{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Autoencoder (AE), Variational Autoencoder (VAE) and Generative Adversarial Network (GAN)\n",
"\n",
"Antonio Fonseca\n",
"\n",
"GeoComput & ML\n",
"\n",
"May 25th, 2021"
]
},
{
"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",
"```"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cpu\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 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",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"print(device)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Loading the dataset and create dataloaders\n",
"mnist_train = datasets.MNIST(root = 'data', train=True, download=True, transform = transforms.ToTensor())\n",
"mnist_test = datasets.MNIST(root = 'data', train=False, download=True, transform = transforms.ToTensor())\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Implementing an Autoencoder\n",
"\n",
"Now that you have a basic neural network set up, we'll go through the steps of training an autoencoder that\n",
"can compress the input down to 2 dimensions, and then (attempt to) reconstruct the original image. This\n",
"will be similar to your previous network with one hidden layer, but with many more.\n",
" - Fill in the Autoencoder class with a stack of layers of the following shape: 784-1000-500-250-2-250-\n",
"500-1000-784 You can make use of the nn.Linear function to automatically manage the creation of\n",
"weight and bias parameters. Between each layer, use a tanh activation.\n",
" - Change the activation function going to the middle (2-dim) layer to linear (keeping the rest as tanh).\n",
" - Use the sigmoid activation function on the output of the last hidden layer.\n",
" - Adapt your training function for the autoencoder. Use the same batch size and number of steps (128\n",
"and 5000), but use the ADAM optimizer instead of Gradient Descent. Use Mean Squared Error for\n",
"your reconstruction loss.\n",
" - After training your model, plot the 2 dimensional embeddings of 1000 digits, colored by the image\n",
"labels.\n",
" - Produce side-by-side plots of one original and reconstructed sample of each digit (0 - 9). You can use\n",
"the save_image function from torchvision.utils.\n",
" - Now for something fun: locate the embeddings of two distinct images, and interpolate between them\n",
"to produce some intermediate point in the latent space. Visualize this point in the 2D embedding.\n",
"Then, run your decoder on this fabricated \"embedding\" to see if it the output looks anything like a\n",
"handwritten digit. You might try interpolating between and within several different classes.\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Section 1"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"class Autoencoder(nn.Module):\n",
" def __init__(self):\n",
" super(Autoencoder, self).__init__()\n",
" self.enc_lin1 = nn.Linear(784, 1000)\n",
" self.enc_lin2 = nn.Linear(1000, 500)\n",
" self.enc_lin3 = nn.Linear(500, 250)\n",
" self.enc_lin4 = nn.Linear(250, 2)\n",
" \n",
" self.dec_lin1 = nn.Linear(2, 250)\n",
" self.dec_lin2 = nn.Linear(250, 500)\n",
" self.dec_lin3 = nn.Linear(500, 1000)\n",
" self.dec_lin4 = nn.Linear(1000, 784)\n",
" \n",
" self.tanh = nn.Tanh()\n",
"\n",
" def encode(self, x):\n",
" x = self.enc_lin1(x)\n",
" x = self.tanh(x)\n",
" x = self.enc_lin2(x)\n",
" x = self.tanh(x)\n",
" x = self.enc_lin3(x)\n",
" x = self.tanh(x)\n",
" x = self.enc_lin4(x)\n",
" z = self.tanh(x)\n",
" # ... additional layers, plus possible nonlinearities.\n",
" return z\n",
"\n",
" def decode(self, z):\n",
" # ditto, but in reverse\n",
" x = self.dec_lin1(z)\n",
" x = self.tanh(x)\n",
" x = self.dec_lin2(x)\n",
" x = self.tanh(x)\n",
" x = self.dec_lin3(x)\n",
" x = self.tanh(x)\n",
" x = self.dec_lin4(x)\n",
" x = self.tanh(x)\n",
" return x\n",
"\n",
" def forward(self, x):\n",
" z = self.encode(x)\n",
" return self.decode(z), z"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"batch_size = 128\n",
"test_loader = torch.utils.data.DataLoader(mnist_test, \n",
" batch_size=batch_size, \n",
" shuffle=False)\n",
"train_loader = torch.utils.data.DataLoader(mnist_train, \n",
" batch_size=batch_size, \n",
" shuffle=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"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 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",
" print('optimizer: {}'.format(optimizer))\n",
" if num_epochs is None:\n",
" num_epochs = 100 # obviously, this is too many. I don't know what this author was thinking.\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.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('out.shape: {}'.format(out.shape))\n",
"\n",
" # Calculate the loss\n",
" targets = targets # add an extra dimension to keep CrossEntropy happy.\n",
" if verbose: print('targets.shape: {}'.format(targets.shape))\n",
" loss = loss_fn(out,model_input)\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",
" print(\" Train loss: {:.4f}. Test loss: {:.4f}. Time: {:.4f}\".format(evaluate(model,train_loader,verbose), evaluate(model,test_loader,verbose), (time.time() - start))) #TODO: implement the evaluate function to provide performance statistics during training.\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",
" for data, targets in evaluation_set:\n",
"\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",
" loss = loss_fn(out,model_input)\n",
" \n",
" if verbose: print('out[:5]: {}'.format(out[:5]))\n",
" loss_all+=loss.item()\n",
" loss = loss_all/len(evaluation_set)\n",
" return loss\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Autoencoding MNIST\n",
""
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Autoencoder - with non-linearity (tanh)\n",
"optimizer: Adam (\n",
"Parameter Group 0\n",
" amsgrad: False\n",
" betas: (0.9, 0.999)\n",
" eps: 1e-08\n",
" lr: 0.01\n",
" weight_decay: 0\n",
")\n",
"n. of epochs: 100\n",
" EPOCH 0. Progress: 0.0%. \n",
" Train loss: 1.0428. Test loss: 1.0423. Time: 10.0248\n",
" EPOCH 10. Progress: 10.0%. \n",
" Train loss: 0.8899. Test loss: 0.8877. Time: 9.6755\n",
" EPOCH 20. Progress: 20.0%. \n",
" Train loss: 0.8899. Test loss: 0.8877. Time: 9.5361\n",
" EPOCH 30. Progress: 30.0%. \n",
" Train loss: 0.8899. Test loss: 0.8877. Time: 9.4851\n",
" EPOCH 40. Progress: 40.0%. \n",
" Train loss: 0.8899. Test loss: 0.8877. Time: 9.6068\n",
" EPOCH 50. Progress: 50.0%. \n",
" Train loss: 0.8899. Test loss: 0.8877. Time: 9.5639\n",
" EPOCH 60. Progress: 60.0%. \n",
" Train loss: 0.8899. Test loss: 0.8877. Time: 9.5320\n",
" EPOCH 70. Progress: 70.0%. \n",
" Train loss: 0.8899. Test loss: 0.8877. Time: 9.5468\n",
" EPOCH 80. Progress: 80.0%. \n",
" Train loss: 0.8899. Test loss: 0.8877. Time: 9.5872\n",
" EPOCH 90. Progress: 90.0%. \n",
" Train loss: 0.8899. Test loss: 0.8877. Time: 9.5837\n",
" EPOCH 100. Progress: 100.0%. \n",
" Train loss: 0.8899. Test loss: 0.8877. Time: 9.5013\n",
"Deleting previous model\n",
"optimizer: Adam (\n",
"Parameter Group 0\n",
" amsgrad: False\n",
" betas: (0.9, 0.999)\n",
" eps: 1e-08\n",
" lr: 0.005\n",
" weight_decay: 0\n",
")\n",
"n. of epochs: 100\n",
" EPOCH 0. Progress: 0.0%. \n",
" Train loss: 1.1027. Test loss: 1.1086. Time: 9.4835\n",
" EPOCH 10. Progress: 10.0%. \n",
" Train loss: 1.1190. Test loss: 1.1250. Time: 9.5613\n",
" EPOCH 20. Progress: 20.0%. \n",
" Train loss: 1.1304. Test loss: 1.1366. Time: 9.3534\n",
" EPOCH 30. Progress: 30.0%. \n",
" Train loss: 1.1336. Test loss: 1.1397. Time: 9.6452\n",
" EPOCH 40. Progress: 40.0%. \n",
" Train loss: 1.1353. Test loss: 1.1414. Time: 9.4842\n",
" EPOCH 50. Progress: 50.0%. \n",
" Train loss: 1.1349. Test loss: 1.1411. Time: 9.4263\n",
" EPOCH 60. Progress: 60.0%. \n",
" Train loss: 1.1353. Test loss: 1.1414. Time: 9.5138\n",
" EPOCH 70. Progress: 70.0%. \n",
" Train loss: 1.1367. Test loss: 1.1428. Time: 9.5135\n",
" EPOCH 80. Progress: 80.0%. \n",
" Train loss: 1.1367. Test loss: 1.1428. Time: 9.5350\n",
" EPOCH 90. Progress: 90.0%. \n",
" Train loss: 1.1363. Test loss: 1.1425. Time: 9.4734\n",
" EPOCH 100. Progress: 100.0%. \n",
" Train loss: 1.1361. Test loss: 1.1422. Time: 9.5480\n",
"Deleting previous model\n",
"optimizer: Adam (\n",
"Parameter Group 0\n",
" amsgrad: False\n",
" betas: (0.9, 0.999)\n",
" eps: 1e-08\n",
" lr: 0.001\n",
" weight_decay: 0\n",
")\n",
"n. of epochs: 100\n",
" EPOCH 0. Progress: 0.0%. \n",
" Train loss: 0.0505. Test loss: 0.0503. Time: 9.5554\n",
" EPOCH 10. Progress: 10.0%. \n",
" Train loss: 0.0406. Test loss: 0.0405. Time: 9.4873\n",
" EPOCH 20. Progress: 20.0%. \n",
" Train loss: 0.0389. Test loss: 0.0389. Time: 9.5403\n",
" EPOCH 30. Progress: 30.0%. \n",
" Train loss: 0.0377. Test loss: 0.0378. Time: 9.2778\n",
" EPOCH 40. Progress: 40.0%. \n",
" Train loss: 0.0368. Test loss: 0.0370. Time: 9.5494\n",
" EPOCH 50. Progress: 50.0%. \n",
" Train loss: 0.0368. Test loss: 0.0369. Time: 9.5141\n",
" EPOCH 60. Progress: 60.0%. \n",
" Train loss: 0.0363. Test loss: 0.0366. Time: 9.5806\n",
" EPOCH 70. Progress: 70.0%. \n",
" Train loss: 0.0359. Test loss: 0.0362. Time: 9.5740\n",
" EPOCH 80. Progress: 80.0%. \n",
" Train loss: 0.0356. Test loss: 0.0359. Time: 9.5521\n",
" EPOCH 90. Progress: 90.0%. \n",
" Train loss: 0.0359. Test loss: 0.0362. Time: 9.4949\n",
" EPOCH 100. Progress: 100.0%. \n",
" Train loss: 0.0356. Test loss: 0.0359. Time: 9.5257\n"
]
}
],
"source": [
"# hid_dim_range = [128,256,512]\n",
"lr_range = [0.01,0.005,0.001]\n",
"print('Autoencoder - with non-linearity (tanh)')\n",
"for lr in lr_range:\n",
" if 'model' in globals():\n",
" print('Deleting previous model')\n",
" del model\n",
" model = Autoencoder().to(device)\n",
" ADAM = torch.optim.Adam(model.parameters(), lr = lr) # This is absurdly high.\n",
" loss_fn = nn.MSELoss().to(device)\n",
" train(model,loss_fn, ADAM, train_loader, test_loader,verbose=False)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Autoencoder - with non-linearity (tanh)\n",
"optimizer: Adam (\n",
"Parameter Group 0\n",
" amsgrad: False\n",
" betas: (0.9, 0.999)\n",
" eps: 1e-08\n",
" lr: 0.001\n",
" weight_decay: 0\n",
")\n",
"n. of epochs: 500\n",
" EPOCH 0. Progress: 0.0%. \n",
" Train loss: 0.0498. Test loss: 0.0500. Time: 9.8799\n",
" EPOCH 10. Progress: 2.0%. \n",
" Train loss: 0.0413. Test loss: 0.0415. Time: 9.5658\n",
" EPOCH 20. Progress: 4.0%. \n",
" Train loss: 0.0390. Test loss: 0.0392. Time: 9.5774\n",
" EPOCH 30. Progress: 6.0%. \n",
" Train loss: 0.0380. Test loss: 0.0381. Time: 9.5850\n",
" EPOCH 40. Progress: 8.0%. \n",
" Train loss: 0.0371. Test loss: 0.0373. Time: 9.5798\n",
" EPOCH 50. Progress: 10.0%. \n",
" Train loss: 0.0368. Test loss: 0.0369. Time: 9.5161\n",
" EPOCH 60. Progress: 12.0%. \n",
" Train loss: 0.0363. Test loss: 0.0365. Time: 9.6184\n",
" EPOCH 70. Progress: 14.000000000000002%. \n",
" Train loss: 0.0359. Test loss: 0.0361. Time: 9.4047\n",
" EPOCH 80. Progress: 16.0%. \n",
" Train loss: 0.0357. Test loss: 0.0359. Time: 9.5648\n",
" EPOCH 90. Progress: 18.0%. \n",
" Train loss: 0.0356. Test loss: 0.0359. Time: 9.5302\n",
" EPOCH 100. Progress: 20.0%. \n",
" Train loss: 0.0355. Test loss: 0.0358. Time: 9.5004\n",
" EPOCH 110. Progress: 22.0%. \n",
" Train loss: 0.0354. Test loss: 0.0358. Time: 9.4044\n",
" EPOCH 120. Progress: 24.0%. \n",
" Train loss: 0.0356. Test loss: 0.0359. Time: 9.4904\n",
" EPOCH 130. Progress: 26.0%. \n",
" Train loss: 0.0350. Test loss: 0.0354. Time: 9.5713\n",
" EPOCH 140. Progress: 28.000000000000004%. \n",
" Train loss: 0.0350. Test loss: 0.0354. Time: 9.5394\n",
" EPOCH 150. Progress: 30.0%. \n",
" Train loss: 0.0350. Test loss: 0.0354. Time: 9.5370\n",
" EPOCH 160. Progress: 32.0%. \n",
" Train loss: 0.0354. Test loss: 0.0357. Time: 9.5828\n",
" EPOCH 170. Progress: 34.0%. \n",
" Train loss: 0.0347. Test loss: 0.0352. Time: 9.2973\n",
" EPOCH 180. Progress: 36.0%. \n",
" Train loss: 0.0346. Test loss: 0.0349. Time: 9.5753\n",
" EPOCH 190. Progress: 38.0%. \n",
" Train loss: 0.0352. Test loss: 0.0356. Time: 9.4743\n",
" EPOCH 200. Progress: 40.0%. \n",
" Train loss: 0.0349. Test loss: 0.0353. Time: 9.5419\n",
" EPOCH 210. Progress: 42.0%. \n",
" Train loss: 0.0345. Test loss: 0.0349. Time: 9.5709\n",
" EPOCH 220. Progress: 44.0%. \n",
" Train loss: 0.0345. Test loss: 0.0349. Time: 9.5569\n",
" EPOCH 230. Progress: 46.0%. \n",
" Train loss: 0.0343. Test loss: 0.0348. Time: 9.2725\n",
" EPOCH 240. Progress: 48.0%. \n",
" Train loss: 0.0343. Test loss: 0.0348. Time: 9.4752\n",
" EPOCH 250. Progress: 50.0%. \n",
" Train loss: 0.0348. Test loss: 0.0352. Time: 9.5952\n",
" EPOCH 260. Progress: 52.0%. \n",
" Train loss: 0.0342. Test loss: 0.0348. Time: 9.5204\n",
" EPOCH 270. Progress: 54.0%. \n",
" Train loss: 0.0342. Test loss: 0.0348. Time: 9.4831\n",
" EPOCH 280. Progress: 56.00000000000001%. \n",
" Train loss: 0.0341. Test loss: 0.0346. Time: 9.4748\n",
" EPOCH 290. Progress: 57.99999999999999%. \n",
" Train loss: 0.0340. Test loss: 0.0347. Time: 9.5644\n",
" EPOCH 300. Progress: 60.0%. \n",
" Train loss: 0.0343. Test loss: 0.0348. Time: 9.2511\n",
" EPOCH 310. Progress: 62.0%. \n",
" Train loss: 0.0340. Test loss: 0.0345. Time: 9.5455\n",
" EPOCH 320. Progress: 64.0%. \n",
" Train loss: 0.0339. Test loss: 0.0345. Time: 9.5771\n",
" EPOCH 330. Progress: 66.0%. \n",
" Train loss: 0.0339. Test loss: 0.0345. Time: 9.5510\n",
" EPOCH 340. Progress: 68.0%. \n",
" Train loss: 0.0343. Test loss: 0.0350. Time: 9.5394\n",
" EPOCH 350. Progress: 70.0%. \n",
" Train loss: 0.0342. Test loss: 0.0348. Time: 9.4558\n",
" EPOCH 360. Progress: 72.0%. \n",
" Train loss: 0.0341. Test loss: 0.0347. Time: 9.4981\n",
" EPOCH 370. Progress: 74.0%. \n",
" Train loss: 0.0337. Test loss: 0.0343. Time: 9.4733\n",
" EPOCH 380. Progress: 76.0%. \n",
" Train loss: 0.0338. Test loss: 0.0344. Time: 9.5276\n",
" EPOCH 390. Progress: 78.0%. \n",
" Train loss: 0.0340. Test loss: 0.0345. Time: 9.6180\n",
" EPOCH 400. Progress: 80.0%. \n",
" Train loss: 0.0339. Test loss: 0.0344. Time: 9.6086\n",
" EPOCH 410. Progress: 82.0%. \n",
" Train loss: 0.0337. Test loss: 0.0344. Time: 9.5417\n",
" EPOCH 420. Progress: 84.0%. \n",
" Train loss: 0.0336. Test loss: 0.0342. Time: 9.4374\n",
" EPOCH 430. Progress: 86.0%. \n",
" Train loss: 0.0336. Test loss: 0.0343. Time: 9.5644\n",
" EPOCH 440. Progress: 88.0%. \n",
" Train loss: 0.0336. Test loss: 0.0343. Time: 9.5794\n",
" EPOCH 450. Progress: 90.0%. \n",
" Train loss: 0.0334. Test loss: 0.0340. Time: 9.5730\n",
" EPOCH 460. Progress: 92.0%. \n",
" Train loss: 0.0337. Test loss: 0.0343. Time: 9.5895\n",
" EPOCH 470. Progress: 94.0%. \n",
" Train loss: 0.0336. Test loss: 0.0343. Time: 9.5377\n",
" EPOCH 480. Progress: 96.0%. \n",
" Train loss: 0.0335. Test loss: 0.0342. Time: 9.4630\n",
" EPOCH 490. Progress: 98.0%. \n",
" Train loss: 0.0340. Test loss: 0.0347. Time: 9.5456\n",
" EPOCH 500. Progress: 100.0%. \n",
" Train loss: 0.0337. Test loss: 0.0344. Time: 9.7685\n"
]
}
],
"source": [
"# Training for longer with the lr that gave best result\n",
"lr_range = [0.001]\n",
"print('Autoencoder - with non-linearity (tanh)')\n",
"for lr in lr_range:\n",
" if 'model' in globals():\n",
" print('Deleting previous model')\n",
" del model\n",
" model = Autoencoder().to(device)\n",
" ADAM = torch.optim.Adam(model.parameters(), lr = lr) \n",
" loss_fn = nn.MSELoss().to(device)\n",
" train(model,loss_fn, ADAM, train_loader, test_loader,num_epochs=500,verbose=False)\n",
" \n",
"# # Save the trained model \n",
"# torch.save(model.state_dict(), './models/model_AE.pt')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Load the model\n",
"model_AE = Autoencoder().to(device)\n",
"model_AE.load_state_dict(torch.load('./models/model_AE.pt',map_location=torch.device(device)))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"targets.shape: torch.Size([1000])\n",
"np.unique(targets): [0 1 2 3 4 5 6 7 8 9]\n",
"latentVar.shape: torch.Size([1000, 2])\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"