h9wxrm" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0072. Train Acc: 89.5425, Test loss: 0.0073. Test Acc: 89.6000. Time/epoch: 4.6712\n", " EPOCH 10. Progress: 50.0%. \n", " Train loss: 0.0031. Train Acc: 96.0950, Test loss: 0.0033. Test Acc: 96.1100. Time/epoch: 4.6559\n", " EPOCH 20. Progress: 100.0%. \n", " Train loss: 0.0027. Train Acc: 96.7000, Test loss: 0.0029. Test Acc: 96.5800. Time/epoch: 4.8368\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train96.7
Accuracy/val96.58
Loss/train0.00267
Loss/val0.0029
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run glamorous-sweep-222 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/w1h9wxrm
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Run summary:


Accuracy/train96.095
Accuracy/val95.81
Loss/train0.00152
Loss/val0.00169
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run worldly-sweep-223 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/jab9p12o
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Run summary:


Accuracy/train97.6125
Accuracy/val97.18
Loss/train0.00193
Loss/val0.00237
epoch50

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Run summary:


Accuracy/train97.3725
Accuracy/val97.2
Loss/train0.00107
Loss/val0.00117
epoch50

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/96qs6je8" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0028. Train Acc: 92.7200, Test loss: 0.0028. Test Acc: 92.6500. Time/epoch: 3.1800\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0013. Train Acc: 96.9675, Test loss: 0.0014. Test Acc: 96.5200. Time/epoch: 3.1467\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0010. Train Acc: 97.5800, Test loss: 0.0012. Test Acc: 97.1100. Time/epoch: 3.1478\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0012. Train Acc: 97.2175, Test loss: 0.0014. Test Acc: 96.7800. Time/epoch: 3.1445\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0009. Train Acc: 97.9575, Test loss: 0.0011. Test Acc: 97.3900. Time/epoch: 2.9880\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0012. Train Acc: 97.2075, Test loss: 0.0015. Test Acc: 96.7100. Time/epoch: 2.9671\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train97.2075
Accuracy/val96.71
Loss/train0.00119
Loss/val0.0015
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run logical-sweep-226 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/96qs6je8
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/if36gw6q" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0117. Train Acc: 84.8025, Test loss: 0.0116. Test Acc: 84.9500. Time/epoch: 4.8100\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0051. Train Acc: 93.5625, Test loss: 0.0052. Test Acc: 93.7500. Time/epoch: 4.8047\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0044. Train Acc: 94.6175, Test loss: 0.0045. Test Acc: 94.6100. Time/epoch: 4.6649\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0040. Train Acc: 94.8675, Test loss: 0.0041. Test Acc: 94.9600. Time/epoch: 4.6971\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0040. Train Acc: 94.8900, Test loss: 0.0041. Test Acc: 95.0300. Time/epoch: 4.6646\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0039. Train Acc: 95.4925, Test loss: 0.0040. Test Acc: 95.3700. Time/epoch: 4.8321\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train95.4925
Accuracy/val95.37
Loss/train0.00385
Loss/val0.00399
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run jolly-sweep-227 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/if36gw6q
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/lny4oi1s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0481. Train Acc: 36.8250, Test loss: 0.0480. Test Acc: 37.5400. Time/epoch: 4.5595\n", " EPOCH 10. Progress: 50.0%. \n", " Train loss: 0.0481. Train Acc: 36.8250, Test loss: 0.0480. Test Acc: 37.5400. Time/epoch: 4.6839\n", " EPOCH 20. Progress: 100.0%. \n", " Train loss: 0.0481. Train Acc: 36.8250, Test loss: 0.0480. Test Acc: 37.5400. Time/epoch: 4.6789\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train36.825
Accuracy/val37.54
Loss/train0.04809
Loss/val0.04802
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run gentle-sweep-228 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/lny4oi1s
Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_071411-lny4oi1s/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: csvwyhyp with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 32\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 50\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.0003\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.05\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_071602-csvwyhyp" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run cosmic-sweep-229 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/csvwyhyp" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0061. Train Acc: 91.8000, Test loss: 0.0062. Test Acc: 91.6500. Time/epoch: 4.9858\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0024. Train Acc: 96.8900, Test loss: 0.0030. Test Acc: 96.6200. Time/epoch: 5.1420\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0021. Train Acc: 97.5225, Test loss: 0.0034. Test Acc: 96.3500. Time/epoch: 5.1794\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0014. Train Acc: 98.2825, Test loss: 0.0033. Test Acc: 96.9800. Time/epoch: 5.1754\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0008. Train Acc: 99.0850, Test loss: 0.0030. Test Acc: 97.6100. Time/epoch: 5.1110\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0006. Train Acc: 99.3250, Test loss: 0.0037. Test Acc: 97.3500. Time/epoch: 4.9807\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train99.325
Accuracy/val97.35
Loss/train0.00063
Loss/val0.00372
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run cosmic-sweep-229 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/csvwyhyp
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/yge6t1ea" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0029. Train Acc: 81.7425, Test loss: 0.0030. Test Acc: 82.0300. Time/epoch: 2.3699\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0027. Train Acc: 89.3325, Test loss: 0.0029. Test Acc: 89.0700. Time/epoch: 2.3301\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0030. Train Acc: 89.0175, Test loss: 0.0032. Test Acc: 88.2100. Time/epoch: 2.3258\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0006. Train Acc: 97.4625, Test loss: 0.0011. Test Acc: 95.1700. Time/epoch: 2.1939\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0006. Train Acc: 97.5300, Test loss: 0.0012. Test Acc: 94.5700. Time/epoch: 2.2067\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0007. Train Acc: 96.6775, Test loss: 0.0014. Test Acc: 94.0000. Time/epoch: 2.3487\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train96.6775
Accuracy/val94.0
Loss/train0.00071
Loss/val0.00143
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run breezy-sweep-230 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/yge6t1ea
Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_072034-yge6t1ea/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: ec2v9ubl with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 64\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 20\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.0001\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.05\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_072245-ec2v9ubl" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run hopeful-sweep-231 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ec2v9ubl" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0036. Train Acc: 89.8275, Test loss: 0.0036. Test Acc: 89.9400. Time/epoch: 3.1595\n", " EPOCH 10. Progress: 50.0%. \n", " Train loss: 0.0013. Train Acc: 96.6900, Test loss: 0.0015. Test Acc: 96.2600. Time/epoch: 3.1593\n", " EPOCH 20. Progress: 100.0%. \n", " Train loss: 0.0011. Train Acc: 97.1875, Test loss: 0.0014. Test Acc: 96.6100. Time/epoch: 3.1437\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/val▂▁▅▁▆▇▇▇▇▇██▇▅██▇▇███
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Run summary:


Accuracy/train97.1875
Accuracy/val96.61
Loss/train0.00112
Loss/val0.00142
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run hopeful-sweep-231 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ec2v9ubl
Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_072245-ec2v9ubl/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: mqjrnn89 with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 64\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 20\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.01\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: rmsprop\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.0005\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_072406-mqjrnn89" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run blooming-sweep-232 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/mqjrnn89" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: rmsprop\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0240. Train Acc: 36.8250, Test loss: 0.0241. Test Acc: 37.5400. Time/epoch: 3.1553\n", " EPOCH 10. Progress: 50.0%. \n", " Train loss: 0.0240. Train Acc: 36.8250, Test loss: 0.0241. Test Acc: 37.5400. Time/epoch: 2.9821\n", " EPOCH 20. Progress: 100.0%. \n", " Train loss: 0.0240. Train Acc: 36.8250, Test loss: 0.0241. Test Acc: 37.5400. Time/epoch: 2.9804\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train36.825
Accuracy/val37.54
Loss/train0.02402
Loss/val0.02408
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run blooming-sweep-232 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/mqjrnn89
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ca726hgm" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0015. Train Acc: 91.7075, Test loss: 0.0016. Test Acc: 91.9400. Time/epoch: 2.4440\n", " EPOCH 10. Progress: 100.0%. \n", " Train loss: 0.0007. Train Acc: 96.3700, Test loss: 0.0008. Test Acc: 96.0600. Time/epoch: 2.3954\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train96.37
Accuracy/val96.06
Loss/train0.0007
Loss/val0.00084
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run astral-sweep-233 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ca726hgm
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/qzkda9ho" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: sgd\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0180. Train Acc: 36.8300, Test loss: 0.0179. Test Acc: 37.5400. Time/epoch: 3.1094\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0151. Train Acc: 63.6725, Test loss: 0.0150. Test Acc: 64.2200. Time/epoch: 3.0681\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0098. Train Acc: 76.4450, Test loss: 0.0098. Test Acc: 76.3900. Time/epoch: 3.0830\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0027. Train Acc: 93.2175, Test loss: 0.0028. Test Acc: 93.1000. Time/epoch: 2.9288\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0020. Train Acc: 94.9225, Test loss: 0.0021. Test Acc: 94.9100. Time/epoch: 2.9379\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0018. Train Acc: 95.4750, Test loss: 0.0019. Test Acc: 95.5300. Time/epoch: 3.1026\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train95.475
Accuracy/val95.53
Loss/train0.00178
Loss/val0.0019
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run rare-sweep-234 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/qzkda9ho
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Accuracy/train96.16
Accuracy/val95.98
Loss/train0.00308
Loss/val0.00336
epoch50

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Run summary:


Accuracy/train95.4875
Accuracy/val95.48
Loss/train0.00184
Loss/val0.00192
epoch50

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Accuracy/train60.5525
Accuracy/val61.19
Loss/train0.02449
Loss/val0.02439
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run hardy-sweep-237 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/havnxul7
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Accuracy/train95.7825
Accuracy/val95.84
Loss/train0.00173
Loss/val0.00178
epoch50

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Accuracy/train94.4625
Accuracy/val94.45
Loss/train0.00207
Loss/val0.00215
epoch10

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Accuracy/train36.825
Accuracy/val37.54
Loss/train0.04805
Loss/val0.04796
epoch20

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/xuisv8ry" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0030. Train Acc: 91.0575, Test loss: 0.0032. Test Acc: 91.1900. Time/epoch: 3.1646\n", " EPOCH 10. Progress: 100.0%. \n", " Train loss: 0.0013. Train Acc: 96.5650, Test loss: 0.0015. Test Acc: 96.3400. Time/epoch: 3.2859\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train96.565
Accuracy/val96.34
Loss/train0.00134
Loss/val0.00153
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run wobbly-sweep-241 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/xuisv8ry
Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_074246-xuisv8ry/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Sweep Agent: Waiting for job.\n", "\u001b[34m\u001b[1mwandb\u001b[0m: Job received.\n", "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 46pg2e94 with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 32\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 50\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1e-05\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: sgd\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.0005\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_074344-46pg2e94" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run clear-sweep-242 to Weights & Biases (docs)
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Run summary:


Accuracy/train96.2025
Accuracy/val96.09
Loss/train0.00306
Loss/val0.0034
epoch50

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Run summary:


Accuracy/train96.9625
Accuracy/val96.61
Loss/train0.0006
Loss/val0.0007
epoch50

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Run summary:


Accuracy/train93.57
Accuracy/val93.52
Loss/train0.00132
Loss/val0.00137
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run sunny-sweep-244 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/kwgjwpfh
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Accuracy/train95.41
Accuracy/val95.52
Loss/train0.00396
Loss/val0.00408
epoch20

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/4bmop0um" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/y33bnpyd" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "optimizer: adam\n", " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0024. Train Acc: 86.4350, Test loss: 0.0024. Test Acc: 86.7200. Time/epoch: 2.4388\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0009. Train Acc: 95.3050, Test loss: 0.0010. Test Acc: 95.1700. Time/epoch: 2.2733\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0008. Train Acc: 95.9575, Test loss: 0.0009. Test Acc: 95.8600. Time/epoch: 2.4097\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0007. Train Acc: 96.5525, Test loss: 0.0008. Test Acc: 96.4200. Time/epoch: 2.4182\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0007. Train Acc: 96.6775, Test loss: 0.0007. Test Acc: 96.4200. Time/epoch: 2.4077\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0006. Train Acc: 97.0450, Test loss: 0.0007. Test Acc: 96.5900. Time/epoch: 2.2643\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train97.045
Accuracy/val96.59
Loss/train0.00062
Loss/val0.0007
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run fast-sweep-246 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/y33bnpyd
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Accuracy/train95.1175
Accuracy/val94.96
Loss/train0.00387
Loss/val0.00416
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run young-sweep-247 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/i9579k9i
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Accuracy/train36.825
Accuracy/val37.54
Loss/train0.04807
Loss/val0.04796
epoch10

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Run summary:


Accuracy/train93.92
Accuracy/val94.04
Loss/train0.00119
Loss/val0.00126
epoch50

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Accuracy/train96.9525
Accuracy/val96.64
Loss/train0.00257
Loss/val0.00295
epoch10

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run graceful-sweep-250 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ruzwy4t8
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Stopping sweep.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Problem at: /tmp/ipykernel_682325/368360005.py 26 train\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Traceback (most recent call last):\n", " File \"/gpfs/gibbs/project/dijk/ahf38/conda_envs/geo_comp2/lib/python3.8/site-packages/wandb/sdk/wandb_init.py\", line 1150, in init\n", " run = wi.init()\n", " File \"/gpfs/gibbs/project/dijk/ahf38/conda_envs/geo_comp2/lib/python3.8/site-packages/wandb/sdk/wandb_init.py\", line 799, in init\n", " run_start_result = run_start_handle.wait(timeout=30)\n", " File \"/gpfs/gibbs/project/dijk/ahf38/conda_envs/geo_comp2/lib/python3.8/site-packages/wandb/sdk/lib/mailbox.py\", line 283, in wait\n", " found, abandoned = self._slot._get_and_clear(timeout=wait_timeout)\n", " File \"/gpfs/gibbs/project/dijk/ahf38/conda_envs/geo_comp2/lib/python3.8/site-packages/wandb/sdk/lib/mailbox.py\", line 130, in _get_and_clear\n", " if self._wait(timeout=timeout):\n", " File \"/gpfs/gibbs/project/dijk/ahf38/conda_envs/geo_comp2/lib/python3.8/site-packages/wandb/sdk/lib/mailbox.py\", line 126, in _wait\n", " return self._event.wait(timeout=timeout)\n", " File \"/gpfs/gibbs/project/dijk/ahf38/conda_envs/geo_comp2/lib/python3.8/threading.py\", line 558, in wait\n", " signaled = self._cond.wait(timeout)\n", " File \"/gpfs/gibbs/project/dijk/ahf38/conda_envs/geo_comp2/lib/python3.8/threading.py\", line 306, in wait\n", " gotit = waiter.acquire(True, timeout)\n", "Exception\n" ] } ], "source": [ "wandb.agent(sweep_id, train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Using a smarter search " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "tags": [] }, "outputs": [], "source": [ "# Make a smarter search \n", "# Configure the sweep – specify the parameters to search through, the search strategy, the optimization metric et all.\n", "sweep_config = {\n", " 'method': 'bayes', #grid, random\n", " 'metric': {\n", " 'name': 'Accuracy/val',\n", " 'goal': 'maximize' \n", " },\n", " 'parameters': {\n", " 'epochs': {\n", " 'values': [10, 20, 50, 100]\n", " },\n", " 'batch_size': {\n", " 'values': [32,64,128, 512]\n", " },\n", " 'weight_decay': {\n", " 'values': [0.0005, 0.005, 0.05]\n", " },\n", " 'learning_rate': {\n", " 'values': [1e-2,1e-3, 1e-4, 3e-4, 3e-5, 1e-5]\n", " },\n", " 'optimizer': {\n", " 'values': ['adam']\n", " }\n", " }\n", "}\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "tags": [] }, "outputs": [], "source": [ "class CNNet(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.conv1 = nn.Conv2d(4, 6, 5)\n", " self.pool = nn.MaxPool2d(2, 2)\n", " self.conv2 = nn.Conv2d(6, 16, 5)\n", " self.fc1 = nn.Linear(16 * 4 * 4, 120)\n", " self.fc2 = nn.Linear(120, 84)\n", " self.fc3 = nn.Linear(84, 6)\n", "\n", " def forward(self, x):\n", " x = self.pool(F.relu(self.conv1(x)))\n", " x = self.pool(F.relu(self.conv2(x)))\n", " x = torch.flatten(x, 1) # flatten all dimensions except batch\n", " x = F.relu(self.fc1(x))\n", " x = F.relu(self.fc2(x))\n", " x = self.fc3(x)\n", " return x\n", "\n", "\n", "model = CNNet()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Create sweep with ID: tyzcsl8s\n", "Sweep URL: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s\n" ] } ], "source": [ "# Initialize a new sweep\n", "# Arguments:\n", "# – sweep_config: the sweep config dictionary defined above\n", "# – entity: Set the username for the sweep\n", "# – project: Set the project name for the sweep\n", "sweep_id = wandb.sweep(sweep_config, entity=\"ahof1704\", project=\"CNN_Sat_sweep\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "tags": [] }, "outputs": [], "source": [ "# Training and Evaluation routines for Sweeping \n", "import time\n", "\n", "loss_fn = nn.CrossEntropyLoss().to(device)\n", "\n", "def train(config=None):\n", " \"\"\"\n", " This is a standard training loop, which leaves some parts to be filled in.\n", " INPUT:\n", " :param model: an untrained pytorch model\n", " :param loss_fn: e.g. Cross Entropy loss of Mean Squared Error.\n", " :param optimizer: the model optimizer, initialized with a learning rate.\n", " :param training_set: The training data, in a dataloader for easy iteration.\n", " :param test_loader: The testing data, in a dataloader for easy iteration.\n", " \"\"\"\n", " \n", " # config_defaults = {\n", " # 'epochs': 2,\n", " # 'batch_size': 128,\n", " # 'weight_decay': 0.0005,\n", " # 'learning_rate': 1e-3,\n", " # 'activation': 'relu',\n", " # 'optimizer': 'nadam',\n", " # 'seed': 42\n", " # }\n", " with wandb.init(config=config):\n", " verbose=False\n", " model = CNNet().to(device)\n", " wandb.watch(model, log=\"all\")\n", " model.train()\n", "\n", " # Config is a variable that holds and saves hyperparameters and inputs\n", " config = wandb.config\n", "\n", " # Define the optimizer\n", " if config.optimizer=='sgd':\n", " optimizer = torch.optim.SGD(model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay, momentum=0.9, nesterov=True)\n", " elif config.optimizer=='rmsprop':\n", " optimizer = torch.optim.RMSprop(model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay)\n", " elif config.optimizer=='adam':\n", " optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate, betas=(0.9, 0.999))\n", " elif config.optimizer=='nadam':\n", " optimizer = torch.optim.Nadam(model.parameters(), lr=config.learning_rate, betas=(0.9, 0.999))\n", "\n", " # -- create dataloaders\n", " train_sampler = SubsetRandomSampler(train_indices)\n", " valid_sampler = SubsetRandomSampler(val_indices)\n", "\n", " dataloaders = {\n", " 'train': torch.utils.data.DataLoader(dataset, batch_size=config.batch_size, sampler=train_sampler),\n", " 'test': torch.utils.data.DataLoader(dataset, batch_size=config.batch_size, sampler=valid_sampler),\n", " 'all': torch.utils.data.DataLoader(dataset, batch_size=5000, shuffle=False),\n", " }\n", " train_loader = dataloaders['train']\n", " test_loader = dataloaders['test']\n", "\n", " # if num_epochs is None:\n", " # num_epochs = 100\n", " # print('n. of epochs: {}'.format(num_epochs))\n", " best_acc=-1\n", " for epoch in range(config.epochs+1):\n", " start = time.time()\n", " # loop through each data point in the training set\n", " for data, targets in train_loader:\n", "\n", " # run the model on the data\n", " model_input = data.permute(0, 3, 2, 1).to(device)\n", " if verbose: print('model_input.shape: {}'.format(model_input.shape))\n", "\n", " # Clear gradients w.r.t. parameters\n", " optimizer.zero_grad()\n", "\n", " out = model(model_input) # The second output is the latent representation\n", " if verbose:\n", " print('targets.shape: {}'.format(targets.shape))\n", " print('targets: {}'.format(targets))\n", " print('out.shape: {}'.format(out.shape))\n", " print('out: {}'.format(out))\n", "\n", " # Calculate the loss\n", " targets = targets.type(torch.LongTensor).to(device) # add an extra dimension to keep CrossEntropy happy.\n", " if verbose: print('targets.shape: {}'.format(targets.shape))\n", " loss = loss_fn(out,targets)\n", " if verbose: print('loss: {}'.format(loss))\n", "\n", " # Find the gradients of our loss via backpropogation\n", " loss.backward()\n", "\n", " # Adjust accordingly with the optimizer\n", " optimizer.step()\n", "\n", " loss_train, acc_train = evaluate(model,train_loader,verbose)\n", " loss_test, acc_test = evaluate(model,test_loader,verbose) \n", " \n", " # Give status reports every 100 epochs\n", " if epoch % 10==0:\n", " print(f\" EPOCH {epoch}. Progress: {epoch/config.epochs*100}%. \")\n", " print(\" Train loss: {:.4f}. Train Acc: {:.4f}, Test loss: {:.4f}. Test Acc: {:.4f}. Time/epoch: {:.4f}\".format(loss_train, acc_train, loss_test, acc_test, (time.time() - start))) #TODO: implement the evaluate function to provide performance statistics during training.\n", "\n", " wandb.log({\n", " \"Loss/train\": loss_train,\n", " \"Loss/val\": loss_test,\n", " \"Accuracy/train\": acc_train,\n", " \"Accuracy/val\": acc_test,\n", " \"epoch\": epoch\n", " }, step=epoch)\n", " \n", " if acc_test>best_acc:\n", " print('saving best checkpoint at epoch: {}, Acc: {}'.format(epoch,acc_test))\n", " best_acc = acc_test\n", " torch.save(model.state_dict(), os.path.join(wandb.run.dir,'model.pt'))\n", " \n", " \n", "\n", "def evaluate(model, evaluation_set, verbose=False):\n", " \"\"\"\n", " Evaluates the given model on the given dataset.\n", " Returns the percentage of correct classifications out of total classifications.\n", " \"\"\"\n", " model.eval()\n", " with torch.no_grad(): # this disables backpropogation, which makes the model run much more quickly.\n", " correct = 0\n", " total = 0\n", " loss_all=0\n", "\n", " for data, targets in evaluation_set:\n", "\n", " # run the model on the data\n", " model_input = data.permute(0, 3, 2, 1).to(device)\n", " if verbose:\n", " print('model_input.shape: {}'.format(model_input.shape))\n", " print('targets.shape: {}'.format(targets.shape))\n", " out = model(model_input)\n", " targets = targets.type(torch.LongTensor).to(device)\n", " loss = loss_fn(out,targets)\n", "\n", " if verbose: print('out[:5]: {}'.format(out[:5]))\n", " loss_all+=loss.item()\n", "\n", " # the class with the highest energy is what we choose as prediction\n", " _, predicted = torch.max(out.data, 1)\n", " total += targets.size(0)\n", " correct += (predicted == targets).sum().item()\n", " acc = 100 * correct / total \n", " loss = loss_all/total\n", " return loss, acc\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: eaergb9w with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 512\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 50\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.001\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.005\n", "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_084935-eaergb9w" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run spring-sweep-1 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/eaergb9w" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0004. Train Acc: 92.3950, Test loss: 0.0004. Test Acc: 92.1800. Time/epoch: 1.5736\n", "saving best checkpoint at epoch: 0, Acc: 92.18\n", "saving best checkpoint at epoch: 1, Acc: 94.77\n", "saving best checkpoint at epoch: 3, Acc: 95.09\n", "saving best checkpoint at epoch: 4, Acc: 95.15\n", "saving best checkpoint at epoch: 6, Acc: 95.75\n", "saving best checkpoint at epoch: 8, Acc: 95.89\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0002. Train Acc: 96.2850, Test loss: 0.0002. Test Acc: 96.1300. Time/epoch: 1.6753\n", "saving best checkpoint at epoch: 10, Acc: 96.13\n", "saving best checkpoint at epoch: 11, Acc: 96.24\n", "saving best checkpoint at epoch: 12, Acc: 96.29\n", "saving best checkpoint at epoch: 13, Acc: 96.31\n", "saving best checkpoint at epoch: 16, Acc: 96.85\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0001. Train Acc: 97.5450, Test loss: 0.0002. Test Acc: 97.2100. Time/epoch: 1.5347\n", "saving best checkpoint at epoch: 20, Acc: 97.21\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0002. Train Acc: 96.6350, Test loss: 0.0002. Test Acc: 96.0200. Time/epoch: 1.5355\n", "saving best checkpoint at epoch: 35, Acc: 97.35\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0001. Train Acc: 97.9400, Test loss: 0.0002. Test Acc: 97.0300. Time/epoch: 1.6826\n", "saving best checkpoint at epoch: 46, Acc: 97.37\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0001. Train Acc: 98.4200, Test loss: 0.0001. Test Acc: 97.5800. Time/epoch: 1.5738\n", "saving best checkpoint at epoch: 50, Acc: 97.58\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train98.42
Accuracy/val97.58
Loss/train8e-05
Loss/val0.00015
epoch50

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run spring-sweep-1 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/eaergb9w
Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_084935-eaergb9w/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: imhc2czd with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 128\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 20\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 3e-05\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.005\n", "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_085111-imhc2czd" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run restful-sweep-2 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/imhc2czd" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0024. Train Acc: 89.1650, Test loss: 0.0024. Test Acc: 89.2400. Time/epoch: 2.2423\n", "saving best checkpoint at epoch: 0, Acc: 89.24\n", "saving best checkpoint at epoch: 1, Acc: 90.78\n", "saving best checkpoint at epoch: 2, Acc: 92.33\n", "saving best checkpoint at epoch: 3, Acc: 92.54\n", "saving best checkpoint at epoch: 4, Acc: 93.38\n", "saving best checkpoint at epoch: 5, Acc: 93.91\n", "saving best checkpoint at epoch: 6, Acc: 93.98\n", "saving best checkpoint at epoch: 8, Acc: 94.29\n", "saving best checkpoint at epoch: 9, Acc: 94.53\n", " EPOCH 10. Progress: 50.0%. \n", " Train loss: 0.0012. Train Acc: 93.6325, Test loss: 0.0012. Test Acc: 93.4200. Time/epoch: 2.1841\n", "saving best checkpoint at epoch: 11, Acc: 94.66\n", "saving best checkpoint at epoch: 12, Acc: 94.94\n", "saving best checkpoint at epoch: 16, Acc: 95.02\n", "saving best checkpoint at epoch: 18, Acc: 95.11\n", "saving best checkpoint at epoch: 19, Acc: 95.26\n", " EPOCH 20. Progress: 100.0%. \n", " Train loss: 0.0009. Train Acc: 95.8200, Test loss: 0.0009. Test Acc: 95.6600. Time/epoch: 2.1753\n", "saving best checkpoint at epoch: 20, Acc: 95.66\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train95.82
Accuracy/val95.66
Loss/train0.00087
Loss/val0.00092
epoch20

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run restful-sweep-2 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/imhc2czd
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ts1a9t68" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0006. Train Acc: 88.8700, Test loss: 0.0006. Test Acc: 88.9000. Time/epoch: 1.7003\n", "saving best checkpoint at epoch: 0, Acc: 88.9\n", "saving best checkpoint at epoch: 1, Acc: 91.84\n", "saving best checkpoint at epoch: 2, Acc: 92.76\n", "saving best checkpoint at epoch: 3, Acc: 93.44\n", "saving best checkpoint at epoch: 4, Acc: 93.47\n", "saving best checkpoint at epoch: 6, Acc: 94.24\n", "saving best checkpoint at epoch: 8, Acc: 94.68\n", "saving best checkpoint at epoch: 9, Acc: 94.9\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0003. Train Acc: 93.0725, Test loss: 0.0003. Test Acc: 93.1100. Time/epoch: 1.6812\n", "saving best checkpoint at epoch: 12, Acc: 95.02\n", "saving best checkpoint at epoch: 14, Acc: 95.07\n", "saving best checkpoint at epoch: 15, Acc: 95.68\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0002. Train Acc: 95.9475, Test loss: 0.0002. Test Acc: 95.7700. Time/epoch: 1.5469\n", "saving best checkpoint at epoch: 20, Acc: 95.77\n", "saving best checkpoint at epoch: 21, Acc: 96.0\n", "saving best checkpoint at epoch: 23, Acc: 96.22\n", "saving best checkpoint at epoch: 24, Acc: 96.25\n", "saving best checkpoint at epoch: 26, Acc: 96.26\n", "saving best checkpoint at epoch: 27, Acc: 96.32\n", "saving best checkpoint at epoch: 28, Acc: 96.52\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0002. Train Acc: 96.6025, Test loss: 0.0002. Test Acc: 96.2700. Time/epoch: 1.7239\n", "saving best checkpoint at epoch: 32, Acc: 96.6\n", "saving best checkpoint at epoch: 34, Acc: 96.78\n", "saving best checkpoint at epoch: 39, Acc: 96.92\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0002. Train Acc: 97.1050, Test loss: 0.0002. Test Acc: 96.7100. Time/epoch: 1.7287\n", "saving best checkpoint at epoch: 46, Acc: 96.96\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0001. Train Acc: 97.3550, Test loss: 0.0002. Test Acc: 96.9100. Time/epoch: 1.5700\n", "saving best checkpoint at epoch: 51, Acc: 97.04\n", "saving best checkpoint at epoch: 54, Acc: 97.17\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0001. Train Acc: 97.4800, Test loss: 0.0002. Test Acc: 97.0700. Time/epoch: 1.5663\n", "saving best checkpoint at epoch: 62, Acc: 97.49\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0001. Train Acc: 97.8925, Test loss: 0.0002. Test Acc: 97.3400. Time/epoch: 1.6849\n", "saving best checkpoint at epoch: 72, Acc: 97.52\n", "saving best checkpoint at epoch: 76, Acc: 97.6\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0001. Train Acc: 98.0125, Test loss: 0.0001. Test Acc: 97.5100. Time/epoch: 1.5435\n", "saving best checkpoint at epoch: 85, Acc: 97.77\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0001. Train Acc: 98.0675, Test loss: 0.0001. Test Acc: 97.4300. Time/epoch: 1.5394\n", "saving best checkpoint at epoch: 92, Acc: 97.79\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0001. Train Acc: 98.5125, Test loss: 0.0001. Test Acc: 97.8000. Time/epoch: 1.6845\n", "saving best checkpoint at epoch: 100, Acc: 97.8\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train98.5125
Accuracy/val97.8
Loss/train8e-05
Loss/val0.00013
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/5imxc6l6" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0005. Train Acc: 90.0775, Test loss: 0.0005. Test Acc: 90.2300. Time/epoch: 1.5671\n", "saving best checkpoint at epoch: 0, Acc: 90.23\n", "saving best checkpoint at epoch: 1, Acc: 93.06\n", "saving best checkpoint at epoch: 3, Acc: 94.35\n", "saving best checkpoint at epoch: 6, Acc: 94.75\n", "saving best checkpoint at epoch: 8, Acc: 95.84\n", "saving best checkpoint at epoch: 9, Acc: 96.75\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0002. Train Acc: 96.2875, Test loss: 0.0002. Test Acc: 96.2400. Time/epoch: 1.6812\n", "saving best checkpoint at epoch: 15, Acc: 96.89\n", "saving best checkpoint at epoch: 17, Acc: 97.11\n", "saving best checkpoint at epoch: 18, Acc: 97.19\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0001. Train Acc: 97.5500, Test loss: 0.0002. Test Acc: 97.2300. Time/epoch: 1.5478\n", "saving best checkpoint at epoch: 20, Acc: 97.23\n", "saving best checkpoint at epoch: 26, Acc: 97.4\n", "saving best checkpoint at epoch: 29, Acc: 97.66\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0001. Train Acc: 98.2575, Test loss: 0.0001. Test Acc: 97.6600. Time/epoch: 1.5383\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0001. Train Acc: 98.3550, Test loss: 0.0001. Test Acc: 97.3800. Time/epoch: 1.6888\n", "saving best checkpoint at epoch: 41, Acc: 97.71\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0002. Train Acc: 96.8825, Test loss: 0.0002. Test Acc: 96.1500. Time/epoch: 1.5808\n", "saving best checkpoint at epoch: 51, Acc: 98.03\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0001. Train Acc: 98.2350, Test loss: 0.0002. Test Acc: 97.0100. Time/epoch: 1.6859\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0001. Train Acc: 98.9475, Test loss: 0.0002. Test Acc: 97.5000. Time/epoch: 1.6416\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0001. Train Acc: 98.7400, Test loss: 0.0002. Test Acc: 97.2400. Time/epoch: 1.5532\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0001. Train Acc: 97.9650, Test loss: 0.0002. Test Acc: 96.6200. Time/epoch: 1.6915\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0001. Train Acc: 98.4825, Test loss: 0.0002. Test Acc: 96.5600. Time/epoch: 1.5437\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train98.4825
Accuracy/val96.56
Loss/train8e-05
Loss/val0.00025
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/kqldb244" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0038. Train Acc: 89.1650, Test loss: 0.0039. Test Acc: 89.0500. Time/epoch: 3.2586\n", "saving best checkpoint at epoch: 0, Acc: 89.05\n", "saving best checkpoint at epoch: 1, Acc: 92.16\n", "saving best checkpoint at epoch: 2, Acc: 92.62\n", "saving best checkpoint at epoch: 3, Acc: 92.98\n", "saving best checkpoint at epoch: 4, Acc: 94.1\n", "saving best checkpoint at epoch: 5, Acc: 94.15\n", "saving best checkpoint at epoch: 6, Acc: 94.77\n", "saving best checkpoint at epoch: 7, Acc: 94.8\n", "saving best checkpoint at epoch: 9, Acc: 95.35\n", " EPOCH 10. Progress: 100.0%. \n", " Train loss: 0.0020. Train Acc: 95.0625, Test loss: 0.0021. Test Acc: 95.0700. Time/epoch: 3.2527\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train95.0625
Accuracy/val95.07
Loss/train0.00197
Loss/val0.00206
epoch10

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/q66lh63h" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0004. Train Acc: 90.1525, Test loss: 0.0004. Test Acc: 90.4800. Time/epoch: 1.7523\n", "saving best checkpoint at epoch: 0, Acc: 90.48\n", "saving best checkpoint at epoch: 1, Acc: 92.32\n", "saving best checkpoint at epoch: 3, Acc: 94.52\n", "saving best checkpoint at epoch: 4, Acc: 95.1\n", "saving best checkpoint at epoch: 5, Acc: 95.55\n", "saving best checkpoint at epoch: 8, Acc: 96.04\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0002. Train Acc: 95.9600, Test loss: 0.0002. Test Acc: 95.8600. Time/epoch: 1.7021\n", "saving best checkpoint at epoch: 11, Acc: 96.22\n", "saving best checkpoint at epoch: 14, Acc: 96.61\n", "saving best checkpoint at epoch: 17, Acc: 96.64\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0002. Train Acc: 96.4425, Test loss: 0.0002. Test Acc: 96.3300. Time/epoch: 1.5584\n", "saving best checkpoint at epoch: 23, Acc: 96.86\n", "saving best checkpoint at epoch: 27, Acc: 97.06\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0001. Train Acc: 97.6200, Test loss: 0.0002. Test Acc: 97.1400. Time/epoch: 1.7035\n", "saving best checkpoint at epoch: 30, Acc: 97.14\n", "saving best checkpoint at epoch: 35, Acc: 97.35\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0002. Train Acc: 96.1100, Test loss: 0.0002. Test Acc: 95.8000. Time/epoch: 1.5532\n", "saving best checkpoint at epoch: 48, Acc: 97.39\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0001. Train Acc: 97.3150, Test loss: 0.0002. Test Acc: 96.7000. Time/epoch: 1.7284\n", "saving best checkpoint at epoch: 52, Acc: 97.42\n", "saving best checkpoint at epoch: 57, Acc: 97.52\n", "saving best checkpoint at epoch: 58, Acc: 97.6\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0001. Train Acc: 97.9300, Test loss: 0.0002. Test Acc: 97.0400. Time/epoch: 1.5781\n", "saving best checkpoint at epoch: 66, Acc: 97.66\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0002. Train Acc: 96.9750, Test loss: 0.0002. Test Acc: 96.4400. Time/epoch: 1.5612\n", "saving best checkpoint at epoch: 74, Acc: 97.68\n", "saving best checkpoint at epoch: 77, Acc: 97.69\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0001. Train Acc: 98.7125, Test loss: 0.0002. Test Acc: 97.4400. Time/epoch: 1.7046\n", "saving best checkpoint at epoch: 83, Acc: 97.93\n", "saving best checkpoint at epoch: 87, Acc: 97.96\n", "saving best checkpoint at epoch: 89, Acc: 97.97\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0000. Train Acc: 99.2050, Test loss: 0.0001. Test Acc: 97.9800. Time/epoch: 1.5516\n", "saving best checkpoint at epoch: 90, Acc: 97.98\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0001. Train Acc: 99.0625, Test loss: 0.0002. Test Acc: 97.6300. Time/epoch: 1.7018\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

Run history:


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Run summary:


Accuracy/train99.0625
Accuracy/val97.63
Loss/train5e-05
Loss/val0.00017
epoch100

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run sweet-sweep-6 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/q66lh63h
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/459efn2e" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0027. Train Acc: 61.6575, Test loss: 0.0027. Test Acc: 62.3500. Time/epoch: 1.5920\n", "saving best checkpoint at epoch: 0, Acc: 62.35\n", "saving best checkpoint at epoch: 1, Acc: 70.78\n", "saving best checkpoint at epoch: 2, Acc: 76.68\n", "saving best checkpoint at epoch: 3, Acc: 85.2\n", "saving best checkpoint at epoch: 4, Acc: 88.49\n", "saving best checkpoint at epoch: 5, Acc: 90.07\n", "saving best checkpoint at epoch: 6, Acc: 90.91\n", "saving best checkpoint at epoch: 7, Acc: 91.36\n", "saving best checkpoint at epoch: 8, Acc: 92.11\n", "saving best checkpoint at epoch: 9, Acc: 92.19\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0004. Train Acc: 92.6975, Test loss: 0.0004. Test Acc: 92.6700. Time/epoch: 1.5554\n", "saving best checkpoint at epoch: 10, Acc: 92.67\n", "saving best checkpoint at epoch: 11, Acc: 92.92\n", "saving best checkpoint at epoch: 12, Acc: 93.29\n", "saving best checkpoint at epoch: 13, Acc: 93.37\n", "saving best checkpoint at epoch: 15, Acc: 93.63\n", "saving best checkpoint at epoch: 16, Acc: 94.18\n", "saving best checkpoint at epoch: 17, Acc: 94.32\n", "saving best checkpoint at epoch: 18, Acc: 94.46\n", "saving best checkpoint at epoch: 19, Acc: 94.54\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0003. Train Acc: 94.5350, Test loss: 0.0003. Test Acc: 94.4500. Time/epoch: 1.5664\n", "saving best checkpoint at epoch: 21, Acc: 94.88\n", "saving best checkpoint at epoch: 23, Acc: 94.98\n", "saving best checkpoint at epoch: 25, Acc: 95.07\n", "saving best checkpoint at epoch: 26, Acc: 95.12\n", "saving best checkpoint at epoch: 28, Acc: 95.36\n", "saving best checkpoint at epoch: 29, Acc: 95.49\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0002. Train Acc: 95.7675, Test loss: 0.0002. Test Acc: 95.5500. Time/epoch: 1.5633\n", "saving best checkpoint at epoch: 30, Acc: 95.55\n", "saving best checkpoint at epoch: 34, Acc: 95.78\n", "saving best checkpoint at epoch: 37, Acc: 95.83\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0002. Train Acc: 96.2775, Test loss: 0.0002. Test Acc: 95.9100. Time/epoch: 1.6901\n", "saving best checkpoint at epoch: 40, Acc: 95.91\n", "saving best checkpoint at epoch: 44, Acc: 95.94\n", "saving best checkpoint at epoch: 45, Acc: 95.95\n", "saving best checkpoint at epoch: 47, Acc: 96.17\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0002. Train Acc: 96.4825, Test loss: 0.0002. Test Acc: 96.1100. Time/epoch: 1.5931\n", "saving best checkpoint at epoch: 51, Acc: 96.28\n", "saving best checkpoint at epoch: 55, Acc: 96.36\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0002. Train Acc: 96.6675, Test loss: 0.0002. Test Acc: 96.4200. Time/epoch: 1.5757\n", "saving best checkpoint at epoch: 60, Acc: 96.42\n", "saving best checkpoint at epoch: 61, Acc: 96.47\n", "saving best checkpoint at epoch: 63, Acc: 96.49\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0002. Train Acc: 96.9775, Test loss: 0.0002. Test Acc: 96.4900. Time/epoch: 1.7017\n", "saving best checkpoint at epoch: 72, Acc: 96.57\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0002. Train Acc: 97.0150, Test loss: 0.0002. Test Acc: 96.4700. Time/epoch: 1.5609\n", "saving best checkpoint at epoch: 81, Acc: 96.72\n", "saving best checkpoint at epoch: 86, Acc: 96.74\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0002. Train Acc: 97.1425, Test loss: 0.0002. Test Acc: 96.6200. Time/epoch: 1.7450\n", "saving best checkpoint at epoch: 94, Acc: 96.79\n", "saving best checkpoint at epoch: 96, Acc: 96.83\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0001. Train Acc: 97.1800, Test loss: 0.0002. Test Acc: 96.8100. Time/epoch: 1.6897\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train97.18
Accuracy/val96.81
Loss/train0.00015
Loss/val0.00018
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/krnzu5ta" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0009. Train Acc: 86.3925, Test loss: 0.0009. Test Acc: 86.5600. Time/epoch: 1.5978\n", "saving best checkpoint at epoch: 0, Acc: 86.56\n", "saving best checkpoint at epoch: 1, Acc: 90.27\n", "saving best checkpoint at epoch: 2, Acc: 92.0\n", "saving best checkpoint at epoch: 3, Acc: 92.55\n", "saving best checkpoint at epoch: 4, Acc: 92.97\n", "saving best checkpoint at epoch: 6, Acc: 93.28\n", "saving best checkpoint at epoch: 8, Acc: 93.92\n", "saving best checkpoint at epoch: 9, Acc: 94.22\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0003. Train Acc: 94.2675, Test loss: 0.0003. Test Acc: 94.1000. Time/epoch: 1.5429\n", "saving best checkpoint at epoch: 11, Acc: 94.25\n", "saving best checkpoint at epoch: 12, Acc: 94.52\n", "saving best checkpoint at epoch: 13, Acc: 94.92\n", "saving best checkpoint at epoch: 15, Acc: 95.2\n", "saving best checkpoint at epoch: 16, Acc: 95.49\n", "saving best checkpoint at epoch: 19, Acc: 95.63\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0002. Train Acc: 95.6400, Test loss: 0.0002. Test Acc: 95.5400. Time/epoch: 1.5730\n", "saving best checkpoint at epoch: 22, Acc: 96.06\n", "saving best checkpoint at epoch: 24, Acc: 96.16\n", "saving best checkpoint at epoch: 29, Acc: 96.18\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0002. Train Acc: 95.9750, Test loss: 0.0002. Test Acc: 95.7100. Time/epoch: 1.5606\n", "saving best checkpoint at epoch: 31, Acc: 96.59\n", "saving best checkpoint at epoch: 32, Acc: 96.68\n", "saving best checkpoint at epoch: 35, Acc: 96.75\n", "saving best checkpoint at epoch: 38, Acc: 96.85\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0001. Train Acc: 97.3400, Test loss: 0.0002. Test Acc: 97.0200. Time/epoch: 1.6947\n", "saving best checkpoint at epoch: 40, Acc: 97.02\n", "saving best checkpoint at epoch: 47, Acc: 97.18\n", "saving best checkpoint at epoch: 49, Acc: 97.3\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0002. Train Acc: 97.1250, Test loss: 0.0002. Test Acc: 96.7600. Time/epoch: 1.5569\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0001. Train Acc: 97.6850, Test loss: 0.0002. Test Acc: 97.3000. Time/epoch: 1.5390\n", "saving best checkpoint at epoch: 64, Acc: 97.57\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0001. Train Acc: 97.8050, Test loss: 0.0001. Test Acc: 97.3200. Time/epoch: 1.6843\n", "saving best checkpoint at epoch: 75, Acc: 97.68\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0001. Train Acc: 98.0475, Test loss: 0.0001. Test Acc: 97.3500. Time/epoch: 1.5452\n", "saving best checkpoint at epoch: 88, Acc: 97.8\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0001. Train Acc: 97.4075, Test loss: 0.0002. Test Acc: 96.8800. Time/epoch: 1.5421\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0001. Train Acc: 98.4850, Test loss: 0.0001. Test Acc: 97.8100. Time/epoch: 1.6910\n", "saving best checkpoint at epoch: 100, Acc: 97.81\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train98.485
Accuracy/val97.81
Loss/train8e-05
Loss/val0.00014
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ngrmf0wm" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0005. Train Acc: 89.7600, Test loss: 0.0005. Test Acc: 89.5100. Time/epoch: 1.5902\n", "saving best checkpoint at epoch: 0, Acc: 89.51\n", "saving best checkpoint at epoch: 1, Acc: 91.22\n", "saving best checkpoint at epoch: 2, Acc: 93.15\n", "saving best checkpoint at epoch: 4, Acc: 94.45\n", "saving best checkpoint at epoch: 5, Acc: 94.67\n", "saving best checkpoint at epoch: 6, Acc: 95.01\n", "saving best checkpoint at epoch: 7, Acc: 95.14\n", "saving best checkpoint at epoch: 9, Acc: 95.95\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0002. Train Acc: 96.2450, Test loss: 0.0002. Test Acc: 96.2300. Time/epoch: 1.6991\n", "saving best checkpoint at epoch: 10, Acc: 96.23\n", "saving best checkpoint at epoch: 12, Acc: 96.61\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0002. Train Acc: 96.8075, Test loss: 0.0002. Test Acc: 96.4200. Time/epoch: 1.5569\n", "saving best checkpoint at epoch: 23, Acc: 96.74\n", "saving best checkpoint at epoch: 28, Acc: 96.76\n", "saving best checkpoint at epoch: 29, Acc: 96.87\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0003. Train Acc: 94.6725, Test loss: 0.0003. Test Acc: 94.3300. Time/epoch: 1.5484\n", "saving best checkpoint at epoch: 36, Acc: 96.95\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0002. Train Acc: 96.6175, Test loss: 0.0002. Test Acc: 96.1000. Time/epoch: 1.6936\n", "saving best checkpoint at epoch: 44, Acc: 97.14\n", "saving best checkpoint at epoch: 48, Acc: 97.23\n", "saving best checkpoint at epoch: 49, Acc: 97.32\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0001. Train Acc: 97.4550, Test loss: 0.0002. Test Acc: 96.5800. Time/epoch: 1.5732\n", "saving best checkpoint at epoch: 55, Acc: 97.37\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0001. Train Acc: 97.6750, Test loss: 0.0002. Test Acc: 96.7000. Time/epoch: 1.5459\n", "saving best checkpoint at epoch: 68, Acc: 97.45\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0001. Train Acc: 98.5400, Test loss: 0.0002. Test Acc: 97.2100. Time/epoch: 1.7067\n", "saving best checkpoint at epoch: 77, Acc: 97.55\n", "saving best checkpoint at epoch: 79, Acc: 97.56\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0001. Train Acc: 98.6975, Test loss: 0.0002. Test Acc: 97.2100. Time/epoch: 1.5635\n", "saving best checkpoint at epoch: 81, Acc: 97.75\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0001. Train Acc: 98.4400, Test loss: 0.0002. Test Acc: 96.9900. Time/epoch: 1.5489\n", "saving best checkpoint at epoch: 97, Acc: 97.82\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0001. Train Acc: 98.9525, Test loss: 0.0002. Test Acc: 97.4600. Time/epoch: 1.6900\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train98.9525
Accuracy/val97.46
Loss/train6e-05
Loss/val0.00019
epoch100

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run valiant-sweep-9 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ngrmf0wm
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/b2px33o8" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0008. Train Acc: 87.1950, Test loss: 0.0008. Test Acc: 86.6900. Time/epoch: 1.6896\n", "saving best checkpoint at epoch: 0, Acc: 86.69\n", "saving best checkpoint at epoch: 1, Acc: 89.34\n", "saving best checkpoint at epoch: 2, Acc: 90.57\n", "saving best checkpoint at epoch: 3, Acc: 91.56\n", "saving best checkpoint at epoch: 4, Acc: 92.49\n", "saving best checkpoint at epoch: 5, Acc: 92.64\n", "saving best checkpoint at epoch: 6, Acc: 92.99\n", "saving best checkpoint at epoch: 7, Acc: 93.3\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0003. Train Acc: 94.0175, Test loss: 0.0003. Test Acc: 93.7900. Time/epoch: 1.7866\n", "saving best checkpoint at epoch: 10, Acc: 93.79\n", "saving best checkpoint at epoch: 12, Acc: 93.92\n", "saving best checkpoint at epoch: 14, Acc: 94.09\n", "saving best checkpoint at epoch: 15, Acc: 94.36\n", "saving best checkpoint at epoch: 17, Acc: 94.5\n", "saving best checkpoint at epoch: 18, Acc: 94.6\n", "saving best checkpoint at epoch: 19, Acc: 94.96\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0002. Train Acc: 95.5375, Test loss: 0.0002. Test Acc: 95.1900. Time/epoch: 1.6947\n", "saving best checkpoint at epoch: 20, Acc: 95.19\n", "saving best checkpoint at epoch: 21, Acc: 95.34\n", "saving best checkpoint at epoch: 25, Acc: 95.45\n", "saving best checkpoint at epoch: 26, Acc: 95.55\n", "saving best checkpoint at epoch: 29, Acc: 95.58\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0002. Train Acc: 96.0200, Test loss: 0.0002. Test Acc: 95.7800. Time/epoch: 1.5499\n", "saving best checkpoint at epoch: 30, Acc: 95.78\n", "saving best checkpoint at epoch: 31, Acc: 95.91\n", "saving best checkpoint at epoch: 35, Acc: 96.26\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0002. Train Acc: 96.3125, Test loss: 0.0002. Test Acc: 95.7600. Time/epoch: 1.5549\n", "saving best checkpoint at epoch: 41, Acc: 96.33\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0002. Train Acc: 96.9525, Test loss: 0.0002. Test Acc: 96.3500. Time/epoch: 1.7089\n", "saving best checkpoint at epoch: 50, Acc: 96.35\n", "saving best checkpoint at epoch: 51, Acc: 96.41\n", "saving best checkpoint at epoch: 52, Acc: 96.62\n", "saving best checkpoint at epoch: 57, Acc: 96.67\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0001. Train Acc: 97.0525, Test loss: 0.0002. Test Acc: 96.5000. Time/epoch: 1.5527\n", "saving best checkpoint at epoch: 64, Acc: 96.76\n", "saving best checkpoint at epoch: 67, Acc: 96.79\n", "saving best checkpoint at epoch: 68, Acc: 96.91\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0001. Train Acc: 97.2625, Test loss: 0.0002. Test Acc: 96.7700. Time/epoch: 1.5684\n", "saving best checkpoint at epoch: 77, Acc: 96.98\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0001. Train Acc: 97.1000, Test loss: 0.0002. Test Acc: 96.6400. Time/epoch: 1.5479\n", "saving best checkpoint at epoch: 84, Acc: 97.1\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0001. Train Acc: 97.7250, Test loss: 0.0002. Test Acc: 97.0200. Time/epoch: 1.5580\n", "saving best checkpoint at epoch: 94, Acc: 97.12\n", "saving best checkpoint at epoch: 96, Acc: 97.13\n", "saving best checkpoint at epoch: 97, Acc: 97.19\n", "saving best checkpoint at epoch: 99, Acc: 97.27\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0001. Train Acc: 97.9525, Test loss: 0.0002. Test Acc: 96.8400. Time/epoch: 1.6912\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train97.9525
Accuracy/val96.84
Loss/train0.00011
Loss/val0.00017
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/00ln6on5" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0006. Train Acc: 87.6175, Test loss: 0.0006. Test Acc: 87.8600. Time/epoch: 1.5812\n", "saving best checkpoint at epoch: 0, Acc: 87.86\n", "saving best checkpoint at epoch: 1, Acc: 90.48\n", "saving best checkpoint at epoch: 2, Acc: 93.51\n", "saving best checkpoint at epoch: 3, Acc: 94.21\n", "saving best checkpoint at epoch: 6, Acc: 94.82\n", "saving best checkpoint at epoch: 7, Acc: 95.4\n", "saving best checkpoint at epoch: 8, Acc: 95.5\n", "saving best checkpoint at epoch: 9, Acc: 96.05\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0002. Train Acc: 95.4650, Test loss: 0.0002. Test Acc: 95.3600. Time/epoch: 1.5420\n", "saving best checkpoint at epoch: 15, Acc: 96.18\n", "saving best checkpoint at epoch: 17, Acc: 96.49\n", "saving best checkpoint at epoch: 19, Acc: 96.67\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0002. Train Acc: 95.8375, Test loss: 0.0002. Test Acc: 95.7100. Time/epoch: 1.5545\n", "saving best checkpoint at epoch: 22, Acc: 96.9\n", "saving best checkpoint at epoch: 27, Acc: 96.99\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0001. Train Acc: 97.1250, Test loss: 0.0002. Test Acc: 96.9700. Time/epoch: 1.5461\n", "saving best checkpoint at epoch: 34, Acc: 97.16\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0001. Train Acc: 97.8200, Test loss: 0.0002. Test Acc: 97.2800. Time/epoch: 1.6927\n", "saving best checkpoint at epoch: 40, Acc: 97.28\n", "saving best checkpoint at epoch: 44, Acc: 97.37\n", "saving best checkpoint at epoch: 46, Acc: 97.47\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0001. Train Acc: 97.8575, Test loss: 0.0002. Test Acc: 97.1200. Time/epoch: 1.5630\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0001. Train Acc: 97.0925, Test loss: 0.0002. Test Acc: 96.1700. Time/epoch: 1.5466\n", "saving best checkpoint at epoch: 63, Acc: 97.6\n", "saving best checkpoint at epoch: 65, Acc: 97.77\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0001. Train Acc: 98.4900, Test loss: 0.0001. Test Acc: 97.5900. Time/epoch: 1.7045\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0001. Train Acc: 98.6075, Test loss: 0.0002. Test Acc: 97.6500. Time/epoch: 1.5509\n", "saving best checkpoint at epoch: 88, Acc: 97.88\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0001. Train Acc: 98.7950, Test loss: 0.0002. Test Acc: 97.5600. Time/epoch: 1.5506\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0001. Train Acc: 98.5575, Test loss: 0.0002. Test Acc: 97.3500. Time/epoch: 1.6969\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train98.5575
Accuracy/val97.35
Loss/train7e-05
Loss/val0.00019
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/5furh1c1" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0005. Train Acc: 89.3375, Test loss: 0.0005. Test Acc: 89.4500. Time/epoch: 1.5920\n", "saving best checkpoint at epoch: 0, Acc: 89.45\n", "saving best checkpoint at epoch: 1, Acc: 92.14\n", "saving best checkpoint at epoch: 3, Acc: 93.8\n", "saving best checkpoint at epoch: 5, Acc: 94.15\n", "saving best checkpoint at epoch: 6, Acc: 95.47\n", "saving best checkpoint at epoch: 7, Acc: 95.69\n", "saving best checkpoint at epoch: 8, Acc: 95.77\n", "saving best checkpoint at epoch: 9, Acc: 96.16\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0002. Train Acc: 96.0925, Test loss: 0.0002. Test Acc: 95.9100. Time/epoch: 1.7116\n", "saving best checkpoint at epoch: 13, Acc: 96.38\n", "saving best checkpoint at epoch: 14, Acc: 96.49\n", "saving best checkpoint at epoch: 17, Acc: 96.56\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0002. Train Acc: 96.6575, Test loss: 0.0002. Test Acc: 96.2700. Time/epoch: 1.5641\n", "saving best checkpoint at epoch: 22, Acc: 96.66\n", "saving best checkpoint at epoch: 23, Acc: 96.75\n", "saving best checkpoint at epoch: 24, Acc: 96.98\n", "saving best checkpoint at epoch: 27, Acc: 97.09\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0001. Train Acc: 97.8000, Test loss: 0.0002. Test Acc: 97.1600. Time/epoch: 1.5668\n", "saving best checkpoint at epoch: 30, Acc: 97.16\n", "saving best checkpoint at epoch: 35, Acc: 97.2\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0001. Train Acc: 97.7275, Test loss: 0.0002. Test Acc: 96.9200. Time/epoch: 1.7048\n", "saving best checkpoint at epoch: 41, Acc: 97.3\n", "saving best checkpoint at epoch: 43, Acc: 97.34\n", "saving best checkpoint at epoch: 45, Acc: 97.5\n", "saving best checkpoint at epoch: 49, Acc: 97.51\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0001. Train Acc: 98.0625, Test loss: 0.0002. Test Acc: 97.2300. Time/epoch: 1.5850\n", "saving best checkpoint at epoch: 58, Acc: 97.58\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0001. Train Acc: 98.4100, Test loss: 0.0002. Test Acc: 97.2400. Time/epoch: 1.5624\n", "saving best checkpoint at epoch: 62, Acc: 97.64\n", "saving best checkpoint at epoch: 68, Acc: 97.65\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0001. Train Acc: 98.5825, Test loss: 0.0002. Test Acc: 97.3700. Time/epoch: 1.7039\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0001. Train Acc: 98.4775, Test loss: 0.0002. Test Acc: 97.0500. Time/epoch: 1.6558\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0000. Train Acc: 99.1525, Test loss: 0.0002. Test Acc: 97.6800. Time/epoch: 1.5644\n", "saving best checkpoint at epoch: 90, Acc: 97.68\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0001. Train Acc: 98.8000, Test loss: 0.0002. Test Acc: 97.2400. Time/epoch: 1.6939\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train98.8
Accuracy/val97.24
Loss/train6e-05
Loss/val0.0002
epoch100

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Accuracy/train96.7125
Accuracy/val95.87
Loss/train0.00016
Loss/val0.00023
epoch50

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/fdsgzvl8" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0006. Train Acc: 86.6300, Test loss: 0.0007. Test Acc: 86.8200. Time/epoch: 1.7127\n", "saving best checkpoint at epoch: 0, Acc: 86.82\n", "saving best checkpoint at epoch: 1, Acc: 89.99\n", "saving best checkpoint at epoch: 2, Acc: 90.95\n", "saving best checkpoint at epoch: 3, Acc: 91.64\n", "saving best checkpoint at epoch: 4, Acc: 91.7\n", "saving best checkpoint at epoch: 5, Acc: 92.49\n", "saving best checkpoint at epoch: 6, Acc: 93.35\n", "saving best checkpoint at epoch: 7, Acc: 93.75\n", "saving best checkpoint at epoch: 9, Acc: 94.04\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0003. Train Acc: 94.6025, Test loss: 0.0003. Test Acc: 94.3900. Time/epoch: 1.5597\n", "saving best checkpoint at epoch: 10, Acc: 94.39\n", "saving best checkpoint at epoch: 11, Acc: 94.58\n", "saving best checkpoint at epoch: 12, Acc: 94.91\n", "saving best checkpoint at epoch: 13, Acc: 95.27\n", "saving best checkpoint at epoch: 15, Acc: 95.4\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0002. Train Acc: 95.4025, Test loss: 0.0002. Test Acc: 95.1000. Time/epoch: 1.5497\n", "saving best checkpoint at epoch: 22, Acc: 95.41\n", "saving best checkpoint at epoch: 23, Acc: 95.66\n", "saving best checkpoint at epoch: 25, Acc: 95.89\n", "saving best checkpoint at epoch: 26, Acc: 96.21\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0002. Train Acc: 96.1775, Test loss: 0.0002. Test Acc: 95.6800. Time/epoch: 1.6917\n", "saving best checkpoint at epoch: 34, Acc: 96.43\n", "saving best checkpoint at epoch: 39, Acc: 96.5\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0002. Train Acc: 95.9325, Test loss: 0.0002. Test Acc: 95.4300. Time/epoch: 1.5507\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0001. Train Acc: 97.3700, Test loss: 0.0002. Test Acc: 96.6800. Time/epoch: 1.7086\n", "saving best checkpoint at epoch: 50, Acc: 96.68\n", "saving best checkpoint at epoch: 52, Acc: 96.7\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0002. Train Acc: 96.6825, Test loss: 0.0002. Test Acc: 96.0100. Time/epoch: 1.5585\n", "saving best checkpoint at epoch: 62, Acc: 96.84\n", "saving best checkpoint at epoch: 65, Acc: 96.89\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0001. Train Acc: 97.5775, Test loss: 0.0002. Test Acc: 96.9800. Time/epoch: 1.5568\n", "saving best checkpoint at epoch: 70, Acc: 96.98\n", "saving best checkpoint at epoch: 79, Acc: 97.0\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0001. Train Acc: 97.9175, Test loss: 0.0002. Test Acc: 96.9300. Time/epoch: 1.6924\n", "saving best checkpoint at epoch: 85, Acc: 97.28\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0001. Train Acc: 98.0975, Test loss: 0.0001. Test Acc: 97.3400. Time/epoch: 1.5694\n", "saving best checkpoint at epoch: 90, Acc: 97.34\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0001. Train Acc: 97.9975, Test loss: 0.0002. Test Acc: 97.2300. Time/epoch: 1.5454\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train97.9975
Accuracy/val97.23
Loss/train0.00011
Loss/val0.00016
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ecs67yd1" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0005. Train Acc: 87.6525, Test loss: 0.0005. Test Acc: 88.0100. Time/epoch: 1.5929\n", "saving best checkpoint at epoch: 0, Acc: 88.01\n", "saving best checkpoint at epoch: 1, Acc: 92.54\n", "saving best checkpoint at epoch: 4, Acc: 94.07\n", "saving best checkpoint at epoch: 5, Acc: 95.03\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0002. Train Acc: 96.1950, Test loss: 0.0002. Test Acc: 95.8200. Time/epoch: 1.5677\n", "saving best checkpoint at epoch: 10, Acc: 95.82\n", "saving best checkpoint at epoch: 14, Acc: 96.33\n", "saving best checkpoint at epoch: 15, Acc: 96.48\n", "saving best checkpoint at epoch: 17, Acc: 96.73\n", "saving best checkpoint at epoch: 18, Acc: 97.03\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0001. Train Acc: 97.1150, Test loss: 0.0002. Test Acc: 96.6800. Time/epoch: 1.5537\n", "saving best checkpoint at epoch: 24, Acc: 97.06\n", "saving best checkpoint at epoch: 27, Acc: 97.29\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0001. Train Acc: 97.8925, Test loss: 0.0002. Test Acc: 97.2900. Time/epoch: 1.5402\n", "saving best checkpoint at epoch: 32, Acc: 97.54\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0001. Train Acc: 98.3425, Test loss: 0.0001. Test Acc: 97.4100. Time/epoch: 1.6852\n", "saving best checkpoint at epoch: 45, Acc: 97.66\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0001. Train Acc: 97.2975, Test loss: 0.0002. Test Acc: 96.2800. Time/epoch: 1.5690\n", "saving best checkpoint at epoch: 51, Acc: 97.78\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0001. Train Acc: 99.0375, Test loss: 0.0001. Test Acc: 97.7500. Time/epoch: 1.5477\n", "saving best checkpoint at epoch: 61, Acc: 97.85\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0000. Train Acc: 99.2150, Test loss: 0.0002. Test Acc: 97.5700. Time/epoch: 1.6997\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0000. Train Acc: 99.0825, Test loss: 0.0002. Test Acc: 97.5000. Time/epoch: 1.5379\n", "saving best checkpoint at epoch: 89, Acc: 97.88\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0000. Train Acc: 99.3100, Test loss: 0.0002. Test Acc: 97.5700. Time/epoch: 1.5444\n", "saving best checkpoint at epoch: 98, Acc: 97.93\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0000. Train Acc: 99.4825, Test loss: 0.0002. Test Acc: 97.7900. Time/epoch: 1.6948\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train99.4825
Accuracy/val97.79
Loss/train3e-05
Loss/val0.00016
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/4c9tchm1" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0005. Train Acc: 89.6000, Test loss: 0.0005. Test Acc: 89.5500. Time/epoch: 1.5700\n", "saving best checkpoint at epoch: 0, Acc: 89.55\n", "saving best checkpoint at epoch: 1, Acc: 90.99\n", "saving best checkpoint at epoch: 3, Acc: 93.52\n", "saving best checkpoint at epoch: 4, Acc: 94.09\n", "saving best checkpoint at epoch: 5, Acc: 94.9\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0002. Train Acc: 95.2525, Test loss: 0.0002. Test Acc: 95.1200. Time/epoch: 1.6870\n", "saving best checkpoint at epoch: 10, Acc: 95.12\n", "saving best checkpoint at epoch: 12, Acc: 95.69\n", "saving best checkpoint at epoch: 13, Acc: 95.87\n", "saving best checkpoint at epoch: 16, Acc: 96.02\n", "saving best checkpoint at epoch: 17, Acc: 96.15\n", "saving best checkpoint at epoch: 18, Acc: 96.47\n", "saving best checkpoint at epoch: 19, Acc: 96.51\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0002. Train Acc: 96.8775, Test loss: 0.0002. Test Acc: 96.2600. Time/epoch: 1.5535\n", "saving best checkpoint at epoch: 26, Acc: 96.7\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0001. Train Acc: 97.3550, Test loss: 0.0002. Test Acc: 96.6500. Time/epoch: 1.5500\n", "saving best checkpoint at epoch: 33, Acc: 97.17\n", "saving best checkpoint at epoch: 37, Acc: 97.24\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0001. Train Acc: 97.7200, Test loss: 0.0002. Test Acc: 96.7300. Time/epoch: 1.6755\n", "saving best checkpoint at epoch: 44, Acc: 97.35\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0001. Train Acc: 98.5475, Test loss: 0.0002. Test Acc: 97.3200. Time/epoch: 1.5598\n", "saving best checkpoint at epoch: 58, Acc: 97.38\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0001. Train Acc: 98.4050, Test loss: 0.0002. Test Acc: 97.1200. Time/epoch: 1.5449\n", "saving best checkpoint at epoch: 68, Acc: 97.49\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0001. Train Acc: 98.8850, Test loss: 0.0002. Test Acc: 97.2900. Time/epoch: 1.6866\n", "saving best checkpoint at epoch: 75, Acc: 97.6\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0001. Train Acc: 98.7425, Test loss: 0.0002. Test Acc: 97.1400. Time/epoch: 1.6972\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0000. Train Acc: 99.2600, Test loss: 0.0002. Test Acc: 97.5400. Time/epoch: 1.5439\n", "saving best checkpoint at epoch: 91, Acc: 97.66\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0000. Train Acc: 99.3775, Test loss: 0.0002. Test Acc: 97.5500. Time/epoch: 1.5368\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train99.3775
Accuracy/val97.55
Loss/train4e-05
Loss/val0.00019
epoch100

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Run summary:


Accuracy/train97.3125
Accuracy/val96.95
Loss/train0.00015
Loss/val0.00017
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/8jae2l3f" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0016. Train Acc: 91.7650, Test loss: 0.0017. Test Acc: 91.6300. Time/epoch: 2.1920\n", "saving best checkpoint at epoch: 0, Acc: 91.63\n", "saving best checkpoint at epoch: 1, Acc: 92.05\n", "saving best checkpoint at epoch: 2, Acc: 93.15\n", "saving best checkpoint at epoch: 3, Acc: 94.19\n", "saving best checkpoint at epoch: 6, Acc: 94.37\n", "saving best checkpoint at epoch: 7, Acc: 95.34\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0008. Train Acc: 96.1525, Test loss: 0.0009. Test Acc: 95.9300. Time/epoch: 2.1749\n", "saving best checkpoint at epoch: 10, Acc: 95.93\n", "saving best checkpoint at epoch: 11, Acc: 96.28\n", "saving best checkpoint at epoch: 15, Acc: 96.49\n", "saving best checkpoint at epoch: 18, Acc: 96.77\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0007. Train Acc: 96.1000, Test loss: 0.0009. Test Acc: 95.4600. Time/epoch: 2.1818\n", "saving best checkpoint at epoch: 21, Acc: 96.82\n", "saving best checkpoint at epoch: 22, Acc: 96.92\n", "saving best checkpoint at epoch: 25, Acc: 97.11\n", "saving best checkpoint at epoch: 26, Acc: 97.21\n", "saving best checkpoint at epoch: 27, Acc: 97.3\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0005. Train Acc: 97.8350, Test loss: 0.0006. Test Acc: 97.3300. Time/epoch: 2.1733\n", "saving best checkpoint at epoch: 30, Acc: 97.33\n", "saving best checkpoint at epoch: 34, Acc: 97.48\n", "saving best checkpoint at epoch: 36, Acc: 97.56\n", "saving best checkpoint at epoch: 39, Acc: 97.64\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0005. Train Acc: 97.6200, Test loss: 0.0007. Test Acc: 96.9800. Time/epoch: 2.1892\n", "saving best checkpoint at epoch: 42, Acc: 97.77\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0004. Train Acc: 98.2975, Test loss: 0.0005. Test Acc: 97.5900. Time/epoch: 2.1758\n", "saving best checkpoint at epoch: 51, Acc: 97.81\n", "saving best checkpoint at epoch: 52, Acc: 97.85\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0003. Train Acc: 98.7050, Test loss: 0.0005. Test Acc: 97.7200. Time/epoch: 2.0515\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0003. Train Acc: 98.6150, Test loss: 0.0006. Test Acc: 97.5200. Time/epoch: 2.1924\n", "saving best checkpoint at epoch: 76, Acc: 97.94\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0002. Train Acc: 99.1000, Test loss: 0.0005. Test Acc: 98.0300. Time/epoch: 2.1864\n", "saving best checkpoint at epoch: 80, Acc: 98.03\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0002. Train Acc: 99.1375, Test loss: 0.0006. Test Acc: 97.8000. Time/epoch: 2.1853\n", "saving best checkpoint at epoch: 92, Acc: 98.09\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0002. Train Acc: 99.1850, Test loss: 0.0006. Test Acc: 97.7100. Time/epoch: 2.1890\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train99.185
Accuracy/val97.71
Loss/train0.00018
Loss/val0.00056
epoch100

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Accuracy/train98.2875
Accuracy/val97.79
Loss/train9e-05
Loss/val0.00013
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/czeyk9sq" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0004. Train Acc: 90.8875, Test loss: 0.0005. Test Acc: 90.9600. Time/epoch: 1.5749\n", "saving best checkpoint at epoch: 0, Acc: 90.96\n", "saving best checkpoint at epoch: 1, Acc: 92.38\n", "saving best checkpoint at epoch: 2, Acc: 93.02\n", "saving best checkpoint at epoch: 4, Acc: 94.22\n", "saving best checkpoint at epoch: 5, Acc: 95.0\n", "saving best checkpoint at epoch: 7, Acc: 95.41\n", "saving best checkpoint at epoch: 8, Acc: 95.49\n", "saving best checkpoint at epoch: 9, Acc: 95.51\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0003. Train Acc: 93.8775, Test loss: 0.0003. Test Acc: 93.8300. Time/epoch: 1.7190\n", "saving best checkpoint at epoch: 11, Acc: 95.54\n", "saving best checkpoint at epoch: 13, Acc: 95.73\n", "saving best checkpoint at epoch: 15, Acc: 96.43\n", "saving best checkpoint at epoch: 16, Acc: 96.52\n", "saving best checkpoint at epoch: 17, Acc: 96.55\n", "saving best checkpoint at epoch: 18, Acc: 96.84\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0002. Train Acc: 95.3950, Test loss: 0.0002. Test Acc: 95.0200. Time/epoch: 1.5531\n", "saving best checkpoint at epoch: 23, Acc: 97.06\n", "saving best checkpoint at epoch: 25, Acc: 97.07\n", "saving best checkpoint at epoch: 26, Acc: 97.14\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0001. Train Acc: 97.4650, Test loss: 0.0002. Test Acc: 96.9200. Time/epoch: 1.5515\n", "saving best checkpoint at epoch: 37, Acc: 97.41\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0002. Train Acc: 96.5525, Test loss: 0.0002. Test Acc: 95.8400. Time/epoch: 1.6876\n", "saving best checkpoint at epoch: 41, Acc: 97.43\n", "saving best checkpoint at epoch: 44, Acc: 97.7\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0001. Train Acc: 98.1425, Test loss: 0.0002. Test Acc: 97.1300. Time/epoch: 1.5799\n", "saving best checkpoint at epoch: 51, Acc: 97.76\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0001. Train Acc: 98.3025, Test loss: 0.0002. Test Acc: 97.4900. Time/epoch: 1.6913\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0001. Train Acc: 98.1900, Test loss: 0.0002. Test Acc: 97.2200. Time/epoch: 1.5658\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0001. Train Acc: 99.0850, Test loss: 0.0001. Test Acc: 97.7700. Time/epoch: 1.5505\n", "saving best checkpoint at epoch: 80, Acc: 97.77\n", "saving best checkpoint at epoch: 84, Acc: 97.88\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0001. Train Acc: 98.5800, Test loss: 0.0002. Test Acc: 97.3400. Time/epoch: 1.7098\n", "saving best checkpoint at epoch: 96, Acc: 97.9\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0001. Train Acc: 98.7425, Test loss: 0.0002. Test Acc: 97.2700. Time/epoch: 1.5533\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train98.7425
Accuracy/val97.27
Loss/train6e-05
Loss/val0.0002
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/jh03s4xr" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0005. Train Acc: 90.6175, Test loss: 0.0005. Test Acc: 90.3600. Time/epoch: 1.7241\n", "saving best checkpoint at epoch: 0, Acc: 90.36\n", "saving best checkpoint at epoch: 1, Acc: 92.2\n", "saving best checkpoint at epoch: 2, Acc: 92.47\n", "saving best checkpoint at epoch: 5, Acc: 93.47\n", "saving best checkpoint at epoch: 6, Acc: 93.51\n", "saving best checkpoint at epoch: 7, Acc: 93.61\n", "saving best checkpoint at epoch: 8, Acc: 94.38\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0002. Train Acc: 94.8975, Test loss: 0.0003. Test Acc: 94.7300. Time/epoch: 1.6912\n", "saving best checkpoint at epoch: 10, Acc: 94.73\n", "saving best checkpoint at epoch: 13, Acc: 94.91\n", "saving best checkpoint at epoch: 14, Acc: 95.02\n", "saving best checkpoint at epoch: 17, Acc: 95.15\n", "saving best checkpoint at epoch: 19, Acc: 95.49\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0002. Train Acc: 95.3975, Test loss: 0.0002. Test Acc: 95.1700. Time/epoch: 1.5470\n", "saving best checkpoint at epoch: 23, Acc: 95.6\n", "saving best checkpoint at epoch: 25, Acc: 95.98\n", "saving best checkpoint at epoch: 28, Acc: 96.2\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0002. Train Acc: 96.1625, Test loss: 0.0002. Test Acc: 95.8200. Time/epoch: 1.6889\n", "saving best checkpoint at epoch: 31, Acc: 96.26\n", "saving best checkpoint at epoch: 33, Acc: 96.29\n", "saving best checkpoint at epoch: 39, Acc: 96.35\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0002. Train Acc: 96.8825, Test loss: 0.0002. Test Acc: 96.4100. Time/epoch: 1.5443\n", "saving best checkpoint at epoch: 40, Acc: 96.41\n", "saving best checkpoint at epoch: 43, Acc: 96.49\n", "saving best checkpoint at epoch: 45, Acc: 96.54\n", "saving best checkpoint at epoch: 46, Acc: 96.7\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0002. Train Acc: 96.6900, Test loss: 0.0002. Test Acc: 96.2900. Time/epoch: 1.5578\n", "saving best checkpoint at epoch: 52, Acc: 96.79\n", "saving best checkpoint at epoch: 59, Acc: 96.89\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0001. Train Acc: 97.4500, Test loss: 0.0002. Test Acc: 96.9500. Time/epoch: 1.6851\n", "saving best checkpoint at epoch: 60, Acc: 96.95\n", "saving best checkpoint at epoch: 64, Acc: 97.07\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0001. Train Acc: 97.0975, Test loss: 0.0002. Test Acc: 96.6600. Time/epoch: 1.5679\n", "saving best checkpoint at epoch: 73, Acc: 97.08\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0001. Train Acc: 97.6800, Test loss: 0.0002. Test Acc: 96.8100. Time/epoch: 1.5486\n", "saving best checkpoint at epoch: 81, Acc: 97.26\n", "saving best checkpoint at epoch: 87, Acc: 97.35\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0001. Train Acc: 97.7450, Test loss: 0.0002. Test Acc: 96.9500. Time/epoch: 1.6946\n", "saving best checkpoint at epoch: 93, Acc: 97.37\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0001. Train Acc: 98.1450, Test loss: 0.0001. Test Acc: 97.3500. Time/epoch: 1.5474\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train98.145
Accuracy/val97.35
Loss/train0.0001
Loss/val0.00015
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/fkj7tlse" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0015. Train Acc: 91.4650, Test loss: 0.0016. Test Acc: 91.4700. Time/epoch: 2.2127\n", "saving best checkpoint at epoch: 0, Acc: 91.47\n", "saving best checkpoint at epoch: 1, Acc: 94.1\n", "saving best checkpoint at epoch: 2, Acc: 94.58\n", "saving best checkpoint at epoch: 4, Acc: 94.71\n", "saving best checkpoint at epoch: 5, Acc: 95.56\n", "saving best checkpoint at epoch: 7, Acc: 95.95\n", "saving best checkpoint at epoch: 9, Acc: 95.97\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0007. Train Acc: 96.1750, Test loss: 0.0008. Test Acc: 95.9400. Time/epoch: 2.1781\n", "saving best checkpoint at epoch: 11, Acc: 96.45\n", "saving best checkpoint at epoch: 18, Acc: 96.7\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0006. Train Acc: 97.3800, Test loss: 0.0007. Test Acc: 96.8600. Time/epoch: 2.1722\n", "saving best checkpoint at epoch: 20, Acc: 96.86\n", "saving best checkpoint at epoch: 21, Acc: 96.97\n", "saving best checkpoint at epoch: 25, Acc: 97.05\n", "saving best checkpoint at epoch: 27, Acc: 97.18\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0005. Train Acc: 97.8575, Test loss: 0.0006. Test Acc: 97.0800. Time/epoch: 2.1942\n", "saving best checkpoint at epoch: 34, Acc: 97.45\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0004. Train Acc: 98.1200, Test loss: 0.0006. Test Acc: 97.1000. Time/epoch: 2.1784\n", "saving best checkpoint at epoch: 43, Acc: 97.48\n", "saving best checkpoint at epoch: 47, Acc: 97.49\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0004. Train Acc: 97.8975, Test loss: 0.0007. Test Acc: 96.9700. Time/epoch: 2.0271\n", "saving best checkpoint at epoch: 51, Acc: 97.7\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0002. Train Acc: 98.9300, Test loss: 0.0006. Test Acc: 97.5600. Time/epoch: 2.0455\n", "saving best checkpoint at epoch: 61, Acc: 97.77\n", "saving best checkpoint at epoch: 63, Acc: 97.81\n", "saving best checkpoint at epoch: 69, Acc: 97.89\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0002. Train Acc: 99.0025, Test loss: 0.0006. Test Acc: 97.5900. Time/epoch: 2.0671\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0002. Train Acc: 99.2900, Test loss: 0.0006. Test Acc: 97.8000. Time/epoch: 2.0382\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0002. Train Acc: 99.1775, Test loss: 0.0006. Test Acc: 97.5900. Time/epoch: 2.2121\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0002. Train Acc: 99.0750, Test loss: 0.0008. Test Acc: 97.3000. Time/epoch: 2.1745\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train99.075
Accuracy/val97.3
Loss/train0.00021
Loss/val0.0008
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/j4buw0pg" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0014. Train Acc: 92.4650, Test loss: 0.0015. Test Acc: 92.5800. Time/epoch: 2.2078\n", "saving best checkpoint at epoch: 0, Acc: 92.58\n", "saving best checkpoint at epoch: 1, Acc: 93.21\n", "saving best checkpoint at epoch: 2, Acc: 94.23\n", "saving best checkpoint at epoch: 3, Acc: 94.3\n", "saving best checkpoint at epoch: 5, Acc: 94.87\n", "saving best checkpoint at epoch: 6, Acc: 96.1\n", "saving best checkpoint at epoch: 9, Acc: 96.51\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0007. Train Acc: 96.4875, Test loss: 0.0008. Test Acc: 96.3200. Time/epoch: 2.1820\n", "saving best checkpoint at epoch: 12, Acc: 96.73\n", "saving best checkpoint at epoch: 13, Acc: 96.95\n", "saving best checkpoint at epoch: 19, Acc: 97.05\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0007. Train Acc: 96.2875, Test loss: 0.0009. Test Acc: 95.7000. Time/epoch: 2.1878\n", "saving best checkpoint at epoch: 21, Acc: 97.54\n", "saving best checkpoint at epoch: 28, Acc: 97.73\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0004. Train Acc: 98.0800, Test loss: 0.0006. Test Acc: 97.2800. Time/epoch: 2.1992\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0003. Train Acc: 98.7725, Test loss: 0.0006. Test Acc: 97.6800. Time/epoch: 2.0404\n", "saving best checkpoint at epoch: 42, Acc: 97.82\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0002. Train Acc: 99.1175, Test loss: 0.0007. Test Acc: 97.6000. Time/epoch: 2.1692\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0003. Train Acc: 98.6675, Test loss: 0.0009. Test Acc: 96.9700. Time/epoch: 2.1849\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0001. Train Acc: 99.3575, Test loss: 0.0007. Test Acc: 97.5300. Time/epoch: 2.1914\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0001. Train Acc: 99.5250, Test loss: 0.0007. Test Acc: 97.6600. Time/epoch: 2.1845\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0002. Train Acc: 99.0950, Test loss: 0.0009. Test Acc: 96.9500. Time/epoch: 2.1932\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0001. Train Acc: 99.5175, Test loss: 0.0010. Test Acc: 97.2000. Time/epoch: 2.1853\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train99.5175
Accuracy/val97.2
Loss/train0.0001
Loss/val0.001
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/9gmhu4iw" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0006. Train Acc: 89.4825, Test loss: 0.0006. Test Acc: 89.4900. Time/epoch: 1.5772\n", "saving best checkpoint at epoch: 0, Acc: 89.49\n", "saving best checkpoint at epoch: 1, Acc: 92.46\n", "saving best checkpoint at epoch: 2, Acc: 93.29\n", "saving best checkpoint at epoch: 4, Acc: 94.32\n", "saving best checkpoint at epoch: 5, Acc: 94.65\n", "saving best checkpoint at epoch: 9, Acc: 94.87\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0002. Train Acc: 95.3025, Test loss: 0.0003. Test Acc: 95.2600. Time/epoch: 1.5569\n", "saving best checkpoint at epoch: 10, Acc: 95.26\n", "saving best checkpoint at epoch: 12, Acc: 95.29\n", "saving best checkpoint at epoch: 13, Acc: 95.33\n", "saving best checkpoint at epoch: 14, Acc: 95.85\n", "saving best checkpoint at epoch: 17, Acc: 95.95\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0002. Train Acc: 95.9000, Test loss: 0.0002. Test Acc: 95.9200. Time/epoch: 1.6925\n", "saving best checkpoint at epoch: 22, Acc: 96.3\n", "saving best checkpoint at epoch: 25, Acc: 96.31\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0002. Train Acc: 96.6225, Test loss: 0.0002. Test Acc: 96.3100. Time/epoch: 1.5483\n", "saving best checkpoint at epoch: 31, Acc: 96.47\n", "saving best checkpoint at epoch: 32, Acc: 96.64\n", "saving best checkpoint at epoch: 37, Acc: 96.74\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0001. Train Acc: 97.1375, Test loss: 0.0002. Test Acc: 96.7300. Time/epoch: 1.6898\n", "saving best checkpoint at epoch: 45, Acc: 96.8\n", "saving best checkpoint at epoch: 47, Acc: 96.87\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0002. Train Acc: 97.1150, Test loss: 0.0002. Test Acc: 96.7400. Time/epoch: 1.5509\n", "saving best checkpoint at epoch: 56, Acc: 97.14\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0001. Train Acc: 97.3250, Test loss: 0.0002. Test Acc: 96.7100. Time/epoch: 1.6891\n", "saving best checkpoint at epoch: 65, Acc: 97.28\n", "saving best checkpoint at epoch: 66, Acc: 97.32\n", "saving best checkpoint at epoch: 69, Acc: 97.38\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0001. Train Acc: 97.7275, Test loss: 0.0002. Test Acc: 97.1200. Time/epoch: 1.5383\n", "saving best checkpoint at epoch: 74, Acc: 97.39\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0001. Train Acc: 97.5500, Test loss: 0.0002. Test Acc: 96.7900. Time/epoch: 1.5497\n", "saving best checkpoint at epoch: 84, Acc: 97.49\n", "saving best checkpoint at epoch: 86, Acc: 97.57\n", "saving best checkpoint at epoch: 89, Acc: 97.59\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0001. Train Acc: 98.2500, Test loss: 0.0001. Test Acc: 97.5300. Time/epoch: 1.5452\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0001. Train Acc: 98.4100, Test loss: 0.0001. Test Acc: 97.6500. Time/epoch: 1.6866\n", "saving best checkpoint at epoch: 100, Acc: 97.65\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train98.41
Accuracy/val97.65
Loss/train9e-05
Loss/val0.00014
epoch100

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run ancient-sweep-24 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/9gmhu4iw
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Accuracy/train99.27
Accuracy/val97.25
Loss/train4e-05
Loss/val0.00021
epoch100

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run icy-sweep-25 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/6urholcs
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/6pdijj9r" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0008. Train Acc: 85.5125, Test loss: 0.0008. Test Acc: 85.2600. Time/epoch: 1.7448\n", "saving best checkpoint at epoch: 0, Acc: 85.26\n", "saving best checkpoint at epoch: 1, Acc: 90.79\n", "saving best checkpoint at epoch: 2, Acc: 92.17\n", "saving best checkpoint at epoch: 3, Acc: 92.47\n", "saving best checkpoint at epoch: 4, Acc: 93.36\n", "saving best checkpoint at epoch: 6, Acc: 93.93\n", "saving best checkpoint at epoch: 7, Acc: 94.28\n", "saving best checkpoint at epoch: 8, Acc: 94.67\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0003. Train Acc: 94.6975, Test loss: 0.0003. Test Acc: 94.7200. Time/epoch: 1.6724\n", "saving best checkpoint at epoch: 10, Acc: 94.72\n", "saving best checkpoint at epoch: 11, Acc: 95.06\n", "saving best checkpoint at epoch: 13, Acc: 95.16\n", "saving best checkpoint at epoch: 15, Acc: 95.65\n", "saving best checkpoint at epoch: 19, Acc: 95.81\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0002. Train Acc: 95.2800, Test loss: 0.0002. Test Acc: 95.0600. Time/epoch: 1.5332\n", "saving best checkpoint at epoch: 21, Acc: 95.9\n", "saving best checkpoint at epoch: 22, Acc: 95.92\n", "saving best checkpoint at epoch: 23, Acc: 96.01\n", "saving best checkpoint at epoch: 24, Acc: 96.04\n", "saving best checkpoint at epoch: 25, Acc: 96.1\n", "saving best checkpoint at epoch: 29, Acc: 96.47\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0002. Train Acc: 96.5400, Test loss: 0.0002. Test Acc: 96.3400. Time/epoch: 1.5346\n", "saving best checkpoint at epoch: 35, Acc: 96.55\n", "saving best checkpoint at epoch: 37, Acc: 96.68\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0002. Train Acc: 97.1650, Test loss: 0.0002. Test Acc: 96.8700. Time/epoch: 1.6825\n", "saving best checkpoint at epoch: 40, Acc: 96.87\n", "saving best checkpoint at epoch: 43, Acc: 96.94\n", "saving best checkpoint at epoch: 49, Acc: 97.12\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0001. Train Acc: 97.3625, Test loss: 0.0002. Test Acc: 96.8200. Time/epoch: 1.5875\n", "saving best checkpoint at epoch: 54, Acc: 97.18\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0001. Train Acc: 97.3975, Test loss: 0.0002. Test Acc: 97.0400. Time/epoch: 1.5533\n", "saving best checkpoint at epoch: 63, Acc: 97.21\n", "saving best checkpoint at epoch: 64, Acc: 97.28\n", "saving best checkpoint at epoch: 67, Acc: 97.31\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0001. Train Acc: 97.9650, Test loss: 0.0001. Test Acc: 97.3900. Time/epoch: 1.6906\n", "saving best checkpoint at epoch: 70, Acc: 97.39\n", "saving best checkpoint at epoch: 71, Acc: 97.46\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0001. Train Acc: 98.0625, Test loss: 0.0001. Test Acc: 97.3700. Time/epoch: 1.5684\n", "saving best checkpoint at epoch: 81, Acc: 97.61\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0001. Train Acc: 98.0600, Test loss: 0.0001. Test Acc: 97.4500. Time/epoch: 1.6941\n", "saving best checkpoint at epoch: 92, Acc: 97.68\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0001. Train Acc: 97.9675, Test loss: 0.0002. Test Acc: 97.1600. Time/epoch: 1.5800\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train97.9675
Accuracy/val97.16
Loss/train0.00011
Loss/val0.00015
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/x5tmqhlb" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0006. Train Acc: 86.6850, Test loss: 0.0006. Test Acc: 86.9800. Time/epoch: 1.7173\n", "saving best checkpoint at epoch: 0, Acc: 86.98\n", "saving best checkpoint at epoch: 1, Acc: 87.34\n", "saving best checkpoint at epoch: 2, Acc: 89.27\n", "saving best checkpoint at epoch: 3, Acc: 93.18\n", "saving best checkpoint at epoch: 5, Acc: 94.76\n", "saving best checkpoint at epoch: 8, Acc: 95.58\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0002. Train Acc: 95.5725, Test loss: 0.0002. Test Acc: 95.4000. Time/epoch: 1.5544\n", "saving best checkpoint at epoch: 11, Acc: 95.61\n", "saving best checkpoint at epoch: 17, Acc: 96.14\n", "saving best checkpoint at epoch: 18, Acc: 96.39\n", "saving best checkpoint at epoch: 19, Acc: 96.71\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0003. Train Acc: 93.0150, Test loss: 0.0004. Test Acc: 92.3900. Time/epoch: 1.5428\n", "saving best checkpoint at epoch: 21, Acc: 96.85\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0002. Train Acc: 96.7325, Test loss: 0.0002. Test Acc: 95.9100. Time/epoch: 1.7182\n", "saving best checkpoint at epoch: 33, Acc: 97.02\n", "saving best checkpoint at epoch: 35, Acc: 97.04\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0001. Train Acc: 97.6800, Test loss: 0.0002. Test Acc: 96.5900. Time/epoch: 1.5697\n", "saving best checkpoint at epoch: 43, Acc: 97.35\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0001. Train Acc: 98.4400, Test loss: 0.0002. Test Acc: 96.8300. Time/epoch: 1.5753\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0001. Train Acc: 98.3000, Test loss: 0.0002. Test Acc: 96.8600. Time/epoch: 1.5504\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0001. Train Acc: 98.2875, Test loss: 0.0003. Test Acc: 96.5100. Time/epoch: 1.6850\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0001. Train Acc: 99.0150, Test loss: 0.0002. Test Acc: 96.9100. Time/epoch: 1.5626\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0000. Train Acc: 99.2300, Test loss: 0.0002. Test Acc: 96.7600. Time/epoch: 1.5536\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0000. Train Acc: 99.5925, Test loss: 0.0003. Test Acc: 96.9600. Time/epoch: 1.6883\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train99.5925
Accuracy/val96.96
Loss/train2e-05
Loss/val0.00025
epoch100

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Accuracy/train99.0325
Accuracy/val97.58
Loss/train5e-05
Loss/val0.00017
epoch100

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Run summary:


Accuracy/train98.7425
Accuracy/val96.74
Loss/train0.0006
Loss/val0.00272
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/0n0s9pss" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0483. Train Acc: 36.8250, Test loss: 0.0481. Test Acc: 37.5400. Time/epoch: 5.1060\n", "saving best checkpoint at epoch: 0, Acc: 37.54\n", " EPOCH 10. Progress: 100.0%. \n", " Train loss: 0.0481. Train Acc: 36.8250, Test loss: 0.0480. Test Acc: 37.5400. Time/epoch: 5.0873\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train36.825
Accuracy/val37.54
Loss/train0.04807
Loss/val0.04798
epoch10

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/8m1lsuw6" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0028. Train Acc: 58.7700, Test loss: 0.0029. Test Acc: 58.9700. Time/epoch: 1.7256\n", "saving best checkpoint at epoch: 0, Acc: 58.97\n", "saving best checkpoint at epoch: 1, Acc: 63.45\n", "saving best checkpoint at epoch: 2, Acc: 80.97\n", "saving best checkpoint at epoch: 3, Acc: 82.18\n", "saving best checkpoint at epoch: 4, Acc: 84.26\n", "saving best checkpoint at epoch: 5, Acc: 85.96\n", "saving best checkpoint at epoch: 6, Acc: 87.13\n", "saving best checkpoint at epoch: 7, Acc: 88.07\n", "saving best checkpoint at epoch: 8, Acc: 88.52\n", "saving best checkpoint at epoch: 9, Acc: 89.06\n", " EPOCH 10. Progress: 20.0%. \n", " Train loss: 0.0005. Train Acc: 89.8100, Test loss: 0.0005. Test Acc: 89.6900. Time/epoch: 1.5481\n", "saving best checkpoint at epoch: 10, Acc: 89.69\n", "saving best checkpoint at epoch: 11, Acc: 90.03\n", "saving best checkpoint at epoch: 12, Acc: 90.48\n", "saving best checkpoint at epoch: 13, Acc: 90.92\n", "saving best checkpoint at epoch: 14, Acc: 91.3\n", "saving best checkpoint at epoch: 16, Acc: 91.78\n", "saving best checkpoint at epoch: 17, Acc: 91.98\n", "saving best checkpoint at epoch: 18, Acc: 92.34\n", "saving best checkpoint at epoch: 19, Acc: 92.5\n", " EPOCH 20. Progress: 40.0%. \n", " Train loss: 0.0004. Train Acc: 92.5675, Test loss: 0.0004. Test Acc: 92.7200. Time/epoch: 1.5397\n", "saving best checkpoint at epoch: 20, Acc: 92.72\n", "saving best checkpoint at epoch: 21, Acc: 93.22\n", "saving best checkpoint at epoch: 24, Acc: 93.49\n", "saving best checkpoint at epoch: 25, Acc: 93.52\n", "saving best checkpoint at epoch: 27, Acc: 93.56\n", "saving best checkpoint at epoch: 28, Acc: 93.92\n", " EPOCH 30. Progress: 60.0%. \n", " Train loss: 0.0003. Train Acc: 93.8200, Test loss: 0.0003. Test Acc: 93.9200. Time/epoch: 1.6722\n", "saving best checkpoint at epoch: 31, Acc: 93.95\n", "saving best checkpoint at epoch: 32, Acc: 94.0\n", "saving best checkpoint at epoch: 33, Acc: 94.07\n", "saving best checkpoint at epoch: 34, Acc: 94.17\n", "saving best checkpoint at epoch: 36, Acc: 94.36\n", "saving best checkpoint at epoch: 38, Acc: 94.55\n", " EPOCH 40. Progress: 80.0%. \n", " Train loss: 0.0003. Train Acc: 94.3475, Test loss: 0.0003. Test Acc: 94.4400. Time/epoch: 1.5486\n", "saving best checkpoint at epoch: 44, Acc: 94.77\n", "saving best checkpoint at epoch: 46, Acc: 94.82\n", " EPOCH 50. Progress: 100.0%. \n", " Train loss: 0.0003. Train Acc: 94.8550, Test loss: 0.0003. Test Acc: 94.8200. Time/epoch: 1.6937\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train94.855
Accuracy/val94.82
Loss/train0.00027
Loss/val0.00029
epoch50

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Time/epoch: 1.5496\n", "saving best checkpoint at epoch: 0, Acc: 65.51\n", "saving best checkpoint at epoch: 1, Acc: 75.52\n", "saving best checkpoint at epoch: 2, Acc: 79.1\n", "saving best checkpoint at epoch: 3, Acc: 82.91\n", "saving best checkpoint at epoch: 4, Acc: 85.02\n", "saving best checkpoint at epoch: 5, Acc: 85.85\n", "saving best checkpoint at epoch: 6, Acc: 86.43\n", "saving best checkpoint at epoch: 7, Acc: 86.81\n", "saving best checkpoint at epoch: 8, Acc: 87.11\n", "saving best checkpoint at epoch: 9, Acc: 87.46\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0006. Train Acc: 87.7750, Test loss: 0.0006. Test Acc: 87.9500. Time/epoch: 1.6865\n", "saving best checkpoint at epoch: 10, Acc: 87.95\n", "saving best checkpoint at epoch: 11, Acc: 88.19\n", "saving best checkpoint at epoch: 12, Acc: 88.5\n", "saving best checkpoint at epoch: 13, Acc: 89.22\n", "saving best checkpoint at epoch: 14, Acc: 89.29\n", "saving best checkpoint at epoch: 15, Acc: 89.67\n", "saving best checkpoint at epoch: 16, Acc: 89.99\n", "saving best checkpoint at epoch: 17, Acc: 90.19\n", "saving best checkpoint at epoch: 18, Acc: 90.64\n", "saving best checkpoint at epoch: 19, Acc: 90.69\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0004. Train Acc: 91.1400, Test loss: 0.0004. Test Acc: 90.8500. Time/epoch: 1.5560\n", "saving best checkpoint at epoch: 20, Acc: 90.85\n", "saving best checkpoint at epoch: 21, Acc: 91.26\n", "saving best checkpoint at epoch: 22, Acc: 91.47\n", "saving best checkpoint at epoch: 23, Acc: 91.6\n", "saving best checkpoint at epoch: 24, Acc: 91.87\n", "saving best checkpoint at epoch: 25, Acc: 92.18\n", "saving best checkpoint at epoch: 27, Acc: 92.37\n", "saving best checkpoint at epoch: 28, Acc: 92.58\n", "saving best checkpoint at epoch: 29, Acc: 92.65\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0003. Train Acc: 92.9575, Test loss: 0.0004. Test Acc: 92.7600. Time/epoch: 1.5399\n", "saving best checkpoint at epoch: 30, Acc: 92.76\n", "saving best checkpoint at epoch: 31, Acc: 92.89\n", "saving best checkpoint at epoch: 32, Acc: 92.92\n", "saving best checkpoint at epoch: 33, Acc: 93.14\n", "saving best checkpoint at epoch: 34, Acc: 93.25\n", "saving best checkpoint at epoch: 35, Acc: 93.36\n", "saving best checkpoint at epoch: 36, Acc: 93.54\n", "saving best checkpoint at epoch: 37, Acc: 93.64\n", "saving best checkpoint at epoch: 39, Acc: 93.74\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0003. Train Acc: 93.8075, Test loss: 0.0003. Test Acc: 93.7600. Time/epoch: 1.6792\n", "saving best checkpoint at epoch: 40, Acc: 93.76\n", "saving best checkpoint at epoch: 41, Acc: 93.88\n", "saving best checkpoint at epoch: 42, Acc: 93.93\n", "saving best checkpoint at epoch: 44, Acc: 93.96\n", "saving best checkpoint at epoch: 45, Acc: 94.04\n", "saving best checkpoint at epoch: 46, Acc: 94.17\n", "saving best checkpoint at epoch: 48, Acc: 94.22\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0003. Train Acc: 94.1300, Test loss: 0.0003. Test Acc: 94.1500. Time/epoch: 1.5607\n", "saving best checkpoint at epoch: 51, Acc: 94.31\n", "saving best checkpoint at epoch: 56, Acc: 94.43\n", "saving best checkpoint at epoch: 58, Acc: 94.5\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0003. Train Acc: 94.2525, Test loss: 0.0003. Test Acc: 94.3900. Time/epoch: 1.5341\n", "saving best checkpoint at epoch: 61, Acc: 94.57\n", "saving best checkpoint at epoch: 67, Acc: 94.72\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0003. Train Acc: 94.7300, Test loss: 0.0003. Test Acc: 94.6200. Time/epoch: 1.6901\n", "saving best checkpoint at epoch: 76, Acc: 94.76\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0002. Train Acc: 94.6675, Test loss: 0.0003. Test Acc: 94.5400. Time/epoch: 1.5367\n", "saving best checkpoint at epoch: 81, Acc: 94.81\n", "saving best checkpoint at epoch: 82, Acc: 94.82\n", "saving best checkpoint at epoch: 85, Acc: 94.83\n", "saving best checkpoint at epoch: 86, Acc: 94.88\n", "saving best checkpoint at epoch: 87, Acc: 94.92\n", "saving best checkpoint at epoch: 89, Acc: 95.02\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0002. Train Acc: 95.1400, Test loss: 0.0003. Test Acc: 94.9800. Time/epoch: 1.5398\n", "saving best checkpoint at epoch: 93, Acc: 95.04\n", "saving best checkpoint at epoch: 95, Acc: 95.09\n", "saving best checkpoint at epoch: 97, Acc: 95.14\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0002. Train Acc: 95.1975, Test loss: 0.0002. Test Acc: 95.0700. Time/epoch: 1.6824\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train95.1975
Accuracy/val95.07
Loss/train0.00023
Loss/val0.00025
epoch100

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Run summary:


Accuracy/train96.9925
Accuracy/val96.6
Loss/train0.00015
Loss/val0.00018
epoch100

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Accuracy/train96.8125
Accuracy/val96.27
Loss/train0.00017
Loss/val0.0002
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/pwurgze0" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0031. Train Acc: 47.5750, Test loss: 0.0031. Test Acc: 47.7700. Time/epoch: 1.5660\n", "saving best checkpoint at epoch: 0, Acc: 47.77\n", "saving best checkpoint at epoch: 1, Acc: 60.28\n", "saving best checkpoint at epoch: 2, Acc: 66.63\n", "saving best checkpoint at epoch: 3, Acc: 71.05\n", "saving best checkpoint at epoch: 4, Acc: 73.34\n", "saving best checkpoint at epoch: 5, Acc: 75.86\n", "saving best checkpoint at epoch: 6, Acc: 79.66\n", "saving best checkpoint at epoch: 7, Acc: 81.76\n", "saving best checkpoint at epoch: 8, Acc: 84.55\n", "saving best checkpoint at epoch: 9, Acc: 86.29\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0006. Train Acc: 86.6850, Test loss: 0.0006. Test Acc: 86.5300. Time/epoch: 1.6877\n", "saving best checkpoint at epoch: 10, Acc: 86.53\n", "saving best checkpoint at epoch: 11, Acc: 87.31\n", "saving best checkpoint at epoch: 12, Acc: 88.37\n", "saving best checkpoint at epoch: 13, Acc: 89.03\n", "saving best checkpoint at epoch: 14, Acc: 89.34\n", "saving best checkpoint at epoch: 15, Acc: 89.58\n", "saving best checkpoint at epoch: 16, Acc: 89.84\n", "saving best checkpoint at epoch: 17, Acc: 90.24\n", "saving best checkpoint at epoch: 18, Acc: 90.44\n", "saving best checkpoint at epoch: 19, Acc: 90.61\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0004. Train Acc: 91.2475, Test loss: 0.0004. Test Acc: 90.9300. Time/epoch: 1.5504\n", "saving best checkpoint at epoch: 20, Acc: 90.93\n", "saving best checkpoint at epoch: 21, Acc: 91.07\n", "saving best checkpoint at epoch: 23, Acc: 91.43\n", "saving best checkpoint at epoch: 25, Acc: 91.56\n", "saving best checkpoint at epoch: 26, Acc: 91.69\n", "saving best checkpoint at epoch: 27, Acc: 91.98\n", "saving best checkpoint at epoch: 28, Acc: 92.02\n", "saving best checkpoint at epoch: 29, Acc: 92.06\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0004. Train Acc: 92.2625, Test loss: 0.0004. Test Acc: 92.0600. Time/epoch: 1.6804\n", "saving best checkpoint at epoch: 31, Acc: 92.13\n", "saving best checkpoint at epoch: 32, Acc: 92.31\n", "saving best checkpoint at epoch: 33, Acc: 92.34\n", "saving best checkpoint at epoch: 34, Acc: 92.37\n", "saving best checkpoint at epoch: 35, Acc: 92.55\n", "saving best checkpoint at epoch: 37, Acc: 92.6\n", "saving best checkpoint at epoch: 38, Acc: 92.78\n", "saving best checkpoint at epoch: 39, Acc: 92.85\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0003. Train Acc: 93.1400, Test loss: 0.0003. Test Acc: 92.8800. Time/epoch: 1.5314\n", "saving best checkpoint at epoch: 40, Acc: 92.88\n", "saving best checkpoint at epoch: 41, Acc: 93.02\n", "saving best checkpoint at epoch: 42, Acc: 93.08\n", "saving best checkpoint at epoch: 44, Acc: 93.1\n", "saving best checkpoint at epoch: 45, Acc: 93.18\n", "saving best checkpoint at epoch: 47, Acc: 93.33\n", "saving best checkpoint at epoch: 49, Acc: 93.45\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0003. Train Acc: 93.9575, Test loss: 0.0003. Test Acc: 93.5600. Time/epoch: 1.7005\n", "saving best checkpoint at epoch: 50, Acc: 93.56\n", "saving best checkpoint at epoch: 51, Acc: 93.59\n", "saving best checkpoint at epoch: 53, Acc: 93.64\n", "saving best checkpoint at epoch: 55, Acc: 93.74\n", "saving best checkpoint at epoch: 56, Acc: 93.81\n", "saving best checkpoint at epoch: 58, Acc: 93.85\n", "saving best checkpoint at epoch: 59, Acc: 93.9\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0003. Train Acc: 94.4750, Test loss: 0.0003. Test Acc: 93.9700. Time/epoch: 1.5386\n", "saving best checkpoint at epoch: 60, Acc: 93.97\n", "saving best checkpoint at epoch: 65, Acc: 94.17\n", "saving best checkpoint at epoch: 67, Acc: 94.22\n", "saving best checkpoint at epoch: 68, Acc: 94.23\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0003. Train Acc: 94.8150, Test loss: 0.0003. Test Acc: 94.2900. Time/epoch: 1.5432\n", "saving best checkpoint at epoch: 70, Acc: 94.29\n", "saving best checkpoint at epoch: 71, Acc: 94.41\n", "saving best checkpoint at epoch: 77, Acc: 94.45\n", "saving best checkpoint at epoch: 78, Acc: 94.6\n", "saving best checkpoint at epoch: 79, Acc: 94.63\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0002. Train Acc: 95.0225, Test loss: 0.0003. Test Acc: 94.6100. Time/epoch: 1.5393\n", "saving best checkpoint at epoch: 81, Acc: 94.65\n", "saving best checkpoint at epoch: 83, Acc: 94.73\n", "saving best checkpoint at epoch: 86, Acc: 94.79\n", "saving best checkpoint at epoch: 88, Acc: 94.81\n", "saving best checkpoint at epoch: 89, Acc: 94.82\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0002. Train Acc: 95.4000, Test loss: 0.0003. Test Acc: 94.8200. Time/epoch: 1.6811\n", "saving best checkpoint at epoch: 91, Acc: 94.88\n", "saving best checkpoint at epoch: 93, Acc: 94.9\n", "saving best checkpoint at epoch: 97, Acc: 94.97\n", "saving best checkpoint at epoch: 98, Acc: 95.07\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0002. Train Acc: 95.3650, Test loss: 0.0003. Test Acc: 94.9600. Time/epoch: 1.6837\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train95.365
Accuracy/val94.96
Loss/train0.00023
Loss/val0.00026
epoch100

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run avid-sweep-35 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/pwurgze0
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ax3vvrwq" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0024. Train Acc: 73.6525, Test loss: 0.0024. Test Acc: 73.7300. Time/epoch: 1.5804\n", "saving best checkpoint at epoch: 0, Acc: 73.73\n", "saving best checkpoint at epoch: 1, Acc: 80.75\n", "saving best checkpoint at epoch: 2, Acc: 86.08\n", "saving best checkpoint at epoch: 3, Acc: 87.72\n", "saving best checkpoint at epoch: 4, Acc: 88.2\n", "saving best checkpoint at epoch: 5, Acc: 89.02\n", "saving best checkpoint at epoch: 6, Acc: 89.41\n", "saving best checkpoint at epoch: 7, Acc: 90.2\n", "saving best checkpoint at epoch: 8, Acc: 90.76\n", "saving best checkpoint at epoch: 9, Acc: 91.02\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0004. Train Acc: 91.3175, Test loss: 0.0004. Test Acc: 91.3100. Time/epoch: 1.6877\n", "saving best checkpoint at epoch: 10, Acc: 91.31\n", "saving best checkpoint at epoch: 11, Acc: 91.9\n", "saving best checkpoint at epoch: 13, Acc: 92.29\n", "saving best checkpoint at epoch: 14, Acc: 92.78\n", "saving best checkpoint at epoch: 15, Acc: 93.1\n", "saving best checkpoint at epoch: 16, Acc: 93.18\n", "saving best checkpoint at epoch: 17, Acc: 93.65\n", "saving best checkpoint at epoch: 18, Acc: 93.88\n", "saving best checkpoint at epoch: 19, Acc: 93.99\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0003. Train Acc: 93.9350, Test loss: 0.0003. Test Acc: 94.2200. Time/epoch: 1.5540\n", "saving best checkpoint at epoch: 20, Acc: 94.22\n", "saving best checkpoint at epoch: 21, Acc: 94.34\n", "saving best checkpoint at epoch: 22, Acc: 94.48\n", "saving best checkpoint at epoch: 24, Acc: 94.51\n", "saving best checkpoint at epoch: 26, Acc: 94.85\n", "saving best checkpoint at epoch: 27, Acc: 95.05\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0003. Train Acc: 94.9075, Test loss: 0.0003. Test Acc: 95.0100. Time/epoch: 1.6922\n", "saving best checkpoint at epoch: 31, Acc: 95.11\n", "saving best checkpoint at epoch: 32, Acc: 95.24\n", "saving best checkpoint at epoch: 33, Acc: 95.42\n", "saving best checkpoint at epoch: 37, Acc: 95.7\n", "saving best checkpoint at epoch: 39, Acc: 95.72\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0002. Train Acc: 95.6025, Test loss: 0.0003. Test Acc: 95.5300. Time/epoch: 1.6833\n", "saving best checkpoint at epoch: 41, Acc: 95.94\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0002. Train Acc: 96.1675, Test loss: 0.0002. Test Acc: 96.0000. Time/epoch: 1.5599\n", "saving best checkpoint at epoch: 50, Acc: 96.0\n", "saving best checkpoint at epoch: 52, Acc: 96.06\n", "saving best checkpoint at epoch: 54, Acc: 96.18\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0002. Train Acc: 96.2350, Test loss: 0.0002. Test Acc: 96.1100. Time/epoch: 1.6841\n", "saving best checkpoint at epoch: 61, Acc: 96.21\n", "saving best checkpoint at epoch: 63, Acc: 96.34\n", "saving best checkpoint at epoch: 69, Acc: 96.35\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0002. Train Acc: 96.4100, Test loss: 0.0002. Test Acc: 96.2100. Time/epoch: 1.5318\n", "saving best checkpoint at epoch: 72, Acc: 96.43\n", "saving best checkpoint at epoch: 75, Acc: 96.57\n", "saving best checkpoint at epoch: 78, Acc: 96.65\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0002. Train Acc: 96.7125, Test loss: 0.0002. Test Acc: 96.4800. Time/epoch: 1.6840\n", "saving best checkpoint at epoch: 83, Acc: 96.67\n", "saving best checkpoint at epoch: 84, Acc: 96.77\n", "saving best checkpoint at epoch: 89, Acc: 96.81\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0002. Train Acc: 96.7375, Test loss: 0.0002. Test Acc: 96.4500. Time/epoch: 1.5350\n", "saving best checkpoint at epoch: 93, Acc: 96.87\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0002. Train Acc: 97.0350, Test loss: 0.0002. Test Acc: 96.7900. Time/epoch: 1.5425\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train97.035
Accuracy/val96.79
Loss/train0.00016
Loss/val0.00018
epoch100

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run denim-sweep-36 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ax3vvrwq
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Time/epoch: 1.5657\n", "saving best checkpoint at epoch: 0, Acc: 53.41\n", "saving best checkpoint at epoch: 1, Acc: 74.71\n", "saving best checkpoint at epoch: 2, Acc: 78.01\n", "saving best checkpoint at epoch: 3, Acc: 80.75\n", "saving best checkpoint at epoch: 4, Acc: 83.03\n", "saving best checkpoint at epoch: 5, Acc: 85.52\n", "saving best checkpoint at epoch: 6, Acc: 87.68\n", "saving best checkpoint at epoch: 7, Acc: 88.61\n", "saving best checkpoint at epoch: 8, Acc: 89.57\n", "saving best checkpoint at epoch: 9, Acc: 90.16\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0005. Train Acc: 90.6600, Test loss: 0.0005. Test Acc: 90.3800. Time/epoch: 1.7099\n", "saving best checkpoint at epoch: 10, Acc: 90.38\n", "saving best checkpoint at epoch: 11, Acc: 90.82\n", "saving best checkpoint at epoch: 12, Acc: 91.08\n", "saving best checkpoint at epoch: 13, Acc: 91.3\n", "saving best checkpoint at epoch: 14, Acc: 91.44\n", "saving best checkpoint at epoch: 15, Acc: 91.64\n", "saving best checkpoint at epoch: 16, Acc: 91.67\n", "saving best checkpoint at epoch: 17, Acc: 91.83\n", "saving best checkpoint at epoch: 18, Acc: 92.07\n", "saving best checkpoint at epoch: 19, Acc: 92.08\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0004. Train Acc: 92.5325, Test loss: 0.0004. Test Acc: 92.2800. Time/epoch: 1.5457\n", "saving best checkpoint at epoch: 20, Acc: 92.28\n", "saving best checkpoint at epoch: 21, Acc: 92.3\n", "saving best checkpoint at epoch: 22, Acc: 92.32\n", "saving best checkpoint at epoch: 24, Acc: 92.45\n", "saving best checkpoint at epoch: 26, Acc: 92.57\n", "saving best checkpoint at epoch: 27, Acc: 92.72\n", "saving best checkpoint at epoch: 29, Acc: 92.79\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0003. Train Acc: 92.9850, Test loss: 0.0003. Test Acc: 92.9800. Time/epoch: 1.6818\n", "saving best checkpoint at epoch: 30, Acc: 92.98\n", "saving best checkpoint at epoch: 31, Acc: 93.0\n", "saving best checkpoint at epoch: 34, Acc: 93.11\n", "saving best checkpoint at epoch: 35, Acc: 93.22\n", "saving best checkpoint at epoch: 38, Acc: 93.31\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0003. Train Acc: 93.4650, Test loss: 0.0003. Test Acc: 93.3400. Time/epoch: 1.5396\n", "saving best checkpoint at epoch: 40, Acc: 93.34\n", "saving best checkpoint at epoch: 41, Acc: 93.4\n", "saving best checkpoint at epoch: 42, Acc: 93.51\n", "saving best checkpoint at epoch: 43, Acc: 93.53\n", "saving best checkpoint at epoch: 44, Acc: 93.67\n", "saving best checkpoint at epoch: 46, Acc: 93.74\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0003. Train Acc: 93.9100, Test loss: 0.0003. Test Acc: 93.9100. Time/epoch: 1.6861\n", "saving best checkpoint at epoch: 50, Acc: 93.91\n", "saving best checkpoint at epoch: 51, Acc: 93.94\n", "saving best checkpoint at epoch: 55, Acc: 94.19\n", "saving best checkpoint at epoch: 57, Acc: 94.22\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0003. Train Acc: 94.1975, Test loss: 0.0003. Test Acc: 94.3200. Time/epoch: 1.5335\n", "saving best checkpoint at epoch: 60, Acc: 94.32\n", "saving best checkpoint at epoch: 65, Acc: 94.67\n", "saving best checkpoint at epoch: 69, Acc: 94.7\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0003. Train Acc: 94.2275, Test loss: 0.0003. Test Acc: 94.4500. Time/epoch: 1.5460\n", "saving best checkpoint at epoch: 72, Acc: 94.75\n", "saving best checkpoint at epoch: 77, Acc: 94.86\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0003. Train Acc: 94.6300, Test loss: 0.0003. Test Acc: 94.8400. Time/epoch: 1.5532\n", "saving best checkpoint at epoch: 82, Acc: 94.89\n", "saving best checkpoint at epoch: 83, Acc: 95.02\n", "saving best checkpoint at epoch: 85, Acc: 95.09\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0003. Train Acc: 94.9225, Test loss: 0.0003. Test Acc: 95.1900. Time/epoch: 1.5430\n", "saving best checkpoint at epoch: 90, Acc: 95.19\n", "saving best checkpoint at epoch: 94, Acc: 95.25\n", "saving best checkpoint at epoch: 95, Acc: 95.3\n", "saving best checkpoint at epoch: 99, Acc: 95.35\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0002. Train Acc: 95.0700, Test loss: 0.0003. Test Acc: 95.2900. Time/epoch: 1.6845\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train95.07
Accuracy/val95.29
Loss/train0.00024
Loss/val0.00026
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/b7mppksc" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0009. Train Acc: 83.2800, Test loss: 0.0010. Test Acc: 82.8800. Time/epoch: 1.5677\n", "saving best checkpoint at epoch: 0, Acc: 82.88\n", "saving best checkpoint at epoch: 1, Acc: 87.72\n", "saving best checkpoint at epoch: 2, Acc: 89.26\n", "saving best checkpoint at epoch: 3, Acc: 91.18\n", "saving best checkpoint at epoch: 5, Acc: 92.1\n", "saving best checkpoint at epoch: 6, Acc: 92.79\n", "saving best checkpoint at epoch: 7, Acc: 93.46\n", "saving best checkpoint at epoch: 9, Acc: 94.3\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0003. Train Acc: 93.6950, Test loss: 0.0003. Test Acc: 93.7600. Time/epoch: 1.6787\n", "saving best checkpoint at epoch: 11, Acc: 94.68\n", "saving best checkpoint at epoch: 12, Acc: 95.18\n", "saving best checkpoint at epoch: 17, Acc: 95.27\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0002. Train Acc: 96.0900, Test loss: 0.0002. Test Acc: 95.9200. Time/epoch: 1.5397\n", "saving best checkpoint at epoch: 20, Acc: 95.92\n", "saving best checkpoint at epoch: 25, Acc: 96.09\n", "saving best checkpoint at epoch: 26, Acc: 96.11\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0002. Train Acc: 96.4625, Test loss: 0.0002. Test Acc: 96.1600. Time/epoch: 1.6787\n", "saving best checkpoint at epoch: 30, Acc: 96.16\n", "saving best checkpoint at epoch: 32, Acc: 96.46\n", "saving best checkpoint at epoch: 39, Acc: 96.49\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0002. Train Acc: 96.9550, Test loss: 0.0002. Test Acc: 96.6700. Time/epoch: 1.5390\n", "saving best checkpoint at epoch: 40, Acc: 96.67\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0002. Train Acc: 96.9625, Test loss: 0.0002. Test Acc: 96.6100. Time/epoch: 1.6923\n", "saving best checkpoint at epoch: 52, Acc: 96.91\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0002. Train Acc: 96.7725, Test loss: 0.0002. Test Acc: 96.4900. Time/epoch: 1.5414\n", "saving best checkpoint at epoch: 66, Acc: 97.08\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0001. Train Acc: 97.2025, Test loss: 0.0002. Test Acc: 96.7100. Time/epoch: 1.5450\n", "saving best checkpoint at epoch: 77, Acc: 97.24\n", "saving best checkpoint at epoch: 78, Acc: 97.28\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0001. Train Acc: 97.7900, Test loss: 0.0002. Test Acc: 97.3300. Time/epoch: 1.5395\n", "saving best checkpoint at epoch: 80, Acc: 97.33\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0001. Train Acc: 97.9050, Test loss: 0.0002. Test Acc: 97.3400. Time/epoch: 1.5505\n", "saving best checkpoint at epoch: 90, Acc: 97.34\n", "saving best checkpoint at epoch: 98, Acc: 97.55\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0001. Train Acc: 97.8975, Test loss: 0.0002. Test Acc: 97.2800. Time/epoch: 1.6822\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train97.8975
Accuracy/val97.28
Loss/train0.00011
Loss/val0.00016
epoch100

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Time/epoch: 1.5490\n", "saving best checkpoint at epoch: 0, Acc: 57.27\n", "saving best checkpoint at epoch: 1, Acc: 77.36\n", "saving best checkpoint at epoch: 2, Acc: 80.7\n", "saving best checkpoint at epoch: 3, Acc: 85.38\n", "saving best checkpoint at epoch: 4, Acc: 87.24\n", "saving best checkpoint at epoch: 5, Acc: 88.48\n", "saving best checkpoint at epoch: 6, Acc: 89.05\n", "saving best checkpoint at epoch: 7, Acc: 89.57\n", "saving best checkpoint at epoch: 8, Acc: 89.87\n", "saving best checkpoint at epoch: 9, Acc: 90.2\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0005. Train Acc: 90.3575, Test loss: 0.0005. Test Acc: 90.4400. Time/epoch: 1.6825\n", "saving best checkpoint at epoch: 10, Acc: 90.44\n", "saving best checkpoint at epoch: 11, Acc: 90.66\n", "saving best checkpoint at epoch: 12, Acc: 91.02\n", "saving best checkpoint at epoch: 13, Acc: 91.16\n", "saving best checkpoint at epoch: 14, Acc: 91.46\n", "saving best checkpoint at epoch: 15, Acc: 91.81\n", "saving best checkpoint at epoch: 16, Acc: 91.89\n", "saving best checkpoint at epoch: 17, Acc: 92.08\n", "saving best checkpoint at epoch: 18, Acc: 92.23\n", "saving best checkpoint at epoch: 19, Acc: 92.37\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0004. Train Acc: 92.1875, Test loss: 0.0004. Test Acc: 92.4400. Time/epoch: 1.5484\n", "saving best checkpoint at epoch: 20, Acc: 92.44\n", "saving best checkpoint at epoch: 21, Acc: 92.59\n", "saving best checkpoint at epoch: 22, Acc: 92.68\n", "saving best checkpoint at epoch: 23, Acc: 92.83\n", "saving best checkpoint at epoch: 24, Acc: 93.0\n", "saving best checkpoint at epoch: 25, Acc: 93.14\n", "saving best checkpoint at epoch: 26, Acc: 93.16\n", "saving best checkpoint at epoch: 27, Acc: 93.24\n", "saving best checkpoint at epoch: 28, Acc: 93.34\n", "saving best checkpoint at epoch: 29, Acc: 93.43\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0003. Train Acc: 93.3100, Test loss: 0.0003. Test Acc: 93.3700. Time/epoch: 1.5422\n", "saving best checkpoint at epoch: 31, Acc: 93.65\n", "saving best checkpoint at epoch: 33, Acc: 93.66\n", "saving best checkpoint at epoch: 34, Acc: 93.71\n", "saving best checkpoint at epoch: 35, Acc: 93.92\n", "saving best checkpoint at epoch: 37, Acc: 93.97\n", "saving best checkpoint at epoch: 38, Acc: 94.09\n", "saving best checkpoint at epoch: 39, Acc: 94.13\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0003. Train Acc: 93.9300, Test loss: 0.0003. Test Acc: 94.2000. Time/epoch: 1.6882\n", "saving best checkpoint at epoch: 40, Acc: 94.2\n", "saving best checkpoint at epoch: 43, Acc: 94.23\n", "saving best checkpoint at epoch: 44, Acc: 94.48\n", "saving best checkpoint at epoch: 47, Acc: 94.56\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0003. Train Acc: 94.5775, Test loss: 0.0003. Test Acc: 94.5800. Time/epoch: 1.5991\n", "saving best checkpoint at epoch: 50, Acc: 94.58\n", "saving best checkpoint at epoch: 51, Acc: 94.63\n", "saving best checkpoint at epoch: 52, Acc: 94.64\n", "saving best checkpoint at epoch: 53, Acc: 94.68\n", "saving best checkpoint at epoch: 54, Acc: 94.72\n", "saving best checkpoint at epoch: 55, Acc: 94.73\n", "saving best checkpoint at epoch: 56, Acc: 94.76\n", "saving best checkpoint at epoch: 57, Acc: 94.94\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0003. Train Acc: 94.9825, Test loss: 0.0003. Test Acc: 94.9600. Time/epoch: 1.6868\n", "saving best checkpoint at epoch: 60, Acc: 94.96\n", "saving best checkpoint at epoch: 63, Acc: 95.0\n", "saving best checkpoint at epoch: 65, Acc: 95.02\n", "saving best checkpoint at epoch: 69, Acc: 95.1\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0002. Train Acc: 95.1475, Test loss: 0.0003. Test Acc: 95.0100. Time/epoch: 1.5420\n", "saving best checkpoint at epoch: 72, Acc: 95.12\n", "saving best checkpoint at epoch: 73, Acc: 95.14\n", "saving best checkpoint at epoch: 74, Acc: 95.33\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0002. Train Acc: 95.4825, Test loss: 0.0002. Test Acc: 95.2300. Time/epoch: 1.6870\n", "saving best checkpoint at epoch: 82, Acc: 95.46\n", "saving best checkpoint at epoch: 86, Acc: 95.48\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0002. Train Acc: 95.7200, Test loss: 0.0002. Test Acc: 95.3800. Time/epoch: 1.5716\n", "saving best checkpoint at epoch: 93, Acc: 95.5\n", "saving best checkpoint at epoch: 96, Acc: 95.54\n", "saving best checkpoint at epoch: 97, Acc: 95.56\n", "saving best checkpoint at epoch: 98, Acc: 95.57\n", "saving best checkpoint at epoch: 99, Acc: 95.59\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0002. Train Acc: 95.9875, Test loss: 0.0002. Test Acc: 95.6400. Time/epoch: 1.5398\n", "saving best checkpoint at epoch: 100, Acc: 95.64\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train95.9875
Accuracy/val95.64
Loss/train0.00021
Loss/val0.00023
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/cq3layly" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0025. Train Acc: 59.3275, Test loss: 0.0026. Test Acc: 59.3100. Time/epoch: 1.5675\n", "saving best checkpoint at epoch: 0, Acc: 59.31\n", "saving best checkpoint at epoch: 1, Acc: 64.63\n", "saving best checkpoint at epoch: 2, Acc: 80.01\n", "saving best checkpoint at epoch: 3, Acc: 85.77\n", "saving best checkpoint at epoch: 4, Acc: 89.11\n", "saving best checkpoint at epoch: 5, Acc: 89.63\n", "saving best checkpoint at epoch: 7, Acc: 90.2\n", "saving best checkpoint at epoch: 8, Acc: 90.61\n", "saving best checkpoint at epoch: 9, Acc: 90.74\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0004. Train Acc: 90.9175, Test loss: 0.0005. Test Acc: 90.9200. Time/epoch: 1.6891\n", "saving best checkpoint at epoch: 10, Acc: 90.92\n", "saving best checkpoint at epoch: 11, Acc: 91.05\n", "saving best checkpoint at epoch: 12, Acc: 91.23\n", "saving best checkpoint at epoch: 13, Acc: 91.3\n", "saving best checkpoint at epoch: 14, Acc: 91.54\n", "saving best checkpoint at epoch: 15, Acc: 91.64\n", "saving best checkpoint at epoch: 16, Acc: 91.76\n", "saving best checkpoint at epoch: 17, Acc: 91.9\n", "saving best checkpoint at epoch: 18, Acc: 92.03\n", "saving best checkpoint at epoch: 19, Acc: 92.17\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0004. Train Acc: 92.3400, Test loss: 0.0004. Test Acc: 92.2800. Time/epoch: 1.5481\n", "saving best checkpoint at epoch: 20, Acc: 92.28\n", "saving best checkpoint at epoch: 21, Acc: 92.44\n", "saving best checkpoint at epoch: 22, Acc: 92.48\n", "saving best checkpoint at epoch: 23, Acc: 92.68\n", "saving best checkpoint at epoch: 24, Acc: 92.73\n", "saving best checkpoint at epoch: 25, Acc: 92.83\n", "saving best checkpoint at epoch: 26, Acc: 92.89\n", "saving best checkpoint at epoch: 27, Acc: 93.03\n", "saving best checkpoint at epoch: 28, Acc: 93.07\n", "saving best checkpoint at epoch: 29, Acc: 93.15\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0003. Train Acc: 93.1200, Test loss: 0.0003. Test Acc: 93.2200. Time/epoch: 1.6815\n", "saving best checkpoint at epoch: 30, Acc: 93.22\n", "saving best checkpoint at epoch: 31, Acc: 93.31\n", "saving best checkpoint at epoch: 33, Acc: 93.33\n", "saving best checkpoint at epoch: 34, Acc: 93.45\n", "saving best checkpoint at epoch: 35, Acc: 93.48\n", "saving best checkpoint at epoch: 36, Acc: 93.58\n", "saving best checkpoint at epoch: 37, Acc: 93.6\n", "saving best checkpoint at epoch: 39, Acc: 93.64\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0003. Train Acc: 93.7725, Test loss: 0.0003. Test Acc: 93.8000. Time/epoch: 1.5409\n", "saving best checkpoint at epoch: 40, Acc: 93.8\n", "saving best checkpoint at epoch: 42, Acc: 93.83\n", "saving best checkpoint at epoch: 44, Acc: 93.9\n", "saving best checkpoint at epoch: 46, Acc: 94.0\n", "saving best checkpoint at epoch: 48, Acc: 94.07\n", "saving best checkpoint at epoch: 49, Acc: 94.11\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0003. Train Acc: 94.0275, Test loss: 0.0003. Test Acc: 94.0700. Time/epoch: 1.7101\n", "saving best checkpoint at epoch: 52, Acc: 94.34\n", "saving best checkpoint at epoch: 55, Acc: 94.35\n", "saving best checkpoint at epoch: 56, Acc: 94.43\n", "saving best checkpoint at epoch: 57, Acc: 94.49\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0003. Train Acc: 94.5300, Test loss: 0.0003. Test Acc: 94.4400. Time/epoch: 1.5592\n", "saving best checkpoint at epoch: 63, Acc: 94.55\n", "saving best checkpoint at epoch: 64, Acc: 94.61\n", "saving best checkpoint at epoch: 65, Acc: 94.7\n", "saving best checkpoint at epoch: 66, Acc: 94.76\n", "saving best checkpoint at epoch: 68, Acc: 94.84\n", "saving best checkpoint at epoch: 69, Acc: 94.94\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0003. Train Acc: 95.0575, Test loss: 0.0003. Test Acc: 94.8500. Time/epoch: 1.5495\n", "saving best checkpoint at epoch: 78, Acc: 94.99\n", "saving best checkpoint at epoch: 79, Acc: 95.05\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0002. Train Acc: 95.3800, Test loss: 0.0003. Test Acc: 95.0700. Time/epoch: 1.5475\n", "saving best checkpoint at epoch: 80, Acc: 95.07\n", "saving best checkpoint at epoch: 81, Acc: 95.14\n", "saving best checkpoint at epoch: 83, Acc: 95.17\n", "saving best checkpoint at epoch: 85, Acc: 95.2\n", "saving best checkpoint at epoch: 88, Acc: 95.28\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0002. Train Acc: 95.6900, Test loss: 0.0002. Test Acc: 95.3800. Time/epoch: 1.6793\n", "saving best checkpoint at epoch: 90, Acc: 95.38\n", "saving best checkpoint at epoch: 92, Acc: 95.44\n", "saving best checkpoint at epoch: 95, Acc: 95.48\n", "saving best checkpoint at epoch: 98, Acc: 95.59\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0002. Train Acc: 95.9650, Test loss: 0.0002. Test Acc: 95.5900. Time/epoch: 1.5405\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train95.965
Accuracy/val95.59
Loss/train0.00022
Loss/val0.00023
epoch100

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run fallen-sweep-40 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/cq3layly
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ep1a94tg" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0028. Train Acc: 50.1925, Test loss: 0.0028. Test Acc: 50.3700. Time/epoch: 1.5582\n", "saving best checkpoint at epoch: 0, Acc: 50.37\n", "saving best checkpoint at epoch: 1, Acc: 70.44\n", "saving best checkpoint at epoch: 2, Acc: 74.06\n", "saving best checkpoint at epoch: 3, Acc: 80.47\n", "saving best checkpoint at epoch: 4, Acc: 86.49\n", "saving best checkpoint at epoch: 5, Acc: 88.17\n", "saving best checkpoint at epoch: 6, Acc: 89.03\n", "saving best checkpoint at epoch: 7, Acc: 89.3\n", "saving best checkpoint at epoch: 8, Acc: 89.58\n", "saving best checkpoint at epoch: 9, Acc: 89.87\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0004. Train Acc: 90.4275, Test loss: 0.0005. Test Acc: 90.0600. Time/epoch: 1.7016\n", "saving best checkpoint at epoch: 10, Acc: 90.06\n", "saving best checkpoint at epoch: 11, Acc: 90.25\n", "saving best checkpoint at epoch: 12, Acc: 90.35\n", "saving best checkpoint at epoch: 13, Acc: 90.62\n", "saving best checkpoint at epoch: 14, Acc: 90.79\n", "saving best checkpoint at epoch: 15, Acc: 90.96\n", "saving best checkpoint at epoch: 16, Acc: 91.23\n", "saving best checkpoint at epoch: 17, Acc: 91.31\n", "saving best checkpoint at epoch: 18, Acc: 91.44\n", "saving best checkpoint at epoch: 19, Acc: 91.65\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0004. Train Acc: 91.9925, Test loss: 0.0004. Test Acc: 91.7700. Time/epoch: 1.5473\n", "saving best checkpoint at epoch: 20, Acc: 91.77\n", "saving best checkpoint at epoch: 21, Acc: 91.86\n", "saving best checkpoint at epoch: 22, Acc: 92.04\n", "saving best checkpoint at epoch: 23, Acc: 92.25\n", "saving best checkpoint at epoch: 24, Acc: 92.37\n", "saving best checkpoint at epoch: 26, Acc: 92.74\n", "saving best checkpoint at epoch: 29, Acc: 92.91\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0003. Train Acc: 93.0325, Test loss: 0.0003. Test Acc: 92.9700. Time/epoch: 1.6872\n", "saving best checkpoint at epoch: 30, Acc: 92.97\n", "saving best checkpoint at epoch: 31, Acc: 93.07\n", "saving best checkpoint at epoch: 34, Acc: 93.26\n", "saving best checkpoint at epoch: 36, Acc: 93.32\n", "saving best checkpoint at epoch: 37, Acc: 93.38\n", "saving best checkpoint at epoch: 38, Acc: 93.48\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0003. Train Acc: 93.5275, Test loss: 0.0003. Test Acc: 93.5600. Time/epoch: 1.5338\n", "saving best checkpoint at epoch: 40, Acc: 93.56\n", "saving best checkpoint at epoch: 43, Acc: 93.67\n", "saving best checkpoint at epoch: 44, Acc: 93.71\n", "saving best checkpoint at epoch: 45, Acc: 93.79\n", "saving best checkpoint at epoch: 48, Acc: 93.89\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0003. Train Acc: 93.8750, Test loss: 0.0003. Test Acc: 93.9000. Time/epoch: 1.7060\n", "saving best checkpoint at epoch: 50, Acc: 93.9\n", "saving best checkpoint at epoch: 51, Acc: 93.99\n", "saving best checkpoint at epoch: 52, Acc: 94.01\n", "saving best checkpoint at epoch: 53, Acc: 94.07\n", "saving best checkpoint at epoch: 54, Acc: 94.13\n", "saving best checkpoint at epoch: 55, Acc: 94.16\n", "saving best checkpoint at epoch: 57, Acc: 94.17\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0003. Train Acc: 94.3050, Test loss: 0.0003. Test Acc: 94.2600. Time/epoch: 1.5478\n", "saving best checkpoint at epoch: 60, Acc: 94.26\n", "saving best checkpoint at epoch: 61, Acc: 94.3\n", "saving best checkpoint at epoch: 62, Acc: 94.31\n", "saving best checkpoint at epoch: 63, Acc: 94.38\n", "saving best checkpoint at epoch: 65, Acc: 94.44\n", "saving best checkpoint at epoch: 66, Acc: 94.47\n", "saving best checkpoint at epoch: 67, Acc: 94.57\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0003. Train Acc: 94.7000, Test loss: 0.0003. Test Acc: 94.5800. Time/epoch: 1.5463\n", "saving best checkpoint at epoch: 70, Acc: 94.58\n", "saving best checkpoint at epoch: 71, Acc: 94.64\n", "saving best checkpoint at epoch: 73, Acc: 94.77\n", "saving best checkpoint at epoch: 75, Acc: 94.9\n", "saving best checkpoint at epoch: 77, Acc: 94.93\n", "saving best checkpoint at epoch: 78, Acc: 94.95\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0002. Train Acc: 95.0325, Test loss: 0.0003. Test Acc: 94.8800. Time/epoch: 1.5405\n", "saving best checkpoint at epoch: 81, Acc: 95.02\n", "saving best checkpoint at epoch: 82, Acc: 95.04\n", "saving best checkpoint at epoch: 84, Acc: 95.07\n", "saving best checkpoint at epoch: 87, Acc: 95.28\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0002. Train Acc: 95.3325, Test loss: 0.0002. Test Acc: 95.2800. Time/epoch: 1.6971\n", "saving best checkpoint at epoch: 92, Acc: 95.36\n", "saving best checkpoint at epoch: 93, Acc: 95.41\n", "saving best checkpoint at epoch: 94, Acc: 95.44\n", "saving best checkpoint at epoch: 98, Acc: 95.5\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0002. Train Acc: 95.6425, Test loss: 0.0002. Test Acc: 95.4200. Time/epoch: 1.5519\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train95.6425
Accuracy/val95.42
Loss/train0.00022
Loss/val0.00023
epoch100

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run stellar-sweep-41 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/ep1a94tg
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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/zabuo6zp" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0028. Train Acc: 44.7750, Test loss: 0.0029. Test Acc: 45.3100. Time/epoch: 1.8260\n", "saving best checkpoint at epoch: 0, Acc: 45.31\n", "saving best checkpoint at epoch: 1, Acc: 75.0\n", "saving best checkpoint at epoch: 2, Acc: 77.6\n", "saving best checkpoint at epoch: 3, Acc: 79.6\n", "saving best checkpoint at epoch: 4, Acc: 81.35\n", "saving best checkpoint at epoch: 5, Acc: 83.25\n", "saving best checkpoint at epoch: 6, Acc: 84.95\n", "saving best checkpoint at epoch: 7, Acc: 86.6\n", "saving best checkpoint at epoch: 8, Acc: 87.62\n", "saving best checkpoint at epoch: 9, Acc: 87.87\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0005. Train Acc: 88.7275, Test loss: 0.0006. Test Acc: 88.6400. Time/epoch: 1.6862\n", "saving best checkpoint at epoch: 10, Acc: 88.64\n", "saving best checkpoint at epoch: 11, Acc: 89.03\n", "saving best checkpoint at epoch: 12, Acc: 89.43\n", "saving best checkpoint at epoch: 13, Acc: 89.86\n", "saving best checkpoint at epoch: 14, Acc: 90.1\n", "saving best checkpoint at epoch: 15, Acc: 90.42\n", "saving best checkpoint at epoch: 16, Acc: 90.83\n", "saving best checkpoint at epoch: 17, Acc: 91.12\n", "saving best checkpoint at epoch: 18, Acc: 91.49\n", "saving best checkpoint at epoch: 19, Acc: 91.58\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0004. Train Acc: 92.0600, Test loss: 0.0004. Test Acc: 91.8800. Time/epoch: 1.5467\n", "saving best checkpoint at epoch: 20, Acc: 91.88\n", "saving best checkpoint at epoch: 21, Acc: 92.08\n", "saving best checkpoint at epoch: 22, Acc: 92.3\n", "saving best checkpoint at epoch: 23, Acc: 92.59\n", "saving best checkpoint at epoch: 24, Acc: 92.62\n", "saving best checkpoint at epoch: 25, Acc: 93.03\n", "saving best checkpoint at epoch: 26, Acc: 93.19\n", "saving best checkpoint at epoch: 27, Acc: 93.3\n", "saving best checkpoint at epoch: 28, Acc: 93.37\n", "saving best checkpoint at epoch: 29, Acc: 93.62\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0003. Train Acc: 93.6075, Test loss: 0.0003. Test Acc: 93.8000. Time/epoch: 1.6839\n", "saving best checkpoint at epoch: 30, Acc: 93.8\n", "saving best checkpoint at epoch: 31, Acc: 94.12\n", "saving best checkpoint at epoch: 32, Acc: 94.21\n", "saving best checkpoint at epoch: 34, Acc: 94.28\n", "saving best checkpoint at epoch: 36, Acc: 94.45\n", "saving best checkpoint at epoch: 37, Acc: 94.58\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0003. Train Acc: 94.4975, Test loss: 0.0003. Test Acc: 94.3800. Time/epoch: 1.5375\n", "saving best checkpoint at epoch: 41, Acc: 94.82\n", "saving best checkpoint at epoch: 44, Acc: 94.83\n", "saving best checkpoint at epoch: 45, Acc: 94.89\n", "saving best checkpoint at epoch: 47, Acc: 94.91\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0003. Train Acc: 95.0825, Test loss: 0.0003. Test Acc: 94.9600. Time/epoch: 1.7034\n", "saving best checkpoint at epoch: 50, Acc: 94.96\n", "saving best checkpoint at epoch: 51, Acc: 95.05\n", "saving best checkpoint at epoch: 52, Acc: 95.07\n", "saving best checkpoint at epoch: 54, Acc: 95.14\n", "saving best checkpoint at epoch: 55, Acc: 95.2\n", "saving best checkpoint at epoch: 57, Acc: 95.23\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0002. Train Acc: 95.2950, Test loss: 0.0003. Test Acc: 95.2600. Time/epoch: 1.5483\n", "saving best checkpoint at epoch: 60, Acc: 95.26\n", "saving best checkpoint at epoch: 61, Acc: 95.29\n", "saving best checkpoint at epoch: 64, Acc: 95.39\n", "saving best checkpoint at epoch: 69, Acc: 95.41\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0002. Train Acc: 95.5300, Test loss: 0.0003. Test Acc: 95.3400. Time/epoch: 1.5433\n", "saving best checkpoint at epoch: 74, Acc: 95.49\n", "saving best checkpoint at epoch: 75, Acc: 95.53\n", "saving best checkpoint at epoch: 76, Acc: 95.67\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0002. Train Acc: 95.6825, Test loss: 0.0002. Test Acc: 95.5800. Time/epoch: 1.5427\n", "saving best checkpoint at epoch: 86, Acc: 95.71\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0002. Train Acc: 95.8800, Test loss: 0.0002. Test Acc: 95.7200. Time/epoch: 1.5577\n", "saving best checkpoint at epoch: 90, Acc: 95.72\n", "saving best checkpoint at epoch: 93, Acc: 95.77\n", "saving best checkpoint at epoch: 96, Acc: 95.84\n", "saving best checkpoint at epoch: 98, Acc: 95.87\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0002. Train Acc: 96.0625, Test loss: 0.0002. Test Acc: 95.8400. Time/epoch: 1.6853\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train96.0625
Accuracy/val95.84
Loss/train0.00021
Loss/val0.00023
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/8zg3wr9b" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0023. Train Acc: 62.2825, Test loss: 0.0023. Test Acc: 62.3800. Time/epoch: 1.6092\n", "saving best checkpoint at epoch: 0, Acc: 62.38\n", "saving best checkpoint at epoch: 2, Acc: 70.38\n", "saving best checkpoint at epoch: 3, Acc: 83.82\n", "saving best checkpoint at epoch: 4, Acc: 86.94\n", "saving best checkpoint at epoch: 5, Acc: 88.81\n", "saving best checkpoint at epoch: 6, Acc: 89.3\n", "saving best checkpoint at epoch: 7, Acc: 90.12\n", "saving best checkpoint at epoch: 8, Acc: 90.95\n", "saving best checkpoint at epoch: 9, Acc: 91.67\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0004. Train Acc: 91.3325, Test loss: 0.0004. Test Acc: 91.4200. Time/epoch: 1.5628\n", "saving best checkpoint at epoch: 11, Acc: 92.36\n", "saving best checkpoint at epoch: 12, Acc: 92.74\n", "saving best checkpoint at epoch: 13, Acc: 93.12\n", "saving best checkpoint at epoch: 15, Acc: 93.58\n", "saving best checkpoint at epoch: 17, Acc: 93.83\n", "saving best checkpoint at epoch: 18, Acc: 93.97\n", "saving best checkpoint at epoch: 19, Acc: 94.14\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0003. Train Acc: 93.9300, Test loss: 0.0003. Test Acc: 94.0000. Time/epoch: 1.6972\n", "saving best checkpoint at epoch: 21, Acc: 94.61\n", "saving best checkpoint at epoch: 23, Acc: 94.62\n", "saving best checkpoint at epoch: 24, Acc: 94.72\n", "saving best checkpoint at epoch: 25, Acc: 95.17\n", "saving best checkpoint at epoch: 28, Acc: 95.23\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0002. Train Acc: 95.2850, Test loss: 0.0003. Test Acc: 95.2100. Time/epoch: 1.5434\n", "saving best checkpoint at epoch: 32, Acc: 95.4\n", "saving best checkpoint at epoch: 33, Acc: 95.47\n", "saving best checkpoint at epoch: 34, Acc: 95.51\n", "saving best checkpoint at epoch: 37, Acc: 95.52\n", "saving best checkpoint at epoch: 39, Acc: 95.62\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0002. Train Acc: 95.5825, Test loss: 0.0003. Test Acc: 95.4300. Time/epoch: 1.6880\n", "saving best checkpoint at epoch: 44, Acc: 95.72\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0002. Train Acc: 95.7925, Test loss: 0.0002. Test Acc: 95.5900. Time/epoch: 1.5607\n", "saving best checkpoint at epoch: 51, Acc: 95.73\n", "saving best checkpoint at epoch: 53, Acc: 95.87\n", "saving best checkpoint at epoch: 56, Acc: 96.05\n", "saving best checkpoint at epoch: 59, Acc: 96.09\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0002. Train Acc: 96.2175, Test loss: 0.0002. Test Acc: 95.9000. Time/epoch: 1.6921\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0002. Train Acc: 96.3975, Test loss: 0.0002. Test Acc: 96.1100. Time/epoch: 1.5492\n", "saving best checkpoint at epoch: 70, Acc: 96.11\n", "saving best checkpoint at epoch: 74, Acc: 96.17\n", "saving best checkpoint at epoch: 76, Acc: 96.24\n", "saving best checkpoint at epoch: 78, Acc: 96.25\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0002. Train Acc: 96.5800, Test loss: 0.0002. Test Acc: 96.2300. Time/epoch: 1.5520\n", "saving best checkpoint at epoch: 81, Acc: 96.27\n", "saving best checkpoint at epoch: 83, Acc: 96.29\n", "saving best checkpoint at epoch: 87, Acc: 96.3\n", "saving best checkpoint at epoch: 89, Acc: 96.31\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0002. Train Acc: 96.6350, Test loss: 0.0002. Test Acc: 96.4500. Time/epoch: 1.7028\n", "saving best checkpoint at epoch: 90, Acc: 96.45\n", "saving best checkpoint at epoch: 95, Acc: 96.56\n", "saving best checkpoint at epoch: 96, Acc: 96.62\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0002. Train Acc: 96.8675, Test loss: 0.0002. Test Acc: 96.4800. Time/epoch: 1.5496\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train96.8675
Accuracy/val96.48
Loss/train0.00016
Loss/val0.00019
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/6hlqcbb7" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0032. Train Acc: 27.5000, Test loss: 0.0032. Test Acc: 27.6300. Time/epoch: 1.7136\n", "saving best checkpoint at epoch: 0, Acc: 27.63\n", "saving best checkpoint at epoch: 1, Acc: 60.87\n", "saving best checkpoint at epoch: 2, Acc: 63.82\n", "saving best checkpoint at epoch: 3, Acc: 68.47\n", "saving best checkpoint at epoch: 4, Acc: 73.94\n", "saving best checkpoint at epoch: 5, Acc: 79.19\n", "saving best checkpoint at epoch: 6, Acc: 83.79\n", "saving best checkpoint at epoch: 7, Acc: 84.81\n", "saving best checkpoint at epoch: 8, Acc: 85.6\n", "saving best checkpoint at epoch: 9, Acc: 85.86\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0007. Train Acc: 86.3950, Test loss: 0.0007. Test Acc: 86.1800. Time/epoch: 1.6959\n", "saving best checkpoint at epoch: 10, Acc: 86.18\n", "saving best checkpoint at epoch: 11, Acc: 86.62\n", "saving best checkpoint at epoch: 12, Acc: 86.94\n", "saving best checkpoint at epoch: 13, Acc: 87.35\n", "saving best checkpoint at epoch: 15, Acc: 87.73\n", "saving best checkpoint at epoch: 16, Acc: 88.01\n", "saving best checkpoint at epoch: 17, Acc: 88.36\n", "saving best checkpoint at epoch: 18, Acc: 88.46\n", "saving best checkpoint at epoch: 19, Acc: 88.75\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0005. Train Acc: 88.9800, Test loss: 0.0005. Test Acc: 89.1200. Time/epoch: 1.5546\n", "saving best checkpoint at epoch: 20, Acc: 89.12\n", "saving best checkpoint at epoch: 21, Acc: 89.39\n", "saving best checkpoint at epoch: 22, Acc: 89.59\n", "saving best checkpoint at epoch: 23, Acc: 89.91\n", "saving best checkpoint at epoch: 24, Acc: 90.04\n", "saving best checkpoint at epoch: 25, Acc: 90.39\n", "saving best checkpoint at epoch: 26, Acc: 90.57\n", "saving best checkpoint at epoch: 27, Acc: 91.01\n", "saving best checkpoint at epoch: 28, Acc: 91.19\n", "saving best checkpoint at epoch: 29, Acc: 91.36\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0004. Train Acc: 91.4800, Test loss: 0.0004. Test Acc: 91.5800. Time/epoch: 1.6980\n", "saving best checkpoint at epoch: 30, Acc: 91.58\n", "saving best checkpoint at epoch: 32, Acc: 91.65\n", "saving best checkpoint at epoch: 33, Acc: 91.88\n", "saving best checkpoint at epoch: 34, Acc: 92.0\n", "saving best checkpoint at epoch: 35, Acc: 92.03\n", "saving best checkpoint at epoch: 36, Acc: 92.09\n", "saving best checkpoint at epoch: 37, Acc: 92.27\n", "saving best checkpoint at epoch: 38, Acc: 92.39\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0003. Train Acc: 92.7150, Test loss: 0.0004. Test Acc: 92.5400. Time/epoch: 1.5434\n", "saving best checkpoint at epoch: 40, Acc: 92.54\n", "saving best checkpoint at epoch: 41, Acc: 92.6\n", "saving best checkpoint at epoch: 42, Acc: 92.7\n", "saving best checkpoint at epoch: 43, Acc: 92.91\n", "saving best checkpoint at epoch: 45, Acc: 93.04\n", "saving best checkpoint at epoch: 47, Acc: 93.1\n", "saving best checkpoint at epoch: 49, Acc: 93.11\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0003. Train Acc: 93.3850, Test loss: 0.0003. Test Acc: 93.3200. Time/epoch: 1.7006\n", "saving best checkpoint at epoch: 50, Acc: 93.32\n", "saving best checkpoint at epoch: 53, Acc: 93.51\n", "saving best checkpoint at epoch: 55, Acc: 93.52\n", "saving best checkpoint at epoch: 56, Acc: 93.59\n", "saving best checkpoint at epoch: 59, Acc: 93.82\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0003. Train Acc: 93.8100, Test loss: 0.0003. Test Acc: 93.8400. Time/epoch: 1.5452\n", "saving best checkpoint at epoch: 60, Acc: 93.84\n", "saving best checkpoint at epoch: 63, Acc: 93.9\n", "saving best checkpoint at epoch: 64, Acc: 94.04\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0003. Train Acc: 94.1000, Test loss: 0.0003. Test Acc: 94.0500. Time/epoch: 1.5450\n", "saving best checkpoint at epoch: 70, Acc: 94.05\n", "saving best checkpoint at epoch: 72, Acc: 94.14\n", "saving best checkpoint at epoch: 73, Acc: 94.25\n", "saving best checkpoint at epoch: 78, Acc: 94.34\n", "saving best checkpoint at epoch: 79, Acc: 94.47\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0003. Train Acc: 94.3850, Test loss: 0.0003. Test Acc: 94.3700. Time/epoch: 1.5437\n", "saving best checkpoint at epoch: 85, Acc: 94.5\n", "saving best checkpoint at epoch: 88, Acc: 94.54\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0003. Train Acc: 94.5150, Test loss: 0.0003. Test Acc: 94.3900. Time/epoch: 1.6996\n", "saving best checkpoint at epoch: 94, Acc: 94.58\n", "saving best checkpoint at epoch: 95, Acc: 94.61\n", "saving best checkpoint at epoch: 98, Acc: 94.77\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0003. Train Acc: 94.7225, Test loss: 0.0003. Test Acc: 94.5100. Time/epoch: 1.5517\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train94.7225
Accuracy/val94.51
Loss/train0.00025
Loss/val0.00026
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/72b50lmo" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0028. Train Acc: 54.5275, Test loss: 0.0028. Test Acc: 54.8700. Time/epoch: 1.7153\n", "saving best checkpoint at epoch: 0, Acc: 54.87\n", "saving best checkpoint at epoch: 1, Acc: 72.12\n", "saving best checkpoint at epoch: 2, Acc: 74.67\n", "saving best checkpoint at epoch: 3, Acc: 78.22\n", "saving best checkpoint at epoch: 4, Acc: 82.12\n", "saving best checkpoint at epoch: 5, Acc: 83.41\n", "saving best checkpoint at epoch: 6, Acc: 84.73\n", "saving best checkpoint at epoch: 7, Acc: 85.61\n", "saving best checkpoint at epoch: 8, Acc: 86.11\n", "saving best checkpoint at epoch: 9, Acc: 86.31\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0006. Train Acc: 86.7925, Test loss: 0.0006. Test Acc: 86.7400. Time/epoch: 1.6951\n", "saving best checkpoint at epoch: 10, Acc: 86.74\n", "saving best checkpoint at epoch: 11, Acc: 86.83\n", "saving best checkpoint at epoch: 12, Acc: 87.38\n", "saving best checkpoint at epoch: 13, Acc: 87.71\n", "saving best checkpoint at epoch: 14, Acc: 87.73\n", "saving best checkpoint at epoch: 15, Acc: 88.21\n", "saving best checkpoint at epoch: 17, Acc: 88.45\n", "saving best checkpoint at epoch: 18, Acc: 88.54\n", "saving best checkpoint at epoch: 19, Acc: 88.73\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0005. Train Acc: 89.1300, Test loss: 0.0005. Test Acc: 89.0500. Time/epoch: 1.5535\n", "saving best checkpoint at epoch: 20, Acc: 89.05\n", "saving best checkpoint at epoch: 21, Acc: 89.47\n", "saving best checkpoint at epoch: 23, Acc: 89.76\n", "saving best checkpoint at epoch: 24, Acc: 90.01\n", "saving best checkpoint at epoch: 25, Acc: 90.09\n", "saving best checkpoint at epoch: 26, Acc: 90.17\n", "saving best checkpoint at epoch: 27, Acc: 90.3\n", "saving best checkpoint at epoch: 28, Acc: 90.34\n", "saving best checkpoint at epoch: 29, Acc: 90.55\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0004. Train Acc: 90.8850, Test loss: 0.0004. Test Acc: 90.7900. Time/epoch: 1.6866\n", "saving best checkpoint at epoch: 30, Acc: 90.79\n", "saving best checkpoint at epoch: 32, Acc: 90.93\n", "saving best checkpoint at epoch: 33, Acc: 91.15\n", "saving best checkpoint at epoch: 34, Acc: 91.37\n", "saving best checkpoint at epoch: 35, Acc: 91.69\n", "saving best checkpoint at epoch: 36, Acc: 91.78\n", "saving best checkpoint at epoch: 37, Acc: 92.49\n", "saving best checkpoint at epoch: 39, Acc: 92.89\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0003. Train Acc: 93.0525, Test loss: 0.0004. Test Acc: 93.2000. Time/epoch: 1.5409\n", "saving best checkpoint at epoch: 40, Acc: 93.2\n", "saving best checkpoint at epoch: 41, Acc: 93.42\n", "saving best checkpoint at epoch: 42, Acc: 93.58\n", "saving best checkpoint at epoch: 43, Acc: 93.75\n", "saving best checkpoint at epoch: 44, Acc: 93.97\n", "saving best checkpoint at epoch: 45, Acc: 94.19\n", "saving best checkpoint at epoch: 48, Acc: 94.2\n", "saving best checkpoint at epoch: 49, Acc: 94.28\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0003. Train Acc: 94.5975, Test loss: 0.0003. Test Acc: 94.4300. Time/epoch: 1.7161\n", "saving best checkpoint at epoch: 50, Acc: 94.43\n", "saving best checkpoint at epoch: 52, Acc: 94.5\n", "saving best checkpoint at epoch: 53, Acc: 94.61\n", "saving best checkpoint at epoch: 57, Acc: 94.75\n", "saving best checkpoint at epoch: 58, Acc: 94.85\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0003. Train Acc: 95.0625, Test loss: 0.0003. Test Acc: 94.8000. Time/epoch: 1.5639\n", "saving best checkpoint at epoch: 61, Acc: 94.87\n", "saving best checkpoint at epoch: 63, Acc: 95.0\n", "saving best checkpoint at epoch: 68, Acc: 95.04\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0002. Train Acc: 95.4000, Test loss: 0.0003. Test Acc: 95.1600. Time/epoch: 1.5486\n", "saving best checkpoint at epoch: 70, Acc: 95.16\n", "saving best checkpoint at epoch: 74, Acc: 95.22\n", "saving best checkpoint at epoch: 78, Acc: 95.23\n", "saving best checkpoint at epoch: 79, Acc: 95.32\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0002. Train Acc: 95.6475, Test loss: 0.0002. Test Acc: 95.3300. Time/epoch: 1.5561\n", "saving best checkpoint at epoch: 80, Acc: 95.33\n", "saving best checkpoint at epoch: 86, Acc: 95.39\n", "saving best checkpoint at epoch: 87, Acc: 95.42\n", "saving best checkpoint at epoch: 88, Acc: 95.48\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0002. Train Acc: 95.8350, Test loss: 0.0002. Test Acc: 95.5700. Time/epoch: 1.5726\n", "saving best checkpoint at epoch: 90, Acc: 95.57\n", "saving best checkpoint at epoch: 95, Acc: 95.67\n", "saving best checkpoint at epoch: 99, Acc: 95.73\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0002. Train Acc: 96.0425, Test loss: 0.0002. Test Acc: 95.7000. Time/epoch: 1.6775\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train96.0425
Accuracy/val95.7
Loss/train0.00021
Loss/val0.00023
epoch100

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Run summary:


Accuracy/train96.8675
Accuracy/val96.55
Loss/train0.00016
Loss/val0.00019
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/fku8awut" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0067. Train Acc: 71.5550, Test loss: 0.0067. Test Acc: 71.4200. Time/epoch: 2.1993\n", "saving best checkpoint at epoch: 0, Acc: 71.42\n", "saving best checkpoint at epoch: 1, Acc: 82.43\n", "saving best checkpoint at epoch: 2, Acc: 86.03\n", "saving best checkpoint at epoch: 3, Acc: 87.63\n", "saving best checkpoint at epoch: 4, Acc: 88.75\n", "saving best checkpoint at epoch: 5, Acc: 89.82\n", "saving best checkpoint at epoch: 6, Acc: 90.3\n", "saving best checkpoint at epoch: 7, Acc: 91.4\n", "saving best checkpoint at epoch: 8, Acc: 91.91\n", "saving best checkpoint at epoch: 9, Acc: 92.25\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0015. Train Acc: 92.2325, Test loss: 0.0016. Test Acc: 92.0700. Time/epoch: 2.0358\n", "saving best checkpoint at epoch: 11, Acc: 92.84\n", "saving best checkpoint at epoch: 12, Acc: 93.05\n", "saving best checkpoint at epoch: 13, Acc: 93.22\n", "saving best checkpoint at epoch: 14, Acc: 93.49\n", "saving best checkpoint at epoch: 16, Acc: 93.58\n", "saving best checkpoint at epoch: 18, Acc: 93.93\n", "saving best checkpoint at epoch: 19, Acc: 93.95\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0012. Train Acc: 94.3050, Test loss: 0.0012. Test Acc: 94.1700. Time/epoch: 2.1656\n", "saving best checkpoint at epoch: 20, Acc: 94.17\n", "saving best checkpoint at epoch: 21, Acc: 94.2\n", "saving best checkpoint at epoch: 22, Acc: 94.49\n", "saving best checkpoint at epoch: 26, Acc: 94.57\n", "saving best checkpoint at epoch: 27, Acc: 94.66\n", "saving best checkpoint at epoch: 28, Acc: 94.87\n", "saving best checkpoint at epoch: 29, Acc: 94.91\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0010. Train Acc: 95.0800, Test loss: 0.0011. Test Acc: 94.9200. Time/epoch: 2.1659\n", "saving best checkpoint at epoch: 30, Acc: 94.92\n", "saving best checkpoint at epoch: 32, Acc: 95.09\n", "saving best checkpoint at epoch: 35, Acc: 95.16\n", "saving best checkpoint at epoch: 36, Acc: 95.25\n", "saving best checkpoint at epoch: 37, Acc: 95.27\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0009. Train Acc: 95.4500, Test loss: 0.0010. Test Acc: 95.1800. Time/epoch: 2.1656\n", "saving best checkpoint at epoch: 42, Acc: 95.33\n", "saving best checkpoint at epoch: 44, Acc: 95.38\n", "saving best checkpoint at epoch: 47, Acc: 95.53\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0009. Train Acc: 95.7500, Test loss: 0.0010. Test Acc: 95.5900. Time/epoch: 2.1695\n", "saving best checkpoint at epoch: 50, Acc: 95.59\n", "saving best checkpoint at epoch: 55, Acc: 95.63\n", "saving best checkpoint at epoch: 56, Acc: 95.65\n", "saving best checkpoint at epoch: 57, Acc: 95.66\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0008. Train Acc: 96.0200, Test loss: 0.0009. Test Acc: 95.7100. Time/epoch: 2.1954\n", "saving best checkpoint at epoch: 60, Acc: 95.71\n", "saving best checkpoint at epoch: 62, Acc: 95.77\n", "saving best checkpoint at epoch: 67, Acc: 95.84\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0008. Train Acc: 96.1300, Test loss: 0.0009. Test Acc: 95.8700. Time/epoch: 2.1853\n", "saving best checkpoint at epoch: 70, Acc: 95.87\n", "saving best checkpoint at epoch: 72, Acc: 95.92\n", "saving best checkpoint at epoch: 74, Acc: 95.98\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0008. Train Acc: 96.1700, Test loss: 0.0009. Test Acc: 95.8600. Time/epoch: 2.1745\n", "saving best checkpoint at epoch: 83, Acc: 96.02\n", "saving best checkpoint at epoch: 86, Acc: 96.2\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0007. Train Acc: 96.4350, Test loss: 0.0009. Test Acc: 96.0700. Time/epoch: 2.1752\n", "saving best checkpoint at epoch: 96, Acc: 96.25\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0007. Train Acc: 96.4725, Test loss: 0.0008. Test Acc: 96.2000. Time/epoch: 2.1812\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train96.4725
Accuracy/val96.2
Loss/train0.00071
Loss/val0.00081
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/hpywdqc7" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0013. Train Acc: 77.2050, Test loss: 0.0013. Test Acc: 77.8200. Time/epoch: 1.5673\n", "saving best checkpoint at epoch: 0, Acc: 77.82\n", "saving best checkpoint at epoch: 1, Acc: 86.93\n", "saving best checkpoint at epoch: 2, Acc: 88.67\n", "saving best checkpoint at epoch: 3, Acc: 90.39\n", "saving best checkpoint at epoch: 4, Acc: 91.36\n", "saving best checkpoint at epoch: 5, Acc: 91.42\n", "saving best checkpoint at epoch: 6, Acc: 91.79\n", "saving best checkpoint at epoch: 7, Acc: 91.83\n", "saving best checkpoint at epoch: 8, Acc: 92.33\n", "saving best checkpoint at epoch: 9, Acc: 92.68\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0003. Train Acc: 93.0575, Test loss: 0.0004. Test Acc: 92.8300. Time/epoch: 1.5820\n", "saving best checkpoint at epoch: 10, Acc: 92.83\n", "saving best checkpoint at epoch: 12, Acc: 93.12\n", "saving best checkpoint at epoch: 13, Acc: 93.18\n", "saving best checkpoint at epoch: 14, Acc: 93.23\n", "saving best checkpoint at epoch: 15, Acc: 93.35\n", "saving best checkpoint at epoch: 17, Acc: 93.43\n", "saving best checkpoint at epoch: 18, Acc: 93.58\n", "saving best checkpoint at epoch: 19, Acc: 93.72\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0003. Train Acc: 93.8725, Test loss: 0.0003. Test Acc: 93.6800. Time/epoch: 1.6931\n", "saving best checkpoint at epoch: 21, Acc: 94.01\n", "saving best checkpoint at epoch: 23, Acc: 94.21\n", "saving best checkpoint at epoch: 24, Acc: 94.23\n", "saving best checkpoint at epoch: 25, Acc: 94.32\n", "saving best checkpoint at epoch: 26, Acc: 94.56\n", "saving best checkpoint at epoch: 27, Acc: 94.6\n", "saving best checkpoint at epoch: 28, Acc: 94.73\n", "saving best checkpoint at epoch: 29, Acc: 94.75\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0002. Train Acc: 95.2750, Test loss: 0.0003. Test Acc: 94.8100. Time/epoch: 1.5527\n", "saving best checkpoint at epoch: 30, Acc: 94.81\n", "saving best checkpoint at epoch: 32, Acc: 95.16\n", "saving best checkpoint at epoch: 34, Acc: 95.23\n", "saving best checkpoint at epoch: 35, Acc: 95.27\n", "saving best checkpoint at epoch: 37, Acc: 95.34\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0002. Train Acc: 95.7825, Test loss: 0.0002. Test Acc: 95.5100. Time/epoch: 1.6911\n", "saving best checkpoint at epoch: 40, Acc: 95.51\n", "saving best checkpoint at epoch: 43, Acc: 95.55\n", "saving best checkpoint at epoch: 44, Acc: 95.66\n", "saving best checkpoint at epoch: 48, Acc: 95.8\n", "saving best checkpoint at epoch: 49, Acc: 95.82\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0002. Train Acc: 96.2400, Test loss: 0.0002. Test Acc: 95.7600. Time/epoch: 1.5508\n", "saving best checkpoint at epoch: 51, Acc: 95.85\n", "saving best checkpoint at epoch: 53, Acc: 95.92\n", "saving best checkpoint at epoch: 57, Acc: 95.94\n", "saving best checkpoint at epoch: 59, Acc: 96.08\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0002. Train Acc: 96.2475, Test loss: 0.0002. Test Acc: 95.7100. Time/epoch: 1.6845\n", "saving best checkpoint at epoch: 62, Acc: 96.09\n", "saving best checkpoint at epoch: 65, Acc: 96.12\n", "saving best checkpoint at epoch: 67, Acc: 96.18\n", "saving best checkpoint at epoch: 68, Acc: 96.28\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0002. Train Acc: 96.6050, Test loss: 0.0002. Test Acc: 96.2500. Time/epoch: 1.5486\n", "saving best checkpoint at epoch: 71, Acc: 96.36\n", "saving best checkpoint at epoch: 74, Acc: 96.41\n", "saving best checkpoint at epoch: 77, Acc: 96.43\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0002. Train Acc: 96.8375, Test loss: 0.0002. Test Acc: 96.4100. Time/epoch: 1.5519\n", "saving best checkpoint at epoch: 81, Acc: 96.52\n", "saving best checkpoint at epoch: 85, Acc: 96.57\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0002. Train Acc: 97.0425, Test loss: 0.0002. Test Acc: 96.5200. Time/epoch: 1.5347\n", "saving best checkpoint at epoch: 92, Acc: 96.58\n", "saving best checkpoint at epoch: 96, Acc: 96.63\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0002. Train Acc: 97.0075, Test loss: 0.0002. Test Acc: 96.4200. Time/epoch: 1.5524\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... 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Run summary:


Accuracy/train97.0075
Accuracy/val96.42
Loss/train0.00016
Loss/val0.00019
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/7y29dvnq" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0028. Train Acc: 52.1700, Test loss: 0.0028. Test Acc: 52.3300. Time/epoch: 1.7185\n", "saving best checkpoint at epoch: 0, Acc: 52.33\n", "saving best checkpoint at epoch: 1, Acc: 73.3\n", "saving best checkpoint at epoch: 2, Acc: 79.67\n", "saving best checkpoint at epoch: 3, Acc: 82.34\n", "saving best checkpoint at epoch: 4, Acc: 84.09\n", "saving best checkpoint at epoch: 5, Acc: 85.16\n", "saving best checkpoint at epoch: 6, Acc: 86.35\n", "saving best checkpoint at epoch: 7, Acc: 87.03\n", "saving best checkpoint at epoch: 8, Acc: 87.66\n", "saving best checkpoint at epoch: 9, Acc: 87.97\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0006. Train Acc: 87.9325, Test loss: 0.0006. Test Acc: 88.1700. Time/epoch: 1.6912\n", "saving best checkpoint at epoch: 10, Acc: 88.17\n", "saving best checkpoint at epoch: 11, Acc: 88.7\n", "saving best checkpoint at epoch: 12, Acc: 88.92\n", "saving best checkpoint at epoch: 13, Acc: 89.15\n", "saving best checkpoint at epoch: 14, Acc: 89.4\n", "saving best checkpoint at epoch: 15, Acc: 89.67\n", "saving best checkpoint at epoch: 17, Acc: 89.96\n", "saving best checkpoint at epoch: 18, Acc: 90.08\n", "saving best checkpoint at epoch: 19, Acc: 90.42\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0004. Train Acc: 90.5025, Test loss: 0.0005. Test Acc: 90.4200. Time/epoch: 1.5442\n", "saving best checkpoint at epoch: 21, Acc: 90.77\n", "saving best checkpoint at epoch: 22, Acc: 90.91\n", "saving best checkpoint at epoch: 23, Acc: 90.95\n", "saving best checkpoint at epoch: 24, Acc: 91.35\n", "saving best checkpoint at epoch: 25, Acc: 91.36\n", "saving best checkpoint at epoch: 26, Acc: 91.58\n", "saving best checkpoint at epoch: 27, Acc: 91.81\n", "saving best checkpoint at epoch: 28, Acc: 91.83\n", "saving best checkpoint at epoch: 29, Acc: 92.01\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0004. Train Acc: 92.3950, Test loss: 0.0004. Test Acc: 92.2000. Time/epoch: 1.6859\n", "saving best checkpoint at epoch: 30, Acc: 92.2\n", "saving best checkpoint at epoch: 31, Acc: 92.61\n", "saving best checkpoint at epoch: 33, Acc: 92.83\n", "saving best checkpoint at epoch: 35, Acc: 93.06\n", "saving best checkpoint at epoch: 36, Acc: 93.13\n", "saving best checkpoint at epoch: 37, Acc: 93.39\n", "saving best checkpoint at epoch: 39, Acc: 93.61\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0003. Train Acc: 93.6300, Test loss: 0.0003. Test Acc: 93.4200. Time/epoch: 1.5401\n", "saving best checkpoint at epoch: 41, Acc: 93.67\n", "saving best checkpoint at epoch: 42, Acc: 93.72\n", "saving best checkpoint at epoch: 43, Acc: 93.76\n", "saving best checkpoint at epoch: 45, Acc: 93.79\n", "saving best checkpoint at epoch: 47, Acc: 93.82\n", "saving best checkpoint at epoch: 48, Acc: 93.91\n", "saving best checkpoint at epoch: 49, Acc: 93.93\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0003. Train Acc: 94.1125, Test loss: 0.0003. Test Acc: 93.9400. Time/epoch: 1.6804\n", "saving best checkpoint at epoch: 50, Acc: 93.94\n", "saving best checkpoint at epoch: 51, Acc: 94.03\n", "saving best checkpoint at epoch: 52, Acc: 94.08\n", "saving best checkpoint at epoch: 53, Acc: 94.17\n", "saving best checkpoint at epoch: 55, Acc: 94.18\n", "saving best checkpoint at epoch: 56, Acc: 94.28\n", "saving best checkpoint at epoch: 58, Acc: 94.29\n", "saving best checkpoint at epoch: 59, Acc: 94.39\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0003. Train Acc: 94.4850, Test loss: 0.0003. Test Acc: 94.4000. Time/epoch: 1.5317\n", "saving best checkpoint at epoch: 60, Acc: 94.4\n", "saving best checkpoint at epoch: 61, Acc: 94.48\n", "saving best checkpoint at epoch: 62, Acc: 94.55\n", "saving best checkpoint at epoch: 66, Acc: 94.63\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0003. Train Acc: 94.8925, Test loss: 0.0003. Test Acc: 94.6800. Time/epoch: 1.5443\n", "saving best checkpoint at epoch: 70, Acc: 94.68\n", "saving best checkpoint at epoch: 72, Acc: 94.79\n", "saving best checkpoint at epoch: 75, Acc: 94.82\n", "saving best checkpoint at epoch: 76, Acc: 94.86\n", "saving best checkpoint at epoch: 79, Acc: 94.87\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0002. Train Acc: 95.1350, Test loss: 0.0003. Test Acc: 94.8200. Time/epoch: 1.5417\n", "saving best checkpoint at epoch: 82, Acc: 94.92\n", "saving best checkpoint at epoch: 83, Acc: 95.03\n", "saving best checkpoint at epoch: 89, Acc: 95.13\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0002. Train Acc: 95.1825, Test loss: 0.0003. Test Acc: 94.8900. Time/epoch: 1.5541\n", "saving best checkpoint at epoch: 93, Acc: 95.19\n", "saving best checkpoint at epoch: 99, Acc: 95.26\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0002. Train Acc: 95.5725, Test loss: 0.0003. Test Acc: 95.2700. Time/epoch: 1.6845\n", "saving best checkpoint at epoch: 100, Acc: 95.27\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train95.5725
Accuracy/val95.27
Loss/train0.00023
Loss/val0.00025
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/kzpcou4q" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0023. Train Acc: 74.3575, Test loss: 0.0024. Test Acc: 74.7700. Time/epoch: 1.5588\n", "saving best checkpoint at epoch: 0, Acc: 74.77\n", "saving best checkpoint at epoch: 1, Acc: 80.79\n", "saving best checkpoint at epoch: 2, Acc: 84.98\n", "saving best checkpoint at epoch: 3, Acc: 87.6\n", "saving best checkpoint at epoch: 4, Acc: 89.37\n", "saving best checkpoint at epoch: 5, Acc: 90.3\n", "saving best checkpoint at epoch: 6, Acc: 90.92\n", "saving best checkpoint at epoch: 7, Acc: 91.53\n", "saving best checkpoint at epoch: 8, Acc: 91.96\n", "saving best checkpoint at epoch: 9, Acc: 92.34\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0004. Train Acc: 92.6275, Test loss: 0.0004. Test Acc: 92.4500. Time/epoch: 1.5460\n", "saving best checkpoint at epoch: 10, Acc: 92.45\n", "saving best checkpoint at epoch: 11, Acc: 93.0\n", "saving best checkpoint at epoch: 12, Acc: 93.51\n", "saving best checkpoint at epoch: 14, Acc: 93.73\n", "saving best checkpoint at epoch: 15, Acc: 93.79\n", "saving best checkpoint at epoch: 17, Acc: 93.91\n", "saving best checkpoint at epoch: 18, Acc: 94.14\n", "saving best checkpoint at epoch: 19, Acc: 94.31\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0003. Train Acc: 94.2775, Test loss: 0.0003. Test Acc: 94.4200. Time/epoch: 1.6847\n", "saving best checkpoint at epoch: 20, Acc: 94.42\n", "saving best checkpoint at epoch: 23, Acc: 94.5\n", "saving best checkpoint at epoch: 24, Acc: 94.62\n", "saving best checkpoint at epoch: 25, Acc: 94.67\n", "saving best checkpoint at epoch: 26, Acc: 94.76\n", "saving best checkpoint at epoch: 29, Acc: 94.78\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0003. Train Acc: 94.4250, Test loss: 0.0003. Test Acc: 94.6300. Time/epoch: 1.5516\n", "saving best checkpoint at epoch: 31, Acc: 94.91\n", "saving best checkpoint at epoch: 32, Acc: 94.98\n", "saving best checkpoint at epoch: 33, Acc: 95.0\n", "saving best checkpoint at epoch: 34, Acc: 95.16\n", "saving best checkpoint at epoch: 38, Acc: 95.39\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0002. Train Acc: 95.4675, Test loss: 0.0002. Test Acc: 95.2200. Time/epoch: 1.6872\n", "saving best checkpoint at epoch: 44, Acc: 95.44\n", "saving best checkpoint at epoch: 46, Acc: 95.64\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0002. Train Acc: 95.6625, Test loss: 0.0002. Test Acc: 95.4500. Time/epoch: 1.5611\n", "saving best checkpoint at epoch: 51, Acc: 95.69\n", "saving best checkpoint at epoch: 53, Acc: 95.78\n", "saving best checkpoint at epoch: 55, Acc: 95.86\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0002. Train Acc: 96.1950, Test loss: 0.0002. Test Acc: 95.8500. Time/epoch: 1.6812\n", "saving best checkpoint at epoch: 65, Acc: 95.95\n", "saving best checkpoint at epoch: 67, Acc: 96.0\n", "saving best checkpoint at epoch: 68, Acc: 96.03\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0002. Train Acc: 96.4625, Test loss: 0.0002. Test Acc: 95.9000. Time/epoch: 1.5452\n", "saving best checkpoint at epoch: 71, Acc: 96.08\n", "saving best checkpoint at epoch: 76, Acc: 96.1\n", "saving best checkpoint at epoch: 77, Acc: 96.18\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0002. Train Acc: 96.5350, Test loss: 0.0002. Test Acc: 96.1900. Time/epoch: 1.6792\n", "saving best checkpoint at epoch: 80, Acc: 96.19\n", "saving best checkpoint at epoch: 83, Acc: 96.21\n", "saving best checkpoint at epoch: 84, Acc: 96.34\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0002. Train Acc: 96.5550, Test loss: 0.0002. Test Acc: 96.2200. Time/epoch: 1.5554\n", "saving best checkpoint at epoch: 96, Acc: 96.36\n", "saving best checkpoint at epoch: 97, Acc: 96.41\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0002. Train Acc: 96.8675, Test loss: 0.0002. Test Acc: 96.3900. Time/epoch: 1.5482\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train96.8675
Accuracy/val96.39
Loss/train0.00016
Loss/val0.00019
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/nso6p69b" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0004. Train Acc: 92.7300, Test loss: 0.0004. Test Acc: 92.6500. Time/epoch: 1.7310\n", "saving best checkpoint at epoch: 0, Acc: 92.65\n", "saving best checkpoint at epoch: 1, Acc: 93.12\n", "saving best checkpoint at epoch: 2, Acc: 94.45\n", "saving best checkpoint at epoch: 4, Acc: 95.02\n", "saving best checkpoint at epoch: 5, Acc: 95.13\n", "saving best checkpoint at epoch: 6, Acc: 95.63\n", "saving best checkpoint at epoch: 7, Acc: 95.66\n", "saving best checkpoint at epoch: 8, Acc: 95.9\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0002. Train Acc: 96.2250, Test loss: 0.0002. Test Acc: 95.7800. Time/epoch: 1.5495\n", "saving best checkpoint at epoch: 11, Acc: 95.98\n", "saving best checkpoint at epoch: 12, Acc: 96.36\n", "saving best checkpoint at epoch: 15, Acc: 96.65\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0001. Train Acc: 97.1975, Test loss: 0.0002. Test Acc: 96.5600. Time/epoch: 1.6899\n", "saving best checkpoint at epoch: 22, Acc: 96.73\n", "saving best checkpoint at epoch: 28, Acc: 96.8\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0001. Train Acc: 97.4850, Test loss: 0.0002. Test Acc: 96.7200. Time/epoch: 1.5552\n", "saving best checkpoint at epoch: 31, Acc: 97.02\n", "saving best checkpoint at epoch: 32, Acc: 97.06\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0001. Train Acc: 97.2125, Test loss: 0.0002. Test Acc: 96.4500. Time/epoch: 1.6810\n", "saving best checkpoint at epoch: 45, Acc: 97.17\n", "saving best checkpoint at epoch: 48, Acc: 97.23\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0001. Train Acc: 97.5950, Test loss: 0.0002. Test Acc: 96.8800. Time/epoch: 1.5647\n", "saving best checkpoint at epoch: 51, Acc: 97.5\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0001. Train Acc: 98.5125, Test loss: 0.0002. Test Acc: 97.4700. Time/epoch: 1.6859\n", "saving best checkpoint at epoch: 64, Acc: 97.56\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0001. Train Acc: 98.3700, Test loss: 0.0002. Test Acc: 97.1100. Time/epoch: 1.5497\n", "saving best checkpoint at epoch: 71, Acc: 97.63\n", "saving best checkpoint at epoch: 76, Acc: 97.73\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0001. Train Acc: 98.7100, Test loss: 0.0002. Test Acc: 97.4800. Time/epoch: 1.6767\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0001. Train Acc: 99.0100, Test loss: 0.0002. Test Acc: 97.7000. Time/epoch: 1.5429\n", "saving best checkpoint at epoch: 93, Acc: 97.79\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0001. Train Acc: 98.1525, Test loss: 0.0003. Test Acc: 96.4600. Time/epoch: 1.5475\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train98.1525
Accuracy/val96.46
Loss/train9e-05
Loss/val0.00025
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/6wn27ev3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0004. Train Acc: 90.5625, Test loss: 0.0004. Test Acc: 90.6000. Time/epoch: 1.6995\n", "saving best checkpoint at epoch: 0, Acc: 90.6\n", "saving best checkpoint at epoch: 1, Acc: 91.8\n", "saving best checkpoint at epoch: 2, Acc: 93.86\n", "saving best checkpoint at epoch: 4, Acc: 94.74\n", "saving best checkpoint at epoch: 5, Acc: 94.94\n", "saving best checkpoint at epoch: 7, Acc: 95.17\n", "saving best checkpoint at epoch: 8, Acc: 95.41\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0002. Train Acc: 96.3775, Test loss: 0.0002. Test Acc: 95.8100. Time/epoch: 1.5637\n", "saving best checkpoint at epoch: 10, Acc: 95.81\n", "saving best checkpoint at epoch: 11, Acc: 96.01\n", "saving best checkpoint at epoch: 12, Acc: 96.05\n", "saving best checkpoint at epoch: 13, Acc: 96.06\n", "saving best checkpoint at epoch: 14, Acc: 96.27\n", "saving best checkpoint at epoch: 15, Acc: 96.51\n", "saving best checkpoint at epoch: 18, Acc: 96.66\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0003. Train Acc: 94.8825, Test loss: 0.0003. Test Acc: 94.6200. Time/epoch: 1.6861\n", "saving best checkpoint at epoch: 22, Acc: 96.73\n", "saving best checkpoint at epoch: 23, Acc: 96.96\n", "saving best checkpoint at epoch: 25, Acc: 97.24\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0002. Train Acc: 96.3625, Test loss: 0.0002. Test Acc: 96.1200. Time/epoch: 1.5534\n", "saving best checkpoint at epoch: 38, Acc: 97.44\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0001. Train Acc: 97.3425, Test loss: 0.0002. Test Acc: 96.7600. Time/epoch: 1.6890\n", "saving best checkpoint at epoch: 41, Acc: 97.61\n", "saving best checkpoint at epoch: 45, Acc: 97.66\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0001. Train Acc: 98.2225, Test loss: 0.0001. Test Acc: 97.5100. Time/epoch: 1.5652\n", "saving best checkpoint at epoch: 57, Acc: 97.69\n", "saving best checkpoint at epoch: 59, Acc: 97.81\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0001. Train Acc: 98.6450, Test loss: 0.0001. Test Acc: 97.5300. Time/epoch: 1.6991\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0001. Train Acc: 98.6975, Test loss: 0.0001. Test Acc: 97.5600. Time/epoch: 1.5375\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0001. Train Acc: 98.5425, Test loss: 0.0001. Test Acc: 97.4900. Time/epoch: 1.6952\n", "saving best checkpoint at epoch: 81, Acc: 97.97\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0002. Train Acc: 96.5300, Test loss: 0.0003. Test Acc: 95.8000. Time/epoch: 1.5501\n", "saving best checkpoint at epoch: 93, Acc: 98.04\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0000. Train Acc: 99.0925, Test loss: 0.0001. Test Acc: 97.9200. Time/epoch: 1.5553\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train99.0925
Accuracy/val97.92
Loss/train5e-05
Loss/val0.00014
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/lwhnokhf" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0017. Train Acc: 70.1950, Test loss: 0.0017. Test Acc: 70.5800. Time/epoch: 1.7054\n", "saving best checkpoint at epoch: 0, Acc: 70.58\n", "saving best checkpoint at epoch: 1, Acc: 83.69\n", "saving best checkpoint at epoch: 2, Acc: 88.34\n", "saving best checkpoint at epoch: 3, Acc: 89.76\n", "saving best checkpoint at epoch: 4, Acc: 90.66\n", "saving best checkpoint at epoch: 5, Acc: 91.08\n", "saving best checkpoint at epoch: 6, Acc: 91.43\n", "saving best checkpoint at epoch: 7, Acc: 91.81\n", "saving best checkpoint at epoch: 8, Acc: 92.42\n", "saving best checkpoint at epoch: 9, Acc: 92.98\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0003. Train Acc: 93.2650, Test loss: 0.0004. Test Acc: 93.3600. Time/epoch: 1.5644\n", "saving best checkpoint at epoch: 10, Acc: 93.36\n", "saving best checkpoint at epoch: 11, Acc: 94.0\n", "saving best checkpoint at epoch: 14, Acc: 94.53\n", "saving best checkpoint at epoch: 16, Acc: 95.06\n", "saving best checkpoint at epoch: 17, Acc: 95.11\n", "saving best checkpoint at epoch: 18, Acc: 95.29\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0002. Train Acc: 95.6125, Test loss: 0.0003. Test Acc: 95.6500. Time/epoch: 1.6869\n", "saving best checkpoint at epoch: 20, Acc: 95.65\n", "saving best checkpoint at epoch: 23, Acc: 95.66\n", "saving best checkpoint at epoch: 24, Acc: 95.75\n", "saving best checkpoint at epoch: 25, Acc: 95.82\n", "saving best checkpoint at epoch: 26, Acc: 95.94\n", "saving best checkpoint at epoch: 27, Acc: 96.0\n", "saving best checkpoint at epoch: 28, Acc: 96.1\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0002. Train Acc: 96.0875, Test loss: 0.0002. Test Acc: 96.0700. Time/epoch: 1.5542\n", "saving best checkpoint at epoch: 34, Acc: 96.3\n", "saving best checkpoint at epoch: 37, Acc: 96.35\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0002. Train Acc: 96.6200, Test loss: 0.0002. Test Acc: 96.3300. Time/epoch: 1.6839\n", "saving best checkpoint at epoch: 42, Acc: 96.48\n", "saving best checkpoint at epoch: 49, Acc: 96.51\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0002. Train Acc: 96.5325, Test loss: 0.0002. Test Acc: 96.3800. Time/epoch: 1.5649\n", "saving best checkpoint at epoch: 51, Acc: 96.67\n", "saving best checkpoint at epoch: 58, Acc: 96.68\n", "saving best checkpoint at epoch: 59, Acc: 96.77\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0002. Train Acc: 97.1075, Test loss: 0.0002. Test Acc: 96.6100. Time/epoch: 1.6875\n", "saving best checkpoint at epoch: 61, Acc: 96.82\n", "saving best checkpoint at epoch: 63, Acc: 96.83\n", "saving best checkpoint at epoch: 64, Acc: 96.92\n", "saving best checkpoint at epoch: 66, Acc: 96.95\n", "saving best checkpoint at epoch: 69, Acc: 96.98\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0001. Train Acc: 97.2900, Test loss: 0.0002. Test Acc: 96.9000. Time/epoch: 1.5406\n", "saving best checkpoint at epoch: 72, Acc: 97.03\n", "saving best checkpoint at epoch: 77, Acc: 97.12\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0001. Train Acc: 97.4675, Test loss: 0.0002. Test Acc: 97.0800. Time/epoch: 1.6812\n", "saving best checkpoint at epoch: 82, Acc: 97.14\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0001. Train Acc: 97.5250, Test loss: 0.0002. Test Acc: 96.9200. Time/epoch: 1.5510\n", "saving best checkpoint at epoch: 94, Acc: 97.22\n", "saving best checkpoint at epoch: 95, Acc: 97.29\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0001. Train Acc: 97.4550, Test loss: 0.0002. Test Acc: 96.6900. Time/epoch: 1.6926\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train97.455
Accuracy/val96.69
Loss/train0.00014
Loss/val0.00017
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/nxmjjnle" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0048. Train Acc: 77.7950, Test loss: 0.0048. Test Acc: 77.8900. Time/epoch: 2.2228\n", "saving best checkpoint at epoch: 0, Acc: 77.89\n", "saving best checkpoint at epoch: 1, Acc: 86.26\n", "saving best checkpoint at epoch: 2, Acc: 88.27\n", "saving best checkpoint at epoch: 3, Acc: 90.18\n", "saving best checkpoint at epoch: 4, Acc: 90.72\n", "saving best checkpoint at epoch: 5, Acc: 91.31\n", "saving best checkpoint at epoch: 6, Acc: 91.46\n", "saving best checkpoint at epoch: 7, Acc: 91.94\n", "saving best checkpoint at epoch: 8, Acc: 92.4\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0014. Train Acc: 92.7650, Test loss: 0.0014. Test Acc: 92.8400. Time/epoch: 2.0406\n", "saving best checkpoint at epoch: 10, Acc: 92.84\n", "saving best checkpoint at epoch: 11, Acc: 92.94\n", "saving best checkpoint at epoch: 12, Acc: 93.0\n", "saving best checkpoint at epoch: 13, Acc: 93.3\n", "saving best checkpoint at epoch: 14, Acc: 93.62\n", "saving best checkpoint at epoch: 15, Acc: 93.71\n", "saving best checkpoint at epoch: 16, Acc: 93.85\n", "saving best checkpoint at epoch: 18, Acc: 93.94\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0012. Train Acc: 93.8000, Test loss: 0.0012. Test Acc: 93.9800. Time/epoch: 2.0324\n", "saving best checkpoint at epoch: 20, Acc: 93.98\n", "saving best checkpoint at epoch: 21, Acc: 94.19\n", "saving best checkpoint at epoch: 22, Acc: 94.22\n", "saving best checkpoint at epoch: 24, Acc: 94.24\n", "saving best checkpoint at epoch: 25, Acc: 94.36\n", "saving best checkpoint at epoch: 27, Acc: 94.38\n", "saving best checkpoint at epoch: 28, Acc: 94.54\n", "saving best checkpoint at epoch: 29, Acc: 94.6\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0010. Train Acc: 94.5650, Test loss: 0.0011. Test Acc: 94.4500. Time/epoch: 2.0292\n", "saving best checkpoint at epoch: 32, Acc: 94.71\n", "saving best checkpoint at epoch: 34, Acc: 94.85\n", "saving best checkpoint at epoch: 35, Acc: 94.87\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0010. Train Acc: 94.8700, Test loss: 0.0011. Test Acc: 94.6800. Time/epoch: 2.1745\n", "saving best checkpoint at epoch: 41, Acc: 94.95\n", "saving best checkpoint at epoch: 43, Acc: 94.96\n", "saving best checkpoint at epoch: 44, Acc: 95.08\n", "saving best checkpoint at epoch: 46, Acc: 95.12\n", "saving best checkpoint at epoch: 47, Acc: 95.19\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0009. Train Acc: 95.3225, Test loss: 0.0010. Test Acc: 95.1500. Time/epoch: 2.1725\n", "saving best checkpoint at epoch: 51, Acc: 95.22\n", "saving best checkpoint at epoch: 53, Acc: 95.25\n", "saving best checkpoint at epoch: 55, Acc: 95.33\n", "saving best checkpoint at epoch: 56, Acc: 95.43\n", "saving best checkpoint at epoch: 58, Acc: 95.48\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0009. Train Acc: 95.6725, Test loss: 0.0009. Test Acc: 95.4300. Time/epoch: 2.0500\n", "saving best checkpoint at epoch: 62, Acc: 95.5\n", "saving best checkpoint at epoch: 63, Acc: 95.55\n", "saving best checkpoint at epoch: 68, Acc: 95.56\n", "saving best checkpoint at epoch: 69, Acc: 95.59\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0008. Train Acc: 95.7775, Test loss: 0.0009. Test Acc: 95.6000. Time/epoch: 2.1815\n", "saving best checkpoint at epoch: 70, Acc: 95.6\n", "saving best checkpoint at epoch: 71, Acc: 95.61\n", "saving best checkpoint at epoch: 74, Acc: 95.65\n", "saving best checkpoint at epoch: 79, Acc: 95.7\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0008. Train Acc: 95.9700, Test loss: 0.0009. Test Acc: 95.6700. Time/epoch: 2.1883\n", "saving best checkpoint at epoch: 81, Acc: 95.79\n", "saving best checkpoint at epoch: 84, Acc: 95.86\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0008. Train Acc: 96.0900, Test loss: 0.0009. Test Acc: 95.8600. Time/epoch: 2.1885\n", "saving best checkpoint at epoch: 91, Acc: 95.91\n", "saving best checkpoint at epoch: 94, Acc: 95.94\n", "saving best checkpoint at epoch: 95, Acc: 95.98\n", "saving best checkpoint at epoch: 98, Acc: 96.0\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0007. Train Acc: 96.3375, Test loss: 0.0008. Test Acc: 95.9400. Time/epoch: 2.1984\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train96.3375
Accuracy/val95.94
Loss/train0.00073
Loss/val0.00082
epoch100

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Time/epoch: 1.5998\n", "saving best checkpoint at epoch: 0, Acc: 24.54\n", "saving best checkpoint at epoch: 1, Acc: 51.54\n", "saving best checkpoint at epoch: 2, Acc: 70.76\n", "saving best checkpoint at epoch: 3, Acc: 75.35\n", "saving best checkpoint at epoch: 4, Acc: 79.33\n", "saving best checkpoint at epoch: 5, Acc: 82.16\n", "saving best checkpoint at epoch: 6, Acc: 83.95\n", "saving best checkpoint at epoch: 7, Acc: 85.36\n", "saving best checkpoint at epoch: 8, Acc: 86.82\n", "saving best checkpoint at epoch: 9, Acc: 88.09\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0006. Train Acc: 88.6925, Test loss: 0.0007. Test Acc: 88.5700. Time/epoch: 1.5446\n", "saving best checkpoint at epoch: 10, Acc: 88.57\n", "saving best checkpoint at epoch: 11, Acc: 89.2\n", "saving best checkpoint at epoch: 12, Acc: 90.04\n", "saving best checkpoint at epoch: 13, Acc: 90.16\n", "saving best checkpoint at epoch: 14, Acc: 90.47\n", "saving best checkpoint at epoch: 15, Acc: 90.9\n", "saving best checkpoint at epoch: 16, Acc: 91.03\n", "saving best checkpoint at epoch: 17, Acc: 91.45\n", "saving best checkpoint at epoch: 18, Acc: 91.68\n", "saving best checkpoint at epoch: 19, Acc: 91.76\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0004. Train Acc: 92.0350, Test loss: 0.0004. Test Acc: 92.0100. Time/epoch: 1.5407\n", "saving best checkpoint at epoch: 20, Acc: 92.01\n", "saving best checkpoint at epoch: 21, Acc: 92.34\n", "saving best checkpoint at epoch: 22, Acc: 92.53\n", "saving best checkpoint at epoch: 24, Acc: 92.56\n", "saving best checkpoint at epoch: 25, Acc: 92.58\n", "saving best checkpoint at epoch: 26, Acc: 92.74\n", "saving best checkpoint at epoch: 27, Acc: 92.88\n", "saving best checkpoint at epoch: 28, Acc: 92.97\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0003. Train Acc: 93.2075, Test loss: 0.0004. Test Acc: 93.0200. Time/epoch: 1.5433\n", "saving best checkpoint at epoch: 30, Acc: 93.02\n", "saving best checkpoint at epoch: 31, Acc: 93.05\n", "saving best checkpoint at epoch: 33, Acc: 93.1\n", "saving best checkpoint at epoch: 34, Acc: 93.2\n", "saving best checkpoint at epoch: 35, Acc: 93.28\n", "saving best checkpoint at epoch: 37, Acc: 93.31\n", "saving best checkpoint at epoch: 38, Acc: 93.45\n", "saving best checkpoint at epoch: 39, Acc: 93.68\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0003. Train Acc: 93.9150, Test loss: 0.0003. Test Acc: 93.8100. Time/epoch: 1.6855\n", "saving best checkpoint at epoch: 40, Acc: 93.81\n", "saving best checkpoint at epoch: 41, Acc: 93.85\n", "saving best checkpoint at epoch: 42, Acc: 93.9\n", "saving best checkpoint at epoch: 44, Acc: 94.0\n", "saving best checkpoint at epoch: 47, Acc: 94.11\n", "saving best checkpoint at epoch: 48, Acc: 94.13\n", "saving best checkpoint at epoch: 49, Acc: 94.18\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0003. Train Acc: 94.3875, Test loss: 0.0003. Test Acc: 94.2400. Time/epoch: 1.5570\n", "saving best checkpoint at epoch: 50, Acc: 94.24\n", "saving best checkpoint at epoch: 52, Acc: 94.35\n", "saving best checkpoint at epoch: 53, Acc: 94.38\n", "saving best checkpoint at epoch: 55, Acc: 94.47\n", "saving best checkpoint at epoch: 58, Acc: 94.5\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0003. Train Acc: 94.6850, Test loss: 0.0003. Test Acc: 94.4300. Time/epoch: 1.5400\n", "saving best checkpoint at epoch: 61, Acc: 94.6\n", "saving best checkpoint at epoch: 66, Acc: 94.62\n", "saving best checkpoint at epoch: 67, Acc: 94.7\n", "saving best checkpoint at epoch: 69, Acc: 94.85\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0002. Train Acc: 95.0875, Test loss: 0.0003. Test Acc: 94.9400. Time/epoch: 1.6995\n", "saving best checkpoint at epoch: 70, Acc: 94.94\n", "saving best checkpoint at epoch: 74, Acc: 94.96\n", "saving best checkpoint at epoch: 79, Acc: 95.0\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0002. Train Acc: 95.2850, Test loss: 0.0003. Test Acc: 94.9500. Time/epoch: 1.5508\n", "saving best checkpoint at epoch: 81, Acc: 95.13\n", "saving best checkpoint at epoch: 87, Acc: 95.21\n", "saving best checkpoint at epoch: 88, Acc: 95.31\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0002. Train Acc: 95.4125, Test loss: 0.0002. Test Acc: 95.1700. Time/epoch: 1.5511\n", "saving best checkpoint at epoch: 91, Acc: 95.36\n", "saving best checkpoint at epoch: 95, Acc: 95.38\n", "saving best checkpoint at epoch: 97, Acc: 95.45\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0002. Train Acc: 95.6550, Test loss: 0.0002. Test Acc: 95.4400. Time/epoch: 1.5432\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... 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Accuracy/train95.655
Accuracy/val95.44
Loss/train0.00021
Loss/val0.00024
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/65z2fmi0" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0052. Train Acc: 75.1550, Test loss: 0.0052. Test Acc: 74.8100. Time/epoch: 2.2607\n", "saving best checkpoint at epoch: 0, Acc: 74.81\n", "saving best checkpoint at epoch: 1, Acc: 84.52\n", "saving best checkpoint at epoch: 2, Acc: 87.27\n", "saving best checkpoint at epoch: 3, Acc: 89.21\n", "saving best checkpoint at epoch: 4, Acc: 89.81\n", "saving best checkpoint at epoch: 5, Acc: 91.0\n", "saving best checkpoint at epoch: 6, Acc: 91.26\n", "saving best checkpoint at epoch: 7, Acc: 91.39\n", "saving best checkpoint at epoch: 8, Acc: 92.05\n", "saving best checkpoint at epoch: 9, Acc: 92.49\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0014. Train Acc: 92.8650, Test loss: 0.0014. Test Acc: 92.9000. Time/epoch: 2.2024\n", "saving best checkpoint at epoch: 10, Acc: 92.9\n", "saving best checkpoint at epoch: 11, Acc: 93.2\n", "saving best checkpoint at epoch: 12, Acc: 93.57\n", "saving best checkpoint at epoch: 13, Acc: 93.81\n", "saving best checkpoint at epoch: 15, Acc: 94.2\n", "saving best checkpoint at epoch: 17, Acc: 94.45\n", "saving best checkpoint at epoch: 18, Acc: 94.71\n", "saving best checkpoint at epoch: 19, Acc: 94.9\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0010. Train Acc: 94.8750, Test loss: 0.0011. Test Acc: 94.7800. Time/epoch: 2.0395\n", "saving best checkpoint at epoch: 21, Acc: 94.92\n", "saving best checkpoint at epoch: 22, Acc: 95.09\n", "saving best checkpoint at epoch: 23, Acc: 95.51\n", "saving best checkpoint at epoch: 28, Acc: 95.58\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0009. Train Acc: 95.8050, Test loss: 0.0010. Test Acc: 95.7800. Time/epoch: 2.0350\n", "saving best checkpoint at epoch: 30, Acc: 95.78\n", "saving best checkpoint at epoch: 31, Acc: 95.88\n", "saving best checkpoint at epoch: 33, Acc: 95.98\n", "saving best checkpoint at epoch: 36, Acc: 96.01\n", "saving best checkpoint at epoch: 37, Acc: 96.07\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0008. Train Acc: 96.1600, Test loss: 0.0009. Test Acc: 96.0800. Time/epoch: 2.1717\n", "saving best checkpoint at epoch: 40, Acc: 96.08\n", "saving best checkpoint at epoch: 41, Acc: 96.15\n", "saving best checkpoint at epoch: 42, Acc: 96.27\n", "saving best checkpoint at epoch: 43, Acc: 96.35\n", "saving best checkpoint at epoch: 47, Acc: 96.41\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0007. Train Acc: 96.6100, Test loss: 0.0008. Test Acc: 96.4000. Time/epoch: 2.1712\n", "saving best checkpoint at epoch: 51, Acc: 96.47\n", "saving best checkpoint at epoch: 53, Acc: 96.51\n", "saving best checkpoint at epoch: 54, Acc: 96.56\n", "saving best checkpoint at epoch: 56, Acc: 96.61\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0007. Train Acc: 96.8450, Test loss: 0.0008. Test Acc: 96.6400. Time/epoch: 2.1877\n", "saving best checkpoint at epoch: 60, Acc: 96.64\n", "saving best checkpoint at epoch: 61, Acc: 96.7\n", "saving best checkpoint at epoch: 65, Acc: 96.79\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0006. Train Acc: 97.0100, Test loss: 0.0007. Test Acc: 96.7000. Time/epoch: 2.1843\n", "saving best checkpoint at epoch: 73, Acc: 96.86\n", "saving best checkpoint at epoch: 74, Acc: 96.87\n", "saving best checkpoint at epoch: 76, Acc: 96.89\n", "saving best checkpoint at epoch: 78, Acc: 96.92\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0006. Train Acc: 97.2650, Test loss: 0.0007. Test Acc: 96.8300. Time/epoch: 2.1802\n", "saving best checkpoint at epoch: 83, Acc: 97.02\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0006. Train Acc: 97.3550, Test loss: 0.0007. Test Acc: 97.0800. Time/epoch: 2.1789\n", "saving best checkpoint at epoch: 90, Acc: 97.08\n", "saving best checkpoint at epoch: 93, Acc: 97.12\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0006. Train Acc: 97.2375, Test loss: 0.0007. Test Acc: 96.9000. Time/epoch: 2.1800\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Accuracy/train97.2375
Accuracy/val96.9
Loss/train0.00058
Loss/val0.00068
epoch100

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Accuracy/train96.8975
Accuracy/val96.36
Loss/train0.00016
Loss/val0.00018
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/2hwteiti" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0037. Train Acc: 83.4850, Test loss: 0.0038. Test Acc: 82.9700. Time/epoch: 2.2066\n", "saving best checkpoint at epoch: 0, Acc: 82.97\n", "saving best checkpoint at epoch: 1, Acc: 86.42\n", "saving best checkpoint at epoch: 2, Acc: 88.52\n", "saving best checkpoint at epoch: 3, Acc: 90.46\n", "saving best checkpoint at epoch: 4, Acc: 91.47\n", "saving best checkpoint at epoch: 5, Acc: 92.1\n", "saving best checkpoint at epoch: 6, Acc: 92.33\n", "saving best checkpoint at epoch: 7, Acc: 93.0\n", "saving best checkpoint at epoch: 8, Acc: 93.36\n", "saving best checkpoint at epoch: 9, Acc: 93.69\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0012. Train Acc: 93.7950, Test loss: 0.0013. Test Acc: 93.6300. Time/epoch: 2.1826\n", "saving best checkpoint at epoch: 11, Acc: 93.88\n", "saving best checkpoint at epoch: 12, Acc: 94.11\n", "saving best checkpoint at epoch: 14, Acc: 94.25\n", "saving best checkpoint at epoch: 15, Acc: 94.26\n", "saving best checkpoint at epoch: 16, Acc: 94.43\n", "saving best checkpoint at epoch: 18, Acc: 94.56\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0010. Train Acc: 94.9325, Test loss: 0.0011. Test Acc: 94.7300. Time/epoch: 2.1813\n", "saving best checkpoint at epoch: 20, Acc: 94.73\n", "saving best checkpoint at epoch: 23, Acc: 94.88\n", "saving best checkpoint at epoch: 25, Acc: 95.04\n", "saving best checkpoint at epoch: 26, Acc: 95.07\n", "saving best checkpoint at epoch: 27, Acc: 95.13\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0009. Train Acc: 95.3350, Test loss: 0.0010. Test Acc: 95.2500. Time/epoch: 2.1791\n", "saving best checkpoint at epoch: 30, Acc: 95.25\n", "saving best checkpoint at epoch: 34, Acc: 95.31\n", "saving best checkpoint at epoch: 37, Acc: 95.37\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0009. Train Acc: 95.7150, Test loss: 0.0010. Test Acc: 95.4500. Time/epoch: 2.1803\n", "saving best checkpoint at epoch: 40, Acc: 95.45\n", "saving best checkpoint at epoch: 41, Acc: 95.49\n", "saving best checkpoint at epoch: 42, Acc: 95.57\n", "saving best checkpoint at epoch: 44, Acc: 95.59\n", "saving best checkpoint at epoch: 45, Acc: 95.61\n", "saving best checkpoint at epoch: 48, Acc: 95.69\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0008. Train Acc: 95.8875, Test loss: 0.0009. Test Acc: 95.5800. Time/epoch: 2.1753\n", "saving best checkpoint at epoch: 53, Acc: 95.82\n", "saving best checkpoint at epoch: 54, Acc: 95.83\n", "saving best checkpoint at epoch: 56, Acc: 95.92\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0008. Train Acc: 96.1975, Test loss: 0.0009. Test Acc: 95.8900. Time/epoch: 2.2064\n", "saving best checkpoint at epoch: 61, Acc: 95.94\n", "saving best checkpoint at epoch: 66, Acc: 96.06\n", "saving best checkpoint at epoch: 69, Acc: 96.17\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0007. Train Acc: 96.4950, Test loss: 0.0008. Test Acc: 96.1600. Time/epoch: 2.1930\n", "saving best checkpoint at epoch: 75, Acc: 96.21\n", "saving best checkpoint at epoch: 79, Acc: 96.32\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0007. Train Acc: 96.6250, Test loss: 0.0008. Test Acc: 96.3200. Time/epoch: 2.0398\n", "saving best checkpoint at epoch: 83, Acc: 96.33\n", "saving best checkpoint at epoch: 88, Acc: 96.49\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0007. Train Acc: 96.7900, Test loss: 0.0008. Test Acc: 96.3500. Time/epoch: 2.1816\n", "saving best checkpoint at epoch: 93, Acc: 96.53\n", "saving best checkpoint at epoch: 95, Acc: 96.56\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0006. Train Acc: 97.0050, Test loss: 0.0008. Test Acc: 96.5000. Time/epoch: 2.1892\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train97.005
Accuracy/val96.5
Loss/train0.00063
Loss/val0.00077
epoch100

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Time/epoch: 2.2290\n", "saving best checkpoint at epoch: 0, Acc: 80.2\n", "saving best checkpoint at epoch: 1, Acc: 85.18\n", "saving best checkpoint at epoch: 2, Acc: 86.77\n", "saving best checkpoint at epoch: 3, Acc: 87.11\n", "saving best checkpoint at epoch: 4, Acc: 88.01\n", "saving best checkpoint at epoch: 5, Acc: 88.54\n", "saving best checkpoint at epoch: 6, Acc: 89.2\n", "saving best checkpoint at epoch: 7, Acc: 89.7\n", "saving best checkpoint at epoch: 8, Acc: 90.1\n", "saving best checkpoint at epoch: 9, Acc: 90.15\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0017. Train Acc: 90.5350, Test loss: 0.0017. Test Acc: 90.6100. Time/epoch: 2.2001\n", "saving best checkpoint at epoch: 10, Acc: 90.61\n", "saving best checkpoint at epoch: 11, Acc: 90.96\n", "saving best checkpoint at epoch: 12, Acc: 91.17\n", "saving best checkpoint at epoch: 13, Acc: 91.55\n", "saving best checkpoint at epoch: 15, Acc: 91.6\n", "saving best checkpoint at epoch: 16, Acc: 91.67\n", "saving best checkpoint at epoch: 17, Acc: 91.97\n", "saving best checkpoint at epoch: 19, Acc: 92.11\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0014. Train Acc: 92.0225, Test loss: 0.0015. Test Acc: 92.1000. Time/epoch: 2.1775\n", "saving best checkpoint at epoch: 22, Acc: 92.21\n", "saving best checkpoint at epoch: 23, Acc: 92.36\n", "saving best checkpoint at epoch: 24, Acc: 92.42\n", "saving best checkpoint at epoch: 27, Acc: 92.76\n", "saving best checkpoint at epoch: 28, Acc: 92.78\n", "saving best checkpoint at epoch: 29, Acc: 92.9\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0013. Train Acc: 92.9600, Test loss: 0.0013. Test Acc: 92.8500. Time/epoch: 2.1839\n", "saving best checkpoint at epoch: 32, Acc: 93.14\n", "saving best checkpoint at epoch: 33, Acc: 93.32\n", "saving best checkpoint at epoch: 37, Acc: 93.35\n", "saving best checkpoint at epoch: 39, Acc: 93.65\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0012. Train Acc: 93.6125, Test loss: 0.0012. Test Acc: 93.4700. Time/epoch: 2.1837\n", "saving best checkpoint at epoch: 41, Acc: 93.66\n", "saving best checkpoint at epoch: 42, Acc: 93.67\n", "saving best checkpoint at epoch: 44, Acc: 93.92\n", "saving best checkpoint at epoch: 45, Acc: 94.03\n", "saving best checkpoint at epoch: 47, Acc: 94.17\n", "saving best checkpoint at epoch: 49, Acc: 94.2\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0011. Train Acc: 94.1275, Test loss: 0.0011. Test Acc: 94.0700. Time/epoch: 2.2040\n", "saving best checkpoint at epoch: 51, Acc: 94.38\n", "saving best checkpoint at epoch: 52, Acc: 94.41\n", "saving best checkpoint at epoch: 55, Acc: 94.58\n", "saving best checkpoint at epoch: 59, Acc: 94.75\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0010. Train Acc: 94.9050, Test loss: 0.0011. Test Acc: 94.8900. Time/epoch: 2.1959\n", "saving best checkpoint at epoch: 60, Acc: 94.89\n", "saving best checkpoint at epoch: 63, Acc: 94.99\n", "saving best checkpoint at epoch: 65, Acc: 95.01\n", "saving best checkpoint at epoch: 67, Acc: 95.08\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0009. Train Acc: 95.2050, Test loss: 0.0010. Test Acc: 95.0600. Time/epoch: 2.1889\n", "saving best checkpoint at epoch: 71, Acc: 95.19\n", "saving best checkpoint at epoch: 72, Acc: 95.22\n", "saving best checkpoint at epoch: 74, Acc: 95.23\n", "saving best checkpoint at epoch: 77, Acc: 95.34\n", "saving best checkpoint at epoch: 79, Acc: 95.42\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0009. Train Acc: 95.6200, Test loss: 0.0010. Test Acc: 95.3700. Time/epoch: 2.0380\n", "saving best checkpoint at epoch: 82, Acc: 95.47\n", "saving best checkpoint at epoch: 85, Acc: 95.55\n", "saving best checkpoint at epoch: 86, Acc: 95.59\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0008. Train Acc: 95.7850, Test loss: 0.0009. Test Acc: 95.4200. Time/epoch: 2.1806\n", "saving best checkpoint at epoch: 92, Acc: 95.6\n", "saving best checkpoint at epoch: 95, Acc: 95.69\n", "saving best checkpoint at epoch: 99, Acc: 95.72\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0008. Train Acc: 95.9600, Test loss: 0.0009. Test Acc: 95.8000. Time/epoch: 2.2229\n", "saving best checkpoint at epoch: 100, Acc: 95.8\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train95.96
Accuracy/val95.8
Loss/train0.00084
Loss/val0.0009
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/q1sio3r7" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0027. Train Acc: 44.4175, Test loss: 0.0027. Test Acc: 43.7000. Time/epoch: 1.5729\n", "saving best checkpoint at epoch: 0, Acc: 43.7\n", "saving best checkpoint at epoch: 1, Acc: 64.22\n", "saving best checkpoint at epoch: 2, Acc: 80.43\n", "saving best checkpoint at epoch: 3, Acc: 83.88\n", "saving best checkpoint at epoch: 4, Acc: 85.42\n", "saving best checkpoint at epoch: 5, Acc: 86.34\n", "saving best checkpoint at epoch: 6, Acc: 87.03\n", "saving best checkpoint at epoch: 7, Acc: 87.7\n", "saving best checkpoint at epoch: 8, Acc: 88.37\n", "saving best checkpoint at epoch: 9, Acc: 88.98\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0007. Train Acc: 89.6000, Test loss: 0.0007. Test Acc: 89.4800. Time/epoch: 1.6844\n", "saving best checkpoint at epoch: 10, Acc: 89.48\n", "saving best checkpoint at epoch: 11, Acc: 89.77\n", "saving best checkpoint at epoch: 12, Acc: 90.51\n", "saving best checkpoint at epoch: 13, Acc: 90.97\n", "saving best checkpoint at epoch: 14, Acc: 91.19\n", "saving best checkpoint at epoch: 15, Acc: 91.22\n", "saving best checkpoint at epoch: 16, Acc: 91.71\n", "saving best checkpoint at epoch: 17, Acc: 91.94\n", "saving best checkpoint at epoch: 18, Acc: 92.23\n", "saving best checkpoint at epoch: 19, Acc: 92.51\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0004. Train Acc: 92.5600, Test loss: 0.0004. Test Acc: 92.4900. Time/epoch: 1.5514\n", "saving best checkpoint at epoch: 21, Acc: 92.77\n", "saving best checkpoint at epoch: 22, Acc: 93.13\n", "saving best checkpoint at epoch: 24, Acc: 93.19\n", "saving best checkpoint at epoch: 25, Acc: 93.29\n", "saving best checkpoint at epoch: 26, Acc: 93.38\n", "saving best checkpoint at epoch: 27, Acc: 93.4\n", "saving best checkpoint at epoch: 28, Acc: 93.45\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0003. Train Acc: 93.2400, Test loss: 0.0003. Test Acc: 93.4300. Time/epoch: 1.6860\n", "saving best checkpoint at epoch: 31, Acc: 93.75\n", "saving best checkpoint at epoch: 33, Acc: 93.8\n", "saving best checkpoint at epoch: 34, Acc: 93.91\n", "saving best checkpoint at epoch: 37, Acc: 94.02\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0003. Train Acc: 93.9200, Test loss: 0.0003. Test Acc: 93.9200. Time/epoch: 1.5416\n", "saving best checkpoint at epoch: 41, Acc: 94.1\n", "saving best checkpoint at epoch: 43, Acc: 94.2\n", "saving best checkpoint at epoch: 45, Acc: 94.27\n", "saving best checkpoint at epoch: 46, Acc: 94.33\n", "saving best checkpoint at epoch: 47, Acc: 94.38\n", "saving best checkpoint at epoch: 49, Acc: 94.49\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0003. Train Acc: 94.4425, Test loss: 0.0003. Test Acc: 94.2900. Time/epoch: 1.7196\n", "saving best checkpoint at epoch: 52, Acc: 94.54\n", "saving best checkpoint at epoch: 57, Acc: 94.64\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0003. Train Acc: 94.6550, Test loss: 0.0003. Test Acc: 94.5200. Time/epoch: 1.5481\n", "saving best checkpoint at epoch: 62, Acc: 94.75\n", "saving best checkpoint at epoch: 63, Acc: 94.78\n", "saving best checkpoint at epoch: 67, Acc: 94.83\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0002. Train Acc: 95.1275, Test loss: 0.0003. Test Acc: 94.8800. Time/epoch: 1.5508\n", "saving best checkpoint at epoch: 70, Acc: 94.88\n", "saving best checkpoint at epoch: 73, Acc: 94.91\n", "saving best checkpoint at epoch: 77, Acc: 94.97\n", "saving best checkpoint at epoch: 78, Acc: 95.01\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0002. Train Acc: 95.2250, Test loss: 0.0002. Test Acc: 95.0900. Time/epoch: 1.5450\n", "saving best checkpoint at epoch: 80, Acc: 95.09\n", "saving best checkpoint at epoch: 83, Acc: 95.14\n", "saving best checkpoint at epoch: 88, Acc: 95.19\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0002. Train Acc: 95.4175, Test loss: 0.0002. Test Acc: 95.0800. Time/epoch: 1.5519\n", "saving best checkpoint at epoch: 93, Acc: 95.31\n", "saving best checkpoint at epoch: 98, Acc: 95.35\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0002. Train Acc: 95.6300, Test loss: 0.0002. Test Acc: 95.3200. Time/epoch: 1.6873\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... 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Run summary:


Accuracy/train95.63
Accuracy/val95.32
Loss/train0.00022
Loss/val0.00023
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/fy142do3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0024. Train Acc: 59.1600, Test loss: 0.0024. Test Acc: 59.5400. Time/epoch: 1.5671\n", "saving best checkpoint at epoch: 0, Acc: 59.54\n", "saving best checkpoint at epoch: 1, Acc: 78.64\n", "saving best checkpoint at epoch: 2, Acc: 82.26\n", "saving best checkpoint at epoch: 3, Acc: 83.73\n", "saving best checkpoint at epoch: 4, Acc: 85.86\n", "saving best checkpoint at epoch: 5, Acc: 86.8\n", "saving best checkpoint at epoch: 6, Acc: 87.06\n", "saving best checkpoint at epoch: 7, Acc: 87.7\n", "saving best checkpoint at epoch: 8, Acc: 88.06\n", "saving best checkpoint at epoch: 9, Acc: 88.22\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0005. Train Acc: 88.4350, Test loss: 0.0005. Test Acc: 88.4800. Time/epoch: 1.5522\n", "saving best checkpoint at epoch: 10, Acc: 88.48\n", "saving best checkpoint at epoch: 11, Acc: 88.61\n", "saving best checkpoint at epoch: 12, Acc: 88.67\n", "saving best checkpoint at epoch: 13, Acc: 88.99\n", "saving best checkpoint at epoch: 14, Acc: 89.26\n", "saving best checkpoint at epoch: 15, Acc: 89.88\n", "saving best checkpoint at epoch: 16, Acc: 90.53\n", "saving best checkpoint at epoch: 17, Acc: 91.03\n", "saving best checkpoint at epoch: 18, Acc: 91.39\n", "saving best checkpoint at epoch: 19, Acc: 91.69\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0004. Train Acc: 92.1900, Test loss: 0.0004. Test Acc: 91.9800. Time/epoch: 1.6872\n", "saving best checkpoint at epoch: 20, Acc: 91.98\n", "saving best checkpoint at epoch: 21, Acc: 92.17\n", "saving best checkpoint at epoch: 22, Acc: 92.58\n", "saving best checkpoint at epoch: 23, Acc: 92.76\n", "saving best checkpoint at epoch: 24, Acc: 92.82\n", "saving best checkpoint at epoch: 25, Acc: 93.06\n", "saving best checkpoint at epoch: 26, Acc: 93.24\n", "saving best checkpoint at epoch: 27, Acc: 93.26\n", "saving best checkpoint at epoch: 28, Acc: 93.48\n", "saving best checkpoint at epoch: 29, Acc: 93.57\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0003. Train Acc: 93.6275, Test loss: 0.0003. Test Acc: 93.6400. Time/epoch: 1.5517\n", "saving best checkpoint at epoch: 30, Acc: 93.64\n", "saving best checkpoint at epoch: 31, Acc: 93.85\n", "saving best checkpoint at epoch: 32, Acc: 93.91\n", "saving best checkpoint at epoch: 33, Acc: 93.98\n", "saving best checkpoint at epoch: 36, Acc: 94.07\n", "saving best checkpoint at epoch: 38, Acc: 94.31\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0003. Train Acc: 94.0300, Test loss: 0.0003. Test Acc: 94.2100. Time/epoch: 1.6954\n", "saving best checkpoint at epoch: 41, Acc: 94.45\n", "saving best checkpoint at epoch: 44, Acc: 94.5\n", "saving best checkpoint at epoch: 45, Acc: 94.66\n", "saving best checkpoint at epoch: 46, Acc: 94.68\n", "saving best checkpoint at epoch: 47, Acc: 94.78\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0003. Train Acc: 94.5575, Test loss: 0.0003. Test Acc: 94.5800. Time/epoch: 1.5592\n", "saving best checkpoint at epoch: 51, Acc: 94.93\n", "saving best checkpoint at epoch: 54, Acc: 94.96\n", "saving best checkpoint at epoch: 56, Acc: 95.0\n", "saving best checkpoint at epoch: 57, Acc: 95.1\n", "saving best checkpoint at epoch: 58, Acc: 95.12\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0002. Train Acc: 95.0225, Test loss: 0.0003. Test Acc: 95.1600. Time/epoch: 1.6906\n", "saving best checkpoint at epoch: 60, Acc: 95.16\n", "saving best checkpoint at epoch: 61, Acc: 95.27\n", "saving best checkpoint at epoch: 63, Acc: 95.28\n", "saving best checkpoint at epoch: 65, Acc: 95.34\n", "saving best checkpoint at epoch: 66, Acc: 95.36\n", "saving best checkpoint at epoch: 69, Acc: 95.46\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0002. Train Acc: 95.3225, Test loss: 0.0002. Test Acc: 95.4800. Time/epoch: 1.5453\n", "saving best checkpoint at epoch: 70, Acc: 95.48\n", "saving best checkpoint at epoch: 77, Acc: 95.58\n", "saving best checkpoint at epoch: 78, Acc: 95.59\n", "saving best checkpoint at epoch: 79, Acc: 95.66\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0002. Train Acc: 95.5175, Test loss: 0.0002. Test Acc: 95.4900. Time/epoch: 1.5550\n", "saving best checkpoint at epoch: 81, Acc: 95.71\n", "saving best checkpoint at epoch: 87, Acc: 95.79\n", "saving best checkpoint at epoch: 88, Acc: 95.8\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0002. Train Acc: 95.8400, Test loss: 0.0002. Test Acc: 95.6200. Time/epoch: 1.5470\n", "saving best checkpoint at epoch: 91, Acc: 95.9\n", "saving best checkpoint at epoch: 96, Acc: 95.94\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0002. Train Acc: 96.0475, Test loss: 0.0002. Test Acc: 95.9700. Time/epoch: 1.5513\n", "saving best checkpoint at epoch: 100, Acc: 95.97\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train96.0475
Accuracy/val95.97
Loss/train0.0002
Loss/val0.00022
epoch100

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Accuracy/train96.7025
Accuracy/val95.99
Loss/train0.00017
Loss/val0.0002
epoch100

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Accuracy/train97.2425
Accuracy/val96.78
Loss/train0.00014
Loss/val0.00018
epoch100

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Run summary:


Accuracy/train96.895
Accuracy/val96.48
Loss/train0.00066
Loss/val0.00075
epoch100

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Time/epoch: 1.6718\n", "saving best checkpoint at epoch: 0, Acc: 63.05\n", "saving best checkpoint at epoch: 1, Acc: 78.97\n", "saving best checkpoint at epoch: 2, Acc: 81.73\n", "saving best checkpoint at epoch: 3, Acc: 83.82\n", "saving best checkpoint at epoch: 4, Acc: 86.09\n", "saving best checkpoint at epoch: 5, Acc: 87.43\n", "saving best checkpoint at epoch: 6, Acc: 88.01\n", "saving best checkpoint at epoch: 7, Acc: 88.61\n", "saving best checkpoint at epoch: 8, Acc: 89.36\n", "saving best checkpoint at epoch: 9, Acc: 89.6\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0005. Train Acc: 90.2900, Test loss: 0.0005. Test Acc: 90.2300. Time/epoch: 1.5514\n", "saving best checkpoint at epoch: 10, Acc: 90.23\n", "saving best checkpoint at epoch: 11, Acc: 90.51\n", "saving best checkpoint at epoch: 12, Acc: 90.79\n", "saving best checkpoint at epoch: 13, Acc: 90.9\n", "saving best checkpoint at epoch: 14, Acc: 91.21\n", "saving best checkpoint at epoch: 15, Acc: 91.25\n", "saving best checkpoint at epoch: 16, Acc: 91.39\n", "saving best checkpoint at epoch: 18, Acc: 91.74\n", "saving best checkpoint at epoch: 19, Acc: 91.9\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0004. Train Acc: 92.3450, Test loss: 0.0004. Test Acc: 92.0300. Time/epoch: 1.5501\n", "saving best checkpoint at epoch: 20, Acc: 92.03\n", "saving best checkpoint at epoch: 21, Acc: 92.32\n", "saving best checkpoint at epoch: 22, Acc: 92.41\n", "saving best checkpoint at epoch: 24, Acc: 92.46\n", "saving best checkpoint at epoch: 25, Acc: 92.81\n", "saving best checkpoint at epoch: 27, Acc: 92.94\n", "saving best checkpoint at epoch: 29, Acc: 93.11\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0003. Train Acc: 93.2250, Test loss: 0.0003. Test Acc: 93.1100. Time/epoch: 1.6964\n", "saving best checkpoint at epoch: 31, Acc: 93.18\n", "saving best checkpoint at epoch: 32, Acc: 93.26\n", "saving best checkpoint at epoch: 33, Acc: 93.3\n", "saving best checkpoint at epoch: 34, Acc: 93.4\n", "saving best checkpoint at epoch: 35, Acc: 93.48\n", "saving best checkpoint at epoch: 36, Acc: 93.53\n", "saving best checkpoint at epoch: 37, Acc: 93.56\n", "saving best checkpoint at epoch: 39, Acc: 93.78\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0003. Train Acc: 93.7650, Test loss: 0.0003. Test Acc: 93.7100. Time/epoch: 1.5660\n", "saving best checkpoint at epoch: 41, Acc: 93.91\n", "saving best checkpoint at epoch: 45, Acc: 94.06\n", "saving best checkpoint at epoch: 46, Acc: 94.19\n", "saving best checkpoint at epoch: 48, Acc: 94.23\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0003. Train Acc: 94.2600, Test loss: 0.0003. Test Acc: 94.1700. Time/epoch: 1.5808\n", "saving best checkpoint at epoch: 52, Acc: 94.24\n", "saving best checkpoint at epoch: 53, Acc: 94.29\n", "saving best checkpoint at epoch: 54, Acc: 94.3\n", "saving best checkpoint at epoch: 55, Acc: 94.42\n", "saving best checkpoint at epoch: 56, Acc: 94.53\n", "saving best checkpoint at epoch: 57, Acc: 94.56\n", "saving best checkpoint at epoch: 59, Acc: 94.62\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0003. Train Acc: 94.6925, Test loss: 0.0003. Test Acc: 94.6100. Time/epoch: 1.5503\n", "saving best checkpoint at epoch: 61, Acc: 94.71\n", "saving best checkpoint at epoch: 63, Acc: 94.81\n", "saving best checkpoint at epoch: 66, Acc: 94.88\n", "saving best checkpoint at epoch: 68, Acc: 94.97\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0002. Train Acc: 95.0325, Test loss: 0.0003. Test Acc: 94.9400. Time/epoch: 1.5489\n", "saving best checkpoint at epoch: 71, Acc: 95.02\n", "saving best checkpoint at epoch: 73, Acc: 95.21\n", "saving best checkpoint at epoch: 78, Acc: 95.38\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0002. Train Acc: 95.4675, Test loss: 0.0003. Test Acc: 95.2600. Time/epoch: 1.6943\n", "saving best checkpoint at epoch: 83, Acc: 95.47\n", "saving best checkpoint at epoch: 85, Acc: 95.56\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0002. Train Acc: 95.6750, Test loss: 0.0002. Test Acc: 95.5200. Time/epoch: 1.5546\n", "saving best checkpoint at epoch: 93, Acc: 95.64\n", "saving best checkpoint at epoch: 97, Acc: 95.71\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0002. Train Acc: 95.7425, Test loss: 0.0002. Test Acc: 95.6300. Time/epoch: 1.6916\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train95.7425
Accuracy/val95.63
Loss/train0.00022
Loss/val0.00024
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/kb19l9b1" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0005. Train Acc: 89.0325, Test loss: 0.0005. Test Acc: 89.2500. Time/epoch: 1.5802\n", "saving best checkpoint at epoch: 0, Acc: 89.25\n", "saving best checkpoint at epoch: 1, Acc: 91.5\n", "saving best checkpoint at epoch: 2, Acc: 92.61\n", "saving best checkpoint at epoch: 3, Acc: 93.49\n", "saving best checkpoint at epoch: 6, Acc: 94.29\n", "saving best checkpoint at epoch: 8, Acc: 95.26\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0004. Train Acc: 91.8675, Test loss: 0.0004. Test Acc: 91.8100. Time/epoch: 1.5471\n", "saving best checkpoint at epoch: 11, Acc: 95.43\n", "saving best checkpoint at epoch: 12, Acc: 95.77\n", "saving best checkpoint at epoch: 16, Acc: 96.02\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0002. Train Acc: 96.5300, Test loss: 0.0002. Test Acc: 96.1600. Time/epoch: 1.6889\n", "saving best checkpoint at epoch: 20, Acc: 96.16\n", "saving best checkpoint at epoch: 23, Acc: 96.18\n", "saving best checkpoint at epoch: 24, Acc: 96.45\n", "saving best checkpoint at epoch: 26, Acc: 96.76\n", "saving best checkpoint at epoch: 28, Acc: 96.8\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0002. Train Acc: 96.9700, Test loss: 0.0002. Test Acc: 96.3800. Time/epoch: 1.5688\n", "saving best checkpoint at epoch: 33, Acc: 96.87\n", "saving best checkpoint at epoch: 36, Acc: 96.92\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0002. Train Acc: 96.7250, Test loss: 0.0002. Test Acc: 96.3500. Time/epoch: 1.6844\n", "saving best checkpoint at epoch: 45, Acc: 97.18\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0001. Train Acc: 97.6050, Test loss: 0.0002. Test Acc: 96.8300. Time/epoch: 1.5565\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0001. Train Acc: 97.8050, Test loss: 0.0002. Test Acc: 96.7700. Time/epoch: 1.6868\n", "saving best checkpoint at epoch: 65, Acc: 97.2\n", "saving best checkpoint at epoch: 67, Acc: 97.35\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0001. Train Acc: 98.1675, Test loss: 0.0002. Test Acc: 97.1300. Time/epoch: 1.5394\n", "saving best checkpoint at epoch: 72, Acc: 97.38\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0001. Train Acc: 98.1200, Test loss: 0.0002. Test Acc: 96.9100. Time/epoch: 1.5546\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0001. Train Acc: 98.8625, Test loss: 0.0002. Test Acc: 97.4200. Time/epoch: 1.5584\n", "saving best checkpoint at epoch: 90, Acc: 97.42\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0001. Train Acc: 97.9950, Test loss: 0.0002. Test Acc: 96.6500. Time/epoch: 1.5454\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train97.995
Accuracy/val96.65
Loss/train0.00011
Loss/val0.00023
epoch100

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Run summary:


Accuracy/train96.99
Accuracy/val96.49
Loss/train0.00016
Loss/val0.00018
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/3gr5qidy" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0030. Train Acc: 46.8275, Test loss: 0.0031. Test Acc: 46.7400. Time/epoch: 1.5724\n", "saving best checkpoint at epoch: 0, Acc: 46.74\n", "saving best checkpoint at epoch: 1, Acc: 72.34\n", "saving best checkpoint at epoch: 2, Acc: 75.68\n", "saving best checkpoint at epoch: 3, Acc: 82.82\n", "saving best checkpoint at epoch: 4, Acc: 86.38\n", "saving best checkpoint at epoch: 5, Acc: 88.03\n", "saving best checkpoint at epoch: 6, Acc: 89.11\n", "saving best checkpoint at epoch: 7, Acc: 89.97\n", "saving best checkpoint at epoch: 8, Acc: 90.77\n", "saving best checkpoint at epoch: 9, Acc: 91.32\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0004. Train Acc: 91.5075, Test loss: 0.0005. Test Acc: 91.4400. Time/epoch: 1.5550\n", "saving best checkpoint at epoch: 10, Acc: 91.44\n", "saving best checkpoint at epoch: 11, Acc: 91.74\n", "saving best checkpoint at epoch: 12, Acc: 92.15\n", "saving best checkpoint at epoch: 13, Acc: 92.35\n", "saving best checkpoint at epoch: 14, Acc: 92.74\n", "saving best checkpoint at epoch: 15, Acc: 92.83\n", "saving best checkpoint at epoch: 16, Acc: 92.95\n", "saving best checkpoint at epoch: 17, Acc: 93.01\n", "saving best checkpoint at epoch: 18, Acc: 93.37\n", "saving best checkpoint at epoch: 19, Acc: 93.51\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0003. Train Acc: 93.6000, Test loss: 0.0003. Test Acc: 93.6500. Time/epoch: 1.5476\n", "saving best checkpoint at epoch: 20, Acc: 93.65\n", "saving best checkpoint at epoch: 22, Acc: 93.94\n", "saving best checkpoint at epoch: 23, Acc: 94.05\n", "saving best checkpoint at epoch: 26, Acc: 94.22\n", "saving best checkpoint at epoch: 27, Acc: 94.25\n", "saving best checkpoint at epoch: 29, Acc: 94.3\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0003. Train Acc: 94.3725, Test loss: 0.0003. Test Acc: 94.4400. Time/epoch: 1.6881\n", "saving best checkpoint at epoch: 30, Acc: 94.44\n", "saving best checkpoint at epoch: 33, Acc: 94.61\n", "saving best checkpoint at epoch: 34, Acc: 94.74\n", "saving best checkpoint at epoch: 36, Acc: 94.81\n", "saving best checkpoint at epoch: 37, Acc: 94.85\n", "saving best checkpoint at epoch: 38, Acc: 94.92\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0003. Train Acc: 94.9700, Test loss: 0.0003. Test Acc: 95.0200. Time/epoch: 1.5364\n", "saving best checkpoint at epoch: 40, Acc: 95.02\n", "saving best checkpoint at epoch: 42, Acc: 95.03\n", "saving best checkpoint at epoch: 45, Acc: 95.06\n", "saving best checkpoint at epoch: 46, Acc: 95.15\n", "saving best checkpoint at epoch: 48, Acc: 95.28\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0002. Train Acc: 95.4650, Test loss: 0.0003. Test Acc: 95.0400. Time/epoch: 1.5621\n", "saving best checkpoint at epoch: 52, Acc: 95.41\n", "saving best checkpoint at epoch: 59, Acc: 95.45\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0002. Train Acc: 95.6175, Test loss: 0.0003. Test Acc: 95.2800. Time/epoch: 1.5489\n", "saving best checkpoint at epoch: 62, Acc: 95.49\n", "saving best checkpoint at epoch: 65, Acc: 95.61\n", "saving best checkpoint at epoch: 69, Acc: 95.71\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0002. Train Acc: 95.8650, Test loss: 0.0002. Test Acc: 95.7200. Time/epoch: 1.5479\n", "saving best checkpoint at epoch: 70, Acc: 95.72\n", "saving best checkpoint at epoch: 75, Acc: 95.82\n", "saving best checkpoint at epoch: 78, Acc: 95.94\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0002. Train Acc: 96.0450, Test loss: 0.0002. Test Acc: 95.8600. Time/epoch: 1.6866\n", "saving best checkpoint at epoch: 84, Acc: 95.95\n", "saving best checkpoint at epoch: 85, Acc: 96.01\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0002. Train Acc: 96.1675, Test loss: 0.0002. Test Acc: 95.8500. Time/epoch: 1.5493\n", "saving best checkpoint at epoch: 92, Acc: 96.02\n", "saving best checkpoint at epoch: 93, Acc: 96.07\n", "saving best checkpoint at epoch: 98, Acc: 96.08\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0002. Train Acc: 96.3375, Test loss: 0.0002. Test Acc: 96.0500. Time/epoch: 1.6948\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... 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Run summary:


Accuracy/train96.3375
Accuracy/val96.05
Loss/train0.0002
Loss/val0.00021
epoch100

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Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_121654-3gr5qidy/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: gyikufvq with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 512\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 100\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 3e-05\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.05\n", "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_121952-gyikufvq" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run devout-sweep-69 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/gyikufvq" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0019. Train Acc: 65.9575, Test loss: 0.0019. Test Acc: 66.3500. Time/epoch: 1.5593\n", "saving best checkpoint at epoch: 0, Acc: 66.35\n", "saving best checkpoint at epoch: 1, Acc: 84.45\n", "saving best checkpoint at epoch: 2, Acc: 86.88\n", "saving best checkpoint at epoch: 3, Acc: 87.92\n", "saving best checkpoint at epoch: 4, Acc: 89.13\n", "saving best checkpoint at epoch: 5, Acc: 90.21\n", "saving best checkpoint at epoch: 6, Acc: 91.28\n", "saving best checkpoint at epoch: 7, Acc: 92.36\n", "saving best checkpoint at epoch: 8, Acc: 92.86\n", "saving best checkpoint at epoch: 9, Acc: 93.58\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0003. Train Acc: 93.9625, Test loss: 0.0003. Test Acc: 94.1600. Time/epoch: 1.5730\n", "saving best checkpoint at epoch: 10, Acc: 94.16\n", "saving best checkpoint at epoch: 12, Acc: 94.72\n", "saving best checkpoint at epoch: 14, Acc: 94.76\n", "saving best checkpoint at epoch: 15, Acc: 95.08\n", "saving best checkpoint at epoch: 16, Acc: 95.2\n", "saving best checkpoint at epoch: 19, Acc: 95.38\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0002. Train Acc: 95.2875, Test loss: 0.0002. Test Acc: 95.5100. Time/epoch: 1.6838\n", "saving best checkpoint at epoch: 20, Acc: 95.51\n", "saving best checkpoint at epoch: 21, Acc: 95.61\n", "saving best checkpoint at epoch: 22, Acc: 95.66\n", "saving best checkpoint at epoch: 23, Acc: 95.69\n", "saving best checkpoint at epoch: 24, Acc: 95.72\n", "saving best checkpoint at epoch: 26, Acc: 95.94\n", "saving best checkpoint at epoch: 29, Acc: 95.98\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0002. Train Acc: 96.0875, Test loss: 0.0002. Test Acc: 95.9700. Time/epoch: 1.5531\n", "saving best checkpoint at epoch: 36, Acc: 96.06\n", "saving best checkpoint at epoch: 37, Acc: 96.1\n", "saving best checkpoint at epoch: 39, Acc: 96.15\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0002. Train Acc: 95.7450, Test loss: 0.0002. Test Acc: 95.6600. Time/epoch: 1.6934\n", "saving best checkpoint at epoch: 44, Acc: 96.21\n", "saving best checkpoint at epoch: 45, Acc: 96.26\n", "saving best checkpoint at epoch: 48, Acc: 96.29\n", "saving best checkpoint at epoch: 49, Acc: 96.3\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0002. Train Acc: 96.6175, Test loss: 0.0002. Test Acc: 96.3800. Time/epoch: 1.5560\n", "saving best checkpoint at epoch: 50, Acc: 96.38\n", "saving best checkpoint at epoch: 54, Acc: 96.42\n", "saving best checkpoint at epoch: 55, Acc: 96.43\n", "saving best checkpoint at epoch: 56, Acc: 96.54\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0002. Train Acc: 96.8975, Test loss: 0.0002. Test Acc: 96.5800. Time/epoch: 1.6839\n", "saving best checkpoint at epoch: 60, Acc: 96.58\n", "saving best checkpoint at epoch: 65, Acc: 96.6\n", "saving best checkpoint at epoch: 67, Acc: 96.65\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0002. Train Acc: 96.9550, Test loss: 0.0002. Test Acc: 96.5900. Time/epoch: 1.5488\n", "saving best checkpoint at epoch: 71, Acc: 96.88\n", "saving best checkpoint at epoch: 79, Acc: 96.93\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0001. Train Acc: 97.3225, Test loss: 0.0002. Test Acc: 96.8800. Time/epoch: 1.5538\n", "saving best checkpoint at epoch: 82, Acc: 96.96\n", "saving best checkpoint at epoch: 85, Acc: 97.03\n", "saving best checkpoint at epoch: 87, Acc: 97.13\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0001. Train Acc: 97.4925, Test loss: 0.0002. Test Acc: 97.1000. Time/epoch: 1.5505\n", "saving best checkpoint at epoch: 96, Acc: 97.14\n", "saving best checkpoint at epoch: 99, Acc: 97.21\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0001. Train Acc: 97.5700, Test loss: 0.0002. Test Acc: 97.1900. Time/epoch: 1.5448\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train97.57
Accuracy/val97.19
Loss/train0.00013
Loss/val0.00017
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/9gn8bvsn" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0004. Train Acc: 90.9175, Test loss: 0.0004. Test Acc: 91.1600. Time/epoch: 1.7233\n", "saving best checkpoint at epoch: 0, Acc: 91.16\n", "saving best checkpoint at epoch: 1, Acc: 91.95\n", "saving best checkpoint at epoch: 2, Acc: 93.96\n", "saving best checkpoint at epoch: 3, Acc: 94.35\n", "saving best checkpoint at epoch: 5, Acc: 94.44\n", "saving best checkpoint at epoch: 7, Acc: 95.08\n", "saving best checkpoint at epoch: 8, Acc: 95.25\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0002. Train Acc: 96.0650, Test loss: 0.0002. Test Acc: 96.1200. Time/epoch: 1.5560\n", "saving best checkpoint at epoch: 10, Acc: 96.12\n", "saving best checkpoint at epoch: 13, Acc: 96.51\n", "saving best checkpoint at epoch: 17, Acc: 96.74\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0002. Train Acc: 94.9700, Test loss: 0.0003. Test Acc: 94.7400. Time/epoch: 1.7078\n", "saving best checkpoint at epoch: 23, Acc: 96.99\n", "saving best checkpoint at epoch: 27, Acc: 97.08\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0002. Train Acc: 96.9325, Test loss: 0.0002. Test Acc: 96.5600. Time/epoch: 1.5596\n", "saving best checkpoint at epoch: 32, Acc: 97.47\n", "saving best checkpoint at epoch: 37, Acc: 97.51\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0002. Train Acc: 95.3600, Test loss: 0.0003. Test Acc: 95.0400. Time/epoch: 1.6962\n", "saving best checkpoint at epoch: 41, Acc: 97.58\n", "saving best checkpoint at epoch: 44, Acc: 97.68\n", "saving best checkpoint at epoch: 45, Acc: 97.8\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0001. Train Acc: 97.6800, Test loss: 0.0002. Test Acc: 97.0900. Time/epoch: 1.5595\n", "saving best checkpoint at epoch: 53, Acc: 97.93\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0001. Train Acc: 98.3700, Test loss: 0.0001. Test Acc: 97.6400. Time/epoch: 1.6867\n", "saving best checkpoint at epoch: 64, Acc: 97.97\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0001. Train Acc: 98.9225, Test loss: 0.0001. Test Acc: 98.1500. Time/epoch: 1.5493\n", "saving best checkpoint at epoch: 70, Acc: 98.15\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0001. Train Acc: 98.6575, Test loss: 0.0001. Test Acc: 97.6200. Time/epoch: 1.5591\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0000. Train Acc: 99.1675, Test loss: 0.0001. Test Acc: 98.0200. Time/epoch: 1.5565\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0001. Train Acc: 98.7725, Test loss: 0.0002. Test Acc: 97.4700. Time/epoch: 1.5640\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train98.7725
Accuracy/val97.47
Loss/train6e-05
Loss/val0.00016
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/m674t9aj" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0025. Train Acc: 38.0850, Test loss: 0.0025. Test Acc: 38.7600. Time/epoch: 1.8129\n", "saving best checkpoint at epoch: 0, Acc: 38.76\n", "saving best checkpoint at epoch: 1, Acc: 73.85\n", "saving best checkpoint at epoch: 2, Acc: 77.87\n", "saving best checkpoint at epoch: 3, Acc: 89.8\n", "saving best checkpoint at epoch: 4, Acc: 90.9\n", "saving best checkpoint at epoch: 5, Acc: 91.07\n", "saving best checkpoint at epoch: 6, Acc: 91.74\n", "saving best checkpoint at epoch: 7, Acc: 92.08\n", "saving best checkpoint at epoch: 8, Acc: 92.34\n", "saving best checkpoint at epoch: 9, Acc: 92.59\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0004. Train Acc: 92.5600, Test loss: 0.0004. Test Acc: 92.6500. Time/epoch: 1.5551\n", "saving best checkpoint at epoch: 10, Acc: 92.65\n", "saving best checkpoint at epoch: 11, Acc: 93.18\n", "saving best checkpoint at epoch: 12, Acc: 93.41\n", "saving best checkpoint at epoch: 13, Acc: 93.44\n", "saving best checkpoint at epoch: 14, Acc: 93.59\n", "saving best checkpoint at epoch: 16, Acc: 93.78\n", "saving best checkpoint at epoch: 17, Acc: 93.92\n", "saving best checkpoint at epoch: 18, Acc: 94.2\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0003. Train Acc: 94.3200, Test loss: 0.0003. Test Acc: 94.5100. Time/epoch: 1.6992\n", "saving best checkpoint at epoch: 20, Acc: 94.51\n", "saving best checkpoint at epoch: 24, Acc: 94.61\n", "saving best checkpoint at epoch: 25, Acc: 94.79\n", "saving best checkpoint at epoch: 26, Acc: 94.95\n", "saving best checkpoint at epoch: 29, Acc: 95.21\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0003. Train Acc: 95.2275, Test loss: 0.0003. Test Acc: 95.3200. Time/epoch: 1.5568\n", "saving best checkpoint at epoch: 30, Acc: 95.32\n", "saving best checkpoint at epoch: 35, Acc: 95.47\n", "saving best checkpoint at epoch: 39, Acc: 95.72\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0002. Train Acc: 95.6475, Test loss: 0.0002. Test Acc: 95.6500. Time/epoch: 1.6973\n", "saving best checkpoint at epoch: 43, Acc: 95.88\n", "saving best checkpoint at epoch: 48, Acc: 95.96\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0002. Train Acc: 95.8100, Test loss: 0.0002. Test Acc: 95.6400. Time/epoch: 1.5618\n", "saving best checkpoint at epoch: 53, Acc: 96.05\n", "saving best checkpoint at epoch: 56, Acc: 96.17\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0002. Train Acc: 96.3250, Test loss: 0.0002. Test Acc: 96.1700. Time/epoch: 1.6886\n", "saving best checkpoint at epoch: 64, Acc: 96.29\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0002. Train Acc: 96.4625, Test loss: 0.0002. Test Acc: 96.2300. Time/epoch: 1.5446\n", "saving best checkpoint at epoch: 75, Acc: 96.42\n", "saving best checkpoint at epoch: 79, Acc: 96.52\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0002. Train Acc: 96.6500, Test loss: 0.0002. Test Acc: 96.3900. Time/epoch: 1.5512\n", "saving best checkpoint at epoch: 88, Acc: 96.57\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0002. Train Acc: 96.7625, Test loss: 0.0002. Test Acc: 96.4100. Time/epoch: 1.5503\n", "saving best checkpoint at epoch: 93, Acc: 96.58\n", "saving best checkpoint at epoch: 99, Acc: 96.65\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0002. Train Acc: 96.7550, Test loss: 0.0002. Test Acc: 96.5000. Time/epoch: 1.5737\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train96.755
Accuracy/val96.5
Loss/train0.00017
Loss/val0.00019
epoch100

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Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/jp3cygfx" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0025. Train Acc: 65.9500, Test loss: 0.0025. Test Acc: 66.2100. Time/epoch: 1.7282\n", "saving best checkpoint at epoch: 0, Acc: 66.21\n", "saving best checkpoint at epoch: 1, Acc: 79.69\n", "saving best checkpoint at epoch: 2, Acc: 81.37\n", "saving best checkpoint at epoch: 3, Acc: 85.2\n", "saving best checkpoint at epoch: 4, Acc: 86.54\n", "saving best checkpoint at epoch: 5, Acc: 87.62\n", "saving best checkpoint at epoch: 6, Acc: 88.27\n", "saving best checkpoint at epoch: 7, Acc: 88.91\n", "saving best checkpoint at epoch: 8, Acc: 89.12\n", "saving best checkpoint at epoch: 9, Acc: 89.44\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0005. Train Acc: 89.7750, Test loss: 0.0005. Test Acc: 89.8100. Time/epoch: 1.5568\n", "saving best checkpoint at epoch: 10, Acc: 89.81\n", "saving best checkpoint at epoch: 11, Acc: 89.83\n", "saving best checkpoint at epoch: 12, Acc: 90.17\n", "saving best checkpoint at epoch: 13, Acc: 90.55\n", "saving best checkpoint at epoch: 14, Acc: 90.71\n", "saving best checkpoint at epoch: 15, Acc: 90.76\n", "saving best checkpoint at epoch: 17, Acc: 90.79\n", "saving best checkpoint at epoch: 18, Acc: 91.04\n", "saving best checkpoint at epoch: 19, Acc: 91.23\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0004. Train Acc: 91.4525, Test loss: 0.0004. Test Acc: 91.6100. Time/epoch: 1.6912\n", "saving best checkpoint at epoch: 20, Acc: 91.61\n", "saving best checkpoint at epoch: 21, Acc: 91.9\n", "saving best checkpoint at epoch: 22, Acc: 92.47\n", "saving best checkpoint at epoch: 23, Acc: 92.89\n", "saving best checkpoint at epoch: 24, Acc: 93.18\n", "saving best checkpoint at epoch: 25, Acc: 93.51\n", "saving best checkpoint at epoch: 28, Acc: 93.6\n", "saving best checkpoint at epoch: 29, Acc: 93.77\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0003. Train Acc: 93.5400, Test loss: 0.0003. Test Acc: 93.8800. Time/epoch: 1.5431\n", "saving best checkpoint at epoch: 30, Acc: 93.88\n", "saving best checkpoint at epoch: 31, Acc: 93.89\n", "saving best checkpoint at epoch: 32, Acc: 94.05\n", "saving best checkpoint at epoch: 34, Acc: 94.09\n", "saving best checkpoint at epoch: 35, Acc: 94.16\n", "saving best checkpoint at epoch: 36, Acc: 94.22\n", "saving best checkpoint at epoch: 37, Acc: 94.23\n", "saving best checkpoint at epoch: 38, Acc: 94.45\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0003. Train Acc: 94.1750, Test loss: 0.0003. Test Acc: 94.2100. Time/epoch: 1.5590\n", "saving best checkpoint at epoch: 41, Acc: 94.51\n", "saving best checkpoint at epoch: 42, Acc: 94.54\n", "saving best checkpoint at epoch: 43, Acc: 94.56\n", "saving best checkpoint at epoch: 46, Acc: 94.6\n", "saving best checkpoint at epoch: 47, Acc: 94.66\n", "saving best checkpoint at epoch: 48, Acc: 94.85\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0003. Train Acc: 94.5675, Test loss: 0.0003. Test Acc: 94.7700. Time/epoch: 1.5568\n", "saving best checkpoint at epoch: 51, Acc: 94.87\n", "saving best checkpoint at epoch: 53, Acc: 94.9\n", "saving best checkpoint at epoch: 55, Acc: 95.05\n", "saving best checkpoint at epoch: 57, Acc: 95.09\n", "saving best checkpoint at epoch: 58, Acc: 95.15\n", "saving best checkpoint at epoch: 59, Acc: 95.2\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0003. Train Acc: 94.9650, Test loss: 0.0003. Test Acc: 95.1100. Time/epoch: 1.6908\n", "saving best checkpoint at epoch: 61, Acc: 95.27\n", "saving best checkpoint at epoch: 62, Acc: 95.33\n", "saving best checkpoint at epoch: 67, Acc: 95.45\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0002. Train Acc: 95.4325, Test loss: 0.0003. Test Acc: 95.5000. Time/epoch: 1.5482\n", "saving best checkpoint at epoch: 70, Acc: 95.5\n", "saving best checkpoint at epoch: 72, Acc: 95.52\n", "saving best checkpoint at epoch: 76, Acc: 95.57\n", "saving best checkpoint at epoch: 78, Acc: 95.64\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0002. Train Acc: 95.5175, Test loss: 0.0002. Test Acc: 95.3300. Time/epoch: 1.5511\n", "saving best checkpoint at epoch: 81, Acc: 95.69\n", "saving best checkpoint at epoch: 83, Acc: 95.72\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0002. Train Acc: 95.8800, Test loss: 0.0002. Test Acc: 95.8100. Time/epoch: 1.5446\n", "saving best checkpoint at epoch: 90, Acc: 95.81\n", "saving best checkpoint at epoch: 96, Acc: 95.88\n", "saving best checkpoint at epoch: 97, Acc: 95.9\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0002. Train Acc: 96.1625, Test loss: 0.0002. Test Acc: 95.8300. Time/epoch: 1.5488\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... 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Run summary:


Accuracy/train96.1625
Accuracy/val95.83
Loss/train0.00021
Loss/val0.00023
epoch100

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Run summary:


Accuracy/train98.45
Accuracy/val97.46
Loss/train8e-05
Loss/val0.00018
epoch100

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Time/epoch: 1.5937\n", "saving best checkpoint at epoch: 0, Acc: 57.08\n", "saving best checkpoint at epoch: 1, Acc: 75.08\n", "saving best checkpoint at epoch: 2, Acc: 81.17\n", "saving best checkpoint at epoch: 3, Acc: 83.53\n", "saving best checkpoint at epoch: 4, Acc: 86.24\n", "saving best checkpoint at epoch: 5, Acc: 87.58\n", "saving best checkpoint at epoch: 6, Acc: 88.49\n", "saving best checkpoint at epoch: 7, Acc: 89.24\n", "saving best checkpoint at epoch: 8, Acc: 89.64\n", "saving best checkpoint at epoch: 9, Acc: 89.91\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0005. Train Acc: 90.0575, Test loss: 0.0005. Test Acc: 90.0700. Time/epoch: 1.5676\n", "saving best checkpoint at epoch: 10, Acc: 90.07\n", "saving best checkpoint at epoch: 11, Acc: 90.23\n", "saving best checkpoint at epoch: 12, Acc: 90.54\n", "saving best checkpoint at epoch: 13, Acc: 90.8\n", "saving best checkpoint at epoch: 14, Acc: 90.96\n", "saving best checkpoint at epoch: 15, Acc: 91.11\n", "saving best checkpoint at epoch: 16, Acc: 91.23\n", "saving best checkpoint at epoch: 17, Acc: 91.41\n", "saving best checkpoint at epoch: 18, Acc: 91.59\n", "saving best checkpoint at epoch: 19, Acc: 91.67\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0004. Train Acc: 91.7100, Test loss: 0.0004. Test Acc: 91.9200. Time/epoch: 1.6876\n", "saving best checkpoint at epoch: 20, Acc: 91.92\n", "saving best checkpoint at epoch: 21, Acc: 91.99\n", "saving best checkpoint at epoch: 22, Acc: 92.02\n", "saving best checkpoint at epoch: 23, Acc: 92.2\n", "saving best checkpoint at epoch: 24, Acc: 92.32\n", "saving best checkpoint at epoch: 25, Acc: 92.42\n", "saving best checkpoint at epoch: 26, Acc: 92.55\n", "saving best checkpoint at epoch: 27, Acc: 92.61\n", "saving best checkpoint at epoch: 28, Acc: 92.68\n", "saving best checkpoint at epoch: 29, Acc: 92.98\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0003. Train Acc: 92.8275, Test loss: 0.0004. Test Acc: 93.1700. Time/epoch: 1.5538\n", "saving best checkpoint at epoch: 30, Acc: 93.17\n", "saving best checkpoint at epoch: 31, Acc: 93.35\n", "saving best checkpoint at epoch: 32, Acc: 93.46\n", "saving best checkpoint at epoch: 33, Acc: 93.55\n", "saving best checkpoint at epoch: 34, Acc: 93.63\n", "saving best checkpoint at epoch: 36, Acc: 93.7\n", "saving best checkpoint at epoch: 38, Acc: 93.86\n", "saving best checkpoint at epoch: 39, Acc: 93.98\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0003. Train Acc: 93.6200, Test loss: 0.0003. Test Acc: 93.8300. Time/epoch: 1.5547\n", "saving best checkpoint at epoch: 41, Acc: 94.08\n", "saving best checkpoint at epoch: 43, Acc: 94.23\n", "saving best checkpoint at epoch: 44, Acc: 94.32\n", "saving best checkpoint at epoch: 45, Acc: 94.38\n", "saving best checkpoint at epoch: 46, Acc: 94.52\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0003. Train Acc: 94.4475, Test loss: 0.0003. Test Acc: 94.5200. Time/epoch: 1.5643\n", "saving best checkpoint at epoch: 52, Acc: 94.65\n", "saving best checkpoint at epoch: 55, Acc: 94.69\n", "saving best checkpoint at epoch: 57, Acc: 94.7\n", "saving best checkpoint at epoch: 58, Acc: 94.74\n", "saving best checkpoint at epoch: 59, Acc: 94.8\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0003. Train Acc: 94.7300, Test loss: 0.0003. Test Acc: 94.7000. Time/epoch: 1.5514\n", "saving best checkpoint at epoch: 63, Acc: 94.81\n", "saving best checkpoint at epoch: 64, Acc: 94.86\n", "saving best checkpoint at epoch: 68, Acc: 94.92\n", "saving best checkpoint at epoch: 69, Acc: 94.93\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0003. Train Acc: 94.9900, Test loss: 0.0003. Test Acc: 94.9300. Time/epoch: 1.5565\n", "saving best checkpoint at epoch: 71, Acc: 94.94\n", "saving best checkpoint at epoch: 72, Acc: 95.0\n", "saving best checkpoint at epoch: 77, Acc: 95.07\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0003. Train Acc: 95.0625, Test loss: 0.0003. Test Acc: 94.9900. Time/epoch: 1.5577\n", "saving best checkpoint at epoch: 83, Acc: 95.16\n", "saving best checkpoint at epoch: 87, Acc: 95.19\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0002. Train Acc: 95.3650, Test loss: 0.0003. Test Acc: 95.1900. Time/epoch: 1.6952\n", "saving best checkpoint at epoch: 91, Acc: 95.26\n", "saving best checkpoint at epoch: 95, Acc: 95.31\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0002. Train Acc: 95.5325, Test loss: 0.0002. Test Acc: 95.3000. Time/epoch: 1.5494\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train95.5325
Accuracy/val95.3
Loss/train0.00023
Loss/val0.00025
epoch100

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Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_123503-6xwudzpi/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: q3dr4w53 with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 512\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 100\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.0003\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.005\n", "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_123803-q3dr4w53" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run treasured-sweep-75 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/q3dr4w53" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0004. Train Acc: 90.4925, Test loss: 0.0004. Test Acc: 90.2800. Time/epoch: 1.5818\n", "saving best checkpoint at epoch: 0, Acc: 90.28\n", "saving best checkpoint at epoch: 1, Acc: 90.41\n", "saving best checkpoint at epoch: 2, Acc: 93.56\n", "saving best checkpoint at epoch: 3, Acc: 93.79\n", "saving best checkpoint at epoch: 4, Acc: 94.32\n", "saving best checkpoint at epoch: 6, Acc: 95.0\n", "saving best checkpoint at epoch: 7, Acc: 95.35\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0002. Train Acc: 95.6825, Test loss: 0.0002. Test Acc: 95.4500. Time/epoch: 1.6953\n", "saving best checkpoint at epoch: 10, Acc: 95.45\n", "saving best checkpoint at epoch: 11, Acc: 95.77\n", "saving best checkpoint at epoch: 13, Acc: 96.25\n", "saving best checkpoint at epoch: 18, Acc: 96.33\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0002. Train Acc: 96.3925, Test loss: 0.0002. Test Acc: 96.1500. Time/epoch: 1.5409\n", "saving best checkpoint at epoch: 21, Acc: 96.64\n", "saving best checkpoint at epoch: 28, Acc: 96.74\n", "saving best checkpoint at epoch: 29, Acc: 96.89\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0002. Train Acc: 96.8200, Test loss: 0.0002. Test Acc: 96.4500. Time/epoch: 1.5950\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0001. Train Acc: 97.3300, Test loss: 0.0002. Test Acc: 96.8400. Time/epoch: 1.5545\n", "saving best checkpoint at epoch: 43, Acc: 97.11\n", "saving best checkpoint at epoch: 47, Acc: 97.18\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0001. Train Acc: 97.0450, Test loss: 0.0002. Test Acc: 96.6500. Time/epoch: 1.7074\n", "saving best checkpoint at epoch: 52, Acc: 97.19\n", "saving best checkpoint at epoch: 55, Acc: 97.39\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0001. Train Acc: 97.4625, Test loss: 0.0002. Test Acc: 96.8300. Time/epoch: 1.5645\n", "saving best checkpoint at epoch: 69, Acc: 97.48\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0001. Train Acc: 97.8475, Test loss: 0.0002. Test Acc: 97.1800. Time/epoch: 1.6847\n", "saving best checkpoint at epoch: 71, Acc: 97.52\n", " EPOCH 80. Progress: 80.0%. \n", " Train loss: 0.0001. Train Acc: 98.0400, Test loss: 0.0002. Test Acc: 97.3000. Time/epoch: 1.5567\n", " EPOCH 90. Progress: 90.0%. \n", " Train loss: 0.0001. Train Acc: 98.6225, Test loss: 0.0001. Test Acc: 97.5600. Time/epoch: 1.7204\n", "saving best checkpoint at epoch: 90, Acc: 97.56\n", "saving best checkpoint at epoch: 91, Acc: 97.69\n", " EPOCH 100. Progress: 100.0%. \n", " Train loss: 0.0001. Train Acc: 98.4675, Test loss: 0.0002. Test Acc: 97.4600. Time/epoch: 1.5554\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

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Run summary:


Accuracy/train98.4675
Accuracy/val97.46
Loss/train8e-05
Loss/val0.00015
epoch100

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run treasured-sweep-75 at: https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/q3dr4w53
Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230518_123803-q3dr4w53/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Sweep Agent: Waiting for job.\n", "\u001b[34m\u001b[1mwandb\u001b[0m: Job received.\n", "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 3jluxc9k with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 512\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 100\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 3e-05\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tweight_decay: 0.005\n", "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.15.3" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /vast/palmer/home.mccleary/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_124109-3jluxc9k" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run silver-sweep-76 to Weights & Biases (docs)
Sweep page: https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ahof1704/CNN_Sat_sweep" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/ahof1704/CNN_Sat_sweep/sweeps/tyzcsl8s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ahof1704/CNN_Sat_sweep/runs/3jluxc9k" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " EPOCH 0. Progress: 0.0%. \n", " Train loss: 0.0022. Train Acc: 74.0225, Test loss: 0.0023. Test Acc: 74.2800. Time/epoch: 1.7199\n", "saving best checkpoint at epoch: 0, Acc: 74.28\n", "saving best checkpoint at epoch: 1, Acc: 88.55\n", "saving best checkpoint at epoch: 2, Acc: 90.66\n", "saving best checkpoint at epoch: 3, Acc: 91.04\n", "saving best checkpoint at epoch: 4, Acc: 91.74\n", "saving best checkpoint at epoch: 5, Acc: 92.05\n", "saving best checkpoint at epoch: 6, Acc: 92.49\n", "saving best checkpoint at epoch: 7, Acc: 92.84\n", "saving best checkpoint at epoch: 8, Acc: 92.95\n", "saving best checkpoint at epoch: 9, Acc: 93.28\n", " EPOCH 10. Progress: 10.0%. \n", " Train loss: 0.0003. Train Acc: 93.6025, Test loss: 0.0003. Test Acc: 93.6100. Time/epoch: 1.5491\n", "saving best checkpoint at epoch: 10, Acc: 93.61\n", "saving best checkpoint at epoch: 11, Acc: 93.7\n", "saving best checkpoint at epoch: 12, Acc: 93.88\n", "saving best checkpoint at epoch: 13, Acc: 94.01\n", "saving best checkpoint at epoch: 14, Acc: 94.25\n", "saving best checkpoint at epoch: 16, Acc: 94.41\n", "saving best checkpoint at epoch: 18, Acc: 94.67\n", "saving best checkpoint at epoch: 19, Acc: 94.68\n", " EPOCH 20. Progress: 20.0%. \n", " Train loss: 0.0003. Train Acc: 94.9875, Test loss: 0.0003. Test Acc: 94.8900. Time/epoch: 1.5437\n", "saving best checkpoint at epoch: 20, Acc: 94.89\n", "saving best checkpoint at epoch: 21, Acc: 94.93\n", "saving best checkpoint at epoch: 24, Acc: 95.08\n", "saving best checkpoint at epoch: 27, Acc: 95.12\n", "saving best checkpoint at epoch: 28, Acc: 95.16\n", " EPOCH 30. Progress: 30.0%. \n", " Train loss: 0.0002. Train Acc: 95.4850, Test loss: 0.0003. Test Acc: 94.9700. Time/epoch: 1.6928\n", "saving best checkpoint at epoch: 31, Acc: 95.26\n", "saving best checkpoint at epoch: 32, Acc: 95.32\n", "saving best checkpoint at epoch: 33, Acc: 95.47\n", "saving best checkpoint at epoch: 34, Acc: 95.54\n", "saving best checkpoint at epoch: 35, Acc: 95.72\n", "saving best checkpoint at epoch: 37, Acc: 95.78\n", " EPOCH 40. Progress: 40.0%. \n", " Train loss: 0.0002. Train Acc: 96.0075, Test loss: 0.0002. Test Acc: 95.8500. Time/epoch: 1.5406\n", "saving best checkpoint at epoch: 40, Acc: 95.85\n", "saving best checkpoint at epoch: 47, Acc: 96.08\n", " EPOCH 50. Progress: 50.0%. \n", " Train loss: 0.0002. Train Acc: 96.1000, Test loss: 0.0002. Test Acc: 95.7900. Time/epoch: 1.7070\n", "saving best checkpoint at epoch: 57, Acc: 96.2\n", " EPOCH 60. Progress: 60.0%. \n", " Train loss: 0.0002. Train Acc: 96.5800, Test loss: 0.0002. Test Acc: 96.1500. Time/epoch: 1.5503\n", "saving best checkpoint at epoch: 62, Acc: 96.24\n", " EPOCH 70. Progress: 70.0%. \n", " Train loss: 0.0002. Train Acc: 96.7575, Test loss: 0.0002. Test Acc: 96.3600. Time/epoch: 1.6897\n", "saving best checkpoint at epoch: 70, Acc: 96.36\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Ctrl + C detected. Stopping sweep.\n" ] }, { "data": { "text/html": [ "Waiting for W&B process to finish... (failed 1). Press Control-C to abort syncing." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "wandb.agent(sweep_id, train)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load the best model based on the sweeping\n", "class CNNet(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.conv1 = nn.Conv2d(4, 6, 5)\n", " self.pool = nn.MaxPool2d(2, 2)\n", " self.conv2 = nn.Conv2d(6, 16, 5)\n", " self.fc1 = nn.Linear(16 * 4 * 4, 120)\n", " self.fc2 = nn.Linear(120, 84)\n", " self.fc3 = nn.Linear(84, 6)\n", "\n", " def forward(self, x):\n", " x = self.pool(F.relu(self.conv1(x)))\n", " x = self.pool(F.relu(self.conv2(x)))\n", " x = torch.flatten(x, 1) # flatten all dimensions except batch\n", " x = F.relu(self.fc1(x))\n", " x = F.relu(self.fc2(x))\n", " x = self.fc3(x)\n", " return x\n", "\n", "\n", "model = CNNet()\n", "\n", "PATH_ = '/home/ahf38/Documents/geo_comp_offline/2022/wandb/run-20230518_090544-krnzu5ta/files'\n", "model.load_state_dict(torch.load(os.path.join(PATH_,'model.pt')))" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([1, 4, 28, 28])\n" ] }, { "data": { "image/png": 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aNEgPPvigWrRooTfeeENvvfXWMXeeeuqpkqQnnnhCgwcPVigUUqtWrTRz5kwtWbJEF198sRo3bqzS0lJNmzZNktSrV68j9n355Ze68MILdeWVV6pbt27KzMzUnj17tHDhQv3xj39U9+7d1blzZ0kHB+e5c+dq+PDhGjBggLZu3arx48crMzNT69ev9/1YnnjiCZ1zzjnq2rWrfvnLX6pp06YqKirShg0btGDBgjq/ifx94uLi9Mgjj+jGG29Ur169NHToUKWnp2vDhg1avXq1nnrqqVr52267TQMHDlQgENDw4cN9H/8hv/vd7743Yz3fD8nKylL//v01ZswYZWZmasaMGXrnnXc0YcKEI/6GM/CfjsEO8OGaa65R48aN9fvf/17Dhg1TUVGR0tLS1L59+zrXgvsxnHfeeZo2bZomTJigfv36qUGDBho6dKjS0tI0ZMiQWtnp06crMzNTU6dO1WOPPab27dvr5ZdfVu/evWttX8jMzNSHH36o8ePHa+LEidq2bZvi4+PVrFkz9e7d+6jv4qWkpGjhwoUaOXKkrrnmGsXGxuqSSy6p806hHzExMVqyZIluu+023XPPPQoEArrgggv00ksv1QxLfnXv3l2/+c1v9Pzzz+tPf/qTqqurtXTpUrVv315vv/22Ro8erdzcXMXFxemUU07Ra6+9VvNvuw6nefPmuv3227VkyRLNnz9fO3fuVCgUUosWLfTAAw/o9ttvrxmer7/+euXn52vKlCmaNm2aTjrpJN1zzz3atm1brYsbWx3anjJ+/Hjdf//9ys/PV1JSklq0aFHz7+z8GjJkiLKysjRhwgTdeOON8jxPTZs2Pey7aZdeeqkiIyPVo0cPtWjR4pjuz8rP+S4d/GWY66+/XqNHj9b69euVlZWlRx99VL/+9a9/0OMEfkoBz/vOVR0B/NdYsWKFunTpopkzZ/4gv2UK9y1YsED9+/fXwoULj3mQ/CE0bdpUp5xyil5//fWf+lCAHxXv2AH/Jd555x2tXLlSZ5xxhqKjo7V69Wr97ne/U4sWLXT55Zf/1IeH/zBr167V5s2ba7ZoHLocDoCfFoMd8F8iISFBb7/9th5//HEVFRUpNTVVF110kR566KE6lxwBvs/w4cP17rvvqkOHDnr++eeP+ksWAH48/CgWAADAEVygGAAAwBEMdgAAAI5gsAMAAHAEgx0AAIAjGOwAAAAcwWAHAADgCAY7AAAARzDYAQAAOILBDgAAwBEMdgAAAI5gsAMAAHAEgx0AAIAjGOwAAAAcwWAHAADgCAY7AAAARzDYAQAAOILBDgAAwBEMdgAAAI5gsAMAAHAEgx0AAIAjGOwAAAAcwWAHAADgCAY7AAAARzDYAQAAOILBDgAAwBEMdgAAAI5gsAMAAHAEgx0AAIAjGOwAAAAcwWAHAADgCAY7AAAARzDYAQAAOILBDgAAwBEMdgAAAI5gsAMAAHAEgx0AAIAjGOwAAAAcwWAHAADgCAY7AAAARzDYAQAAOILBDgAAwBEMdgAAAI5gsAMAAHAEgx0AAIAjGOwAAAAcwWD3I3vuuecUCAT04YcfnpC+QCCgW2655YR0fbtzzJgxx/Sx//znP3XzzTfr1FNPVXx8vNLT09WrVy8tWbLkuI/p2/8lJiaqe/fuWrhw4XH1Wo0ZM0aBQOCYPrawsFAPPvigunfvroyMDMXFxenUU0/VhAkTVFpaeoKPFADw34zBDifUrFmz9MEHH+iGG27Q/Pnz9eyzzyoyMlI9e/bUn//85+PqHjBggFauXKl3331XkydPVm5urvr16/ejDXfHasuWLXr88cfVoUMH/fGPf9Rrr72mAQMGaMyYMerbt688z/upDxEA4Ijwn/oA4Ja77rpLDz/8cK3b+vTpow4dOmjcuHEaNGjQMXenp6fr7LPPliR17txZnTp1UvPmzfX444/r4osvPuzHVFRUKBAIKDz8pzvVmzVrppycHMXGxtbcdt555yk2NlZ33nmn3n33XZ1zzjk/2fEBANzBO3b/hkpLSzVy5Ei1b99eiYmJSk5OVqdOnTR//vwjfswzzzyjli1bKjIyUm3atNFLL71UJ5Obm6thw4apYcOGioiIULNmzTR27FhVVlaesGNPS0urc1swGNQZZ5yhrVu3nrD7kaTs7GzVr19fmzdvliQtW7ZMgUBAL7zwgkaOHKkGDRooMjJSGzZskCQtWrRIPXv2VEJCgmJiYtSlSxctXry4Tu/ChQvVvn17RUZGqlmzZnUGVb9iY2NrDXWHdOzYUZJO+PMCAPjvxWD3b6isrEwFBQW64447NG/ePM2aNUvnnHOOLr/88sP+OPO1117Tk08+qXHjxmnOnDlq0qSJrrrqKs2ZM6cmk5ubq44dO+qtt97SqFGj9Ne//lVDhgzRQw89pKFDh37vMTVt2lRNmzY9psdTWVmpv//972rbtu0xffyR7NmzR7t371b9+vVr3f6b3/xGW7Zs0ZQpU7RgwQKlpaVpxowZuuCCC5SQkKDnn39eL7/8spKTk3XhhRfWGu4WL16sSy65RPHx8XrppZc0ceJEvfzyy5o+fXqd+z/07+6WLVt2TMd/6N8dnujnBQDwX8zDj2r69OmeJG/VqlXmj6msrPQqKiq8IUOGeKeffnqt/yfJi46O9nJzc2vlTz75ZK958+Y1tw0bNsyLi4vzNm/eXOvjH374YU+St2bNmlqdo0ePrpXLzs72srOzzcf8bffdd58nyZs3b94xffyhYxo+fLhXUVHhlZeXe+vWrfMuuugiT5I3efJkz/M8b+nSpZ4k79xzz631scXFxV5ycrLXr1+/WrdXVVV5p512mtexY8ea28466ywvKyvLKykpqbmtsLDQS05O9r775TJ27FgvGAx6y5Yt8/14Vq9e7UVHR3uXXXaZ748FAOBIeMfu39Qrr7yiLl26KC4uTuHh4QqFQpo6darWrVtXJ9uzZ0+lp6fX/DkYDGrgwIHasGGDtm3bJkl6/fXX1aNHD2VlZamysrLmv4suukiStHz58qMez4YNG2p+pOnHs88+qwcffFAjR47UJZdc4vvjv+0Pf/iDQqGQIiIi1Lp1a61YsULjxo3T8OHDa+V+9rOf1frzihUrVFBQoMGDB9d67NXV1erdu7dWrVql4uJiFRcXa9WqVbr88ssVFRVV8/Hx8fHq169fneMZNWqUKisr1a1bN1+PIycnR3379lWjRo307LPP+vpYAACOhl+e+Dc0d+5cXXHFFfr5z3+uO++8UxkZGQoPD9fTTz+tadOm1clnZGQc8bbdu3erYcOGysvL04IFCxQKhQ57n7t27TqxD0LS9OnTNWzYMN10002aOHHicfddccUVuvPOOxUIBBQfH6/s7GwFg8E6uczMzFp/zsvLk3Twt2qPpKCgQIFAQNXV1Ud9Po/X5s2b1aNHD4WHh2vx4sVKTk4+Ib0AAEgMdv+WZsyYoWbNmmn27Nm1rp1WVlZ22Hxubu4Rb0tJSZEkpaamql27dnrwwQcP25GVlXW8h13L9OnTdeONN2rw4MGaMmXKMV8D7tvq16+v//mf//ne3HfvKzU1VZI0adKkmt+q/a709PSa36A92vN5PDZv3qzu3bvL8zwtW7ZMDRs2PO5OAAC+jcHu31AgEFBEREStASU3N/eIvxW7ePFi5eXl1fw4tqqqSrNnz1Z2dnbN8NC3b1+98cYbys7OVr169X7Q43/uued044036pprrtGzzz57Qoa649GlSxclJSVp7dq1R72Yc0REhDp27Ki5c+dq4sSJNT+OLSoq0oIFC47rGLZs2aLu3burqqpKy5YtU5MmTY6rDwCAw2Gw+4ksWbJEOTk5dW7v06eP+vbtq7lz52r48OEaMGCAtm7dqvHjxyszM1Pr16+v8zGpqak677zz9Nvf/laxsbH6wx/+oC+++KLWJU/GjRund955R507d9att96qVq1aqbS0VDk5OXrjjTc0ZcqUo76D1Lx5c0n63n9n98orr2jIkCFq3769hg0bpg8++KDW/z/99NMVGRkp6eDlSXr06KHRo0cf86YLi7i4OE2aNEmDBw9WQUGBBgwYoLS0NO3cuVOrV6/Wzp079fTTT0uSxo8fr969e+v888/XyJEjVVVVpQkTJig2NlYFBQW1eseNG6dx48Zp8eLFR/13dvn5+erRo4e++eYbTZ06Vfn5+crPz6/5/w0bNuTdOwDACcFg9xO5++67D3v7pk2bdP311ys/P19TpkzRtGnTdNJJJ+mee+7Rtm3bNHbs2Dof079/f7Vt21b333+/tmzZouzsbM2cOVMDBw6syWRmZurDDz/U+PHjNXHiRG3btk3x8fFq1qyZevfu/b3v4lmvdbdw4UJVV1fro48+UpcuXQ77+A5dNmX//v01x/ZDu+aaa9S4cWP9/ve/17Bhw1RUVKS0tDS1b99e1113XU3u/PPP17x583T//fdr4MCBysjI0PDhw1VSUlLnua+urlZVVdX3bo5Yu3atNm7cWHMc3/VDD7YAgP8eAe/7visBP5C77rpLs2bN0vr162v9FioAADg2XO4EP5mlS5fqt7/9LUMdAAAnCO/YAQAAOIJ37AAAABzBYAcAAOAIBjsAAABHMNgBAAA4gsEOAADAEQx2AAAAjjBvnrjumnPNpTFpJ5ty779m37/Zc9AwczY/d5Mp99GSt82dqU07mXKF+/aZOwNB+5VmqkrKTLnoxGRzZ0W5rVOSSsrLbbnKoLkzKizCng1UmXKR1RXmzoow2zYNSdpfvsWUa3Na3W0bR1Ivtb4pV7jjY3NnWCjenI1Lsa0x272n2Ny5/av3zNnkrFNNuVj7aaI5s161h78jwcdO45AxZ/9qkBJ9ZBsZc3k+Opv7yFr5efxHXspX20c+Olv4yK4y5qp9dG70kbV+/s/00bnDR9b2CivZX2GkugswDy/DR+daH9m+xlyuj84DPrLvG3N7fXQWG65Qxzt2AAAAjmCwAwAAcASDHQAAgCMY7AAAABzBYAcAAOAIBjsAAABHMNgBAAA4gsEOAADAEQx2AAAAjjBvnigpsV6XWqrYY7uOc3bnlubO3A32K9onNrZd0T6qnrlSyWm2sFe+29xZvNe2IUOSmrbubMpV+ti88NXnH5qzEdG2a7iHwqzX5Jeqykvs959p29LQ6lT7ddk3bbBvdIgsiTXltn3xrrkzkN3alEtv1sbcGR1l319QsLfQlPv6k2XmzphY++aT/Xm2bR5lIdtxHi8/mxdsrzCS/StM6uEja9vtI33to9O6h2anj85WPrLzjbmuPjrt37WkpB+g8zQfWet+F+s2A0n6Hx/ZKGPO9p3ooLeMOT8bMqwbSiRpszHn53Nq/w4rxRhze310WvCOHQAAgCMY7AAAABzBYAcAAOAIBjsAAABHMNgBAAA4gsEOAADAEQx2AAAAjmCwAwAAcASDHQAAgCPMmyead7rEXJrz+XJTrmGzjubO4n3fmLMJ9TNNubBIc6WqvSJTLjrOPitX+dgS0bi1bUtHZYW9c/8B+5aMgu22bFTQ9txLUlUoYM6WFdvuf/NX9g0l+/bkmbPxsbbNE2lNzzZ3Nmptuy5/+YFd5s6KUtt5KklB49NfUVZt74xPNWcrSm2bJ9IbNTF3Hg8/V5S3Zk/y0WnbrXKQdb9Iko/O9cZcLx+dN/rIvmHMneej8zEf2VuNub/66LRuk5CkeGPuNh+dtu/EBz1tzF3lo9N6/m/10TnSR/ZLY866oUKyP0+SZH01zvDRacE7dgAAAI5gsAMAAHAEgx0AAIAjGOwAAAAcwWAHAADgCAY7AAAARzDYAQAAOILBDgAAwBEMdgAAAI5gsAMAAHCEeaVYQd4Oc2lZ6R5TLixknytLqveZs9EJtvVPzVt1NnfuK7Ctn4pNizJ3tjzrSnM2FF5uy4WC5s7IqAhztqLEttYqOibF3BmMMJ9+qiorNeV2bdtg7oyLDpmzoXLb/q3dn31m7mzWtIEpV15oX/0WTLQum5KCxpVukdGeuTMhxX5OBcpsi3Ti4uzn1PG40EfWfubY/cZHdqgx19dHZx9j7swsH6XJ9uhZn9tysSPsnXsft2dXGnNL7JXmNWGS9KgxF3uzvbN4sj3bxpibZa/Uw8YvlLtv8lHqY6fXdPs2RLOZPrLPG3Mbj+VAjoJ37AAAABzBYAcAAOAIBjsAAABHMNgBAAA4gsEOAADAEQx2AAAAjmCwAwAAcASDHQAAgCMY7AAAABzBYAcAAOAI806nLevXmEtTs1qYcoW7bWu6JKm6ZL85W1FWYuv07IuBAqE4Uy46Kt3cGR2dZs6WHbCtVEtMtN9/7tp15myDxiebcqUFhebO2AT7+qu9e4tMuUofu54anNTKnA2G2/4OVJ5sX79VZdz+Foq1rciTpECYjzV9+2xr4lqd2cncGVZqX2lXL7WlKVe2P9/ceTxsZ9hB1vVjl99n79z9oD37fENjcOsie+kTvUyxT0bYK+PtmyiV7d1pyn0VmGjuvNV+90oy5m5J9VHqZxue9aV7rb3y5x19ZI075d4bY+/U9cacbbvgQfaXGHUzrhTzsaVMc3xkbZOQtNpHpwXv2AEAADiCwQ4AAMARDHYAAACOYLADAABwBIMdAACAIxjsAAAAHMFgBwAA4AgGOwAAAEcw2AEAADjCvHkid/NX5tLYGNtGgdIw+zaJ5IbZ5uymz1eZcuveX27ulHGhwJYKe+XOrz80Z+OSG5hyOyK/NncmZtg3PzRudbopV32g2NyZnNbInN2+1Xb+7dqdY+6Mio8xZ5NSk0y5YMB+qflAyLYmY9e+PebO2Cr7lor66c1NuT277OsDtqxfYs5mNBtkyh3Yv83ceTzsZ4PUxJjL9bFNIsO2eEGSNM+4fOHSF2zbJCTpzRG2XO8e5krpMh/ZctuDKvBRmeUjm2B7ifO1JWH7X+3ZBlcYg0/aO7XYR3ajLXb2Uh+do22xB/5or/ylj7vvbdzmsdK+BEvW00SSfOx9OaF4xw4AAMARDHYAAACOYLADAABwBIMdAACAIxjsAAAAHMFgBwAA4AgGOwAAAEcw2AEAADiCwQ4AAMARDHYAAACOMK8Ui0y0L2f5ZvMHplz9praVRpIUCiWYs4GICFOusMpcqfT0xqZcYlpTc+eBvbvM2cr9tvVT+7ZsMHdGxxj3rUja/Ok/TLnmJ59l7oxPqG/OhrTelDuQu8XcuS8YNGejIluYcpXVlebO6Hjb/RcVfGPuVHymORqTGmnKVfn4Oklp1M6cLdq305QLhtuO83gd8JGdY8w95GdP2Sx7tMMP0Nn7NWPwBnunlvzGnv3qIVPs7Md93P9H9uiEP9tyd99n7wz6WCkm20uMtNf2/U2S9G65OVplXGkW9LH6btrfbLn7fezpyv3YR9a4Ksz+nUgq8pE9z5izLxe14R07AAAARzDYAQAAOILBDgAAwBEMdgAAAI5gsAMAAHAEgx0AAIAjGOwAAAAcwWAHAADgCAY7AAAAR5g3T0R4uebS2DTb1e/rZbQ0d4YFoszZ3Bzb5cajIgLmzuhY25aGpORm5s6ctbYNHZKUktbGlAtE2jcPlPtYKRAKxplyJWUV5s7Na1aZswW5O0y56NgMc+eePFunJEXFxJpySemNzJ3VxovCF3xj36aRkmnf/JBv3FISiow3d4YSbBtaJCl/h22bSCj042yeKPWRNV+p3sfSEC2xRx+5zJZ74o3H7aV9R9hyw+yVwwO2bRKS9Afr5fetaz8kybhNQZKSjJsntNzeaf+uKWVYe2fZt0nojYfN0eCcO2zBrva7v2GwMXi3vTPex+aJ2HNtuVv22Tv7rLZnrQ/f9t3djnfsAAAAHMFgBwAA4AgGOwAAAEcw2AEAADiCwQ4AAMARDHYAAACOYLADAABwBIMdAACAIxjsAAAAHMFgBwAA4IiA53meJTjoeuMOG0lemG1VVXSKff1WWZl9/VVezkZTLhSdYr//vbtMuf0l9sVEByqqzdnKypAxafp0SpIC9rtXWPl+Uy6iosDcGRUTY85Gxts+V2FR5i158oJBczZQmmfKpTfINndGxNYz5VYvXGjubNzmZHM2d9NaUy463b4mLBiXbM5GhCpNufL9OebOtxfb1pQdzuCAfcWg9VH6WRU01PolLknl59tyEe/YO20b5nyt1NK1m+zZCcbvB2/5uH/7hkPpHGPOfopLDXxkhxtzp/noXOkj+6Qx94t+9s6TF5hiv/7SXvmYcU2YJPPuv8F/sVda14RJ0lxjbquPzvmGkY137AAAABzBYAcAAOAIBjsAAABHMNgBAAA4gsEOAADAEQx2AAAAjmCwAwAAcASDHQAAgCMY7AAAABzBYAcAAOAI8/6luHD7+qW83O22YFWsubNon32lWP4Xq025lCYtzZ31s0+x3feHfzV3FlcmmbORoURTrrrKvlIrzMdY70XFm3Lhqen2zupiczY81rZSrGj/NnNnSPZzKjre9vxXVh0wd0aF23YTtettXB8lKSWruTlbWGj7Oi3IM349S0oKRZizsfFJplxyg47mzuPxfKo9W2LbMKhoz76KUQtetWdvM64K+9heqX8Yc372HzW1r43UZFtszz32ynqb7VnNMeZutVd+5mNN3KnW88/+cqAX/mbPXvuLdabce4HW5s6zjevnHnvcXKllPr5Muv/elhvgY6XY3+1RdTbmfGxUM+EdOwAAAEcw2AEAADiCwQ4AAMARDHYAAACOYLADAABwBIMdAACAIxjsAAAAHMFgBwAA4AgGOwAAAEeY1xQk1q9nLq2qtl19f1+BfUtAZcFeczY6ZJxXq6vNncFQlCmX2aSTuXP7N/nmrFdpO1bPvkxBVdWeOVsZFjDlqveXmTsjwu1bMkqr95pyUdG2DRWS1KhRY3N2787PTLnURvarsu/d8YUp17DF6ebOKh8nQFSy7bkKHthn7oyJSTJnSw7sNeXSW7Qydx6PNcZtEpJk28Mi7QjYL5N/trfKfgD9Km25EvvrkXnzwugO9s57/2nP9rW9xiyyN+rns32E75xny3W+1Fx56ggf929cP/DMb+2Vw7yTfBzAYFOqjY9GnW3MzbdXlvq5/6dtMdt394N8nP2absz52Twx1pDhHTsAAABHMNgBAAA4gsEOAADAEQx2AAAAjmCwAwAAcASDHQAAgCMY7AAAABzBYAcAAOAIBjsAAABHMNgBAAA4wr5SLC3Z3mocF0OR9lVFgYBxhY6k5MZtbcHY+ubOLauWm3LloSRzZ3gwwZw9UFJg7AyZO/2sn/JkW/dT5dlyklRh32imUIUtV15RaO4s2r/TnK3f6BRTLm/zp+bO1PQGplxllX2Jjldmf1KTMxra7l/2NXHh0fbPf2yc7f7DAxHmzuPRNtVHeJ4t1tjH/qtHA2eas7dbdxD1t9//O8bO84Mf2Utz7efDVwttuZ/72b/U8nf2bN9LTbGvV9ors42vW5KkVTGm2DD9r4/S03xkR5tSCb+3N34Vacv5WenV28eWvHnGz9WlPrYWPuXj/LN+h82yV5rwjh0AAIAjGOwAAAAcwWAHAADgCAY7AAAARzDYAQAAOILBDgAAwBEMdgAAAI5gsAMAAHAEgx0AAIAjGOwAAAAcYV4pVlJqX/8VXS/elEuon2buTGmQac7mf7PHlFv3j9fMncWlttU4IfuWMFV79n0zwTDbp8oLVPu4f3s2rNqYrbSvKVPAvv4sEG77O0hkhG0tjyQdKLKtaZOkqEjb409MtS+HKS0psgXD7GuZwnys36ooP2DKJWc0sXdWmV9SVFFRbsqVltvP0+MxbZc9e8Oltlyhj87bvXPt4Tv+Zst9YT8fzh9g+3yoh7lSMp7iktTyqedswdjr7KXx99izubbnP/sS43MvSfM727P/u8KWC46wd662R/Xic6bYnrvsj7+lZ3w9vtr2WiRJE2aZo7rbulLvfXunbbo5yPrZT/LRacE7dgAAAI5gsAMAAHAEgx0AAIAjGOwAAAAcwWAHAADgCAY7AAAARzDYAQAAOILBDgAAwBEMdgAAAI6wXybei7RHPVsuFGm/KrpXbd8oEPQ2mXINW9mvqL9vl+3K2AX7i82d4ZX2zRPVxmtTV4fbr9IfCBg/UZLCPdtGiUDAvnkiXPZjrao0bt4I2c/T4sJ8czYy0va5SstqZO7cv8d2WX6vutLcWR00R5WQlGjKhUXVM3fu2W/fUHNg135TrrLY/viPx04/4dtssYT7k82Vfw/Yr+hvfuV6xLhNQvbHf9JfzJV+Fk+o8c+uswVv9VH6Sx/ZAcbn/xwfnfOM2yQk6d5FttyNveydL75pz2qhKVWvtY/KDNv3zSfy7JV3+1jQIuPr4Rs+7t++W0j6xJh71UfnMEOGd+wAAAAcwWAHAADgCAY7AAAARzDYAQAAOILBDgAAwBEMdgAAAI5gsAMAAHAEgx0AAIAjGOwAAAAcwWAHAADgCPNKsdJS+wwYKCo05aoqfayUqrCtJpGkrNbtTLnGoQRz55crl5hyRZ9+Zu6Mic82Z/dVBky5XQdKzJ3BoH2jXGSYbf1Yo9anmTvLCgvM2YKvPzblwmRfU5fYpIE5Gx8Tst2/j5VqwaBt30140HbfklTt4+9qAWNvWan9a6+82H7+5X/9hS231fZ6crx2+Mgu+K0tt/m39nP8lhE+DmCjMTfRXtm45a9swcGTzJ1v/9l+/zuNq8ra2CsVbdzSJUmaaczF2iuXNLRnz/MesQV/Y+/Uu73tWT9fAEYvGld13dbK3rnEvnnPvHrPzzm110fW2tvZR6cF79gBAAA4gsEOAADAEQx2AAAAjmCwAwAAcASDHQAAgCMY7AAAABzBYAcAAOAIBjsAAABHMNgBAAA4gsEOAADAEQHP80y7ogYN6WcuLc7/yJRr3LqXubO62r5+rLJsnylXP6u1uTMUtO2R8bxoc+emNZ+as2tWv2fKVVTY108FI5PN2ci4GFMuPjPd3FmQs8qcbXrymaZcRJTtOCWpMsy+/ioq3PZ3oOgo+9+V9u/bZcrFRNczd3qyrZ6TpLBQpClXWWVfk7Ztx25zNverr0y5smr7Dqc1n35gzn7XqQH7c5dlzJ3v4/7vuNhH+Gxjzs82tn8Yczt9dFpXn0l6wfgSX+Hj7m+4z0f4MWNuso/Ot31kTzfm7vy1ubIkYH1QUnSqMWjfMKj/NWb9nFI3+MiWGnP2RZjSCh/Z7om23P/aRhZJ0r2GkY137AAAABzBYAcAAOAIBjsAAABHMNgBAAA4gsEOAADAEQx2AAAAjmCwAwAAcASDHQAAgCMY7AAAABwRbg1GJzYylyakNDHl9hbtMHdWVFqvIS1lZZ5k6yyvNHfGJdpmYK/CtMjjYLbKfrnpmKDt6v+Vsm0TkKT42CRzdk/BdlMud8/n5s7m/9PTnG1wcltTrrrCfp589Zn9GuLVkbYvlaysDubOyFjbRomiffnmzlDAvvkkLBA05SrKy82deetWm7NVIduWkGB4lLnzeKT4yHYz5mxfNQc9v9Ce/bsxe4uP+28/xBi0vbwe1MoePXmALdfcx91rr4+s8W2OPdfbK1/3cffXPmkMlvvYJmFf2CQ1NObusFfea1wu9MJf7J1f26OyvcJJfl5h1vvIzjB+i/exoEX3GjK8YwcAAOAIBjsAAABHMNgBAAA4gsEOAADAEQx2AAAAjmCwAwAAcASDHQAAgCMY7AAAABzBYAcAAOAIBjsAAABHmFeKBcIrzKWRMYmm3IFKW06S4sIbm7OFxftNuaiQbU2XJMXEJphy5VW2+5aktj0uM2dLqm2ryor27TJ3JqQmmbOZcbaVXuuWvWjujPSx/mznRtsil+KCrebOsoJt5mx8RoYpV1F2wNxZWW7Lhnys1AqGWZfoSOWVtvM/FB1v7gyPta80Kyuz/b0y8CP9/dO24Owg64LF+j467a8GUpYx96CPzv5TbblrfawJ++eX9qz1u4HtlfhfHvWRNT6peffZK+0LDqWxxpNltI8vh3wfK8XSrCvFcu2d7xhXheXZK32t3xp9pzHY1975nnWfoKQzjTn7dGXDO3YAAACOYLADAABwBIMdAACAIxjsAAAAHMFgBwAA4AgGOwAAAEcw2AEAADiCwQ4AAMARDHYAAACOYLADAABwhHmlWHVVwFwaERlnylWVbjd3enH1zNmcTxeZck1b9zR37i+yLYcJ87H+acNHH5izxfsLTbmsFmebO3dtWWfORhtXqsX4WFO27ctl5myDZrblLJHR9jV1DU+2LoaSDhR8bcoZt3RJkqoDtr9XVVf7KA3av06jY5NNub2FRebOwv17zNnwiDRTzistMXceD/viNCnCmDvVR6d9GZ10vvE077bP3jndGiy2d57xsj371BW23C097J3q7SP7kS3m592QbB/ZoSFbLtfH/inbq9ZBK4wbFi+dbO/sZcyd/wt75zNT7FnNssU+mWivbOHj7m835jr46LTgHTsAAABHMNgBAAA4gsEOAADAEQx2AAAAjmCwAwAAcASDHQAAgCMY7AAAABzBYAcAAOAIBjsAAABHmDdPREfar8teWmLb0uBVVpo7A/LM2YikTFMuGJtq7tyw7jNTbvuH/zR3Br1Ic9arLDPlqvYHzZ0RUcZLnUtKjLNtnjjp9K7mzu07d5izgVjb30GSkjPMnV5krDm7c5ttS4jnVZs7o6NTTLmNOf8wdyaknWzOFu3eacrlbFhj7gwLZpmz4TFJplz9kxqYO4+Hjwv6q7kx95aPzmt8ZP9g3CjRzUfnsBhbrty4oUCS3jduk5CkW2wLi7R9qb3TtoPIH/u+JKmfj+yLxhPw6pn2zoytPg7gXltsmI8vFOuSiBd9bJM4xx6VbAuL1MbHOT3bx93PNeZW+Oi04B07AAAARzDYAQAAOILBDgAAwBEMdgAAAI5gsAMAAHAEgx0AAIAjGOwAAAAcwWAHAADgCAY7AAAARzDYAQAAOMK8Ukz2TVXKy3nflGvY8lxzZ+GuXHM2IaaxKbdpzSfmzj15X5tyFbH2pzQiyr7SrLqiwJTbV2x/nqIros3ZnVtta6XCouuZOyt32491f0maKReX1MTcuWtzjjkbGd3IlMvbal+TFhNvWykWDNjPk2/Wfm7OllVEmHLF+82V8gJR5mxVScCU27tzl/0AjoN9GZ1k3Wq13Een/ZmTbmtmy23f5KO0gy0WMcBeGRphz/7aeJ4Ns1fqRR9Z6+e/s4/OYh/Zk6zBd+yd+c/Zs08ac7+zV2qzMXe1n7eYbC/FkqRPXrXljDFJ0l4f2TnGnJ91hlcbMrxjBwAA4AgGOwAAAEcw2AEAADiCwQ4AAMARDHYAAACOYLADAABwBIMdAACAIxjsAAAAHMFgBwAA4AjzmoSiwnxzaSAiZMrtyd9i7qyotq++2LbxI1Ou8EC5uVMRto0KaW17mCt3rbcdpyRVVZeYcqd2vdLcWZxrvS64VLD1A1Ou4SnnmzsD1fbNFwdyd5tyn35tv4Z4WHysORsVk2zKFRzIMXdGxBaZcgd22jd0FO22f52GpzQw5TwfOxHCgrZtFpIUCrN9/suLbFtXjpf5yv+SOhlzdzS0d5Zvs2eXGDdK+Lh7tf2HLbfGtlhIknR2lj27yLi05eQ4e+dbr9izsy+y5ayfe0lq3MuefXiRLXe2MSdJf7RH1deYe9BHZytjbnC1vbPI/m1L1j1AV9kr5ePp1wpjLt5HpwXv2AEAADiCwQ4AAMARDHYAAACOYLADAABwBIMdAACAIxjsAAAAHMFgBwAA4AgGOwAAAEcw2AEAADiCwQ4AAMAR5pVi8fGJ5tKYGNvOl6qKKnOnV21fa1RuXIEUG2/fd3OgpNjWGZdk70y0r7Tat92Wi4q279vZXWxfVRWbnmHKJTVpau7cvv0LczZv5zemXFyc/f5L8o17mSQp2bjSLlRprkxokG7KRSTZv04SGtrXtFWFJZly2zd+Zu4Mi7cv5qqq2mcLVu4ydx6P2T6yxi9H/dnHmrDTfNz/cOtfyX2Urtlpy+X6eEyR1p1Osq+0Ktlv74y+zp61LQ20r6mSpKd97J+ybsp6ysfzf6s9al4V5mf1nvGU0nIfnbbvRAc1MubW++i0fu1L9udqq49OC96xAwAAcASDHQAAgCMY7AAAABzBYAcAAOAIBjsAAABHMNgBAAA4gsEOAADAEQx2AAAAjmCwAwAAcASDHQAAgCPMK8XCfMyAew8UmnKl++y7YcJj7ItEous1MeXKy3ysaorKNOU2f/w3c2dKum2llCRFNmlsyu39Zp25s7Rkoznb7PQBts7i3ebO4gP2VVHRiSmmXDBoPqXVqvOl5uzuvbZFMrtz15g7C3d+bcqdclZPc2eYj8e/ffsWUy53k32lWHr9BuZsUZFtTVxxVZm583iU+sguNeb8/M05yUf2k2pbbuPHJ/7+zzvd3tndx/23Meb+0NHead5pJSnemDvbx+M/+6OW9nCjr0yxN32sFKuwRzUhxpbbc8DeWa+1LbfA/m3L10ov6+f0Hz46c3xkexlzr/rotOAdOwAAAEcw2AEAADiCwQ4AAMARDHYAAACOYLADAABwBIMdAACAIxjsAAAAHMFgBwAA4AgGOwAAAEeYL1O//0CJufSbrz8y5TJPOs/cWbR3rzm7Y+NqU65+VgdzZ3W4Z8rFpNo2JEhSaWmuOZvRoKEpF4wyXpJeUpuzLzNnq6ttV//fv2+fubNJux7m7EdL55hysVGV5s7oFOOl1iXtW2+7Knx5yR5zZ1yC7ar0RXm2DRWSFIq2b56Ij7Q9/vi4gLkzEG7f5pKSZdtSkd7AvnXmePi4oL5OM+asV76XpCwf2duNuat8dFpfjXb72CbR38f99zHm3v3A3tklz8cBGBcBLfDx+LsFbK8bkv38+9J+94rwkW1uPICtPjo7GzdKzPbROcxHdocxZ58E/G2oeduYi/XRacE7dgAAAI5gsAMAAHAEgx0AAIAjGOwAAAAcwWAHAADgCAY7AAAARzDYAQAAOILBDgAAwBEMdgAAAI5gsAMAAHCEef9Q6Z695tKEVNvCnbV/e9XcGYptbM7GJGWbcsXFBebO8DDbWqWEeqnmzojIeuZsSj3b4y85YF/4EhmfZM5u+XyFKRcVa1+MlLv+PXM2Oqa+KVe0N99+/1u/MWdLimy7iepl2c/TXZvfN+UatbjS3BkdYV8ptmOHcYmUZ1/TVvTNGnPWq7StKQwG/Sz7OnaJPrLWVUWdfHTO8JG1L2O0sy4j3Oij85c+sp8Zc9bnXpLmGdeESdJOY+6vPu6/lY/sI8bcXh+dftbUPWHM/cZH5++NuW0+Oit8ZK0+8ZG1rhOU7CsFbUtQ7XjHDgAAwBEMdgAAAI5gsAMAAHAEgx0AAIAjGOwAAAAcwWAHAADgCAY7AAAARzDYAQAAOILBDgAAwBEMdgAAAI4w7x8KBG0rtSQpWBVh6wwEzZ3lnj1bUWZbOhII2FclxSfbFg5Z1yRJUmxcjDkbMC5SiU2wL0YKr6wyZ0v32dZPJdZvY+5s1Nq+GKli/VpTrsqznXuSlL/ja3M2NinDlGvX+RJz55q/TTblwkPmSgWi7CvFYhMSTLkDu+z3n9zY/joRE9/Ulovxs+zr2FlXSkmS9dXIz/oj+1eO9IUxZ18wKF1kzNlftaR7fWRtX2FSso/ORj6y1rVWv/bRmeIja301HOej81Qf2a7G3EM+Oq3P/2U+Oh/zkbWuv7vLR+cbPrKxxtyJfoeNd+wAAAAcwWAHAADgCAY7AAAARzDYAQAAOILBDgAAwBEMdgAAAI5gsAMAAHAEgx0AAIAjGOwAAAAcYb5MfVxiurl0x8f/MOWiEzLNneURSeaswvabYoHqanNlSWmxKZcYGWnuDKu2Z1Vtu4Z9ZES8uXJXbo4526hFZ1MuEBFl7szfZL//PTtt19CPjLRflz4UYdu8IEkqsl2Xfl/+BnNlVvPTTbnICOv1y6WyEtt5Kkn5Wz405Vr3OMXcWa9+S3M2NjHVlNuWs87ceTzsrwZSkjG3yEen/SvHvtHC/qot/dGYK/XRad9tIx0w5uy7XeybBySplTG3xEfnbB/ZvcZcQx+dfo7VuvkkyUfnl8acn68TP6yP6R0fnet9ZOsbc2U+Oi14xw4AAMARDHYAAACOYLADAABwBIMdAACAIxjsAAAAHMFgBwAA4AgGOwAAAEcw2AEAADiCwQ4AAMARDHYAAACOCHie5/3UBwEAAIDjxzt2AAAAjmCwAwAAcASDHQAAgCMY7AAAABzBYAcAAOAIBjsAAABHMNgBAAA4gsEOAADAEQx2AAAAjvj/OzkQtiKSlkEAAAAASUVORK5CYII=", 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wwQerurpay5Yt03PPPacJEybs9sd03bp1U7NmzfRf//VfGjlypNLT0zVt2jQtWLBgj2/jpZdeqp///Oe69NJLdeutt6pTp0567rnn9MILL+zxzMMPP1ySdM8992jQoEFKT0/XwQcfrGnTpmn27Nk6++yz1b59e1VXV2vy5MmSpNNOO2238z788EOdeeaZ+ta3vqVTTjlFrVu31oYNGzRr1iz9+te/Vo8ePdStWzdJ2xfnGTNmaNiwYerfv78+/vhjjR07Vq1bt9bixYtj35Z77rlHJ510kk4++WRdddVV6tChgyorK7VkyRI988wzO/0m8hfJy8vTnXfeqSuuuEKnnXaahgwZopYtW2rJkiVasGCBfvnLXzbJX3PNNRo4cKASiYSGDRsW+/rv8LOf/ewLM+7zfYc2bdro3HPP1ahRo9S6dWtNnTpVL774osaNG7fb33AG/t2x2AExXHzxxWrfvr1uv/12DR06VJWVlSotLVXXrl13OhfcP0OvXr00efJkjRs3Tn379lXbtm01ZMgQlZaWavDgwU2yU6ZMUevWrTVp0iT9/Oc/V9euXfXYY4+pd+/eTdoXWrdurTfffFNjx47V+PHjtXLlSuXn56tjx47q3bv3536KV1JSolmzZmn48OG6+OKLlZubq/POO2+nTwrjyMnJ0ezZs3XNNdfopptuUiKR0BlnnKFHH320cVmKq0ePHvrRj36khx56SL/5zW+UTCY1Z84cde3aVb///e81cuRIlZeXKy8vT4cddpiefvrpxn/btSsHHnigrrvuOs2ePVszZ87U2rVrlZ6erk6dOumWW27Rdddd17g8X3755VqzZo0mTJigyZMn64ADDtBNN92klStXNjm5sWtHe8rYsWN18803a82aNSoqKlKnTp0a/51dXIMHD1abNm00btw4XXHFFYqiSB06dNjlp2nnn3++MjMz1bNnT3Xq1GmPjueK83yXtv8yzOWXX66RI0dq8eLFatOmje666y794Ac/+FKvJ/BVSkTRZ87qCOA/xuuvv67u3btr2rRpX8pvmSJ8zzzzjM4991zNmjVrjxfJL0OHDh102GGH6dlnn/2qrwrwT8UndsB/iBdffFHz5s3TMccco+zsbC1YsEA/+9nP1KlTJ/Xr1++rvnr4N7No0SItX768sUVjx+lwAHy1WOyA/xAFBQX6/e9/r7vvvluVlZVq3ry5zjrrLN122207nXIE+CLDhg3Ta6+9pqOPPloPPfTQ5/6SBYB/Hn4UCwAAEAhOUAwAABAIFjsAAIBAsNgBAAAEgsUOAAAgECx2AAAAgWCxAwAACASLHQAAQCBY7AAAAALBYgcAABAIFjsAAIBAsNgBAAAEgsUOAAAgECx2AAAAgWCxAwAACASLHQAAQCBY7AAAAALBYgcAABAIFjsAAIBAsNgBAAAEgsUOAAAgECx2AAAAgWCxAwAACASLHQAAQCBY7AAAAALBYgcAABAIFjsAAIBAsNgBAAAEgsUOAAAgECx2AAAAgWCxAwAACASLHQAAQCBY7AAAAALBYgcAABAIFjsAAIBAsNgBAAAEgsUOAAAgECx2AAAAgWCxAwAACASLHQAAQCBY7AAAAALBYgcAABAIFjsAAIBAsNgBAAAEgsUOAAAgECx2AAAAgWCx+yd78MEHlUgk9Oabb+6TeYlEQt/73vf2yax/nDlq1Kg9+tq33npL3/3ud3X44YcrPz9fLVu21GmnnabZs2fv9XX6x/8KCwvVo0cPzZo1a6/mukaNGqVEIrFHX7t582bdeuut6tGjh1q1aqW8vDwdfvjhGjdunKqrq/fxNQUA/CdjscM+9cgjj+iNN97Qd77zHc2cOVMPPPCAMjMzdeqpp+q///u/92p2//79NW/ePL322mu67777VF5err59+/7Tlrs9tWLFCt199906+uij9etf/1pPP/20+vfvr1GjRumcc85RFEVf9VUEAAQi7au+AgjLjTfeqDvuuKPJZX369NHRRx+tMWPG6NJLL93j2S1bttTXvvY1SVK3bt104okn6sADD9Tdd9+ts88+e5dfU1dXp0QiobS0r+6p3rFjRy1btky5ubmNl/Xq1Uu5ubm64YYb9Nprr+mkk076yq4fACAcfGL3L6i6ulrDhw9X165dVVhYqOLiYp144omaOXPmbr9m4sSJOuigg5SZmalDDz1Ujz766E6Z8vJyDR06VO3atVNGRoY6duyo0aNHq76+fp9d99LS0p0uS01N1THHHKOPP/54nx1HksrKytSiRQstX75ckjR37lwlEgk9/PDDGj58uNq2bavMzEwtWbJEkvTSSy/p1FNPVUFBgXJyctS9e3e9/PLLO82dNWuWunbtqszMTHXs2HGnRTWu3NzcJkvdDscff7wk7fP7BQDwn4vF7l9QTU2NKioqdP311+upp57SI488opNOOkn9+vXb5Y8zn376af3iF7/QmDFj9MQTT2j//ffXhRdeqCeeeKIxU15eruOPP14vvPCCRowYod/97ncaPHiwbrvtNg0ZMuQLr1OHDh3UoUOHPbo99fX1evXVV9WlS5c9+vrd2bBhg9avX68WLVo0ufxHP/qRVqxYoQkTJuiZZ55RaWmppk6dqjPOOEMFBQV66KGH9Nhjj6m4uFhnnnlmk+Xu5Zdf1nnnnaf8/Hw9+uijGj9+vB577DFNmTJlp+Pv+Hd3c+fO3aPrv+PfHe7r+wUA8B8swj/VlClTIknR/Pnz7a+pr6+P6urqosGDB0dHHXVUk/8nKcrOzo7Ky8ub5A855JDowAMPbLxs6NChUV5eXrR8+fImX3/HHXdEkqKFCxc2mTly5MgmubKysqisrMy+zv/oJz/5SSQpeuqpp/bo63dcp2HDhkV1dXVRbW1t9P7770dnnXVWJCm67777oiiKojlz5kSSoq9//etNvraqqioqLi6O+vbt2+TyhoaG6Mgjj4yOP/74xstOOOGEqE2bNtG2bdsaL9u8eXNUXFwcffblMnr06Cg1NTWaO3du7NuzYMGCKDs7O7rgggtify0AALvDJ3b/oh5//HF1795deXl5SktLU3p6uiZNmqT3339/p+ypp56qli1bNv45NTVVAwcO1JIlS7Ry5UpJ0rPPPquePXuqTZs2qq+vb/zvrLPOkiS98sorn3t9lixZ0vgjzTgeeOAB3XrrrRo+fLjOO++82F//j371q18pPT1dGRkZ6ty5s15//XWNGTNGw4YNa5L7xje+0eTPr7/+uioqKjRo0KAmtz2ZTKp3796aP3++qqqqVFVVpfnz56tfv37Kyspq/Pr8/Hz17dt3p+szYsQI1dfX65RTTol1O5YtW6ZzzjlH++23nx544IFYXwsAwOfhlyf+Bc2YMUMDBgzQN7/5Td1www1q1aqV0tLSdP/992vy5Mk75Vu1arXby9avX6927dpp9erVeuaZZ5Senr7LY65bt27f3ghJU6ZM0dChQ3XllVdq/Pjxez1vwIABuuGGG5RIJJSfn6+ysjKlpqbulGvdunWTP69evVrS9t+q3Z2KigolEgklk8nPvT/31vLly9WzZ0+lpaXp5ZdfVnFx8T6ZCwCAxGL3L2nq1Knq2LGjpk+f3uTcaTU1NbvMl5eX7/aykpISSVLz5s11xBFH6NZbb93ljDZt2uzt1W5iypQpuuKKKzRo0CBNmDBhj88B949atGihY4899gtznz1W8+bNJUn33ntv42/VflbLli0bf4P28+7PvbF8+XL16NFDURRp7ty5ateu3V7PBADgH7HY/QtKJBLKyMhosqCUl5fv9rdiX375Za1evbrxx7ENDQ2aPn26ysrKGpeHc845R88995zKysrUrFmzL/X6P/jgg7riiit08cUX64EHHtgnS93e6N69u4qKirRo0aLPPZlzRkaGjj/+eM2YMUPjx49v/HFsZWWlnnnmmb26DitWrFCPHj3U0NCguXPnav/999+reQAA7AqL3Vdk9uzZWrZs2U6X9+nTR+ecc45mzJihYcOGqX///vr44481duxYtW7dWosXL97pa5o3b65evXrppz/9qXJzc/WrX/1KH3zwQZNTnowZM0YvvviiunXrpquvvloHH3ywqqurtWzZMj333HOaMGHC536CdOCBB0rSF/47u8cff1yDBw9W165dNXToUL3xxhtN/v9RRx2lzMxMSdtPT9KzZ0+NHDlyj5suHHl5ebr33ns1aNAgVVRUqH///iotLdXatWu1YMECrV27Vvfff78kaezYserdu7dOP/10DR8+XA0NDRo3bpxyc3NVUVHRZO6YMWM0ZswYvfzyy5/77+zWrFmjnj176tNPP9WkSZO0Zs0arVmzpvH/t2vXjk/vAAD7BIvdV+SHP/zhLi//6KOPdPnll2vNmjWaMGGCJk+erAMOOEA33XSTVq5cqdGjR+/0Neeee666dOmim2++WStWrFBZWZmmTZumgQMHNmZat26tN998U2PHjtX48eO1cuVK5efnq2PHjurdu/cXfornnutu1qxZSiaTevvtt9W9e/dd3r4dp03ZsmVL43X7sl188cVq3769br/9dg0dOlSVlZUqLS1V165dddlllzXmTj/9dD311FO6+eabNXDgQLVq1UrDhg3Ttm3bdrrvk8mkGhoavrA5YtGiRVq6dGnj9fisL3uxBQD850hEX/RdCfiS3HjjjXrkkUe0ePHiJr+FCgAA9gynO8FXZs6cOfrpT3/KUgcAwD7CJ3YAAACB4BM7AACAQLDYAQAABILFDgAAIBAsdgAAAIFgsQMAAAgEix0AAEAg7OaJoUP/yx6akZVv5dZ98hd7Zs3mDXa2sGUHK3fQ0T3tmeXL37NytZvX2jMVo0K1rsFrfqjavNGemZWdZ2dTU7y/A2RkZdszM2Kcv67BzKWm+WUquXlFdjY7v7mVq02k2jM/emuOldtaVWXPzMryH9NEarqZy7FnRpH/d8X0TO/x37Z1sz3z0enT7exnlcboNC40c51iHH9jjGx/M/fzL2FmRoyZ5TGy+5m552LM3Boj633XkopjzIxjlZlrFWPm0hhZ9904zqdBHczc/Bgz/XdY/za5318kqeKLI43c+6ouxswq4wx1fGIHAAAQCBY7AACAQLDYAQAABILFDgAAIBAsdgAAAIFgsQMAAAgEix0AAEAgWOwAAAACwWIHAAAQCPs0/etXr/SHpnv7YlqMs/SnZ+Ta2Qyz0mHZn/9gz2xIeuemdnOSVLNti53NzMq0chkZfvNA0iuz2C4lacW2bPTPy52S5j/+bqNEIsZfVWpjNBrU1Hp3Vsl+ne2ZmVnec7p6a7U9MyurwM4mUr2WkLr6GntmlOK3N9TXeedbT03120T2hts8IPlnv49zlvxDY2TdM+oPjTHTPfu911ey3QUxsu59dUCMmU/EyLrfYWpjzGwRI7vazMXoNtLBMbLvmrmvxZjpvnP1iTHz7RhZ99tBSYyZ62Nk3e+GlTFmOvjEDgAAIBAsdgAAAIFgsQMAAAgEix0AAEAgWOwAAAACwWIHAAAQCBY7AACAQLDYAQAABILFDgAAIBD2Kd0z072z1EtSss4733QUo3kiJSXDztbXR1YuI8M/h3qU8O6qohL/XOMbN3xqZxvqtlm5hHfTJcU7o39KwmvUyM4vtGcmUv2/VyTNmgy3oUKS0lL9xz87M8fK1W7xzyFes22rd+xsv00kIa8hRJJqtnrnRa/atsmeWVC0n53NyimyctXVG+2ZeyPOGeXdloI4bRZ+t450hZn7ZoyZj5q57OIYQ72XzXaHe7E//c4f+asYh+9g1jT84EN/5qAYx/+1mWsTY2YcvcxcnOYL990wzidMh8XIzjFzfreO5HVAxROnocbBJ3YAAACBYLEDAAAIBIsdAABAIFjsAAAAAsFiBwAAEAgWOwAAgECw2AEAAASCxQ4AACAQLHYAAACBYLEDAAAIhN2/lKKEPbQh8uqn4lSKpceoVUpJ90o/UmPMTG7zylE2Vnxiz0wk/L06M8srJ2pI1vozc/yauOqqjVYuNdOv9Ko1a9IkKbewmRdM8WvCiprvb2dbtOxk5ZYt+Ys9s6rSq+rKyfCfpzX1fqWY+/rLzPSPX1vj1aRJUmaWV6KVnvFllPjs7IQYWbP9Sq1izPTL2KSXzNxFMWZmn+TlFv6vP7PLgzGuwCVe7NsxRja73c+uuNHL9Y9x/K4nxgjP82JLY4yM0/5mNqrp+o7+zB985OWW+CNjVf9dZeZejTFzZYyse//737U8fGIHAAAQCBY7AACAQLDYAQAABILFDgAAIBAsdgAAAIFgsQMAAAgEix0AAEAgWOwAAAACwWIHAAAQCBY7AACAQNj9T7W11fbQRMKrH0vLyvJnpvj1Y9U1Zv3Xho/tmanmbUo21NkzC5r5hUNpWV6tU0rk7+oNUY2dzcjPsXJZuV71mSQl6v3HNK+khZVLzyq0Z5a0PczObltfYeWWvPOiPVP1Zk1fpn8/JVL8Srco6dWPpaVl+MdP97O1tVVWrnqrl9tbXWNk3fqvtTFmlsfIDjzLDPpvsfrNk15uyP3+TPX9vp+dda8Va/bjGMfv6ke9V7jU3b3vJc3+nZ89zcz1i/FxzAq/YVDtv2EG/W+but6sFGsb4zb9KsZtGva0GbzMn3mP+0SR9ISZ8zYWH5/YAQAABILFDgAAIBAsdgAAAIFgsQMAAAgEix0AAEAgWOwAAAACwWIHAAAQCBY7AACAQLDYAQAABMI+TX1+UWt7aJ15Rvlk0m9pqK/zmy/SMzKtXF6h3/xQV7vVyhUUl9gzcwuK/eMna61cRprXUCFJVZXr7GxOfoGVS6Sl2zOLmrW0sw1mLiujuT3zrd9NtbMb131i5VJT/eaH1DSvzWPbtg32zIws73GSpCjhNVqk1PttEtmZ3m2SpAbzOd0Q431ib3jXZrtjzdyiGDO/c5Sf/cBsNHg2xvEvMHMbrvJn5l7ltUlI0nQzd8mF/vH/doaf7Xqgl5sfo02iV0c/u8ZsaXgtRvPCHD+qm91vXS/5M93mhUPjtEm087PPn+vl/uSP1NsxshtjZPclPrEDAAAIBIsdAABAIFjsAAAAAsFiBwAAEAgWOwAAgECw2AEAAASCxQ4AACAQLHYAAACBYLEDAAAIBIsdAABAIBJRFEVO8JKLvm0PbaivsXIZmdn2zLoar6ZMkhIJb18tKGphz6w1j5+R4VdKJdISdjYZeYVHqSn+rl5U6leq1VRvs3KZeUX2zKrNm+xsbrNSK7f83dfsmdWbttjZ2oT1MlHZYb3smTk5ba1cxfrl9szK9SvtbF1VhZVrqHcL3SSZNWWSlJHtvf6T8u57SXrk0cft7Gddk/Bfj/uZubUxjm82WkmShphthBd7D7EkabSZKxvgz9QPYmTdnrYTY8y8MkbWa42U7ogx029YlFabuTj36e0xsgvMXIz7v9Z8i3/XH2k/TJLklnbGaEmzn6aSf/e7d70kLTNWNj6xAwAACASLHQAAQCBY7AAAAALBYgcAABAIFjsAAIBAsNgBAAAEgsUOAAAgECx2AAAAgWCxAwAACIRfk5D0z/5e77ZENPhtEinK8LPmye+3VfqnZc/I8o5fZ7ZuSFIixhn9M7OyrFyzdu3tmYWtOtjZjWu8RoPK1Z/YMzes8rOr3v+LlWtIxrhPY7RkZKZ6L5W1K5faM4tbJK1cIuk/p9LS/Jd0ZjOveSSpentmbr7XECJJG9cv845f5TeU7I3FMbIXm7mPY8yM0e+h9eZb18MxZiY6msHnYgz9UYzsVDPn1n5I0m0xsmalwSC3IULSwBiHd5tHDnogxtDaGN0nh3pNTDEKg1Rwp5c7ZLg/M06byxNm7vrB/szjJvnZNmYuTpuGg0/sAAAAAsFiBwAAEAgWOwAAgECw2AEAAASCxQ4AACAQLHYAAACBYLEDAAAIBIsdAABAIFjsAAAAAsFiBwAAEAi7f6jdQcfbQxMZmVZu6Xu/t2fWbtpoZ9PTvX21JopRk1bvVXplZOfYM6OkX9WUlu71pG2LcT+VHNDMzjbULbNyyxe9ac9MJPz6q4ysPCuXnV1gz0xNzbaziYT3nKqvr7ZnbjErtepjPE8Kilra2ey8YitXVeNX7zVr4Vfa5ZUUWbmVH7xhz9wbXqHSdr81c4NizOx6bozwVWZuTIyZZhVjrJ6yvjGylWauLsbMY/3on//g5R46zJ9Z9Z6frXWDG/2ZdyX8Z/V15sc8BT/zj7/MrArr8L/+zNxX/Oz1M71cFKMm7Dw/qhfMnP3Ym/jEDgAAIBAsdgAAAIFgsQMAAAgEix0AAEAgWOwAAAACwWIHAAAQCBY7AACAQLDYAQAABILFDgAAIBAsdgAAAIGwO51atPTqhyRpzeplVi4nJ92emZHT1s669U+bKz61Z6YkvFxd7RZ7Zmqaf/vrG7xaqa1VNfbMbRs22tnM/CIrl5VdYs9MN6vnJCkl1X2q+jVliswHVVKUTHpBNyeprnablauv8wtnNtcvt7MNW9dbudqGrfbMmnT/MS1o3cHKHXZyP3vm3vBKA7f7uft2uH+MoTPf8bMjjvJya/2Rf13i5Q4a4s98d6WfPfwsM7jAn6kz/WjX081gjEqx3MtiZJ8yg739mdfFeErpGC9We5M/0n3nWniSP7PL1/1sldlGeI8/Uvkxsp3M3LsxZjr4xA4AACAQLHYAAACBYLEDAAAIBIsdAABAIFjsAAAAAsFiBwAAEAgWOwAAgECw2AEAAASCxQ4AACAQ9mn6Kzcss4fWbf3YyuW3aG7PPKBLT//4td75rlcvedOe+fHit61cWhTZM7Mz/ZaGjMxsK9fhCP8U3jVbNtvZZW/OtXL1DV6bgiQ1VHttGpKUmp5r5XJz/fOCN9TV2dmk+bimpfptIkqYzRcxRiYb/JaKqi0brVxWQZE9M7OwwM5urVpn5RJbvNzemh8nnOPFPohx5v+DE2abhKTEL7xcudkmIUkHmc+zNe/7M+MUb6jBzP1vjJk/9aNvTfNyx9wd4/h3xciWm7njYszseokdvSv5sJW7LsaDOs8swhkUubUjkvr9zo7mnujlfuytLJKkoTHaVD4wc6n+SAuf2AEAAASCxQ4AACAQLHYAAACBYLEDAAAIBIsdAABAIFjsAAAAAsFiBwAAEAgWOwAAgECw2AEAAASCxQ4AACAQdqVYdk6GPTSZUmzlElmZ9szKjX+zs9u2VFq51Svfs2emJLy+m0SGV/0lSfkt2tjZnPwWVm7z6k/smdVb/UqxjRvWW7m0dP8xTc/Ms7MNSa9+qz7pV7rVNbgdRlLSzKZl+Lc/JWVfF8lISvq3SfIq3eq2ea8nSfrkr171niRl5nj1b8XNW9sz94b/Dic9Y9YK9f2JP3PDrX62WbWXa+W9FW/nvcWo9GB/5ENP+9lBL1xg5dYknrRnln7oH/8Yd+yr/syF/+Nnu7hVab/2Z0oL7eR10Te9YP/H7ZmDbjSDqX5NmMb6UZlvXbN/5o80X3qSpG5mzmxes/GJHQAAQCBY7AAAAALBYgcAABAIFjsAAIBAsNgBAAAEgsUOAAAgECx2AAAAgWCxAwAACASLHQAAQCBY7AAAAAJhV4p9/LcF9tDsPK8qqDC9lT3zkyVv2dnUjCwr17J9J39mwrurarZW2TMVo36rPpm0cssWxOi7Sfj1W5nZhVYuNS3dnpmWkWNnU7ybr+rqrfbM9Cz/+JnpXramxn/8k+ZjWl9Xa89MxKgUS0/1atqSW/37VMk6O1q/1ev7qd68zj/+XvCfudIpbvCX/sxmh8W4Am4H0YH+yBVveLn3YtR0DYpu8cN/u9mKlUY/8Gdm/dzP9jJzHfyRXdb6Wfux2vi8PXJ9oredXSuvDnCpPVHq88TlXnDMFHvmwhg1fV1u93K92vkz7zHrBOOIU1Pm4BM7AACAQLDYAQAABILFDgAAIBAsdgAAAIFgsQMAAAgEix0AAEAgWOwAAAACwWIHAAAQCBY7AACAQNjNE8mk31LQUFdv5Wpq/PMtl7TwTw3dEHln9M9t6TdfJBu8259R6595v2LFx3a23i1JSPjNA1WbN9jZ7KxcK1ef9NoMJElRjR91n34xmheSddvsbF3Se06n2FdUUiLViqWn+p0IUeRdT0lKmM0jUYyHVAk/XG+2hDTEaBPZG3FKAp51g5v8mZUxsjnvebkif6T6ms0X7Y+NMbSD1yYhSbrRi234rt8m0SzOg9r8CDNY5M/c7w9+dmNfL7fOb5Mouck/fIn5Mkv5hT+zPOE1SrT6hj+zy1g/+9ShXu4df6T87/DSYjNXFGOmg0/sAAAAAsFiBwAAEAgWOwAAgECw2AEAAASCxQ4AACAQLHYAAACBYLEDAAAIBIsdAABAIFjsAAAAAsFiBwAAEAi7Uqy+1q9qSsnJsHJZ2UX2zG0NG+1s5bpPrVx281J7Zt1Wr35q3ad+TVjL/Q62s6s++tDK1dRusWempfn1T4kU76nS5sBj7JlRfa2d3bh2hTdTMSrFzOq57YO9Ipk49V/1kVmplxajpizGX9US5s1Pz8mzZ6an+7e/edsyK5dT5L9O90ZRjKz7Kj8hxsxHYmQnuo9zsT/zKbOm7Pw4N6pnjOwwrzex2fdjVMw1v9zP/j+v/kp+o5n0oxhZjfBiRz7jj7w0xuHNl+5BV8eY6dZ/+d82pJP8aC8zt1+Mw8+PkTUbzWLVGTr4xA4AACAQLHYAAACBYLEDAAAIBIsdAABAIFjsAAAAAsFiBwAAEAgWOwAAgECw2AEAAASCxQ4AACAQLHYAAACBsCvFFKXa0eptXlXS+k9X2jO3Vm2ys6kJb1/d/KlXPSZJiTSvJq2gWSt7Zkp6pp1td9CRVq622qs+k6SGqio7u23Tei+38RN7Zl2MSjH3ryAZqd7jJEnJhno7W13nVbWl5RXYM1PqvZdfIvIrxVLM574kRfJuf1qa/9rPLyyxs3nNW1i5ZL3/OO0N79ps51YA+fecdHiM7EKzDq6L22kk6Xw3GKOmquEUP5taYFaFPeXP1DizJkySfnyBl1v6pD1y7nD/8D2qj7Ny16/yZ94RpyvLfK7UxqiJy/ilGYxREzZ0iZ+dOMrLLTFzknSFH7VrAmOUW1r4xA4AACAQLHYAAACBYLEDAAAIBIsdAABAIFjsAAAAAsFiBwAAEAgWOwAAgECw2AEAAASCxQ4AACAQdvPEli2V9tBo80Yrl5rqn5c9inFu5syMLCu3ucJrU5CkhHlG/6Lmrf2ZSf/2F7X0TiF+QGfv7OWStGXTOju76oM/eTMrltszEzGaNwqat7Ny+SVeTpJyC/2ugZpq775KxmjTSCa953RJaVt7ZkZ6tp396K/eY5qV4c9s1sq//5N1XkPNwnkv2jOl62Nkm/o4RvZAM3fy/v7MQ/2Xju43c13+15+pp73YmhhtEqUVMY5v3v7nj/JH9jbLLCRJD5qNEjFuU48zYhz/MS92R06MmcP88KsJ787yv2tK57tvsTEep/QYx9dtXiw/xsiJMbJuX1ZhjJkOPrEDAAAIBIsdAABAIFjsAAAAAsFiBwAAEAgWOwAAgECw2AEAAASCxQ4AACAQLHYAAACBYLEDAAAIBIsdAABAIOxKsbT0OD0mkZWqra6yJyZiVIrVJmusXLLOr39KSU1YuXUrl9gzE6sW29kNK0us3P5dT7Nntj6wg53dVLHWy336kT2zMLfAzqaleU/VVR/92Z7Zuu3BdjbF7LHJL2xuz6zbttHKfbzI74VqW3a4nS1t197KrVnuP09X/dUvHKqv22blWh1wiD1zb3ilfdu59/LEGDVhQy/wswPM9qvN/tumCpZ6udLf+zN1TIzsUu/7Ru923nuxJCn7l352+Pe8XBt/5Afv+dlDoges3POJK+yZvR/0u7pO/p0ZnGmP1PwJXu64In/mL83nqSRNP8DLDbzWn/nc3X42w8zFqWlz8IkdAABAIFjsAAAAAsFiBwAAEAgWOwAAgECw2AEAAASCxQ4AACAQLHYAAACBYLEDAAAIBIsdAABAIFjsAAAAAmFXimVk5NlDU1K8ypco6ffdpKb6O2iUrHeD9sxkrVd/5laPSVIiM9vOZhcUW7m0NLfERKpY9lc7W1jQ0spFtXX2zJqaajtbu2GdlavasMqe+cmWSjublZ1p5TbEePwLm7eycgUFXp2cJFWU+5Vuqeneyz8zJ8ue2bx1Bzv7twVeVVqzNl712d7aP0b2iS/h+ENjVDUd1NMMzr7QH5r6iJd70B+p/n50fcJ77ZT4rYnSCWZNmKQVFV6u/Xqzp0rSISfE6L9q61WF9fabEPXq5X72NjP33AB/5gdm7rhj/Zlv+Xe/BpoNj/Pv9mee50dlvqJ0XIyZDj6xAwAACASLHQAAQCBY7AAAAALBYgcAABAIFjsAAIBAsNgBAAAEgsUOAAAgECx2AAAAgWCxAwAACITdPBEp8qf6J9/3R8aYGZnramZ+vj2zoFlH79i1tfbM7FyvTUKSijt4pxvfumGNPXPtx+55waX0DK99IFnfYM/MzSu0szVbveaPoqLW9szs7AI7m2Y2n6TFaJ5IT023clUb/cc0zuskJc97/rftdKg9My3Xb6hp3mo/K7f0ndfsmXsjRkeALjZzfg+LpP8XI/uAF5uRcM99L/WLcwe4YlR0lMwxg35hjPRNP9reva6X+3fU9Df84w/03g5U65fr6GM/qufM48t7K5YkHe4G3cdeUpEf1SCvsEiTY8xcHyN7u5nLiTHTwSd2AAAAgWCxAwAACASLHQAAQCBY7AAAAALBYgcAABAIFjsAAIBAsNgBAAAEgsUOAAAgECx2AAAAgWCxAwAACIRdKZaT51cFba3aZOUaGvz6rUTC30GzCryqqJRM++YrJc3rWyne/xB7Zl6BXym2cbXXI/POnOn2zJR0v8gkO8urn0rN9Gdu27LZzibMrqzUjEx75uaqLXY2N9u7Xdu2VtszKzettXINdX6HT1GLVnZWSa/+bcva1fbIvFq/RCs3t8TMVdgz90ZRjGy5mXspxsxBG2OEO3mxtUtizHSrqtwbL0lHx8j+r5k7N8bM52Nkezzr5S49xx458NgYxz/Ai2XM9EdedJWfffhuL3feLH/mAjN3aIyatLIY9WMTenq5GHdprPcJ82WqV2LMdPCJHQAAQCBY7AAAAALBYgcAABAIFjsAAIBAsNgBAAAEgsUOAAAgECx2AAAAgWCxAwAACASLHQAAQCDs6oWs7Gx7aGqq1xKQlublJCmvsJmd3bbNazRoVlRqz8xp3tLKVazwT/W+pr7ezlZXVlq53ELvekpSQen+drZm21Yrt22r3yYRKbKzaWbzSOSPVKbZJiFJufleS0jVpjX2zKotXqNEdrbXeiJJhSX+45+amWvlNqxZac8sKWxtZytrvedKbmELe+beWB8j670apYFxrsDtfvTmpJe7JU5px/FmbnSMmX4Rjmrf93JVP/VnNjvNzz7T02uU6Ou/HPWjN/3sfmZ22HJ/pjr70ffM3CU/82cOWmQG2/gzdasfzT7Jy/3cbT2R9Oqlfnbcf3u5ODffwSd2AAAAgWCxAwAACASLHQAAQCBY7AAAAALBYgcAABAIFjsAAIBAsNgBAAAEgsUOAAAgECx2AAAAgWCxAwAACIRdKVZU5FcVrSv/yMrFqZRqVupXFaVUpHpBs6ZKkt5/9Xkr11DtVW9JUjLG7c8sLLFyHY47057ZrF0XO7tm+UIrt27ZAntmsrbGzjbU1Vq5RIr/mNbV+ccv/3Spd/ykXxOXmeXV9GVkZdkzN1aus7NdDj7WyhU1b2vPTGT617V6lVdV9uE7b9gz98YhMbJrzZxXUrXdZLMmTJK8MjpJT/gzrzHbEO+J83FARozof5k5t89N0rJpfravm41x+ztd6GffNXMv+k2Qmu1HNe5qL3fXTf5M8zuxrmnnz7zFbzjUKjPXzR+poWZNmCSVmblPYhzfwSd2AAAAgWCxAwAACASLHQAAQCBY7AAAAALBYgcAABAIFjsAAIBAsNgBAAAEgsUOAAAgECx2AAAAgWCxAwAACIRdKbbsr36tT031ZitXUNLKnllfU21nMzK8Hpt1y/5qz4waElau47Gn2TPT0/y+nfIV71u5dWb1lyRtXrPCzubk5lu53Jw8e2at/A6lKM27/7NyC+yZDZF//PyC9lZuc0W5PbOuZouVy2tRas9UssGObljvXdfsEr/Ob/Pq9X5200YrV9xiP3vm3vCvueSWrE2KMdOtKZNi/I38FX/m0WZudoyarF5+w6Im5ni5w/yR6v7tGOHrzFwLf2SvGIf/zhle7tXf+zNve9LPLrzAy8W5TfZz2u3+knTz3TGugHmfTj7UH7k4xuFdMV6mFj6xAwAACASLHQAAQCBY7AAAAALBYgcAABAIFjsAAIBAsNgBAAAEgsUOAAAgECx2AAAAgWCxAwAACITdPLF10yZ7aE6e11KQkZplz1y96m92trbSa76oq6+1Z7Y4wDs1dW6hf5b+hF98oLRM77TstVu82y5JVZsr/CtQUGLFMnNy7ZFFrdzz90uVm7xzmKdmpNoz27Q70M6mZ3gvlZJWfkvEls1e10FttddQIUnFLTvY2U2bvONXrPHbNCpjvE98YrakZGc3t2fujfdiZN13riUxZl4ZI+ueKL/vtH1//DjND9vMNglJGrq/l7truT+ze5GfnbHayy0yc5JU50c1+gAvd3KhP3O22SYhSb2O9XLvvunPPL2zlxvtFStJkm681s/eZObiPKc7xMjON3NVMWY6+MQOAAAgECx2AAAAgWCxAwAACASLHQAAQCBY7AAAAALBYgcAABAIFjsAAIBAsNgBAAAEgsUOAAAgECx2AAAAgbArxdKyvZowSaqt97qy1pR/Ys+Ms4JGDV5VWCLhD1234kMrt2293zdTUNLOzrZs5fXN1Fb5lWLbNq3zs9u8WqvIrN6SpMwMvxun+ACvm6Zig/+cSlW1nd1audXKRVHCnuk+/fKLWtgz62vr7eyaj72avuqKDfbMBjspufdUMhmje++fZD8z5xfcSW/HyM42czEavbTUzPmvGn+mJJ1pVoVd93V/ZtV9frbf2WbuJX/mqzV+9v9N8HI/jtE9d8Kv/awWeTGv3HG76WZVmP8OJ2Xn+dl8s40xRkudVsXIuq+VohgzHXxiBwAAEAgWOwAAgECw2AEAAASCxQ4AACAQLHYAAACBYLEDAAAIBIsdAABAIFjsAAAAAsFiBwAAEAgWOwAAgEAkoiiKnODgQYPsocmkVyyUkF8VVFdtdoNISkkz99XIP35S1t2kRIq/K9dUbbSzUUOdlcvOLbZn5uQV2dmqLV6tVE52tn/8fL+mrt6siaup8Z8nhcV+kU1WrtljE+OvSknvpafsXL96beN6v/AnIz3XyuXEqDT7ZOkSO5sw69cqN6yxZ/73b5+ys59VmvDr4NyHuU2M4/tlhH5VWFGMme47R0aMmekxsm5N26sxZp4eIzvAzMWp1JoaI3uxmWsbY2bLGNmNZu6yGDN7mblDY8xcECPrvnPGaV4ripFdb+a8wsq/zzS+b/CJHQAAQCBY7AAAAALBYgcAABAIFjsAAIBAsNgBAAAEgsUOAAAgECx2AAAAgWCxAwAACASLHQAAQCDS3GBdg9cmIUnRl9A8IaXayWS9NzctI9OemZnuZVPT/HOt52QV2NnqbZVWziwzkCTV1/n3fyLFu11bqqrsmdtqa+ysW+iRmuK3B2xY45/rP7/Ia76wW08kyWw6SIn8535Ojt9SkUh4j2n1po32zPrqbXa2tsZ7/Ovr/feevVH9JcyM01IQ52/ZXg9NvDPauy0Bi2LMPDZG9j0zF6dNwm2zkKRJZm5zjJmdYmQPMXPvxpg5Mkb2aDN3VYyZt5q5rBgz47RUbIyRda2KkXXfueI0tDj4xA4AACAQLHYAAACBYLEDAAAIBIsdAABAIFjsAAAAAsFiBwAAEAgWOwAAgECw2AEAAASCxQ4AACAQLHYAAACBSERRnBIqAAAA/KviEzsAAIBAsNgBAAAEgsUOAAAgECx2AAAAgWCxAwAACASLHQAAQCBY7AAAAALBYgcAABAIFjsAAIBA/H8B3v0d052euQAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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", 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Change the dimension to fit into the model\n", "x_test = test_images.transpose(3,0,1,2)\n", "t_test = test_labels.transpose()\n", "\n", "model = model.to(device)\n", "model.eval()\n", "# with torch.no_grad():\n", "\n", "# Retrieve output from the image\n", "# idx_to_plot = 2\n", "for idx_to_plot in range(10):\n", " image = x_test[idx_to_plot,:,:,:]\n", " image_orig = image.copy()\n", " image = torch.FloatTensor(image).permute(2, 1, 0).to(device)\n", "\n", " # Make input tensor require gradient\n", " # X.requires_grad_()\n", " image = image[None,:].requires_grad_()\n", " print(image.shape)\n", " output = model(image)\n", "\n", " # Catch the output\n", " output_idx = output.argmax()\n", " output_max = output[0, output_idx]\n", "\n", " # Do backpropagation to get the derivative of the output based on the image\n", " output_max.backward()\n", "\n", " # Retireve the saliency map and also pick the maximum value from channels on each pixel.\n", " # In this case, we look at dim=1. Recall the shape (batch_size, channel, width, height)\n", " saliency, _ = torch.max(image.grad.data.abs(), dim=1) \n", " saliency = saliency.reshape(28, 28)\n", "\n", " # # Reshape the image\n", " # image = image.reshape(-1, 28, 28)\n", "\n", " # Visualize the image and the saliency map\n", " fig, ax = plt.subplots(1, 2)\n", " # x_train[count,:,:,0:3]\n", " # print(image.shape)\n", " # image = image.permute(3, 2, 1,0).squeeze()\n", " # print(image.shape)\n", " ax[0].imshow(image_orig[:,:,0:3])\n", " ax[0].axis('off')\n", " ax[1].imshow(saliency.cpu(), cmap='hot')\n", " ax[1].axis('off')\n", " ax[0].set_title('Label: {}, Pred: {}'.format(t_test[idx_to_plot].argmax(),output_idx.cpu()))\n", " plt.tight_layout()\n", " fig.suptitle('The Image and Its Saliency Map')\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 4 }