{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Estimating nitrogen concentrations in streams and rivers using NN\n", "\n", "Antonio Fonseca\n", "\n", "GeoComput & ML\n", "\n", "May 20th, 2021\n", "\n", "Exercise base on the: \n", "\n", "**[Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework](https://www.nature.com/articles/s41597-020-0478-7)**\n", "\n", "Longzhu Q. Shen, Giuseppe Amatulli, Tushar Sethi, Peter Raymond & Sami Domisch \n", "Scientific Data volume 7, Article number: 161 (2020) \n", "\n", "Lecture: [Artificial Neural Networks for geo-data](http://spatial-ecology.net/docs/source/lectures/lect_20210520_NNs_day1.pdf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "\n", "The field of deep learning begins with the assumption that everything is a function, and leverages powerful tools like Gradient Descent to efficiently learn these functions. Although many deep learning tasks (like classification) require supervised learning (with labels, testing, and training sets), a rich subset of the field has developed potent methods for automated, non-linear unsupervised learning, in which all you need to provide is the data. These unsupervised methods include Autoencoders, Variational Autoencoders, and Generative Adversarial Networks. They can be used to visualize data or to compress it; to generate novel data, and even to learn the functions underlying your data. In this assignment, you'll gain hands-on experience using simple networks to perform classification and regression on a variety of datasets, and will then apply these techniques to generate new samples using Variational Autoencoders and GANs. \n", "\n", "This assignment will also serve as a hands-on introduction to PyTorch. At present, PyTorch is the single most popular machine learning library in Python. It provides a framework of pre-built classes and helper functions to greatly simplify the creation of neural networks and their paraphernalia. Before PyTorch and its ilk, machine learning researchers were known to spend days juggling weight and bias vectors, or tediously implementing their own data processing functions. With PyTorch, this takes minutes. \n", "\n", "Before diving into this assignment, you'll need to install PyTorch and some other important packages (see below). The PyTorch website provides an interactive quick-start guide to tailor the installation to your system's configuration https://pytorch.org/get-started/locally/. (The installation instructions will ask you to install torchvision in addition to torch. Thankfully we have a VM prepared for you!)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Needed packages for this lecture:\n", "```\n", "conda install pandas\n", "conda install -c conda-forge scikit-learn \n", "conda install -c anaconda seaborn\n", "conda install -c conda-forge tensorflow\n", "conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch\n", "conda install ipywidgets\n", "```\n", "\n", "\n", "**BackGround**\n", "\n", "- Geoenviornmental variables \n", "- Ground observationd : Nitrogen in US streams \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 os\n", "import copy\n", "import json\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 pandas as pd \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": "markdown", "metadata": {}, "source": [ "## Loading data and Logistic Regression" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n", "Failed to download (trying next):\n", "HTTP Error 503: Service Unavailable\n", "\n", "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n", "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to data/MNIST/raw/train-images-idx3-ubyte.gz\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2ef8aa8ad75949b28b1fef7646f1fa59", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=9912422.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Extracting data/MNIST/raw/train-images-idx3-ubyte.gz to data/MNIST/raw\n", "\n", "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n", "Failed to download (trying next):\n", "HTTP Error 503: Service Unavailable\n", "\n", "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\n", "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to data/MNIST/raw/train-labels-idx1-ubyte.gz\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "de929aa4713a4c82a77406412d85446a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=28881.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Extracting data/MNIST/raw/train-labels-idx1-ubyte.gz to data/MNIST/raw\n", "\n", "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n", "Failed to download (trying next):\n", "HTTP Error 503: Service Unavailable\n", "\n", "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\n", "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to data/MNIST/raw/t10k-images-idx3-ubyte.gz\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9d4836c67e774e62a6b8bbc45a6967c7", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=1648877.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Extracting data/MNIST/raw/t10k-images-idx3-ubyte.gz to data/MNIST/raw\n", "\n", "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n", "Failed to download (trying next):\n", "HTTP Error 503: Service Unavailable\n", "\n", "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n", "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3d130ebe28c843babb9205b3017f0380", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=4542.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Extracting data/MNIST/raw/t10k-labels-idx1-ubyte.gz to data/MNIST/raw\n", "\n", "Processing...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/user/miniconda3/lib/python3.8/site-packages/torchvision/datasets/mnist.py:502: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /opt/conda/conda-bld/pytorch_1616554788289/work/torch/csrc/utils/tensor_numpy.cpp:143.)\n", " return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Done!\n" ] } ], "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": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Prepare the dataset\n", "class MyDataset(Dataset):\n", " def __init__(self, data, target):\n", " \n", " self.data = torch.from_numpy(data).float()\n", " self.target = torch.from_numpy(target).float()\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)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "def DataPreProcessing(verbose=False):\n", " dataset = pd.read_csv(\"/media/sf_LVM_shared/my_SE_data/exercise/txt/US_TN_season_1_proc.csv\")\t\n", " \n", " #Check for NaN in this table and drop them if there are\n", " dataset.isna().sum()\n", " dataset.dropna()\n", "\n", " # Remove extra variables from the dataset (keep just the 47 predictors and the 'bcmean' (what we are predicting)\n", " dataset = dataset.drop([\"RVunif_bc\",\"mean\",\"std\",\"cv\",\"longitude\",\"latitude\",\"RVunif\"],axis=1)\n", " if verbose:\n", " print('Example of the dataset: \\n',dataset.head())\n", "\n", " dataset_orig = dataset.copy()\n", "\n", " # Rescale: differences in scales accross input variables may increase the difficulty of the problem being modeled and results on unstable weights for connections\n", " sc = MinMaxScaler(feature_range = (0,1)) #Scaling features to a range between 0 and 1\n", "\n", " # Scaling and translating each feature to our chosen range\n", " dataset = sc.fit_transform(dataset) \n", " dataset = pd.DataFrame(dataset, columns = dataset_orig.columns)\n", " if verbose:\n", " print('dataset (after transform): \\n',dataset.head())\n", " dataset_scaled = dataset.copy() #Just backup\n", " inverse_data = sc.inverse_transform(dataset) #just to make sure it works\n", " inverse_data = pd.DataFrame(inverse_data, columns = dataset_orig.columns)\n", " if verbose: \n", " print('inverse_data: \\n',inverse_data.head())\n", " \n", "\n", " #Check the overall stats\n", " train_stats = dataset.describe()\n", " train_stats.pop('bcmean') #because that is what we are trying to predict\n", " train_stats = train_stats.transpose()\n", " if verbose: print('train_stats: ',train_stats) #now train_stats has 47 predictors (as described in the paper).\n", "\n", " \n", " labels = dataset.pop('bcmean')\n", " if verbose: print('labels.describe: ',labels.describe())\n", " dataset = MyDataset(dataset.to_numpy(), labels.to_numpy())\n", " dataset_size = len(dataset)\n", " if verbose: print('dataset_size: {}'.format(dataset_size))\n", " validation_split=0.3\n", "\n", " batch_size=25 #How many samples are actually going to be selected\n", "\n", " # -- split dataset\n", " indices = list(range(dataset_size))\n", " split = int(np.floor(validation_split*dataset_size))\n", " if verbose: print('samples in validation: {}'.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", " train_sampler = RandomSampler(train_indices)\n", " valid_sampler = RandomSampler(val_indices)\n", "\n", " dataloaders = {\n", " 'train': torch.utils.data.DataLoader(dataset, batch_size=batch_size, num_workers=1, sampler=train_sampler),\n", " 'val': torch.utils.data.DataLoader(dataset, batch_size=batch_size, num_workers=1, sampler=valid_sampler),\n", " 'test': torch.utils.data.DataLoader(dataset, batch_size=dataset_size, num_workers=1, shuffle=False),\n", " 'all_val': torch.utils.data.DataLoader(dataset, batch_size=split, num_workers=1, shuffle=True),\n", " }\n", "\n", "\n", " if verbose: \n", " #Inspect the original mean (still missing some formatting)\n", " sns.set()\n", " f, (ax1,ax2) = plt.subplots(2, 1,sharex=True)\n", " sns.distplot(labels,hist=True,kde=False,bins=75,color='darkblue', ax=ax1, axlabel=False)\n", " sns.kdeplot(labels,bw=0.15,legend=True,color='darkblue', ax=ax2)\n", "\n", " ax1.set_title('Original histogram')\n", " ax1.legend(['bcmean'])\n", " ax2.set_title('KDE')\n", " ax2.set_xlabel('Mean Concentration N')\n", " ax1.set_ylabel('Count')\n", " ax2.set_ylabel('Dist')\n", " \n", " # Inspect the joint distribution of a few pairs of columns from the training set\n", " # We can observe that the process of scalling the data did not affect the skewness of the data\n", " if verbose: \n", " sns.pairplot(dataset_scaled[[\"lc09\", \"lc07\", \"hydro05\", \"hydro07\",\"soil01\",\"dem\"]], diag_kind=\"kde\")\n", " plt.show()\n", " \n", " return dataloaders\n", "\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Example of the dataset: \n", " lc01 lc02 lc03 lc04 lc05 lc06 lc07 lc08 lc09 lc10 ... hydro14 \\\n", "0 66 0 1 33 0 0 0 0 0 0 ... 4418 \n", "1 53 0 2 43 0 0 0 0 0 0 ... 3710 \n", "2 28 0 15 45 23 22 12 0 0 0 ... 38819396 \n", "3 59 0 2 33 0 0 6 0 0 0 ... 7735 \n", "4 52 0 2 43 0 0 1 0 0 0 ... 3999 \n", "\n", " hydro15 hydro16 hydro17 hydro18 hydro19 dem slope order \\\n", "0 68 182937 21313 182937 21313 498 407 2 \n", "1 68 155202 18107 155202 18107 365 402 2 \n", "2 29 310689856 136347296 310689856 136347296 1470 492 8 \n", "3 67 302169 37266 302169 37266 342 467 2 \n", "4 68 167904 19575 167904 19575 341 391 2 \n", "\n", " bcmean \n", "0 -1.435592 \n", "1 -1.070739 \n", "2 -0.474586 \n", "3 -0.747083 \n", "4 -0.589795 \n", "\n", "[5 rows x 48 columns]\n", "dataset (after transform): \n", " lc01 lc02 lc03 lc04 lc05 lc06 lc07 lc08 lc09 \\\n", "0 0.880000 0.0 0.010204 0.478261 0.0000 0.00000 0.000000 0.0 0.0 \n", "1 0.706667 0.0 0.020408 0.623188 0.0000 0.00000 0.000000 0.0 0.0 \n", "2 0.373333 0.0 0.153061 0.652174 0.2875 0.30137 0.127660 0.0 0.0 \n", "3 0.786667 0.0 0.020408 0.478261 0.0000 0.00000 0.063830 0.0 0.0 \n", "4 0.693333 0.0 0.020408 0.623188 0.0000 0.00000 0.010638 0.0 0.0 \n", "\n", " lc10 ... hydro14 hydro15 hydro16 hydro17 hydro18 hydro19 \\\n", "0 0.0 ... 0.000024 0.759036 0.000140 0.000035 0.000140 0.000035 \n", "1 0.0 ... 0.000020 0.759036 0.000118 0.000030 0.000118 0.000030 \n", "2 0.0 ... 0.209210 0.289157 0.237482 0.225111 0.237482 0.225111 \n", "3 0.0 ... 0.000042 0.746988 0.000231 0.000061 0.000231 0.000061 \n", "4 0.0 ... 0.000021 0.759036 0.000128 0.000032 0.000128 0.000032 \n", "\n", " dem slope order bcmean \n", "0 0.143854 0.374539 0.25 0.028016 \n", "1 0.105202 0.369926 0.25 0.130145 \n", "2 0.426330 0.452952 1.00 0.297018 \n", "3 0.098518 0.429889 0.25 0.220742 \n", "4 0.098227 0.359779 0.25 0.264769 \n", "\n", "[5 rows x 48 columns]\n", "inverse_data: \n", " lc01 lc02 lc03 lc04 lc05 lc06 lc07 lc08 lc09 lc10 ... \\\n", "0 66.0 0.0 1.0 33.0 0.0 0.0 0.0 0.0 0.0 0.0 ... \n", "1 53.0 0.0 2.0 43.0 0.0 0.0 0.0 0.0 0.0 0.0 ... \n", "2 28.0 0.0 15.0 45.0 23.0 22.0 12.0 0.0 0.0 0.0 ... \n", "3 59.0 0.0 2.0 33.0 0.0 0.0 6.0 0.0 0.0 0.0 ... \n", "4 52.0 0.0 2.0 43.0 0.0 0.0 1.0 0.0 0.0 0.0 ... \n", "\n", " hydro14 hydro15 hydro16 hydro17 hydro18 hydro19 \\\n", "0 4418.0 68.0 182937.0 21313.0 182937.0 21313.0 \n", "1 3710.0 68.0 155202.0 18107.0 155202.0 18107.0 \n", "2 38819396.0 29.0 310689856.0 136347296.0 310689856.0 136347296.0 \n", "3 7735.0 67.0 302169.0 37266.0 302169.0 37266.0 \n", "4 3999.0 68.0 167904.0 19575.0 167904.0 19575.0 \n", "\n", " dem slope order bcmean \n", "0 498.0 407.0 2.0 -1.435592 \n", "1 365.0 402.0 2.0 -1.070739 \n", "2 1470.0 492.0 8.0 -0.474586 \n", "3 342.0 467.0 2.0 -0.747083 \n", "4 341.0 391.0 2.0 -0.589795 \n", "\n", "[5 rows x 48 columns]\n", "train_stats: count mean std min 25% 50% 75% max\n", "lc01 1118.0 0.137448 0.193109 0.0 0.013333 0.053333 0.173333 1.0\n", "lc02 1118.0 0.008497 0.057215 0.0 0.000000 0.000000 0.000000 1.0\n", "lc03 1118.0 0.204839 0.247051 0.0 0.020408 0.081633 0.364796 1.0\n", "lc04 1118.0 0.220464 0.175918 0.0 0.101449 0.159420 0.275362 1.0\n", "lc05 1118.0 0.033777 0.107309 0.0 0.000000 0.000000 0.012500 1.0\n", "lc06 1118.0 0.149607 0.162242 0.0 0.041096 0.095890 0.191781 1.0\n", "lc07 1118.0 0.339512 0.271629 0.0 0.106383 0.265957 0.542553 1.0\n", "lc08 1118.0 0.005134 0.057887 0.0 0.000000 0.000000 0.000000 1.0\n", "lc09 1118.0 0.080954 0.188890 0.0 0.000000 0.012048 0.048193 1.0\n", "lc10 1118.0 0.004584 0.054904 0.0 0.000000 0.000000 0.000000 1.0\n", "lc11 1118.0 0.014183 0.067649 0.0 0.000000 0.000000 0.000000 1.0\n", "lc12 1118.0 0.018244 0.069379 0.0 0.000000 0.000000 0.015873 1.0\n", "prec 1118.0 0.010144 0.066673 0.0 0.000039 0.000184 0.001226 1.0\n", "tmin 1118.0 0.497205 0.158301 0.0 0.400538 0.492608 0.586022 1.0\n", "tmax 1118.0 0.528114 0.172596 0.0 0.405479 0.526027 0.642123 1.0\n", "soil01 1118.0 0.090513 0.072919 0.0 0.053125 0.071875 0.112500 1.0\n", "soil02 1118.0 0.492454 0.187220 0.0 0.358974 0.461538 0.615385 1.0\n", "soil03 1118.0 0.517907 0.172268 0.0 0.431373 0.509804 0.588235 1.0\n", "soil04 1118.0 0.493104 0.173381 0.0 0.387097 0.483871 0.580645 1.0\n", "soil05 1118.0 0.383697 0.128425 0.0 0.331081 0.405405 0.459459 1.0\n", "soil06 1118.0 0.211986 0.160954 0.0 0.114286 0.171429 0.257143 1.0\n", "soil07 1118.0 0.289927 0.159142 0.0 0.138889 0.291667 0.416667 1.0\n", "soil08 1118.0 0.701262 0.114678 0.0 0.656499 0.718833 0.773210 1.0\n", "soil09 1118.0 0.978158 0.090370 0.0 1.000000 1.000000 1.000000 1.0\n", "soil10 1118.0 0.210858 0.122884 0.0 0.130435 0.217391 0.260870 1.0\n", "hydro01 1118.0 0.448534 0.193156 0.0 0.320513 0.431624 0.585470 1.0\n", "hydro02 1118.0 0.396587 0.152295 0.0 0.290598 0.358974 0.435897 1.0\n", "hydro03 1118.0 0.400296 0.187627 0.0 0.281250 0.375000 0.531250 1.0\n", "hydro04 1118.0 0.550702 0.166883 0.0 0.467990 0.567450 0.618271 1.0\n", "hydro05 1118.0 0.570012 0.182000 0.0 0.433333 0.560000 0.718333 1.0\n", "hydro06 1118.0 0.447054 0.170048 0.0 0.346204 0.439791 0.562173 1.0\n", "hydro07 1118.0 0.584048 0.161894 0.0 0.509434 0.586478 0.672956 1.0\n", "hydro08 1118.0 0.650470 0.212953 0.0 0.537500 0.712500 0.790625 1.0\n", "hydro09 1118.0 0.471208 0.243332 0.0 0.294964 0.386091 0.740408 1.0\n", "hydro10 1118.0 0.593690 0.191173 0.0 0.469613 0.588398 0.745856 1.0\n", "hydro11 1118.0 0.434005 0.176386 0.0 0.320917 0.418338 0.564470 1.0\n", "hydro12 1118.0 0.011632 0.073962 0.0 0.000046 0.000223 0.001267 1.0\n", "hydro13 1118.0 0.012683 0.077900 0.0 0.000042 0.000218 0.001422 1.0\n", "hydro14 1118.0 0.010287 0.069454 0.0 0.000042 0.000209 0.001256 1.0\n", "hydro15 1118.0 0.258196 0.216782 0.0 0.096386 0.168675 0.385542 1.0\n", "hydro16 1118.0 0.012388 0.077245 0.0 0.000041 0.000211 0.001403 1.0\n", "hydro17 1118.0 0.010475 0.069597 0.0 0.000047 0.000219 0.001358 1.0\n", "hydro18 1118.0 0.012388 0.077245 0.0 0.000041 0.000211 0.001403 1.0\n", "hydro19 1118.0 0.010475 0.069597 0.0 0.000047 0.000219 0.001358 1.0\n", "dem 1118.0 0.155945 0.206628 0.0 0.047733 0.085731 0.140294 1.0\n", "slope 1118.0 0.139799 0.166496 0.0 0.035978 0.071033 0.174354 1.0\n", "order 1118.0 0.338327 0.229210 0.0 0.125000 0.375000 0.500000 1.0\n", "labels.describe: count 1118.000000\n", "mean 0.511352\n", "std 0.229915\n", "min 0.000000\n", "25% 0.341402\n", "50% 0.509617\n", "75% 0.676309\n", "max 1.000000\n", "Name: bcmean, dtype: float64\n", "dataset_size: 1118\n", "samples in validation: 335\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/user/miniconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", " warnings.warn(msg, FutureWarning)\n", "/home/user/miniconda3/lib/python3.8/site-packages/seaborn/distributions.py:1659: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.15 for `bw_method`, but please see the docs for the new parameters and update your code.\n", " warnings.warn(msg, FutureWarning)\n" ] }, { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Dim of raw sample image: torch.Size([25, 47])\n", "\n", "Dim of labels: torch.Size([25])\n", "\n", "max 1.0, min 0.0\n", "max_labels 0.9762871265411377, min 0.10735689848661423\n" ] } ], "source": [ "dataloaders = DataPreProcessing(verbose=True)\n", "\n", "#Check out how the dataloeader works\n", "dataiter = iter(dataloaders['val'])\n", "samples_tmp, labels_tmp = dataiter.next()\n", "\n", "print('Dim of raw sample image: {}\\n'.format(samples_tmp.shape))\n", "print('Dim of labels: {}\\n'.format(labels_tmp.shape))\n", "print('max {}, min {}'.format(torch.max(samples_tmp),torch.min(samples_tmp)))\n", "print('max_labels {}, min {}'.format(torch.max(labels_tmp),torch.min(labels_tmp)))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Training and Evaluation routines\n", "def train(model,loss_fn, optimizer, train_loader, test_loader, config_str, 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", " path_to_save = './plots'\n", " if not os.path.exists(path_to_save):\n", " os.makedirs(path_to_save)\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", " train_loss=[]\n", " val_loss=[]\n", " r2train=[]\n", " r2val=[]\n", " for epoch in range(num_epochs+1):\n", " # loop through each data point in the training set\n", " all_loss_train=0\n", " for data, targets in train_loader:\n", " start = time.time()\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('model_input: {}'.format(model_input))\n", " \n", " # Clear gradients w.r.t. parameters\n", " optimizer.zero_grad()\n", " \n", " out = model(model_input).squeeze()\n", " if verbose:\n", " print('targets: {}'.format(targets.shape))\n", " print('out: {}'.format(out.shape))\n", "\n", " # Calculate the loss\n", " targets = targets.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", " all_loss_train += loss.item()\n", " train_loss.append(all_loss_train/len(train_loader))\n", " \n", " with torch.no_grad():\n", " all_loss_val=0\n", " for data, targets in test_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", " out = model(model_input).squeeze()\n", " targets = targets.to(device) # add an extra dimension to keep CrossEntropy happy.\n", " loss = loss_fn(out,targets)\n", " all_loss_val += loss.item()\n", " val_loss.append(all_loss_val/len(test_loader))\n", " \n", " \n", " # Give status reports every 100 epochs\n", " if epoch % 10==0:\n", " print(f\" EPOCH {epoch}. Progress: {epoch/num_epochs*100}%. \")\n", " r2train.append(evaluate(model,train_loader,verbose))\n", " r2val.append(evaluate(model,test_loader,verbose))\n", " print(\" Training R^2: {:.4f}. Test R^2: {:.4f}. Loss Train: {:.4f}. Loss Val: {:.4f}. Time: {:.4f}\".format(r2train[-1], r2val[-1], \n", " train_loss[-1], val_loss[-1], 10*(time.time() - start))) #TODO: implement the evaluate function to provide performance statistics during training.\n", " \n", " # Plot \n", " plt.close('all')\n", " fig,ax = plt.subplots(1,2,figsize=(10,5))\n", " ax[0].plot(np.arange(num_epochs+1),train_loss, label='Training')\n", " ax[0].plot(np.arange(num_epochs+1),val_loss, label='Test')\n", " ax[0].set_xlabel('Epochs')\n", " ax[0].set_ylabel('Loss')\n", " ax[0].legend()\n", " \n", " ax[1].plot(np.arange(0,num_epochs+1,10),r2train, label='Training')\n", " ax[1].plot(np.arange(0,num_epochs+1,10),r2val, label='Test')\n", " ax[1].set_xlabel('Epochs')\n", " ax[1].set_ylabel('Loss')\n", " ax[1].legend()\n", " \n", " plt.show()\n", " print('saving ', os.path.join(path_to_save, config_str + '.png'))\n", " fig.savefig(os.path.join(path_to_save, config_str + '.png'), bbox_inches='tight')\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", " r_score = []\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) #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", " predicted = model(model_input).squeeze()\n", "\n", " if verbose:\n", " print('predicted[:5]: {}'.format(predicted[:5].cpu()))\n", " print('targets[:5]: {}'.format(targets[:5]))\n", "\n", " r_score.append(r2_score(targets, predicted.cpu()))\n", " if verbose: print('r2_score(targets, out): ',r2_score(targets, predicted.cpu()))\n", "\n", " r_score = np.array(r_score)\n", " r_score = r_score.mean()\n", " \n", " return r_score\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Testing a regular FFnet\n", "class LogisticRegression(nn.Module):\n", " def __init__(self, in_dim,out_dim,verbose=False):\n", " super(LogisticRegression, self).__init__()\n", " # Linear function\n", " self.fc1 = nn.Linear(in_dim, out_dim) \n", " torch.nn.init.zeros_(self.fc1.weight)\n", " torch.nn.init.zeros_(self.fc1.bias)\n", "\n", " def forward(self, x):\n", " # Linear function\n", " out = self.fc1(x)\n", " return out" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "tags": [] }, "outputs": [], "source": [ "lr_range = [0.01, 0.001,0.0001]\n", "weight_decay_range = [0,0.1,0.01,0.001]\n", "momentum_range = [0,0.1,0.01,0.001]\n", "dampening_range = [0,0.1,0.01,0.001]\n", "nesterov_range = [False]\n", "for lr in lr_range:\n", " for momentum in momentum_range:\n", " for weight_decay in weight_decay_range:\n", " for nesterov in nesterov_range:\n", " for dampening in dampening_range:\n", " print('\\nlr: {}, momentum: {}, weight_decay: {}, dampening: {}, nesterov: {} '.format(lr, momentum, weight_decay, dampening, nesterov))\n", " model = LogisticRegression(in_dim=47,out_dim=1, verbose=False).to(device)\n", " print(model)\n", " SGD = torch.optim.SGD(model.parameters(), lr = lr, momentum=momentum, dampening=dampening, weight_decay=weight_decay, nesterov=nesterov) # This is absurdly high.\n", " # initialize the loss function. You don't want to use this one, so change it accordingly\n", " loss_fn = nn.MSELoss()\n", " config_str = 'lr' + str(lr) + '_momentum' + str(momentum) + '_wdecay' + str(weight_decay) + '_dampening' + str(dampening) +'_nesterov' + str(nesterov)\n", " train(model,loss_fn, SGD, dataloaders['train'], dataloaders['val'], config_str,num_epochs=100, verbose=False)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "lr: 0.01, momentum: 0.1, weight_decay: 0.001, dampening: 0, nesterov: False \n", "LogisticRegression(\n", " (fc1): Linear(in_features=47, out_features=1, bias=True)\n", ")\n", "optimizer: SGD (\n", "Parameter Group 0\n", " dampening: 0\n", " lr: 0.01\n", " momentum: 0.1\n", " nesterov: False\n", " weight_decay: 0.001\n", ")\n", "n. of epochs: 500\n", " EPOCH 0. Progress: 0.0%. \n", " Training R^2: 0.1292. Test R^2: 0.1760. Loss Train: 0.0809. Loss Val: 0.0527. Time: 5.9460\n", " EPOCH 10. Progress: 2.0%. \n", " Training R^2: 0.4196. Test R^2: 0.3470. Loss Train: 0.0322. Loss Val: 0.0412. Time: 2.5306\n", " EPOCH 20. Progress: 4.0%. \n", " Training R^2: 0.4320. Test R^2: 0.3854. Loss Train: 0.0303. Loss Val: 0.0404. Time: 2.1711\n", " EPOCH 30. Progress: 6.0%. \n", " Training R^2: 0.4587. Test R^2: 0.4069. Loss Train: 0.0289. Loss Val: 0.0396. Time: 3.1840\n", " EPOCH 40. Progress: 8.0%. \n", " Training R^2: 0.4671. Test R^2: 0.3842. Loss Train: 0.0282. Loss Val: 0.0383. Time: 2.2062\n", " EPOCH 50. Progress: 10.0%. \n", " Training R^2: 0.4788. Test R^2: 0.4042. Loss Train: 0.0279. Loss Val: 0.0382. Time: 2.1512\n", " EPOCH 60. Progress: 12.0%. \n", " Training R^2: 0.4832. Test R^2: 0.3908. Loss Train: 0.0277. Loss Val: 0.0405. Time: 2.3680\n", " EPOCH 70. Progress: 14.000000000000002%. \n", " Training R^2: 0.4932. Test R^2: 0.3883. Loss Train: 0.0272. Loss Val: 0.0390. Time: 2.0704\n", " EPOCH 80. Progress: 16.0%. \n", " Training R^2: 0.4834. Test R^2: 0.4274. Loss Train: 0.0272. Loss Val: 0.0373. Time: 2.1498\n", " EPOCH 90. Progress: 18.0%. \n", " Training R^2: 0.4877. Test R^2: 0.4394. Loss Train: 0.0268. Loss Val: 0.0375. Time: 2.1138\n", " EPOCH 100. Progress: 20.0%. \n", " Training R^2: 0.4941. Test R^2: 0.3365. Loss Train: 0.0270. Loss Val: 0.0365. Time: 2.3166\n", " EPOCH 110. Progress: 22.0%. \n", " Training R^2: 0.4872. Test R^2: 0.3795. Loss Train: 0.0267. Loss Val: 0.0376. Time: 2.1446\n", " EPOCH 120. Progress: 24.0%. \n", " Training R^2: 0.4927. Test R^2: 0.4362. Loss Train: 0.0264. Loss Val: 0.0374. Time: 2.8796\n", " EPOCH 130. Progress: 26.0%. \n", " Training R^2: 0.4973. Test R^2: 0.4363. Loss Train: 0.0267. Loss Val: 0.0379. Time: 3.0462\n", " EPOCH 140. Progress: 28.000000000000004%. \n", " Training R^2: 0.4965. Test R^2: 0.4315. Loss Train: 0.0267. Loss Val: 0.0377. Time: 2.1279\n", " EPOCH 150. Progress: 30.0%. \n", " Training R^2: 0.4898. Test R^2: 0.3951. Loss Train: 0.0267. Loss Val: 0.0361. Time: 2.0948\n", " EPOCH 160. Progress: 32.0%. \n", " Training R^2: 0.5092. Test R^2: 0.4080. Loss Train: 0.0267. Loss Val: 0.0386. Time: 2.1599\n", " EPOCH 170. Progress: 34.0%. \n", " Training R^2: 0.5045. Test R^2: 0.4345. Loss Train: 0.0263. Loss Val: 0.0363. Time: 2.7793\n", " EPOCH 180. Progress: 36.0%. \n", " Training R^2: 0.4790. Test R^2: 0.4126. Loss Train: 0.0262. Loss Val: 0.0354. Time: 2.1298\n", " EPOCH 190. Progress: 38.0%. \n", " Training R^2: 0.4640. Test R^2: 0.4252. Loss Train: 0.0262. Loss Val: 0.0364. Time: 2.1042\n", " EPOCH 200. Progress: 40.0%. \n", " Training R^2: 0.5035. Test R^2: 0.4357. Loss Train: 0.0261. Loss Val: 0.0350. Time: 2.1428\n", " EPOCH 210. Progress: 42.0%. \n", " Training R^2: 0.5116. Test R^2: 0.4321. Loss Train: 0.0259. Loss Val: 0.0359. Time: 2.1280\n", " EPOCH 220. Progress: 44.0%. \n", " Training R^2: 0.5220. Test R^2: 0.4098. Loss Train: 0.0263. Loss Val: 0.0364. Time: 2.0515\n", " EPOCH 230. Progress: 46.0%. \n", " Training R^2: 0.5080. Test R^2: 0.3754. Loss Train: 0.0262. Loss Val: 0.0354. Time: 2.1203\n", " EPOCH 240. Progress: 48.0%. \n", " Training R^2: 0.5181. Test R^2: 0.4549. Loss Train: 0.0259. Loss Val: 0.0357. Time: 2.0732\n", " EPOCH 250. Progress: 50.0%. \n", " Training R^2: 0.5137. Test R^2: 0.4251. Loss Train: 0.0260. Loss Val: 0.0371. Time: 2.1298\n", " EPOCH 260. Progress: 52.0%. \n", " Training R^2: 0.5134. Test R^2: 0.4464. Loss Train: 0.0261. Loss Val: 0.0360. Time: 2.3224\n", " EPOCH 270. Progress: 54.0%. \n", " Training R^2: 0.4937. Test R^2: 0.4324. Loss Train: 0.0257. Loss Val: 0.0357. Time: 2.0788\n", " EPOCH 280. Progress: 56.00000000000001%. \n", " Training R^2: 0.5045. Test R^2: 0.4489. Loss Train: 0.0257. Loss Val: 0.0367. Time: 2.2380\n", " EPOCH 290. Progress: 57.99999999999999%. \n", " Training R^2: 0.5001. Test R^2: 0.4146. Loss Train: 0.0260. Loss Val: 0.0359. Time: 2.1239\n", " EPOCH 300. Progress: 60.0%. \n", " Training R^2: 0.5207. Test R^2: 0.4330. Loss Train: 0.0260. Loss Val: 0.0355. Time: 2.6492\n", " EPOCH 310. Progress: 62.0%. \n", " Training R^2: 0.5231. Test R^2: 0.4120. Loss Train: 0.0267. Loss Val: 0.0363. Time: 2.3547\n", " EPOCH 320. Progress: 64.0%. \n", " Training R^2: 0.5032. Test R^2: 0.4650. Loss Train: 0.0257. Loss Val: 0.0366. Time: 2.1155\n", " EPOCH 330. Progress: 66.0%. \n", " Training R^2: 0.5227. Test R^2: 0.4368. Loss Train: 0.0259. Loss Val: 0.0365. Time: 2.0535\n", " EPOCH 340. Progress: 68.0%. \n", " Training R^2: 0.5147. Test R^2: 0.4457. Loss Train: 0.0262. Loss Val: 0.0385. Time: 2.6143\n", " EPOCH 350. Progress: 70.0%. \n", " Training R^2: 0.5198. Test R^2: 0.4558. Loss Train: 0.0260. Loss Val: 0.0356. Time: 3.3361\n", " EPOCH 360. Progress: 72.0%. \n", " Training R^2: 0.5272. Test R^2: 0.4013. Loss Train: 0.0259. Loss Val: 0.0356. Time: 2.0422\n", " EPOCH 370. Progress: 74.0%. \n", " Training R^2: 0.4989. Test R^2: 0.4503. Loss Train: 0.0263. Loss Val: 0.0345. Time: 2.2167\n", " EPOCH 380. Progress: 76.0%. \n", " Training R^2: 0.5141. Test R^2: 0.4529. Loss Train: 0.0261. Loss Val: 0.0352. Time: 2.3632\n", " EPOCH 390. Progress: 78.0%. \n", " Training R^2: 0.4922. Test R^2: 0.4053. Loss Train: 0.0258. Loss Val: 0.0360. Time: 3.0163\n", " EPOCH 400. Progress: 80.0%. \n", " Training R^2: 0.5315. Test R^2: 0.4407. Loss Train: 0.0259. Loss Val: 0.0352. Time: 2.4278\n", " EPOCH 410. Progress: 82.0%. \n", " Training R^2: 0.4209. Test R^2: 0.4272. Loss Train: 0.0257. Loss Val: 0.0351. Time: 2.7372\n", " EPOCH 420. Progress: 84.0%. \n", " Training R^2: 0.5020. Test R^2: 0.4106. Loss Train: 0.0256. Loss Val: 0.0351. Time: 2.3255\n", " EPOCH 430. Progress: 86.0%. \n", " Training R^2: 0.5092. Test R^2: 0.4564. Loss Train: 0.0256. Loss Val: 0.0353. Time: 2.3361\n", " EPOCH 440. Progress: 88.0%. \n", " Training R^2: 0.5206. Test R^2: 0.3941. Loss Train: 0.0262. Loss Val: 0.0344. Time: 2.4134\n", " EPOCH 450. Progress: 90.0%. \n", " Training R^2: 0.5316. Test R^2: 0.4250. Loss Train: 0.0253. Loss Val: 0.0352. Time: 2.3447\n", " EPOCH 460. Progress: 92.0%. \n", " Training R^2: 0.5000. Test R^2: 0.3961. Loss Train: 0.0260. Loss Val: 0.0354. Time: 2.1631\n", " EPOCH 470. Progress: 94.0%. \n", " Training R^2: 0.5037. Test R^2: 0.4630. Loss Train: 0.0254. Loss Val: 0.0347. Time: 2.8979\n", " EPOCH 480. Progress: 96.0%. \n", " Training R^2: 0.5328. Test R^2: 0.3998. Loss Train: 0.0255. Loss Val: 0.0366. Time: 2.0738\n", " EPOCH 490. Progress: 98.0%. \n", " Training R^2: 0.5091. Test R^2: 0.4204. Loss Train: 0.0254. Loss Val: 0.0349. Time: 1.9961\n", " EPOCH 500. Progress: 100.0%. \n", " Training R^2: 0.5191. Test R^2: 0.4330. Loss Train: 0.0256. Loss Val: 0.0351. Time: 3.1330\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "saving ./plots/lr0.01LONGER_momentum0.1_wdecay0.001_dampening0_nesterovFalse.png\n", "\n", "lr: 0.01, momentum: 0.9, weight_decay: 0.001, dampening: 0, nesterov: False \n", "LogisticRegression(\n", " (fc1): Linear(in_features=47, out_features=1, bias=True)\n", ")\n", "optimizer: SGD (\n", "Parameter Group 0\n", " dampening: 0\n", " lr: 0.01\n", " momentum: 0.9\n", " nesterov: False\n", " weight_decay: 0.001\n", ")\n", "n. of epochs: 500\n", " EPOCH 0. Progress: 0.0%. \n", " Training R^2: 0.2370. Test R^2: 0.1497. Loss Train: 0.0843. Loss Val: 0.0538. Time: 3.2603\n", " EPOCH 10. Progress: 2.0%. \n", " Training R^2: 0.4846. Test R^2: 0.4289. Loss Train: 0.0270. Loss Val: 0.0367. Time: 2.6736\n", " EPOCH 20. Progress: 4.0%. \n", " Training R^2: 0.5036. Test R^2: 0.4182. Loss Train: 0.0268. Loss Val: 0.0379. Time: 2.7447\n", " EPOCH 30. Progress: 6.0%. \n", " Training R^2: 0.5200. Test R^2: 0.4422. Loss Train: 0.0266. Loss Val: 0.0359. Time: 2.2237\n", " EPOCH 40. Progress: 8.0%. \n", " Training R^2: 0.5165. Test R^2: 0.4516. Loss Train: 0.0260. Loss Val: 0.0370. Time: 7.8190\n", " EPOCH 50. Progress: 10.0%. \n", " Training R^2: 0.4289. Test R^2: 0.4077. Loss Train: 0.0289. Loss Val: 0.0379. Time: 2.4827\n", " EPOCH 60. Progress: 12.0%. \n", " Training R^2: 0.5101. Test R^2: 0.4489. Loss Train: 0.0254. Loss Val: 0.0353. Time: 2.3484\n", " EPOCH 70. Progress: 14.000000000000002%. \n", " Training R^2: 0.5097. Test R^2: 0.4530. Loss Train: 0.0268. Loss Val: 0.0355. Time: 3.0138\n", " EPOCH 80. Progress: 16.0%. \n", " Training R^2: 0.5224. Test R^2: 0.4542. Loss Train: 0.0260. Loss Val: 0.0354. Time: 2.6839\n", " EPOCH 90. Progress: 18.0%. \n", " Training R^2: 0.5271. Test R^2: 0.3807. Loss Train: 0.0260. Loss Val: 0.0349. Time: 1.8238\n", " EPOCH 100. Progress: 20.0%. \n", " Training R^2: 0.5101. Test R^2: 0.4279. Loss Train: 0.0261. Loss Val: 0.0359. Time: 2.3156\n", " EPOCH 110. Progress: 22.0%. \n", " Training R^2: 0.5306. Test R^2: 0.4605. Loss Train: 0.0258. Loss Val: 0.0345. Time: 1.8360\n", " EPOCH 120. Progress: 24.0%. \n", " Training R^2: 0.5264. Test R^2: 0.4653. Loss Train: 0.0254. Loss Val: 0.0367. Time: 2.4769\n", " EPOCH 130. Progress: 26.0%. \n", " Training R^2: 0.5018. Test R^2: 0.4304. Loss Train: 0.0263. Loss Val: 0.0352. Time: 2.2373\n", " EPOCH 140. Progress: 28.000000000000004%. \n", " Training R^2: 0.5286. Test R^2: 0.4372. Loss Train: 0.0258. Loss Val: 0.0349. Time: 2.5191\n", " EPOCH 150. Progress: 30.0%. \n", " Training R^2: 0.4869. Test R^2: 0.4419. Loss Train: 0.0272. Loss Val: 0.0362. Time: 2.7092\n", " EPOCH 160. Progress: 32.0%. \n", " Training R^2: 0.5121. Test R^2: 0.4195. Loss Train: 0.0263. Loss Val: 0.0374. Time: 2.3216\n", " EPOCH 170. Progress: 34.0%. \n", " Training R^2: 0.5133. Test R^2: 0.4503. Loss Train: 0.0263. Loss Val: 0.0357. Time: 2.1948\n", " EPOCH 180. Progress: 36.0%. \n", " Training R^2: 0.5382. Test R^2: 0.3948. Loss Train: 0.0274. Loss Val: 0.0352. Time: 2.3185\n", " EPOCH 190. Progress: 38.0%. \n", " Training R^2: 0.4829. Test R^2: 0.4170. Loss Train: 0.0268. Loss Val: 0.0367. Time: 2.4032\n", " EPOCH 200. Progress: 40.0%. \n", " Training R^2: 0.5256. Test R^2: 0.4373. Loss Train: 0.0262. Loss Val: 0.0350. Time: 2.3591\n", " EPOCH 210. Progress: 42.0%. \n", " Training R^2: 0.5272. Test R^2: 0.4283. Loss Train: 0.0256. Loss Val: 0.0354. Time: 2.1982\n", " EPOCH 220. Progress: 44.0%. \n", " Training R^2: 0.5143. Test R^2: 0.4589. Loss Train: 0.0259. Loss Val: 0.0345. Time: 3.1806\n", " EPOCH 230. Progress: 46.0%. \n", " Training R^2: 0.5401. Test R^2: 0.4181. Loss Train: 0.0261. Loss Val: 0.0343. Time: 2.5634\n", " EPOCH 240. Progress: 48.0%. \n", " Training R^2: 0.5014. Test R^2: 0.4760. Loss Train: 0.0255. Loss Val: 0.0352. Time: 2.0837\n", " EPOCH 250. Progress: 50.0%. \n", " Training R^2: 0.5255. Test R^2: 0.4414. Loss Train: 0.0268. Loss Val: 0.0347. Time: 1.9711\n", " EPOCH 260. Progress: 52.0%. \n", " Training R^2: 0.5492. Test R^2: 0.4560. Loss Train: 0.0256. Loss Val: 0.0350. Time: 2.4712\n", " EPOCH 270. Progress: 54.0%. \n", " Training R^2: 0.3971. Test R^2: 0.4073. Loss Train: 0.0258. Loss Val: 0.0380. Time: 1.8694\n", " EPOCH 280. Progress: 56.00000000000001%. \n", " Training R^2: 0.4833. Test R^2: 0.4405. Loss Train: 0.0252. Loss Val: 0.0342. Time: 1.9649\n", " EPOCH 290. Progress: 57.99999999999999%. \n", " Training R^2: 0.5295. Test R^2: 0.4399. Loss Train: 0.0255. Loss Val: 0.0349. Time: 1.8793\n", " EPOCH 300. Progress: 60.0%. \n", " Training R^2: 0.5335. Test R^2: 0.4543. Loss Train: 0.0255. Loss Val: 0.0347. Time: 2.0025\n", " EPOCH 310. Progress: 62.0%. \n", " Training R^2: 0.4871. Test R^2: 0.4498. Loss Train: 0.0256. Loss Val: 0.0348. Time: 1.9823\n", " EPOCH 320. Progress: 64.0%. \n", " Training R^2: 0.5254. Test R^2: 0.4363. Loss Train: 0.0265. Loss Val: 0.0346. Time: 2.0350\n", " EPOCH 330. Progress: 66.0%. \n", " Training R^2: 0.4671. Test R^2: 0.4699. Loss Train: 0.0263. Loss Val: 0.0348. Time: 2.0128\n", " EPOCH 340. Progress: 68.0%. \n", " Training R^2: 0.4482. Test R^2: 0.4442. Loss Train: 0.0263. Loss Val: 0.0365. Time: 1.9249\n", " EPOCH 350. Progress: 70.0%. \n", " Training R^2: 0.5402. Test R^2: 0.4583. Loss Train: 0.0253. Loss Val: 0.0346. Time: 2.1697\n", " EPOCH 360. Progress: 72.0%. \n", " Training R^2: 0.5130. Test R^2: 0.4348. Loss Train: 0.0281. Loss Val: 0.0350. Time: 1.9188\n", " EPOCH 370. Progress: 74.0%. \n", " Training R^2: 0.5427. Test R^2: 0.4000. Loss Train: 0.0257. Loss Val: 0.0349. Time: 1.9767\n", " EPOCH 380. Progress: 76.0%. \n", " Training R^2: 0.5321. Test R^2: 0.4589. Loss Train: 0.0261. Loss Val: 0.0366. Time: 1.8689\n", " EPOCH 390. Progress: 78.0%. \n", " Training R^2: 0.5216. Test R^2: 0.4523. Loss Train: 0.0263. Loss Val: 0.0352. Time: 1.7124\n", " EPOCH 400. Progress: 80.0%. \n", " Training R^2: 0.4896. Test R^2: 0.4235. Loss Train: 0.0262. Loss Val: 0.0367. Time: 1.9804\n", " EPOCH 410. Progress: 82.0%. \n", " Training R^2: 0.5069. Test R^2: 0.4444. Loss Train: 0.0251. Loss Val: 0.0351. Time: 2.0140\n", " EPOCH 420. Progress: 84.0%. \n", " Training R^2: 0.4853. Test R^2: 0.4509. Loss Train: 0.0262. Loss Val: 0.0361. Time: 1.9275\n", " EPOCH 430. Progress: 86.0%. \n", " Training R^2: 0.5214. Test R^2: 0.4328. Loss Train: 0.0255. Loss Val: 0.0358. Time: 1.9275\n", " EPOCH 440. Progress: 88.0%. \n", " Training R^2: 0.5252. Test R^2: 0.4525. Loss Train: 0.0254. Loss Val: 0.0357. Time: 2.0793\n", " EPOCH 450. Progress: 90.0%. \n", " Training R^2: 0.5405. Test R^2: 0.4435. Loss Train: 0.0254. Loss Val: 0.0339. Time: 1.9715\n", " EPOCH 460. Progress: 92.0%. \n", " Training R^2: 0.5270. Test R^2: 0.4243. Loss Train: 0.0266. Loss Val: 0.0346. Time: 1.9307\n", " EPOCH 470. Progress: 94.0%. \n", " Training R^2: 0.5364. Test R^2: 0.4669. Loss Train: 0.0254. Loss Val: 0.0343. Time: 2.1174\n", " EPOCH 480. Progress: 96.0%. \n", " Training R^2: 0.4949. Test R^2: 0.4394. Loss Train: 0.0261. Loss Val: 0.0339. Time: 1.9938\n", " EPOCH 490. Progress: 98.0%. \n", " Training R^2: 0.4509. Test R^2: 0.4009. Loss Train: 0.0257. Loss Val: 0.0414. Time: 1.9509\n", " EPOCH 500. Progress: 100.0%. \n", " Training R^2: 0.5098. Test R^2: 0.4527. Loss Train: 0.0258. Loss Val: 0.0337. Time: 2.0501\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "saving ./plots/lr0.01LONGER_momentum0.9_wdecay0.001_dampening0_nesterovFalse.png\n" ] } ], "source": [ "# Best configuration for longer\n", "lr_range = [0.01]\n", "weight_decay_range = [0.001]\n", "momentum_range = [0.1,0.9]\n", "dampening_range = [0]\n", "nesterov_range = [False]\n", "for lr in lr_range:\n", " for momentum in momentum_range:\n", " for weight_decay in weight_decay_range:\n", " for nesterov in nesterov_range:\n", " for dampening in dampening_range:\n", " try: \n", " print('\\nlr: {}, momentum: {}, weight_decay: {}, dampening: {}, nesterov: {} '.format(lr, momentum, weight_decay, dampening, nesterov))\n", " model = LogisticRegression(in_dim=47,out_dim=1, verbose=False).to(device)\n", " print(model)\n", " SGD = torch.optim.SGD(model.parameters(), lr = lr, momentum=momentum, dampening=dampening, weight_decay=weight_decay, nesterov=nesterov) # This is absurdly high.\n", " # initialize the loss function. You don't want to use this one, so change it accordingly\n", " loss_fn = nn.MSELoss()\n", " config_str = 'lr' + str(lr) + 'LONGER_momentum' + str(momentum) + '_wdecay' + str(weight_decay) + '_dampening' + str(dampening) +'_nesterov' + str(nesterov)\n", " train(model,loss_fn, SGD, dataloaders['train'], dataloaders['val'], config_str,num_epochs=500, verbose=False)\n", " except:\n", " pass" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "predicted.shape: torch.Size([335])\n", "predicted[:20]: \ttensor([0.4197, 0.4249, 0.6119, 0.8562, 0.6343, 0.3911, 0.4445, 0.2016, 0.4953,\n", " 0.5134, 0.2582, 0.4030, 0.3671, 0.4673, 0.5299, 0.7208, 0.5424, 0.1291,\n", " 0.6937, 0.1303])\n", "targets[:20]: \t\ttensor([0.3880, 0.2883, 0.7370, 0.9044, 0.5947, 0.5528, 0.3247, 0.1974, 0.5815,\n", " 0.8917, 0.3609, 0.3433, 0.3853, 0.5961, 0.9056, 0.7102, 0.6711, 0.0362,\n", " 0.5681, 0.2078])\n", "predicted.shape: torch.Size([1118])\n", "predicted[:20]: \ttensor([0.3200, 0.3313, 0.6222, 0.3567, 0.3359, 0.4297, 0.3794, 0.4444, 0.5455,\n", " 0.5131, 0.5163, 0.5323, 0.5762, 0.5698, 0.5697, 0.5701, 0.5701, 0.5715,\n", " 0.5823, 0.5200])\n", "targets[:20]: \t\ttensor([0.0280, 0.1301, 0.2970, 0.2207, 0.2648, 0.3342, 0.2873, 0.3362, 0.6023,\n", " 0.5031, 0.5335, 0.6247, 0.5454, 0.4926, 0.4442, 0.5030, 0.5472, 0.5128,\n", " 0.5702, 0.5017])\n" ] } ], "source": [ "# Test\n", "with torch.no_grad():\n", " data, targets_val = next(iter(dataloaders['all_val']))\n", " model_input = data.to(device)\n", " predicted_val = model(model_input).squeeze()\n", "# _, predicted = torch.max(out, 1)\n", " print('predicted.shape: {}'.format(predicted_val.shape))\n", " print('predicted[:20]: \\t{}'.format(predicted_val[:20]))\n", " print('targets[:20]: \\t\\t{}'.format(targets_val[:20]))\n", " \n", "# Test\n", "with torch.no_grad():\n", " data, targets = next(iter(dataloaders['test']))\n", " model_input = data.to(device)\n", " predicted = model(model_input).squeeze()\n", " print('predicted.shape: {}'.format(predicted.shape))\n", " print('predicted[:20]: \\t{}'.format(predicted[:20]))\n", " print('targets[:20]: \\t\\t{}'.format(targets[:20]))" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Time for a real test\n", "path_to_save = './plots'\n", "if not os.path.exists(path_to_save):\n", " os.makedirs(path_to_save)\n", " \n", "fig, (ax1,ax2) = plt.subplots(1,2, sharey=True)\n", "# test_predictions = model(normed_test_data).flatten()\n", "r = r2_score(targets_val, predicted_val.cpu())\n", "ax1.scatter(targets_val, predicted_val.cpu(),alpha=0.5, label='$R^2$ = %.3f' % (r))\n", "ax1.legend(loc=\"upper left\")\n", "ax1.set_xlabel('True Values [Mean Conc.]')\n", "ax1.set_ylabel('Predictions [Mean Conc.]')\n", "ax1.axis('equal')\n", "ax1.axis('square')\n", "ax1.set_xlim([0,1])\n", "ax1.set_ylim([0,1])\n", "_ = ax1.plot([-100, 100], [-100, 100], 'r:')\n", "ax1.set_title('Test dataset')\n", "fig.set_figheight(30)\n", "fig.set_figwidth(10)\n", "\n", "#Whole dataset\n", "r = r2_score(targets, predicted.cpu())\n", "ax2.scatter(targets, predicted.cpu(), alpha=0.5, label='$R^2$ = %.3f' % (r))\n", "ax2.legend(loc=\"upper left\")\n", "ax2.set_xlabel('True Values [Mean Conc.]')\n", "ax2.set_ylabel('Predictions [Mean Conc.]')\n", "ax2.axis('equal')\n", "ax2.axis('square')\n", "ax2.set_xlim([0,1])\n", "ax2.set_ylim([0,1])\n", "_ = ax2.plot([-100, 100], [-100, 100], 'r:')\n", "ax2.set_title('Whole dataset')\n", "fig.savefig(os.path.join(path_to_save, 'LogisticReg_LONGER_R2Score_' + config_str + '.png'), bbox_inches='tight')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Question 1\n", "\n", "__What percentage classification accuracy did your simple network achieve? Make a table with the configurations you tested and results you obtained!__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Feed-forward Neural Network\n", "\n", "This time, keeping the rest of your logistic model fixed:\n", "- Create one hidden layer (with 128 units) between the input and output by creating another weight and bias variable.\n", "- Try training this without a non-linearity between the layers (linear activation), and then try adding a sigmoid non-linearity both before the hidden layer and after the hidden layer, recording your test accuracy results for each in a table.\n", "- Try adjusting the learning rate (by making it smaller) if your model is not onverging/improving accuracy. You might also try increasing the number of epochs used.\n", "- Experiment with the non-linearity used before the middle layer. Here are some activation functions to choose from: relu, softplus, elu, tanh.\n", "- Lastly, experiment with the width of the hidden layer, keeping the activation function that performs best. Remember to add these results to your table.\n", "\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "class FeedForwardNet(nn.Module):\n", " def __init__(self, in_dim,hid_dim,out_dim,verbose=False):\n", " super(FeedForwardNet, self).__init__()\n", " # Linear function\n", " self.fc1 = nn.Linear(in_dim, hid_dim)\n", " self.fc2 = nn.Linear(hid_dim, out_dim)\n", "\n", " def forward(self, x):\n", " # Linear function\n", " out = self.fc1(x)\n", " out = self.fc2(out)\n", " return out" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "lr: 0.001, momentum: 0.9, weight_decay: 0.001, dampening: 0, nesterov: False \n", "FeedForwardNet(\n", " (fc1): Linear(in_features=47, out_features=64, bias=True)\n", " (fc2): Linear(in_features=64, out_features=1, bias=True)\n", ")\n", "optimizer: SGD (\n", "Parameter Group 0\n", " dampening: 0\n", " lr: 0.001\n", " momentum: 0.9\n", " nesterov: False\n", " weight_decay: 0.001\n", ")\n", "n. of epochs: 500\n", " EPOCH 0. Progress: 0.0%. \n", " Training R^2: -0.1002. Test R^2: -0.1883. Loss Train: 0.1547. Loss Val: 0.0770. Time: 2.8679\n", " EPOCH 10. Progress: 2.0%. \n", " Training R^2: 0.3881. Test R^2: 0.2935. Loss Train: 0.0333. Loss Val: 0.0443. Time: 2.2752\n", " EPOCH 20. Progress: 4.0%. \n", " Training R^2: 0.4002. Test R^2: 0.3021. Loss Train: 0.0306. Loss Val: 0.0407. Time: 7.0543\n", " EPOCH 30. Progress: 6.0%. \n", " Training R^2: 0.4327. Test R^2: 0.3883. Loss Train: 0.0296. Loss Val: 0.0408. Time: 2.1320\n", " EPOCH 40. Progress: 8.0%. \n", " Training R^2: 0.4659. Test R^2: 0.3870. Loss Train: 0.0287. Loss Val: 0.0389. Time: 2.2508\n", " EPOCH 50. Progress: 10.0%. \n", " Training R^2: 0.4600. Test R^2: 0.3723. Loss Train: 0.0281. Loss Val: 0.0391. Time: 2.6312\n", " EPOCH 60. Progress: 12.0%. \n", " Training R^2: 0.4675. Test R^2: 0.4191. Loss Train: 0.0280. Loss Val: 0.0381. Time: 2.1675\n", " EPOCH 70. Progress: 14.000000000000002%. \n", " Training R^2: 0.4880. Test R^2: 0.3722. Loss Train: 0.0277. Loss Val: 0.0394. Time: 3.2555\n", " EPOCH 80. Progress: 16.0%. \n", " Training R^2: 0.4800. Test R^2: 0.4036. Loss Train: 0.0273. Loss Val: 0.0388. Time: 3.5889\n", " EPOCH 90. Progress: 18.0%. \n", " Training R^2: 0.4860. Test R^2: 0.4194. Loss Train: 0.0267. Loss Val: 0.0368. Time: 2.2345\n", " EPOCH 100. Progress: 20.0%. \n", " Training R^2: 0.4921. Test R^2: 0.4067. Loss Train: 0.0269. Loss Val: 0.0372. Time: 2.6351\n", " EPOCH 110. Progress: 22.0%. \n", " Training R^2: 0.4988. Test R^2: 0.4150. Loss Train: 0.0276. Loss Val: 0.0368. Time: 2.4887\n", " EPOCH 120. Progress: 24.0%. \n", " Training R^2: 0.4870. Test R^2: 0.3986. Loss Train: 0.0266. Loss Val: 0.0366. Time: 2.3648\n", " EPOCH 130. Progress: 26.0%. \n", " Training R^2: 0.5127. Test R^2: 0.4201. Loss Train: 0.0265. Loss Val: 0.0361. Time: 2.3501\n", " EPOCH 140. Progress: 28.000000000000004%. \n", " Training R^2: 0.4983. Test R^2: 0.4107. Loss Train: 0.0265. Loss Val: 0.0359. Time: 2.6944\n", " EPOCH 150. Progress: 30.0%. \n", " Training R^2: 0.4872. Test R^2: 0.4233. Loss Train: 0.0272. Loss Val: 0.0364. Time: 2.9972\n", " EPOCH 160. Progress: 32.0%. \n", " Training R^2: 0.4895. Test R^2: 0.4423. Loss Train: 0.0263. Loss Val: 0.0364. Time: 3.7616\n", " EPOCH 170. Progress: 34.0%. \n", " Training R^2: 0.5143. Test R^2: 0.4369. Loss Train: 0.0262. Loss Val: 0.0362. Time: 2.2601\n", " EPOCH 180. Progress: 36.0%. \n", " Training R^2: 0.5009. Test R^2: 0.4351. Loss Train: 0.0269. Loss Val: 0.0372. Time: 2.2206\n", " EPOCH 190. Progress: 38.0%. \n", " Training R^2: 0.4984. Test R^2: 0.4373. Loss Train: 0.0259. Loss Val: 0.0361. Time: 2.2813\n", " EPOCH 200. Progress: 40.0%. \n", " Training R^2: 0.5306. Test R^2: 0.4306. Loss Train: 0.0265. Loss Val: 0.0359. Time: 2.2075\n", " EPOCH 210. Progress: 42.0%. \n", " Training R^2: 0.5060. Test R^2: 0.4286. Loss Train: 0.0260. Loss Val: 0.0362. Time: 2.2509\n", " EPOCH 220. Progress: 44.0%. \n", " Training R^2: 0.5179. Test R^2: 0.4087. Loss Train: 0.0267. Loss Val: 0.0351. Time: 2.0607\n", " EPOCH 230. Progress: 46.0%. \n", " Training R^2: 0.4969. Test R^2: 0.4329. Loss Train: 0.0260. Loss Val: 0.0359. Time: 3.0709\n", " EPOCH 240. Progress: 48.0%. \n", " Training R^2: 0.5218. Test R^2: 0.4391. Loss Train: 0.0259. Loss Val: 0.0359. Time: 2.5126\n", " EPOCH 250. Progress: 50.0%. \n", " Training R^2: 0.5194. Test R^2: 0.4123. Loss Train: 0.0260. Loss Val: 0.0363. Time: 3.0602\n", " EPOCH 260. Progress: 52.0%. \n", " Training R^2: 0.4955. Test R^2: 0.4243. Loss Train: 0.0262. Loss Val: 0.0354. Time: 2.8255\n", " EPOCH 270. Progress: 54.0%. \n", " Training R^2: 0.4839. Test R^2: 0.4214. Loss Train: 0.0262. Loss Val: 0.0357. Time: 2.8350\n", " EPOCH 280. Progress: 56.00000000000001%. \n", " Training R^2: 0.5136. Test R^2: 0.4427. Loss Train: 0.0262. Loss Val: 0.0361. Time: 2.8567\n", " EPOCH 290. Progress: 57.99999999999999%. \n", " Training R^2: 0.5287. Test R^2: 0.4405. Loss Train: 0.0266. Loss Val: 0.0369. Time: 2.5230\n", " EPOCH 300. Progress: 60.0%. \n", " Training R^2: 0.5248. Test R^2: 0.4311. Loss Train: 0.0257. Loss Val: 0.0376. Time: 4.1712\n", " EPOCH 310. Progress: 62.0%. \n", " Training R^2: 0.5220. Test R^2: 0.4311. Loss Train: 0.0256. Loss Val: 0.0356. Time: 2.7101\n", " EPOCH 320. Progress: 64.0%. \n", " Training R^2: 0.5235. Test R^2: 0.4212. Loss Train: 0.0260. Loss Val: 0.0357. Time: 2.4286\n", " EPOCH 330. Progress: 66.0%. \n", " Training R^2: 0.3678. Test R^2: 0.4150. Loss Train: 0.0262. Loss Val: 0.0366. Time: 2.4606\n", " EPOCH 340. Progress: 68.0%. \n", " Training R^2: 0.5088. Test R^2: 0.4462. Loss Train: 0.0266. Loss Val: 0.0349. Time: 2.1945\n", " EPOCH 350. Progress: 70.0%. \n", " Training R^2: 0.5135. Test R^2: 0.4145. Loss Train: 0.0271. Loss Val: 0.0354. Time: 2.5988\n", " EPOCH 360. Progress: 72.0%. \n", " Training R^2: 0.5219. Test R^2: 0.3990. Loss Train: 0.0256. Loss Val: 0.0356. Time: 2.5520\n", " EPOCH 370. Progress: 74.0%. \n", " Training R^2: 0.4989. Test R^2: 0.4453. Loss Train: 0.0254. Loss Val: 0.0372. Time: 2.2263\n", " EPOCH 380. Progress: 76.0%. \n", " Training R^2: 0.5259. Test R^2: 0.4489. Loss Train: 0.0256. Loss Val: 0.0375. Time: 2.3282\n", " EPOCH 390. Progress: 78.0%. \n", " Training R^2: 0.5307. Test R^2: 0.4354. Loss Train: 0.0256. Loss Val: 0.0354. Time: 2.7453\n", " EPOCH 400. Progress: 80.0%. \n", " Training R^2: 0.5182. Test R^2: 0.3862. Loss Train: 0.0257. Loss Val: 0.0357. Time: 2.2663\n", " EPOCH 410. Progress: 82.0%. \n", " Training R^2: 0.5016. Test R^2: 0.4173. Loss Train: 0.0256. Loss Val: 0.0354. Time: 2.8625\n", " EPOCH 420. Progress: 84.0%. \n", " Training R^2: 0.5244. Test R^2: 0.4451. Loss Train: 0.0257. Loss Val: 0.0353. Time: 2.4082\n", " EPOCH 430. Progress: 86.0%. \n", " Training R^2: 0.5214. Test R^2: 0.4493. Loss Train: 0.0267. Loss Val: 0.0348. Time: 2.2367\n", " EPOCH 440. Progress: 88.0%. \n", " Training R^2: 0.5235. Test R^2: 0.4562. Loss Train: 0.0255. Loss Val: 0.0357. Time: 2.6790\n", " EPOCH 450. Progress: 90.0%. \n", " Training R^2: 0.4598. Test R^2: 0.4491. Loss Train: 0.0255. Loss Val: 0.0346. Time: 2.3281\n", " EPOCH 460. Progress: 92.0%. \n", " Training R^2: 0.5301. Test R^2: 0.4335. Loss Train: 0.0252. Loss Val: 0.0359. Time: 2.3309\n", " EPOCH 470. Progress: 94.0%. \n", " Training R^2: 0.5316. Test R^2: 0.4573. Loss Train: 0.0256. Loss Val: 0.0364. Time: 2.3654\n", " EPOCH 480. Progress: 96.0%. \n", " Training R^2: 0.4818. Test R^2: 0.4548. Loss Train: 0.0255. Loss Val: 0.0356. Time: 2.9039\n", " EPOCH 490. Progress: 98.0%. \n", " Training R^2: 0.5159. Test R^2: 0.4394. Loss Train: 0.0256. Loss Val: 0.0348. Time: 2.6864\n", " EPOCH 500. Progress: 100.0%. \n", " Training R^2: 0.5061. Test R^2: 0.4538. Loss Train: 0.0254. Loss Val: 0.0348. Time: 2.3089\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "saving ./plots/lr0.001FFNet_momentum0.9_wdecay0.001_dampening0_nesterovFalse_HidDim64.png\n", "\n", "lr: 0.001, momentum: 0.9, weight_decay: 0.001, dampening: 0, nesterov: False \n", "FeedForwardNet(\n", " (fc1): Linear(in_features=47, out_features=128, bias=True)\n", " (fc2): Linear(in_features=128, out_features=1, bias=True)\n", ")\n", "optimizer: SGD (\n", "Parameter Group 0\n", " dampening: 0\n", " lr: 0.001\n", " momentum: 0.9\n", " nesterov: False\n", " weight_decay: 0.001\n", ")\n", "n. of epochs: 500\n", " EPOCH 0. Progress: 0.0%. \n", " Training R^2: -0.1650. Test R^2: 0.1370. Loss Train: 0.1189. Loss Val: 0.0560. Time: 2.2674\n", " EPOCH 10. Progress: 2.0%. \n", " Training R^2: 0.3564. Test R^2: 0.3440. Loss Train: 0.0348. Loss Val: 0.0429. Time: 3.4910\n", " EPOCH 20. Progress: 4.0%. \n", " Training R^2: 0.4045. Test R^2: 0.3636. Loss Train: 0.0316. Loss Val: 0.0414. Time: 2.2355\n", " EPOCH 30. Progress: 6.0%. \n", " Training R^2: 0.4447. Test R^2: 0.3739. Loss Train: 0.0294. Loss Val: 0.0425. Time: 2.1547\n", " EPOCH 40. Progress: 8.0%. \n", " Training R^2: 0.4718. Test R^2: 0.3802. Loss Train: 0.0298. Loss Val: 0.0385. Time: 2.1527\n", " EPOCH 50. Progress: 10.0%. \n", " Training R^2: 0.4640. Test R^2: 0.4105. Loss Train: 0.0283. Loss Val: 0.0374. Time: 2.1591\n", " EPOCH 60. Progress: 12.0%. \n", " Training R^2: 0.4905. Test R^2: 0.4069. Loss Train: 0.0281. Loss Val: 0.0376. Time: 2.1030\n", " EPOCH 70. Progress: 14.000000000000002%. \n", " Training R^2: 0.4900. Test R^2: 0.3919. Loss Train: 0.0270. Loss Val: 0.0376. Time: 2.2291\n", " EPOCH 80. Progress: 16.0%. \n", " Training R^2: 0.4953. Test R^2: 0.4300. Loss Train: 0.0271. Loss Val: 0.0387. Time: 2.1138\n", " EPOCH 90. Progress: 18.0%. \n", " Training R^2: 0.4747. Test R^2: 0.4137. Loss Train: 0.0270. Loss Val: 0.0377. Time: 2.2466\n", " EPOCH 100. Progress: 20.0%. \n", " Training R^2: 0.5014. Test R^2: 0.4071. Loss Train: 0.0268. Loss Val: 0.0369. Time: 2.1457\n", " EPOCH 110. Progress: 22.0%. \n", " Training R^2: 0.5030. Test R^2: 0.4142. Loss Train: 0.0267. Loss Val: 0.0379. Time: 2.0818\n", " EPOCH 120. Progress: 24.0%. \n", " Training R^2: 0.4969. Test R^2: 0.4068. Loss Train: 0.0269. Loss Val: 0.0379. Time: 2.1583\n", " EPOCH 130. Progress: 26.0%. \n", " Training R^2: 0.5030. Test R^2: 0.4296. Loss Train: 0.0266. Loss Val: 0.0364. Time: 2.1612\n", " EPOCH 140. Progress: 28.000000000000004%. \n", " Training R^2: 0.5082. Test R^2: 0.4010. Loss Train: 0.0265. Loss Val: 0.0361. Time: 2.1903\n", " EPOCH 150. Progress: 30.0%. \n", " Training R^2: 0.5041. Test R^2: 0.4510. Loss Train: 0.0260. Loss Val: 0.0359. Time: 2.0836\n", " EPOCH 160. Progress: 32.0%. \n", " Training R^2: 0.5151. Test R^2: 0.4335. Loss Train: 0.0259. Loss Val: 0.0389. Time: 2.3125\n", " EPOCH 170. Progress: 34.0%. \n", " Training R^2: 0.4959. Test R^2: 0.4220. Loss Train: 0.0262. Loss Val: 0.0364. Time: 2.1887\n", " EPOCH 180. Progress: 36.0%. \n", " Training R^2: 0.4698. Test R^2: 0.4247. Loss Train: 0.0261. Loss Val: 0.0366. Time: 2.2226\n", " EPOCH 190. Progress: 38.0%. \n", " Training R^2: 0.5144. Test R^2: 0.3948. Loss Train: 0.0267. Loss Val: 0.0356. Time: 2.1010\n", " EPOCH 200. Progress: 40.0%. \n", " Training R^2: 0.5189. Test R^2: 0.4354. Loss Train: 0.0274. Loss Val: 0.0359. Time: 2.1634\n", " EPOCH 210. Progress: 42.0%. \n", " Training R^2: 0.5187. Test R^2: 0.4082. Loss Train: 0.0261. Loss Val: 0.0355. Time: 2.1772\n", " EPOCH 220. Progress: 44.0%. \n", " Training R^2: 0.4928. Test R^2: 0.4425. Loss Train: 0.0259. Loss Val: 0.0356. Time: 2.2574\n", " EPOCH 230. Progress: 46.0%. \n", " Training R^2: 0.5200. Test R^2: 0.4205. Loss Train: 0.0258. Loss Val: 0.0364. Time: 2.3135\n", " EPOCH 240. Progress: 48.0%. \n", " Training R^2: 0.5169. Test R^2: 0.3820. Loss Train: 0.0261. Loss Val: 0.0363. Time: 2.1045\n", " EPOCH 250. Progress: 50.0%. \n", " Training R^2: 0.5190. Test R^2: 0.4478. Loss Train: 0.0257. Loss Val: 0.0361. Time: 2.0785\n", " EPOCH 260. Progress: 52.0%. \n", " Training R^2: 0.5122. Test R^2: 0.4269. Loss Train: 0.0259. Loss Val: 0.0356. Time: 2.0899\n", " EPOCH 270. Progress: 54.0%. \n", " Training R^2: 0.5235. Test R^2: 0.4715. Loss Train: 0.0257. Loss Val: 0.0351. Time: 2.3125\n", " EPOCH 280. Progress: 56.00000000000001%. \n", " Training R^2: 0.4812. Test R^2: 0.4334. Loss Train: 0.0256. Loss Val: 0.0361. Time: 2.3766\n", " EPOCH 290. Progress: 57.99999999999999%. \n", " Training R^2: 0.5019. Test R^2: 0.4556. Loss Train: 0.0261. Loss Val: 0.0365. Time: 2.0649\n", " EPOCH 300. Progress: 60.0%. \n", " Training R^2: 0.5156. Test R^2: 0.4563. Loss Train: 0.0265. Loss Val: 0.0371. Time: 2.3816\n", " EPOCH 310. Progress: 62.0%. \n", " Training R^2: 0.5344. Test R^2: 0.4310. Loss Train: 0.0258. Loss Val: 0.0349. Time: 2.0712\n", " EPOCH 320. Progress: 64.0%. \n", " Training R^2: 0.5257. Test R^2: 0.4413. Loss Train: 0.0255. Loss Val: 0.0360. Time: 2.1857\n", " EPOCH 330. Progress: 66.0%. \n", " Training R^2: 0.4794. Test R^2: 0.4378. Loss Train: 0.0252. Loss Val: 0.0364. Time: 2.0993\n", " EPOCH 340. Progress: 68.0%. \n", " Training R^2: 0.5277. Test R^2: 0.4453. Loss Train: 0.0253. Loss Val: 0.0353. Time: 2.1207\n", " EPOCH 350. Progress: 70.0%. \n", " Training R^2: 0.5116. Test R^2: 0.4509. Loss Train: 0.0257. Loss Val: 0.0359. Time: 2.1579\n", " EPOCH 360. Progress: 72.0%. \n", " Training R^2: 0.5204. Test R^2: 0.4529. Loss Train: 0.0254. Loss Val: 0.0349. Time: 2.4573\n", " EPOCH 370. Progress: 74.0%. \n", " Training R^2: 0.5156. Test R^2: 0.4320. Loss Train: 0.0254. Loss Val: 0.0348. Time: 4.0994\n", " EPOCH 380. Progress: 76.0%. \n", " Training R^2: 0.5010. Test R^2: 0.4440. Loss Train: 0.0265. Loss Val: 0.0355. Time: 2.2201\n", " EPOCH 390. Progress: 78.0%. \n", " Training R^2: 0.4996. Test R^2: 0.4569. Loss Train: 0.0257. Loss Val: 0.0349. Time: 2.3245\n", " EPOCH 400. Progress: 80.0%. \n", " Training R^2: 0.5232. Test R^2: 0.4469. Loss Train: 0.0256. Loss Val: 0.0355. Time: 2.3519\n", " EPOCH 410. Progress: 82.0%. \n", " Training R^2: 0.5372. Test R^2: 0.4467. Loss Train: 0.0253. Loss Val: 0.0348. Time: 2.4484\n", " EPOCH 420. Progress: 84.0%. \n", " Training R^2: 0.5324. Test R^2: 0.4684. Loss Train: 0.0257. Loss Val: 0.0352. Time: 2.1310\n", " EPOCH 430. Progress: 86.0%. \n", " Training R^2: 0.4772. Test R^2: 0.4459. Loss Train: 0.0254. Loss Val: 0.0357. Time: 2.2095\n", " EPOCH 440. Progress: 88.0%. \n", " Training R^2: 0.5210. Test R^2: 0.4543. Loss Train: 0.0256. Loss Val: 0.0343. Time: 2.1805\n", " EPOCH 450. Progress: 90.0%. \n", " Training R^2: 0.5117. Test R^2: 0.4440. Loss Train: 0.0253. Loss Val: 0.0365. Time: 2.0739\n", " EPOCH 460. Progress: 92.0%. \n", " Training R^2: 0.5289. Test R^2: 0.4707. Loss Train: 0.0260. Loss Val: 0.0354. Time: 2.1470\n", " EPOCH 470. Progress: 94.0%. \n", " Training R^2: 0.5273. Test R^2: 0.4631. Loss Train: 0.0259. Loss Val: 0.0358. Time: 2.1357\n", " EPOCH 480. Progress: 96.0%. \n", " Training R^2: 0.5195. Test R^2: 0.4381. Loss Train: 0.0256. Loss Val: 0.0358. Time: 2.1766\n", " EPOCH 490. Progress: 98.0%. \n", " Training R^2: 0.5120. Test R^2: 0.4202. Loss Train: 0.0254. Loss Val: 0.0348. Time: 2.1652\n", " EPOCH 500. Progress: 100.0%. \n", " Training R^2: 0.5386. Test R^2: 0.4514. Loss Train: 0.0254. Loss Val: 0.0345. Time: 2.0892\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "saving ./plots/lr0.001FFNet_momentum0.9_wdecay0.001_dampening0_nesterovFalse_HidDim128.png\n" ] } ], "source": [ "# Best configuration for longer\n", "lr_range = [0.001]\n", "hid_dim_range = [64,128]\n", "weight_decay_range = [0.001]\n", "momentum_range = [0.9]\n", "dampening_range = [0]\n", "nesterov_range = [False]\n", "for lr in lr_range:\n", " for momentum in momentum_range:\n", " for weight_decay in weight_decay_range:\n", " for nesterov in nesterov_range:\n", " for dampening in dampening_range:\n", " for hid_dim in hid_dim_range:\n", " try: \n", " print('\\nlr: {}, momentum: {}, weight_decay: {}, dampening: {}, nesterov: {} '.format(lr, momentum, weight_decay, dampening, nesterov))\n", " model = FeedForwardNet(in_dim=47,hid_dim=hid_dim, out_dim=1, verbose=False).to(device)\n", " print(model)\n", " SGD = torch.optim.SGD(model.parameters(), lr = lr, momentum=momentum, dampening=dampening, weight_decay=weight_decay, nesterov=nesterov) # This is absurdly high.\n", " # initialize the loss function. You don't want to use this one, so change it accordingly\n", " loss_fn = nn.MSELoss()\n", " config_str = 'lr' + str(lr) + 'FFNet_momentum' + str(momentum) + '_wdecay' + str(weight_decay) + '_dampening' + str(dampening) +'_nesterov' + str(nesterov) + '_HidDim' + str(hid_dim)\n", " train(model,loss_fn, SGD, dataloaders['train'], dataloaders['val'], config_str,num_epochs=500, verbose=False)\n", " except:\n", " pass" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "lr: 0.1, momentum: 0.9, weight_decay: 0.001, dampening: 0, nesterov: False \n", "FeedforwardNeuralNetModel(\n", " (fc1): Linear(in_features=47, out_features=64, bias=True)\n", " (relu): ReLU()\n", " (fc2): Linear(in_features=64, out_features=1, bias=True)\n", ")\n", "optimizer: SGD (\n", "Parameter Group 0\n", " dampening: 0\n", " lr: 0.1\n", " momentum: 0.9\n", " nesterov: False\n", " weight_decay: 0.001\n", ")\n", "n. of epochs: 500\n", " EPOCH 0. Progress: 0.0%. \n", " Training R^2: 0.3646. Test R^2: 0.3192. Loss Train: 0.0818. Loss Val: 0.0423. Time: 2.5027\n", " EPOCH 10. Progress: 2.0%. \n", " Training R^2: 0.1156. Test R^2: 0.0489. Loss Train: 0.0308. Loss Val: 0.0594. Time: 2.2376\n", " EPOCH 20. Progress: 4.0%. \n", " Training R^2: 0.5008. Test R^2: 0.3898. Loss Train: 0.0249. Loss Val: 0.0345. Time: 2.1767\n", " EPOCH 30. Progress: 6.0%. \n", " Training R^2: 0.4401. Test R^2: 0.4661. Loss Train: 0.0269. Loss Val: 0.0349. Time: 2.1309\n", " EPOCH 40. Progress: 8.0%. \n", " Training R^2: 0.3220. Test R^2: 0.4064. Loss Train: 0.0248. Loss Val: 0.0372. Time: 2.3498\n", " EPOCH 50. Progress: 10.0%. \n", " Training R^2: 0.5217. Test R^2: 0.4487. Loss Train: 0.0277. Loss Val: 0.0333. Time: 2.7613\n", " EPOCH 60. Progress: 12.0%. \n", " Training R^2: 0.4267. Test R^2: 0.3631. Loss Train: 0.0303. Loss Val: 0.0404. Time: 2.1412\n", " EPOCH 70. Progress: 14.000000000000002%. \n", " Training R^2: 0.5459. Test R^2: 0.4925. Loss Train: 0.0289. Loss Val: 0.0316. Time: 2.1796\n", " EPOCH 80. Progress: 16.0%. \n", " Training R^2: 0.5843. Test R^2: 0.5128. Loss Train: 0.0227. Loss Val: 0.0298. Time: 2.2113\n", " EPOCH 90. Progress: 18.0%. \n", " Training R^2: 0.5063. Test R^2: 0.4281. Loss Train: 0.0249. Loss Val: 0.0350. Time: 2.5134\n", " EPOCH 100. Progress: 20.0%. \n", " Training R^2: 0.5870. Test R^2: 0.5082. Loss Train: 0.0230. Loss Val: 0.0314. Time: 2.3755\n", " EPOCH 110. Progress: 22.0%. \n", " Training R^2: 0.5838. Test R^2: 0.5122. Loss Train: 0.0248. Loss Val: 0.0296. Time: 2.1351\n", " EPOCH 120. Progress: 24.0%. \n", " Training R^2: 0.5589. Test R^2: 0.4741. Loss Train: 0.0267. Loss Val: 0.0315. Time: 2.1909\n", " EPOCH 130. Progress: 26.0%. \n", " Training R^2: 0.5485. Test R^2: 0.4603. Loss Train: 0.0239. Loss Val: 0.0342. Time: 2.1711\n", " EPOCH 140. Progress: 28.000000000000004%. \n", " Training R^2: 0.5472. Test R^2: 0.5175. Loss Train: 0.0244. Loss Val: 0.0301. Time: 2.1524\n", " EPOCH 150. Progress: 30.0%. \n", " Training R^2: 0.5417. Test R^2: 0.5201. Loss Train: 0.0276. Loss Val: 0.0314. Time: 2.1584\n", " EPOCH 160. Progress: 32.0%. \n", " Training R^2: 0.5917. Test R^2: 0.5359. Loss Train: 0.0244. Loss Val: 0.0289. Time: 2.3367\n", " EPOCH 170. Progress: 34.0%. \n", " Training R^2: 0.5555. Test R^2: 0.4634. Loss Train: 0.0261. Loss Val: 0.0346. Time: 2.0601\n", " EPOCH 180. Progress: 36.0%. \n", " Training R^2: 0.4819. Test R^2: 0.3625. Loss Train: 0.0251. Loss Val: 0.0363. Time: 2.1442\n", " EPOCH 190. Progress: 38.0%. \n", " Training R^2: 0.4647. Test R^2: 0.3741. Loss Train: 0.0245. Loss Val: 0.0385. Time: 2.0878\n", " EPOCH 200. Progress: 40.0%. \n", " Training R^2: 0.5655. Test R^2: 0.4926. Loss Train: 0.0234. Loss Val: 0.0311. Time: 2.3180\n", " EPOCH 210. Progress: 42.0%. \n", " Training R^2: 0.5308. Test R^2: 0.4326. Loss Train: 0.0240. Loss Val: 0.0345. Time: 2.2221\n", " EPOCH 220. Progress: 44.0%. \n", " Training R^2: 0.5925. Test R^2: 0.5164. Loss Train: 0.0253. Loss Val: 0.0316. Time: 2.1547\n", " EPOCH 230. Progress: 46.0%. \n", " Training R^2: 0.5802. Test R^2: 0.4898. Loss Train: 0.0255. Loss Val: 0.0318. Time: 2.9204\n", " EPOCH 240. Progress: 48.0%. \n", " Training R^2: 0.5904. Test R^2: 0.5499. Loss Train: 0.0236. Loss Val: 0.0290. Time: 2.1554\n", " EPOCH 250. Progress: 50.0%. \n", " Training R^2: 0.5527. Test R^2: 0.5071. Loss Train: 0.0262. Loss Val: 0.0305. Time: 2.1597\n", " EPOCH 260. Progress: 52.0%. \n", " Training R^2: 0.5447. Test R^2: 0.4810. Loss Train: 0.0255. Loss Val: 0.0335. Time: 2.1949\n", " EPOCH 270. Progress: 54.0%. \n", " Training R^2: 0.5747. Test R^2: 0.4692. Loss Train: 0.0230. Loss Val: 0.0324. Time: 2.1299\n", " EPOCH 280. Progress: 56.00000000000001%. \n", " Training R^2: 0.5909. Test R^2: 0.5150. Loss Train: 0.0233. Loss Val: 0.0316. Time: 2.0958\n", " EPOCH 290. Progress: 57.99999999999999%. \n", " Training R^2: 0.5533. Test R^2: 0.4990. Loss Train: 0.0307. Loss Val: 0.0322. Time: 2.2280\n", " EPOCH 300. Progress: 60.0%. \n", " Training R^2: 0.5911. Test R^2: 0.5194. Loss Train: 0.0244. Loss Val: 0.0296. Time: 2.0876\n", " EPOCH 310. Progress: 62.0%. \n", " Training R^2: 0.5781. Test R^2: 0.5346. Loss Train: 0.0246. Loss Val: 0.0312. Time: 2.1579\n", " EPOCH 320. Progress: 64.0%. \n", " Training R^2: 0.5869. Test R^2: 0.4697. Loss Train: 0.0293. Loss Val: 0.0308. Time: 2.1631\n", " EPOCH 330. Progress: 66.0%. \n", " Training R^2: 0.5359. Test R^2: 0.5073. Loss Train: 0.0249. Loss Val: 0.0322. Time: 2.1336\n", " EPOCH 340. Progress: 68.0%. \n", " Training R^2: 0.3939. Test R^2: 0.3366. Loss Train: 0.0255. Loss Val: 0.0414. Time: 2.5117\n", " EPOCH 350. Progress: 70.0%. \n", " Training R^2: 0.5605. Test R^2: 0.5145. Loss Train: 0.0245. Loss Val: 0.0322. Time: 2.1713\n", " EPOCH 360. Progress: 72.0%. \n", " Training R^2: 0.5575. Test R^2: 0.4871. Loss Train: 0.0250. Loss Val: 0.0341. Time: 2.1935\n", " EPOCH 370. Progress: 74.0%. \n", " Training R^2: 0.5218. Test R^2: 0.4586. Loss Train: 0.0244. Loss Val: 0.0341. Time: 2.1901\n", " EPOCH 380. Progress: 76.0%. \n", " Training R^2: 0.3631. Test R^2: 0.2953. Loss Train: 0.0306. Loss Val: 0.0404. Time: 2.3216\n", " EPOCH 390. Progress: 78.0%. \n", " Training R^2: 0.5393. Test R^2: 0.4570. Loss Train: 0.0252. Loss Val: 0.0342. Time: 2.1549\n", " EPOCH 400. Progress: 80.0%. \n", " Training R^2: 0.5360. Test R^2: 0.4534. Loss Train: 0.0275. Loss Val: 0.0353. Time: 2.1271\n", " EPOCH 410. Progress: 82.0%. \n", " Training R^2: 0.5757. Test R^2: 0.4784. Loss Train: 0.0249. Loss Val: 0.0299. Time: 2.1469\n", " EPOCH 420. Progress: 84.0%. \n", " Training R^2: 0.5373. Test R^2: 0.5167. Loss Train: 0.0230. Loss Val: 0.0320. Time: 2.0999\n", " EPOCH 430. Progress: 86.0%. \n", " Training R^2: 0.5818. Test R^2: 0.4904. Loss Train: 0.0247. Loss Val: 0.0332. Time: 2.1413\n", " EPOCH 440. Progress: 88.0%. \n", " Training R^2: 0.4773. Test R^2: 0.4302. Loss Train: 0.0263. Loss Val: 0.0355. Time: 2.1924\n", " EPOCH 450. Progress: 90.0%. \n", " Training R^2: 0.4953. Test R^2: 0.4957. Loss Train: 0.0241. Loss Val: 0.0323. Time: 2.0620\n", " EPOCH 460. Progress: 92.0%. \n", " Training R^2: 0.5256. Test R^2: 0.5028. Loss Train: 0.0247. Loss Val: 0.0302. Time: 2.1600\n", " EPOCH 470. Progress: 94.0%. \n", " Training R^2: 0.3495. Test R^2: 0.4748. Loss Train: 0.0278. Loss Val: 0.0320. Time: 2.2276\n", " EPOCH 480. Progress: 96.0%. \n", " Training R^2: 0.4896. Test R^2: 0.4707. Loss Train: 0.0258. Loss Val: 0.0345. Time: 2.3685\n", " EPOCH 490. Progress: 98.0%. \n", " Training R^2: 0.6014. Test R^2: 0.5493. Loss Train: 0.0254. Loss Val: 0.0291. Time: 2.1611\n", " EPOCH 500. Progress: 100.0%. \n", " Training R^2: 0.5324. Test R^2: 0.5026. Loss Train: 0.0244. Loss Val: 0.0318. Time: 2.2871\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "saving ./plots/lr0.1FFNet_ReLU_momentum0.9_wdecay0.001_dampening0_nesterovFalse_HidDim64.png\n", "\n", "lr: 0.1, momentum: 0.9, weight_decay: 0.001, dampening: 0, nesterov: False \n", "FeedforwardNeuralNetModel(\n", " (fc1): Linear(in_features=47, out_features=128, bias=True)\n", " (relu): ReLU()\n", " (fc2): Linear(in_features=128, out_features=1, bias=True)\n", ")\n", "optimizer: SGD (\n", "Parameter Group 0\n", " dampening: 0\n", " lr: 0.1\n", " momentum: 0.9\n", " nesterov: False\n", " weight_decay: 0.001\n", ")\n", "n. of epochs: 500\n", " EPOCH 0. Progress: 0.0%. \n", " Training R^2: 0.2283. Test R^2: 0.2728. Loss Train: 0.0909. Loss Val: 0.0450. Time: 2.7173\n", " EPOCH 10. Progress: 2.0%. \n", " Training R^2: 0.5042. Test R^2: 0.3939. Loss Train: 0.0308. Loss Val: 0.0374. Time: 2.1023\n", " EPOCH 20. Progress: 4.0%. \n", " Training R^2: 0.4693. Test R^2: 0.4194. Loss Train: 0.0277. Loss Val: 0.0383. Time: 2.0253\n", " EPOCH 30. Progress: 6.0%. \n", " Training R^2: 0.5159. Test R^2: 0.4670. Loss Train: 0.0281. Loss Val: 0.0342. Time: 1.9902\n", " EPOCH 40. Progress: 8.0%. \n", " Training R^2: 0.5297. Test R^2: 0.4673. Loss Train: 0.0303. Loss Val: 0.0332. Time: 2.0638\n", " EPOCH 50. Progress: 10.0%. \n", " Training R^2: 0.4762. Test R^2: 0.4752. Loss Train: 0.0288. Loss Val: 0.0344. Time: 2.0154\n", " EPOCH 60. Progress: 12.0%. \n", " Training R^2: 0.5820. Test R^2: 0.4874. Loss Train: 0.0242. Loss Val: 0.0311. Time: 1.9918\n", " EPOCH 70. Progress: 14.000000000000002%. \n", " Training R^2: 0.5141. Test R^2: 0.5016. Loss Train: 0.0249. Loss Val: 0.0318. Time: 2.0654\n", " EPOCH 80. Progress: 16.0%. \n", " Training R^2: 0.5299. Test R^2: 0.4122. Loss Train: 0.0248. Loss Val: 0.0366. Time: 2.0807\n", " EPOCH 90. Progress: 18.0%. \n", " Training R^2: 0.5084. Test R^2: 0.4630. Loss Train: 0.0238. Loss Val: 0.0351. Time: 2.0694\n", " EPOCH 100. Progress: 20.0%. \n", " Training R^2: 0.5886. Test R^2: 0.5127. Loss Train: 0.0267. Loss Val: 0.0292. Time: 2.3217\n", " EPOCH 110. Progress: 22.0%. \n", " Training R^2: 0.5817. Test R^2: 0.5371. Loss Train: 0.0245. Loss Val: 0.0301. Time: 1.9418\n", " EPOCH 120. Progress: 24.0%. \n", " Training R^2: 0.4942. Test R^2: 0.3745. Loss Train: 0.0241. Loss Val: 0.0372. Time: 2.0107\n", " EPOCH 130. Progress: 26.0%. \n", " Training R^2: 0.5857. Test R^2: 0.5444. Loss Train: 0.0229. Loss Val: 0.0286. Time: 2.0223\n", " EPOCH 140. Progress: 28.000000000000004%. \n", " Training R^2: 0.3522. Test R^2: 0.2840. Loss Train: 0.0253. Loss Val: 0.0461. Time: 2.0770\n", " EPOCH 150. Progress: 30.0%. \n", " Training R^2: 0.6004. Test R^2: 0.5261. Loss Train: 0.0258. Loss Val: 0.0298. Time: 2.6616\n", " EPOCH 160. Progress: 32.0%. \n", " Training R^2: 0.5011. Test R^2: 0.4417. Loss Train: 0.0314. Loss Val: 0.0363. Time: 2.1031\n", " EPOCH 170. Progress: 34.0%. \n", " Training R^2: 0.5446. Test R^2: 0.4964. Loss Train: 0.0241. Loss Val: 0.0298. Time: 2.7993\n", " EPOCH 180. Progress: 36.0%. \n", " Training R^2: 0.5949. Test R^2: 0.5359. Loss Train: 0.0219. Loss Val: 0.0297. Time: 1.7726\n", " EPOCH 190. Progress: 38.0%. \n", " Training R^2: 0.5681. Test R^2: 0.5055. Loss Train: 0.0234. Loss Val: 0.0325. Time: 2.2046\n", " EPOCH 200. Progress: 40.0%. \n", " Training R^2: 0.5713. Test R^2: 0.4666. Loss Train: 0.0258. Loss Val: 0.0318. Time: 2.1275\n", " EPOCH 210. Progress: 42.0%. \n", " Training R^2: 0.4814. Test R^2: 0.4655. Loss Train: 0.0252. Loss Val: 0.0334. Time: 2.6702\n", " EPOCH 220. Progress: 44.0%. \n", " Training R^2: 0.5877. Test R^2: 0.5165. Loss Train: 0.0226. Loss Val: 0.0284. Time: 2.3608\n", " EPOCH 230. Progress: 46.0%. \n", " Training R^2: 0.2601. Test R^2: 0.2126. Loss Train: 0.0261. Loss Val: 0.0508. Time: 2.8814\n", " EPOCH 240. Progress: 48.0%. \n", " Training R^2: 0.3508. Test R^2: 0.3333. Loss Train: 0.0272. Loss Val: 0.0426. Time: 2.2305\n", " EPOCH 250. Progress: 50.0%. \n", " Training R^2: 0.5467. Test R^2: 0.5143. Loss Train: 0.0257. Loss Val: 0.0296. Time: 2.4325\n", " EPOCH 260. Progress: 52.0%. \n", " Training R^2: 0.4763. Test R^2: 0.4690. Loss Train: 0.0284. Loss Val: 0.0351. Time: 2.6164\n", " EPOCH 270. Progress: 54.0%. \n", " Training R^2: 0.5053. Test R^2: 0.4363. Loss Train: 0.0280. Loss Val: 0.0361. Time: 2.3808\n", " EPOCH 280. Progress: 56.00000000000001%. \n", " Training R^2: 0.5457. Test R^2: 0.5389. Loss Train: 0.0236. Loss Val: 0.0302. Time: 2.1011\n", " EPOCH 290. Progress: 57.99999999999999%. \n", " Training R^2: 0.5960. Test R^2: 0.5262. Loss Train: 0.0260. Loss Val: 0.0295. Time: 2.2730\n", " EPOCH 300. Progress: 60.0%. \n", " Training R^2: 0.5312. Test R^2: 0.5019. Loss Train: 0.0225. Loss Val: 0.0328. Time: 2.2471\n", " EPOCH 310. Progress: 62.0%. \n", " Training R^2: 0.5914. Test R^2: 0.5194. Loss Train: 0.0227. Loss Val: 0.0310. Time: 2.1525\n", " EPOCH 320. Progress: 64.0%. \n", " Training R^2: 0.4003. Test R^2: 0.3783. Loss Train: 0.0240. Loss Val: 0.0390. Time: 2.0833\n", " EPOCH 330. Progress: 66.0%. \n", " Training R^2: 0.6086. Test R^2: 0.5362. Loss Train: 0.0260. Loss Val: 0.0285. Time: 1.9955\n", " EPOCH 340. Progress: 68.0%. \n", " Training R^2: 0.4901. Test R^2: 0.4330. Loss Train: 0.0242. Loss Val: 0.0356. Time: 2.1022\n", " EPOCH 350. Progress: 70.0%. \n", " Training R^2: 0.5079. Test R^2: 0.4892. Loss Train: 0.0285. Loss Val: 0.0334. Time: 2.0113\n", " EPOCH 360. Progress: 72.0%. \n", " Training R^2: 0.5812. Test R^2: 0.5080. Loss Train: 0.0222. Loss Val: 0.0305. Time: 2.2167\n", " EPOCH 370. Progress: 74.0%. \n", " Training R^2: 0.4940. Test R^2: 0.4996. Loss Train: 0.0241. Loss Val: 0.0316. Time: 2.0167\n", " EPOCH 380. Progress: 76.0%. \n", " Training R^2: 0.5079. Test R^2: 0.4638. Loss Train: 0.0228. Loss Val: 0.0327. Time: 2.1083\n", " EPOCH 390. Progress: 78.0%. \n", " Training R^2: 0.5362. Test R^2: 0.4818. Loss Train: 0.0228. Loss Val: 0.0343. Time: 2.0472\n", " EPOCH 400. Progress: 80.0%. \n", " Training R^2: 0.3227. Test R^2: 0.2568. Loss Train: 0.0262. Loss Val: 0.0475. Time: 2.1222\n", " EPOCH 410. Progress: 82.0%. \n", " Training R^2: 0.5846. Test R^2: 0.5075. Loss Train: 0.0231. Loss Val: 0.0304. Time: 2.0978\n", " EPOCH 420. Progress: 84.0%. \n", " Training R^2: 0.5472. Test R^2: 0.4843. Loss Train: 0.0258. Loss Val: 0.0327. Time: 2.0564\n", " EPOCH 430. Progress: 86.0%. \n", " Training R^2: 0.6086. Test R^2: 0.5734. Loss Train: 0.0248. Loss Val: 0.0289. Time: 2.3157\n", " EPOCH 440. Progress: 88.0%. \n", " Training R^2: 0.5590. Test R^2: 0.5247. Loss Train: 0.0233. Loss Val: 0.0302. Time: 2.2981\n", " EPOCH 450. Progress: 90.0%. \n", " Training R^2: 0.5330. Test R^2: 0.4452. Loss Train: 0.0237. Loss Val: 0.0330. Time: 2.2508\n", " EPOCH 460. Progress: 92.0%. \n", " Training R^2: 0.5879. Test R^2: 0.5059. Loss Train: 0.0243. Loss Val: 0.0320. Time: 2.3504\n", " EPOCH 470. Progress: 94.0%. \n", " Training R^2: 0.5669. Test R^2: 0.4830. Loss Train: 0.0298. Loss Val: 0.0332. Time: 2.4113\n", " EPOCH 480. Progress: 96.0%. \n", " Training R^2: 0.5283. Test R^2: 0.4607. Loss Train: 0.0257. Loss Val: 0.0355. Time: 2.3665\n", " EPOCH 490. Progress: 98.0%. \n", " Training R^2: 0.5325. Test R^2: 0.4850. Loss Train: 0.0241. Loss Val: 0.0329. Time: 2.3898\n", " EPOCH 500. Progress: 100.0%. \n", " Training R^2: 0.5753. Test R^2: 0.5075. Loss Train: 0.0240. Loss Val: 0.0324. Time: 2.6969\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "saving ./plots/lr0.1FFNet_ReLU_momentum0.9_wdecay0.001_dampening0_nesterovFalse_HidDim128.png\n", "\n", "lr: 0.01, momentum: 0.9, weight_decay: 0.001, dampening: 0, nesterov: False \n", "FeedforwardNeuralNetModel(\n", " (fc1): Linear(in_features=47, out_features=64, bias=True)\n", " (relu): ReLU()\n", " (fc2): Linear(in_features=64, out_features=1, bias=True)\n", ")\n", "optimizer: SGD (\n", "Parameter Group 0\n", " dampening: 0\n", " lr: 0.01\n", " momentum: 0.9\n", " nesterov: False\n", " weight_decay: 0.001\n", ")\n", "n. of epochs: 500\n", " EPOCH 0. Progress: 0.0%. \n", " Training R^2: 0.2071. Test R^2: 0.1615. Loss Train: 0.0837. Loss Val: 0.0541. Time: 5.2521\n", " EPOCH 10. Progress: 2.0%. \n", " Training R^2: 0.4945. Test R^2: 0.4320. Loss Train: 0.0276. Loss Val: 0.0365. Time: 2.5691\n", " EPOCH 20. Progress: 4.0%. \n", " Training R^2: 0.4887. Test R^2: 0.3529. Loss Train: 0.0265. Loss Val: 0.0354. Time: 2.6201\n", " EPOCH 30. Progress: 6.0%. \n", " Training R^2: 0.4845. Test R^2: 0.3973. Loss Train: 0.0252. Loss Val: 0.0370. Time: 3.0503\n", " EPOCH 40. Progress: 8.0%. \n", " Training R^2: 0.5197. Test R^2: 0.4612. Loss Train: 0.0244. Loss Val: 0.0340. Time: 2.6444\n", " EPOCH 50. Progress: 10.0%. \n", " Training R^2: 0.5593. Test R^2: 0.4810. Loss Train: 0.0240. Loss Val: 0.0317. Time: 2.2123\n", " EPOCH 60. Progress: 12.0%. \n", " Training R^2: 0.5602. Test R^2: 0.5208. Loss Train: 0.0232. Loss Val: 0.0313. Time: 2.2148\n", " EPOCH 70. Progress: 14.000000000000002%. \n", " Training R^2: 0.5714. Test R^2: 0.5037. Loss Train: 0.0232. Loss Val: 0.0301. Time: 2.3758\n", " EPOCH 80. Progress: 16.0%. \n", " Training R^2: 0.5782. Test R^2: 0.4720. Loss Train: 0.0219. Loss Val: 0.0300. Time: 2.3277\n", " EPOCH 90. Progress: 18.0%. \n", " Training R^2: 0.5353. Test R^2: 0.5136. Loss Train: 0.0246. Loss Val: 0.0319. Time: 2.3695\n", " EPOCH 100. Progress: 20.0%. \n", " Training R^2: 0.5828. Test R^2: 0.5317. Loss Train: 0.0217. Loss Val: 0.0282. Time: 2.6416\n", " EPOCH 110. Progress: 22.0%. \n", " Training R^2: 0.5944. Test R^2: 0.5701. Loss Train: 0.0214. Loss Val: 0.0278. Time: 2.5943\n", " EPOCH 120. Progress: 24.0%. \n", " Training R^2: 0.5940. Test R^2: 0.5253. Loss Train: 0.0215. Loss Val: 0.0288. Time: 2.2874\n", " EPOCH 130. Progress: 26.0%. \n", " Training R^2: 0.6363. Test R^2: 0.5689. Loss Train: 0.0219. Loss Val: 0.0269. Time: 2.2321\n", " EPOCH 140. Progress: 28.000000000000004%. \n", " Training R^2: 0.6164. Test R^2: 0.5844. Loss Train: 0.0205. Loss Val: 0.0286. Time: 2.7046\n", " EPOCH 150. Progress: 30.0%. \n", " Training R^2: 0.6418. Test R^2: 0.5830. Loss Train: 0.0201. Loss Val: 0.0257. Time: 2.2595\n", " EPOCH 160. Progress: 32.0%. \n", " Training R^2: 0.6178. Test R^2: 0.5370. Loss Train: 0.0212. Loss Val: 0.0260. Time: 2.1360\n", " EPOCH 170. Progress: 34.0%. \n", " Training R^2: 0.6210. Test R^2: 0.6071. Loss Train: 0.0195. Loss Val: 0.0257. Time: 2.0929\n", " EPOCH 180. Progress: 36.0%. \n", " Training R^2: 0.6152. Test R^2: 0.5949. Loss Train: 0.0199. Loss Val: 0.0257. Time: 2.3402\n", " EPOCH 190. Progress: 38.0%. \n", " Training R^2: 0.6336. Test R^2: 0.6208. Loss Train: 0.0190. Loss Val: 0.0240. Time: 4.4691\n", " EPOCH 200. Progress: 40.0%. \n", " Training R^2: 0.6342. Test R^2: 0.6176. Loss Train: 0.0185. Loss Val: 0.0250. Time: 2.1974\n", " EPOCH 210. Progress: 42.0%. \n", " Training R^2: 0.6577. Test R^2: 0.6409. Loss Train: 0.0188. Loss Val: 0.0253. Time: 2.3948\n", " EPOCH 220. Progress: 44.0%. \n", " Training R^2: 0.6419. Test R^2: 0.5804. Loss Train: 0.0188. Loss Val: 0.0257. Time: 2.3758\n", " EPOCH 230. Progress: 46.0%. \n", " Training R^2: 0.6658. Test R^2: 0.6293. Loss Train: 0.0184. Loss Val: 0.0236. Time: 2.4037\n", " EPOCH 240. Progress: 48.0%. \n", " Training R^2: 0.6620. Test R^2: 0.6130. Loss Train: 0.0185. Loss Val: 0.0243. Time: 3.1687\n", " EPOCH 250. Progress: 50.0%. \n", " Training R^2: 0.6337. Test R^2: 0.6433. Loss Train: 0.0186. Loss Val: 0.0234. Time: 2.2961\n", " EPOCH 260. Progress: 52.0%. \n", " Training R^2: 0.6647. Test R^2: 0.6205. Loss Train: 0.0184. Loss Val: 0.0239. Time: 2.2908\n", " EPOCH 270. Progress: 54.0%. \n", " Training R^2: 0.6879. Test R^2: 0.6262. Loss Train: 0.0177. Loss Val: 0.0229. Time: 2.6227\n", " EPOCH 280. Progress: 56.00000000000001%. \n", " Training R^2: 0.6833. Test R^2: 0.6393. Loss Train: 0.0173. Loss Val: 0.0225. Time: 2.2992\n", " EPOCH 290. Progress: 57.99999999999999%. \n", " Training R^2: 0.6869. Test R^2: 0.6364. Loss Train: 0.0177. Loss Val: 0.0223. Time: 3.4453\n", " EPOCH 300. Progress: 60.0%. \n", " Training R^2: 0.6897. Test R^2: 0.6678. Loss Train: 0.0171. Loss Val: 0.0216. Time: 2.4738\n", " EPOCH 310. Progress: 62.0%. \n", " Training R^2: 0.6930. Test R^2: 0.6566. Loss Train: 0.0175. Loss Val: 0.0214. Time: 2.7012\n", " EPOCH 320. Progress: 64.0%. \n", " Training R^2: 0.6923. Test R^2: 0.6470. Loss Train: 0.0167. Loss Val: 0.0232. Time: 2.5880\n", " EPOCH 330. Progress: 66.0%. \n", " Training R^2: 0.6725. Test R^2: 0.6499. Loss Train: 0.0167. Loss Val: 0.0215. Time: 2.1894\n", " EPOCH 340. Progress: 68.0%. \n", " Training R^2: 0.6971. Test R^2: 0.6649. Loss Train: 0.0177. Loss Val: 0.0206. Time: 2.2053\n", " EPOCH 350. Progress: 70.0%. \n", " Training R^2: 0.6934. Test R^2: 0.5029. Loss Train: 0.0160. Loss Val: 0.0212. Time: 2.2440\n", " EPOCH 360. Progress: 72.0%. \n", " Training R^2: 0.6842. Test R^2: 0.6561. Loss Train: 0.0169. Loss Val: 0.0217. Time: 3.0857\n", " EPOCH 370. Progress: 74.0%. \n", " Training R^2: 0.6782. Test R^2: 0.6649. Loss Train: 0.0165. Loss Val: 0.0225. Time: 3.0184\n", " EPOCH 380. Progress: 76.0%. \n", " Training R^2: 0.6628. Test R^2: 0.6578. Loss Train: 0.0167. Loss Val: 0.0231. Time: 2.4221\n", " EPOCH 390. Progress: 78.0%. \n", " Training R^2: 0.6963. Test R^2: 0.7008. Loss Train: 0.0161. Loss Val: 0.0207. Time: 2.3058\n", " EPOCH 400. Progress: 80.0%. \n", " Training R^2: 0.7049. Test R^2: 0.6829. Loss Train: 0.0167. Loss Val: 0.0199. Time: 2.3138\n", " EPOCH 410. Progress: 82.0%. \n", " Training R^2: 0.6270. Test R^2: 0.6322. Loss Train: 0.0178. Loss Val: 0.0232. Time: 2.2893\n", " EPOCH 420. Progress: 84.0%. \n", " Training R^2: 0.7030. Test R^2: 0.6925. Loss Train: 0.0163. Loss Val: 0.0204. Time: 2.3054\n", " EPOCH 430. Progress: 86.0%. \n", " Training R^2: 0.7005. Test R^2: 0.6811. Loss Train: 0.0164. Loss Val: 0.0197. Time: 2.3668\n", " EPOCH 440. Progress: 88.0%. \n", " Training R^2: 0.6835. Test R^2: 0.6851. Loss Train: 0.0155. Loss Val: 0.0216. Time: 2.5748\n", " EPOCH 450. Progress: 90.0%. \n", " Training R^2: 0.6968. Test R^2: 0.6856. Loss Train: 0.0159. Loss Val: 0.0204. Time: 2.3139\n", " EPOCH 460. Progress: 92.0%. \n", " Training R^2: 0.6903. Test R^2: 0.5642. Loss Train: 0.0160. Loss Val: 0.0213. Time: 2.2897\n", " EPOCH 470. Progress: 94.0%. \n", " Training R^2: 0.7064. Test R^2: 0.6803. Loss Train: 0.0167. Loss Val: 0.0207. Time: 2.3248\n", " EPOCH 480. Progress: 96.0%. \n", " Training R^2: 0.7177. Test R^2: 0.6822. Loss Train: 0.0152. Loss Val: 0.0198. Time: 2.3316\n", " EPOCH 490. Progress: 98.0%. \n", " Training R^2: 0.7157. Test R^2: 0.6994. Loss Train: 0.0158. Loss Val: 0.0194. Time: 2.3627\n", " EPOCH 500. Progress: 100.0%. \n", " Training R^2: 0.6759. Test R^2: 0.6570. Loss Train: 0.0158. Loss Val: 0.0204. Time: 2.3460\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "saving ./plots/lr0.01FFNet_ReLU_momentum0.9_wdecay0.001_dampening0_nesterovFalse_HidDim64.png\n", "\n", "lr: 0.01, momentum: 0.9, weight_decay: 0.001, dampening: 0, nesterov: False \n", "FeedforwardNeuralNetModel(\n", " (fc1): Linear(in_features=47, out_features=128, bias=True)\n", " (relu): ReLU()\n", " (fc2): Linear(in_features=128, out_features=1, bias=True)\n", ")\n", "optimizer: SGD (\n", "Parameter Group 0\n", " dampening: 0\n", " lr: 0.01\n", " momentum: 0.9\n", " nesterov: False\n", " weight_decay: 0.001\n", ")\n", "n. of epochs: 500\n", " EPOCH 0. Progress: 0.0%. \n", " Training R^2: 0.1922. Test R^2: 0.1661. Loss Train: 0.0977. Loss Val: 0.0542. Time: 2.8262\n", " EPOCH 10. Progress: 2.0%. \n", " Training R^2: 0.4624. Test R^2: 0.3758. Loss Train: 0.0272. Loss Val: 0.0403. Time: 2.8381\n", " EPOCH 20. Progress: 4.0%. \n", " Training R^2: 0.5202. Test R^2: 0.4412. Loss Train: 0.0261. Loss Val: 0.0368. Time: 2.4058\n", " EPOCH 30. Progress: 6.0%. \n", " Training R^2: 0.5272. Test R^2: 0.4667. Loss Train: 0.0244. Loss Val: 0.0363. Time: 2.6949\n", " EPOCH 40. Progress: 8.0%. \n", " Training R^2: 0.5523. Test R^2: 0.4698. Loss Train: 0.0242. Loss Val: 0.0325. Time: 2.5087\n", " EPOCH 50. Progress: 10.0%. \n", " Training R^2: 0.5638. Test R^2: 0.5104. Loss Train: 0.0232. Loss Val: 0.0314. Time: 2.4473\n", " EPOCH 60. Progress: 12.0%. \n", " Training R^2: 0.5776. Test R^2: 0.5289. Loss Train: 0.0226. Loss Val: 0.0305. Time: 2.5074\n", " EPOCH 70. Progress: 14.000000000000002%. \n", " Training R^2: 0.5527. Test R^2: 0.5220. Loss Train: 0.0225. Loss Val: 0.0308. Time: 2.5925\n", " EPOCH 80. Progress: 16.0%. \n", " Training R^2: 0.5839. Test R^2: 0.5442. Loss Train: 0.0217. Loss Val: 0.0308. Time: 2.3873\n", " EPOCH 90. Progress: 18.0%. \n", " Training R^2: 0.5832. Test R^2: 0.5532. Loss Train: 0.0213. Loss Val: 0.0297. Time: 2.4564\n", " EPOCH 100. Progress: 20.0%. \n", " Training R^2: 0.6024. Test R^2: 0.5454. Loss Train: 0.0209. Loss Val: 0.0282. Time: 2.4184\n", " EPOCH 110. Progress: 22.0%. \n", " Training R^2: 0.6247. Test R^2: 0.5949. Loss Train: 0.0203. Loss Val: 0.0266. Time: 2.6069\n", " EPOCH 120. Progress: 24.0%. \n", " Training R^2: 0.6297. Test R^2: 0.6127. Loss Train: 0.0206. Loss Val: 0.0257. Time: 2.3823\n", " EPOCH 130. Progress: 26.0%. \n", " Training R^2: 0.6306. Test R^2: 0.5292. Loss Train: 0.0198. Loss Val: 0.0265. Time: 2.3792\n", " EPOCH 140. Progress: 28.000000000000004%. \n", " Training R^2: 0.6307. Test R^2: 0.6146. Loss Train: 0.0198. Loss Val: 0.0251. Time: 2.5422\n", " EPOCH 150. Progress: 30.0%. \n", " Training R^2: 0.6490. Test R^2: 0.5916. Loss Train: 0.0199. Loss Val: 0.0247. Time: 2.3709\n", " EPOCH 160. Progress: 32.0%. \n", " Training R^2: 0.6454. Test R^2: 0.6160. Loss Train: 0.0194. Loss Val: 0.0242. Time: 2.4208\n", " EPOCH 170. Progress: 34.0%. \n", " Training R^2: 0.6512. Test R^2: 0.6117. Loss Train: 0.0186. Loss Val: 0.0237. Time: 2.4226\n", " EPOCH 180. Progress: 36.0%. \n", " Training R^2: 0.6708. Test R^2: 0.5843. Loss Train: 0.0191. Loss Val: 0.0235. Time: 2.4393\n", " EPOCH 190. Progress: 38.0%. \n", " Training R^2: 0.6683. Test R^2: 0.6493. Loss Train: 0.0180. Loss Val: 0.0238. Time: 2.4745\n", " EPOCH 200. Progress: 40.0%. \n", " Training R^2: 0.6799. Test R^2: 0.6506. Loss Train: 0.0183. Loss Val: 0.0223. Time: 2.3793\n", " EPOCH 210. Progress: 42.0%. \n", " Training R^2: 0.6681. Test R^2: 0.6399. Loss Train: 0.0174. Loss Val: 0.0240. Time: 3.3691\n", " EPOCH 220. Progress: 44.0%. \n", " Training R^2: 0.6829. Test R^2: 0.6597. Loss Train: 0.0184. Loss Val: 0.0222. Time: 2.5731\n", " EPOCH 230. Progress: 46.0%. \n", " Training R^2: 0.6830. Test R^2: 0.6617. Loss Train: 0.0171. Loss Val: 0.0224. Time: 2.4132\n", " EPOCH 240. Progress: 48.0%. \n", " Training R^2: 0.6737. Test R^2: 0.6520. Loss Train: 0.0175. Loss Val: 0.0217. Time: 2.5320\n", " EPOCH 250. Progress: 50.0%. \n", " Training R^2: 0.6877. Test R^2: 0.6565. Loss Train: 0.0176. Loss Val: 0.0219. Time: 2.4302\n", " EPOCH 260. Progress: 52.0%. \n", " Training R^2: 0.6386. Test R^2: 0.6378. Loss Train: 0.0181. Loss Val: 0.0236. Time: 2.3277\n", " EPOCH 270. Progress: 54.0%. \n", " Training R^2: 0.6866. Test R^2: 0.6669. Loss Train: 0.0183. Loss Val: 0.0214. Time: 2.7861\n", " EPOCH 280. Progress: 56.00000000000001%. \n", " Training R^2: 0.6795. Test R^2: 0.6910. Loss Train: 0.0170. Loss Val: 0.0204. Time: 2.8787\n", " EPOCH 290. Progress: 57.99999999999999%. \n", " Training R^2: 0.6791. Test R^2: 0.6636. Loss Train: 0.0166. Loss Val: 0.0210. Time: 2.2770\n", " EPOCH 300. Progress: 60.0%. \n", " Training R^2: 0.6888. Test R^2: 0.6586. Loss Train: 0.0170. Loss Val: 0.0237. Time: 2.6371\n", " EPOCH 310. Progress: 62.0%. \n", " Training R^2: 0.6771. Test R^2: 0.6642. Loss Train: 0.0163. Loss Val: 0.0210. Time: 2.5277\n", " EPOCH 320. Progress: 64.0%. \n", " Training R^2: 0.6739. Test R^2: 0.6389. Loss Train: 0.0168. Loss Val: 0.0213. Time: 2.4118\n", " EPOCH 330. Progress: 66.0%. \n", " Training R^2: 0.7075. Test R^2: 0.6793. Loss Train: 0.0158. Loss Val: 0.0203. Time: 2.4315\n", " EPOCH 340. Progress: 68.0%. \n", " Training R^2: 0.7008. Test R^2: 0.6798. Loss Train: 0.0162. Loss Val: 0.0216. Time: 2.4063\n", " EPOCH 350. Progress: 70.0%. \n", " Training R^2: 0.6858. Test R^2: 0.6689. Loss Train: 0.0164. Loss Val: 0.0205. Time: 2.3764\n", " EPOCH 360. Progress: 72.0%. \n", " Training R^2: 0.6970. Test R^2: 0.6683. Loss Train: 0.0170. Loss Val: 0.0207. Time: 2.3104\n", " EPOCH 370. Progress: 74.0%. \n", " Training R^2: 0.7051. Test R^2: 0.6635. Loss Train: 0.0159. Loss Val: 0.0201. Time: 2.3505\n", " EPOCH 380. Progress: 76.0%. \n", " Training R^2: 0.6774. Test R^2: 0.6889. Loss Train: 0.0161. Loss Val: 0.0201. Time: 2.3744\n", " EPOCH 390. Progress: 78.0%. \n", " Training R^2: 0.6908. Test R^2: 0.6898. Loss Train: 0.0166. Loss Val: 0.0200. Time: 2.3658\n", " EPOCH 400. Progress: 80.0%. \n", " Training R^2: 0.7163. Test R^2: 0.7087. Loss Train: 0.0154. Loss Val: 0.0199. Time: 2.5999\n", " EPOCH 410. Progress: 82.0%. \n", " Training R^2: 0.7052. Test R^2: 0.7244. Loss Train: 0.0159. Loss Val: 0.0178. Time: 2.3856\n", " EPOCH 420. Progress: 84.0%. \n", " Training R^2: 0.7283. Test R^2: 0.7120. Loss Train: 0.0160. Loss Val: 0.0193. Time: 2.5393\n", " EPOCH 430. Progress: 86.0%. \n", " Training R^2: 0.7103. Test R^2: 0.6982. Loss Train: 0.0155. Loss Val: 0.0189. Time: 3.6950\n", " EPOCH 440. Progress: 88.0%. \n", " Training R^2: 0.6793. Test R^2: 0.7085. Loss Train: 0.0156. Loss Val: 0.0189. Time: 2.3910\n", " EPOCH 450. Progress: 90.0%. \n", " Training R^2: 0.7306. Test R^2: 0.7129. Loss Train: 0.0157. Loss Val: 0.0200. Time: 2.4181\n", " EPOCH 460. Progress: 92.0%. \n", " Training R^2: 0.5886. Test R^2: 0.5491. Loss Train: 0.0151. Loss Val: 0.0269. Time: 2.9231\n", " EPOCH 470. Progress: 94.0%. \n", " Training R^2: 0.7126. Test R^2: 0.7135. Loss Train: 0.0152. Loss Val: 0.0182. Time: 2.1260\n", " EPOCH 480. Progress: 96.0%. \n", " Training R^2: 0.6784. Test R^2: 0.6746. Loss Train: 0.0156. Loss Val: 0.0200. Time: 2.0990\n", " EPOCH 490. Progress: 98.0%. \n", " Training R^2: 0.7294. Test R^2: 0.6995. Loss Train: 0.0157. Loss Val: 0.0182. Time: 2.8657\n", " EPOCH 500. Progress: 100.0%. \n", " Training R^2: 0.6277. Test R^2: 0.6065. Loss Train: 0.0169. Loss Val: 0.0234. Time: 2.5456\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "saving ./plots/lr0.01FFNet_ReLU_momentum0.9_wdecay0.001_dampening0_nesterovFalse_HidDim128.png\n" ] } ], "source": [ "# Testing a regular FFnet\n", "class FeedforwardNeuralNetModel(nn.Module):\n", " def __init__(self, in_dim, hid_dim, out_dim):\n", " super(FeedforwardNeuralNetModel, self).__init__()\n", " # Linear function\n", " self.fc1 = nn.Linear(in_dim, hid_dim) \n", " # Non-linearity\n", " self.relu = nn.ReLU()\n", " # Linear function (readout)\n", " self.fc2 = nn.Linear(hid_dim, out_dim) \n", "\n", " def forward(self, x):\n", " # Linear function\n", " out = self.fc1(x)\n", " # Non-linearity\n", " out = self.relu(out)\n", " # Linear function (readout)\n", " out = self.fc2(out)\n", " return out\n", "\n", "# Best configuration for longer\n", "lr_range = [0.1,0.01]\n", "hid_dim_range = [64,128]\n", "weight_decay_range = [0.001]\n", "momentum_range = [0.9]\n", "dampening_range = [0]\n", "nesterov_range = [False]\n", "for lr in lr_range:\n", " for momentum in momentum_range:\n", " for weight_decay in weight_decay_range:\n", " for nesterov in nesterov_range:\n", " for dampening in dampening_range:\n", " for hid_dim in hid_dim_range:\n", "# try: \n", " print('\\nlr: {}, momentum: {}, weight_decay: {}, dampening: {}, nesterov: {} '.format(lr, momentum, weight_decay, dampening, nesterov))\n", " model = FeedforwardNeuralNetModel(in_dim=47,hid_dim=hid_dim, out_dim=1).to(device)\n", " print(model)\n", " SGD = torch.optim.SGD(model.parameters(), lr = lr, momentum=momentum, dampening=dampening, weight_decay=weight_decay, nesterov=nesterov) # This is absurdly high.\n", " # initialize the loss function. You don't want to use this one, so change it accordingly\n", " loss_fn = nn.MSELoss()\n", " config_str = 'lr' + str(lr) + 'FFNet_ReLU_momentum' + str(momentum) + '_wdecay' + str(weight_decay) + '_dampening' + str(dampening) +'_nesterov' + str(nesterov) + '_HidDim' + str(hid_dim)\n", " train(model,loss_fn, SGD, dataloaders['train'], dataloaders['val'], config_str,num_epochs=500, verbose=False)\n", "# except:\n", "# pass" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "lr: 0.01, momentum: 0.9, weight_decay: 0.001, dampening: 0, nesterov: False \n", "FeedforwardNeuralNetModel(\n", " (fc1): Linear(in_features=47, out_features=64, bias=True)\n", " (relu): ReLU()\n", " (fc2): Linear(in_features=64, out_features=1, bias=True)\n", ")\n", "optimizer: SGD (\n", "Parameter Group 0\n", " dampening: 0\n", " lr: 0.01\n", " momentum: 0.9\n", " nesterov: False\n", " weight_decay: 0.001\n", ")\n", "n. of epochs: 1000\n", " EPOCH 0. Progress: 0.0%. \n", " Training R^2: 0.2232. Test R^2: 0.2320. Loss Train: 0.0805. Loss Val: 0.0466. Time: 2.9867\n", " EPOCH 10. Progress: 1.0%. \n", " Training R^2: 0.4549. Test R^2: 0.3706. Loss Train: 0.0290. Loss Val: 0.0387. Time: 3.3358\n", " EPOCH 20. Progress: 2.0%. \n", " Training R^2: 0.5057. Test R^2: 0.4227. Loss Train: 0.0263. Loss Val: 0.0358. Time: 2.7223\n", " EPOCH 30. Progress: 3.0%. \n", " Training R^2: 0.5079. Test R^2: 0.4562. Loss Train: 0.0252. Loss Val: 0.0335. Time: 2.2713\n", " EPOCH 40. Progress: 4.0%. \n", " Training R^2: 0.5576. Test R^2: 0.4683. Loss Train: 0.0244. Loss Val: 0.0334. Time: 2.2900\n", " EPOCH 50. Progress: 5.0%. \n", " Training R^2: 0.5486. Test R^2: 0.4982. Loss Train: 0.0242. Loss Val: 0.0314. Time: 2.3105\n", " EPOCH 60. Progress: 6.0%. \n", " Training R^2: 0.5516. Test R^2: 0.4947. Loss Train: 0.0228. Loss Val: 0.0319. Time: 2.3228\n", " EPOCH 70. Progress: 7.000000000000001%. \n", " Training R^2: 0.5863. Test R^2: 0.5308. Loss Train: 0.0225. Loss Val: 0.0325. Time: 2.7094\n", " EPOCH 80. Progress: 8.0%. \n", " Training R^2: 0.6004. Test R^2: 0.4622. Loss Train: 0.0220. Loss Val: 0.0288. Time: 3.0514\n", " EPOCH 90. Progress: 9.0%. \n", " Training R^2: 0.5927. Test R^2: 0.5153. Loss Train: 0.0225. Loss Val: 0.0295. Time: 2.8121\n", " EPOCH 100. Progress: 10.0%. \n", " Training R^2: 0.5910. Test R^2: 0.5321. Loss Train: 0.0228. Loss Val: 0.0294. Time: 2.1782\n", " EPOCH 110. Progress: 11.0%. \n", " Training R^2: 0.5820. Test R^2: 0.5354. Loss Train: 0.0217. Loss Val: 0.0299. Time: 3.8535\n", " EPOCH 120. Progress: 12.0%. \n", " Training R^2: 0.6145. Test R^2: 0.5886. Loss Train: 0.0210. Loss Val: 0.0273. Time: 2.5599\n", " EPOCH 130. Progress: 13.0%. \n", " Training R^2: 0.6297. Test R^2: 0.5751. Loss Train: 0.0206. Loss Val: 0.0264. Time: 3.8149\n", " EPOCH 140. Progress: 14.000000000000002%. \n", " Training R^2: 0.6195. Test R^2: 0.5630. Loss Train: 0.0208. Loss Val: 0.0277. Time: 4.3139\n", " EPOCH 150. Progress: 15.0%. \n", " Training R^2: 0.6359. Test R^2: 0.6050. Loss Train: 0.0202. Loss Val: 0.0265. Time: 2.9903\n", " EPOCH 160. Progress: 16.0%. \n", " Training R^2: 0.6253. Test R^2: 0.6161. Loss Train: 0.0199. Loss Val: 0.0258. Time: 5.2340\n", " EPOCH 170. Progress: 17.0%. \n", " Training R^2: 0.6169. Test R^2: 0.6163. Loss Train: 0.0199. Loss Val: 0.0255. Time: 2.9977\n", " EPOCH 180. Progress: 18.0%. \n", " Training R^2: 0.6311. Test R^2: 0.6027. Loss Train: 0.0202. Loss Val: 0.0256. Time: 2.3679\n", " EPOCH 190. Progress: 19.0%. \n", " Training R^2: 0.6460. Test R^2: 0.5935. Loss Train: 0.0188. Loss Val: 0.0256. Time: 2.5715\n", " EPOCH 200. Progress: 20.0%. \n", " Training R^2: 0.6415. Test R^2: 0.6116. Loss Train: 0.0191. Loss Val: 0.0239. Time: 2.8218\n", " EPOCH 210. Progress: 21.0%. \n", " Training R^2: 0.6490. Test R^2: 0.6230. Loss Train: 0.0189. Loss Val: 0.0238. Time: 2.2883\n", " EPOCH 220. Progress: 22.0%. \n", " Training R^2: 0.6347. Test R^2: 0.5862. Loss Train: 0.0189. Loss Val: 0.0255. Time: 2.2429\n", " EPOCH 230. Progress: 23.0%. \n", " Training R^2: 0.6424. Test R^2: 0.5863. Loss Train: 0.0186. Loss Val: 0.0251. Time: 2.4448\n", " EPOCH 240. Progress: 24.0%. \n", " Training R^2: 0.6649. Test R^2: 0.5864. Loss Train: 0.0184. Loss Val: 0.0243. Time: 2.5898\n", " EPOCH 250. Progress: 25.0%. \n", " Training R^2: 0.6604. Test R^2: 0.6103. Loss Train: 0.0186. Loss Val: 0.0249. Time: 2.4353\n", " EPOCH 260. Progress: 26.0%. \n", " Training R^2: 0.6760. Test R^2: 0.6581. Loss Train: 0.0186. Loss Val: 0.0222. Time: 2.2146\n", " EPOCH 270. Progress: 27.0%. \n", " Training R^2: 0.6397. Test R^2: 0.5926. Loss Train: 0.0180. Loss Val: 0.0258. Time: 2.3377\n", " EPOCH 280. Progress: 28.000000000000004%. \n", " Training R^2: 0.6660. Test R^2: 0.6523. Loss Train: 0.0195. Loss Val: 0.0246. Time: 2.7603\n", " EPOCH 290. Progress: 28.999999999999996%. \n", " Training R^2: 0.6841. Test R^2: 0.6416. Loss Train: 0.0187. Loss Val: 0.0227. Time: 2.7224\n", " EPOCH 300. Progress: 30.0%. \n", " Training R^2: 0.6880. Test R^2: 0.6541. Loss Train: 0.0176. Loss Val: 0.0216. Time: 2.6406\n", " EPOCH 310. Progress: 31.0%. \n", " Training R^2: 0.6694. Test R^2: 0.6531. Loss Train: 0.0183. Loss Val: 0.0221. Time: 2.6460\n", " EPOCH 320. Progress: 32.0%. \n", " Training R^2: 0.6774. Test R^2: 0.6436. Loss Train: 0.0169. Loss Val: 0.0211. Time: 2.4566\n", " EPOCH 330. Progress: 33.0%. \n", " Training R^2: 0.6632. Test R^2: 0.6591. Loss Train: 0.0169. Loss Val: 0.0212. Time: 2.9468\n", " EPOCH 340. Progress: 34.0%. \n", " Training R^2: 0.6894. Test R^2: 0.6351. Loss Train: 0.0171. Loss Val: 0.0211. Time: 2.5672\n", " EPOCH 350. Progress: 35.0%. \n", " Training R^2: 0.6896. Test R^2: 0.6695. Loss Train: 0.0164. Loss Val: 0.0212. Time: 2.6775\n", " EPOCH 360. Progress: 36.0%. \n", " Training R^2: 0.6232. Test R^2: 0.6326. Loss Train: 0.0165. Loss Val: 0.0247. Time: 2.3944\n", " EPOCH 370. Progress: 37.0%. \n", " Training R^2: 0.6804. Test R^2: 0.6576. Loss Train: 0.0164. Loss Val: 0.0210. Time: 2.1897\n", " EPOCH 380. Progress: 38.0%. \n", " Training R^2: 0.6921. Test R^2: 0.6647. Loss Train: 0.0169. Loss Val: 0.0205. Time: 2.2228\n", " EPOCH 390. Progress: 39.0%. \n", " Training R^2: 0.6972. Test R^2: 0.6491. Loss Train: 0.0162. Loss Val: 0.0218. Time: 2.2152\n", " EPOCH 400. Progress: 40.0%. \n", " Training R^2: 0.6947. Test R^2: 0.6753. Loss Train: 0.0167. Loss Val: 0.0212. Time: 2.1881\n", " EPOCH 410. Progress: 41.0%. \n", " Training R^2: 0.7018. Test R^2: 0.6643. Loss Train: 0.0167. Loss Val: 0.0226. Time: 3.0118\n", " EPOCH 420. Progress: 42.0%. \n", " Training R^2: 0.7000. Test R^2: 0.7003. Loss Train: 0.0163. Loss Val: 0.0201. Time: 2.6304\n", " EPOCH 430. Progress: 43.0%. \n", " Training R^2: 0.6625. Test R^2: 0.6237. Loss Train: 0.0167. Loss Val: 0.0233. Time: 3.1598\n", " EPOCH 440. Progress: 44.0%. \n", " Training R^2: 0.7060. Test R^2: 0.6623. Loss Train: 0.0159. Loss Val: 0.0197. Time: 3.0086\n", " EPOCH 450. Progress: 45.0%. \n", " Training R^2: 0.6988. Test R^2: 0.6952. Loss Train: 0.0162. Loss Val: 0.0192. Time: 2.2963\n", " EPOCH 460. Progress: 46.0%. \n", " Training R^2: 0.7103. Test R^2: 0.6961. Loss Train: 0.0159. Loss Val: 0.0202. Time: 2.3076\n", " EPOCH 470. Progress: 47.0%. \n", " Training R^2: 0.7002. Test R^2: 0.7008. Loss Train: 0.0170. Loss Val: 0.0192. Time: 2.2157\n", " EPOCH 480. Progress: 48.0%. \n", " Training R^2: 0.7115. Test R^2: 0.6744. Loss Train: 0.0161. Loss Val: 0.0197. Time: 2.8708\n", " EPOCH 490. Progress: 49.0%. \n", " Training R^2: 0.7282. Test R^2: 0.7196. Loss Train: 0.0155. Loss Val: 0.0197. Time: 2.3536\n", " EPOCH 500. Progress: 50.0%. \n", " Training R^2: 0.7260. Test R^2: 0.6962. Loss Train: 0.0154. Loss Val: 0.0194. Time: 2.3680\n", " EPOCH 510. Progress: 51.0%. \n", " Training R^2: 0.7025. Test R^2: 0.6908. Loss Train: 0.0154. Loss Val: 0.0186. Time: 2.7710\n", " EPOCH 520. Progress: 52.0%. \n", " Training R^2: 0.6770. Test R^2: 0.6661. Loss Train: 0.0169. Loss Val: 0.0222. Time: 2.5762\n", " EPOCH 530. Progress: 53.0%. \n", " Training R^2: 0.5970. Test R^2: 0.6155. Loss Train: 0.0161. Loss Val: 0.0228. Time: 2.3154\n", " EPOCH 540. Progress: 54.0%. \n", " Training R^2: 0.7074. Test R^2: 0.6608. Loss Train: 0.0157. Loss Val: 0.0196. Time: 2.7320\n", " EPOCH 550. Progress: 55.00000000000001%. \n", " Training R^2: 0.7204. Test R^2: 0.7196. Loss Train: 0.0150. Loss Val: 0.0184. Time: 2.1466\n", " EPOCH 560. Progress: 56.00000000000001%. \n", " Training R^2: 0.7081. Test R^2: 0.7074. Loss Train: 0.0157. Loss Val: 0.0182. Time: 2.1762\n", " EPOCH 570. Progress: 56.99999999999999%. \n", " Training R^2: 0.6813. Test R^2: 0.6728. Loss Train: 0.0157. Loss Val: 0.0205. Time: 2.0891\n", " EPOCH 580. Progress: 57.99999999999999%. \n", " Training R^2: 0.7157. Test R^2: 0.6813. Loss Train: 0.0155. Loss Val: 0.0204. Time: 2.4246\n", " EPOCH 590. Progress: 59.0%. \n", " Training R^2: 0.7274. Test R^2: 0.6947. Loss Train: 0.0150. Loss Val: 0.0180. Time: 2.9342\n", " EPOCH 600. Progress: 60.0%. \n", " Training R^2: 0.6775. Test R^2: 0.6712. Loss Train: 0.0152. Loss Val: 0.0212. Time: 2.6074\n", " EPOCH 610. Progress: 61.0%. \n", " Training R^2: 0.7253. Test R^2: 0.6968. Loss Train: 0.0153. Loss Val: 0.0178. Time: 2.2037\n", " EPOCH 620. Progress: 62.0%. \n", " Training R^2: 0.6835. Test R^2: 0.6555. Loss Train: 0.0146. Loss Val: 0.0216. Time: 2.4210\n", " EPOCH 630. Progress: 63.0%. \n", " Training R^2: 0.7259. Test R^2: 0.6918. Loss Train: 0.0150. Loss Val: 0.0189. Time: 2.6791\n", " EPOCH 640. Progress: 64.0%. \n", " Training R^2: 0.7129. Test R^2: 0.6974. Loss Train: 0.0152. Loss Val: 0.0183. Time: 2.9655\n", " EPOCH 650. Progress: 65.0%. \n", " Training R^2: 0.7340. Test R^2: 0.7267. Loss Train: 0.0147. Loss Val: 0.0176. Time: 2.6578\n", " EPOCH 660. Progress: 66.0%. \n", " Training R^2: 0.7294. Test R^2: 0.7002. Loss Train: 0.0153. Loss Val: 0.0188. Time: 2.6740\n", " EPOCH 670. Progress: 67.0%. \n", " Training R^2: 0.7191. Test R^2: 0.7300. Loss Train: 0.0149. Loss Val: 0.0180. Time: 3.2143\n", " EPOCH 680. Progress: 68.0%. \n", " Training R^2: 0.7424. Test R^2: 0.7137. Loss Train: 0.0152. Loss Val: 0.0173. Time: 2.7885\n", " EPOCH 690. Progress: 69.0%. \n", " Training R^2: 0.6990. Test R^2: 0.6807. Loss Train: 0.0148. Loss Val: 0.0180. Time: 3.0227\n", " EPOCH 700. Progress: 70.0%. \n", " Training R^2: 0.7389. Test R^2: 0.7138. Loss Train: 0.0148. Loss Val: 0.0182. Time: 3.1861\n", " EPOCH 710. Progress: 71.0%. \n", " Training R^2: 0.7239. Test R^2: 0.7211. Loss Train: 0.0148. Loss Val: 0.0178. Time: 2.8397\n", " EPOCH 720. Progress: 72.0%. \n", " Training R^2: 0.7309. Test R^2: 0.7357. Loss Train: 0.0142. Loss Val: 0.0171. Time: 2.7046\n", " EPOCH 730. Progress: 73.0%. \n", " Training R^2: 0.7172. Test R^2: 0.7073. Loss Train: 0.0145. Loss Val: 0.0191. Time: 2.6449\n", " EPOCH 740. Progress: 74.0%. \n", " Training R^2: 0.7328. Test R^2: 0.7340. Loss Train: 0.0147. Loss Val: 0.0179. Time: 2.3361\n", " EPOCH 750. Progress: 75.0%. \n", " Training R^2: 0.7431. Test R^2: 0.6926. Loss Train: 0.0144. Loss Val: 0.0172. Time: 2.4534\n", " EPOCH 760. Progress: 76.0%. \n", " Training R^2: 0.7266. Test R^2: 0.7168. Loss Train: 0.0142. Loss Val: 0.0192. Time: 2.3319\n", " EPOCH 770. Progress: 77.0%. \n", " Training R^2: 0.7168. Test R^2: 0.7135. Loss Train: 0.0143. Loss Val: 0.0198. Time: 2.4297\n", " EPOCH 780. Progress: 78.0%. \n", " Training R^2: 0.7356. Test R^2: 0.7314. Loss Train: 0.0152. Loss Val: 0.0169. Time: 2.6842\n", " EPOCH 790. Progress: 79.0%. \n", " Training R^2: 0.7401. Test R^2: 0.7071. Loss Train: 0.0146. Loss Val: 0.0174. Time: 2.5626\n", " EPOCH 800. Progress: 80.0%. \n", " Training R^2: 0.7335. Test R^2: 0.7114. Loss Train: 0.0149. Loss Val: 0.0179. Time: 2.3640\n", " EPOCH 810. Progress: 81.0%. \n", " Training R^2: 0.6838. Test R^2: 0.7167. Loss Train: 0.0153. Loss Val: 0.0186. Time: 2.4697\n", " EPOCH 820. Progress: 82.0%. \n", " Training R^2: 0.7174. Test R^2: 0.7257. Loss Train: 0.0145. Loss Val: 0.0176. Time: 2.3813\n", " EPOCH 830. Progress: 83.0%. \n", " Training R^2: 0.7178. Test R^2: 0.7105. Loss Train: 0.0153. Loss Val: 0.0178. Time: 2.5285\n", " EPOCH 840. Progress: 84.0%. \n", " Training R^2: 0.7328. Test R^2: 0.7158. Loss Train: 0.0143. Loss Val: 0.0181. Time: 2.5079\n", " EPOCH 850. Progress: 85.0%. \n", " Training R^2: 0.7306. Test R^2: 0.7422. Loss Train: 0.0145. Loss Val: 0.0172. Time: 2.3710\n", " EPOCH 860. Progress: 86.0%. \n", " Training R^2: 0.7161. Test R^2: 0.6795. Loss Train: 0.0139. Loss Val: 0.0200. Time: 2.3648\n", " EPOCH 870. Progress: 87.0%. \n", " Training R^2: 0.7056. Test R^2: 0.7028. Loss Train: 0.0161. Loss Val: 0.0182. Time: 2.4802\n", " EPOCH 880. Progress: 88.0%. \n", " Training R^2: 0.7299. Test R^2: 0.7267. Loss Train: 0.0141. Loss Val: 0.0169. Time: 2.9453\n", " EPOCH 890. Progress: 89.0%. \n", " Training R^2: 0.7232. Test R^2: 0.7243. Loss Train: 0.0143. Loss Val: 0.0177. Time: 2.3615\n", " EPOCH 900. Progress: 90.0%. \n", " Training R^2: 0.7262. Test R^2: 0.7361. Loss Train: 0.0147. Loss Val: 0.0182. Time: 2.3210\n", " EPOCH 910. Progress: 91.0%. \n", " Training R^2: 0.7386. Test R^2: 0.6966. Loss Train: 0.0143. Loss Val: 0.0179. Time: 2.4063\n", " EPOCH 920. Progress: 92.0%. \n", " Training R^2: 0.7289. Test R^2: 0.7135. Loss Train: 0.0148. Loss Val: 0.0183. Time: 2.3717\n", " EPOCH 930. Progress: 93.0%. \n", " Training R^2: 0.7319. Test R^2: 0.7358. Loss Train: 0.0148. Loss Val: 0.0171. Time: 2.3222\n", " EPOCH 940. Progress: 94.0%. \n", " Training R^2: 0.7294. Test R^2: 0.7323. Loss Train: 0.0144. Loss Val: 0.0189. Time: 2.3810\n", " EPOCH 950. Progress: 95.0%. \n", " Training R^2: 0.7457. Test R^2: 0.7434. Loss Train: 0.0152. Loss Val: 0.0165. Time: 2.4457\n", " EPOCH 960. Progress: 96.0%. \n", " Training R^2: 0.7436. Test R^2: 0.7233. Loss Train: 0.0155. Loss Val: 0.0170. Time: 2.4220\n", " EPOCH 970. Progress: 97.0%. \n", " Training R^2: 0.7127. Test R^2: 0.6912. Loss Train: 0.0143. Loss Val: 0.0172. Time: 2.6444\n", " EPOCH 980. Progress: 98.0%. \n", " Training R^2: 0.7222. Test R^2: 0.7002. Loss Train: 0.0147. Loss Val: 0.0188. Time: 2.4263\n", " EPOCH 990. Progress: 99.0%. \n", " Training R^2: 0.7367. Test R^2: 0.7214. Loss Train: 0.0143. Loss Val: 0.0174. Time: 2.2914\n", " EPOCH 1000. Progress: 100.0%. \n", " Training R^2: 0.7344. Test R^2: 0.7440. Loss Train: 0.0151. Loss Val: 0.0177. Time: 2.4692\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "saving ./plots/LONGER_FFNet_ReLU_lr0.01_momentum0.9_wdecay0.001_dampening0_nesterovFalse_HidDim64.png\n" ] } ], "source": [ "# Training the best for longer\n", "# Testing a regular FFnet\n", "class FeedforwardNeuralNetModel(nn.Module):\n", " def __init__(self, in_dim, hid_dim, out_dim):\n", " super(FeedforwardNeuralNetModel, self).__init__()\n", " # Linear function\n", " self.fc1 = nn.Linear(in_dim, hid_dim) \n", " # Non-linearity\n", " self.relu = nn.ReLU()\n", " # Linear function (readout)\n", " self.fc2 = nn.Linear(hid_dim, out_dim) \n", "\n", " def forward(self, x):\n", " # Linear function\n", " out = self.fc1(x)\n", " # Non-linearity\n", " out = self.relu(out)\n", " # Linear function (readout)\n", " out = self.fc2(out)\n", " return out\n", "\n", "# Best configuration for longer\n", "lr_range = [0.01]\n", "hid_dim_range = [64]\n", "weight_decay_range = [0.001]\n", "momentum_range = [0.9]\n", "dampening_range = [0]\n", "nesterov_range = [False]\n", "for lr in lr_range:\n", " for momentum in momentum_range:\n", " for weight_decay in weight_decay_range:\n", " for nesterov in nesterov_range:\n", " for dampening in dampening_range:\n", " for hid_dim in hid_dim_range:\n", "# try: \n", " print('\\nlr: {}, momentum: {}, weight_decay: {}, dampening: {}, nesterov: {} '.format(lr, momentum, weight_decay, dampening, nesterov))\n", " model = FeedforwardNeuralNetModel(in_dim=47,hid_dim=hid_dim, out_dim=1).to(device)\n", " print(model)\n", " SGD = torch.optim.SGD(model.parameters(), lr = lr, momentum=momentum, dampening=dampening, weight_decay=weight_decay, nesterov=nesterov) # This is absurdly high.\n", " # initialize the loss function. You don't want to use this one, so change it accordingly\n", " loss_fn = nn.MSELoss()\n", " config_str = 'LONGER_FFNet_ReLU_lr' + str(lr) + '_momentum' + str(momentum) + '_wdecay' + str(weight_decay) + '_dampening' + str(dampening) +'_nesterov' + str(nesterov) + '_HidDim' + str(hid_dim)\n", " train(model,loss_fn, SGD, dataloaders['train'], dataloaders['val'], config_str,num_epochs=1000, verbose=False)\n", "# except:\n", "# pass" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "predicted.shape: torch.Size([335])\n", "predicted[:20]: \ttensor([0.5857, 0.5157, 0.6183, 0.4734, 0.4436, 0.3312, 0.6941, 0.7504, 0.5765,\n", " 0.1581, 0.6536, 0.8482, 0.3342, 0.6194, 0.4693, 0.5088, 0.6190, 0.2262,\n", " 0.6980, 0.4626])\n", "targets[:20]: \t\ttensor([0.5908, 0.3096, 0.5722, 0.6008, 0.4230, 0.1593, 0.6624, 0.7025, 0.5815,\n", " 0.1031, 0.5105, 0.9683, 0.2082, 0.6711, 0.5554, 0.6664, 0.4915, 0.1425,\n", " 0.6479, 0.5573])\n", "predicted.shape: torch.Size([1118])\n", "predicted[:20]: \ttensor([0.2354, 0.2575, 0.4211, 0.2812, 0.2703, 0.4035, 0.3168, 0.4194, 0.5279,\n", " 0.5243, 0.5281, 0.5377, 0.5387, 0.5360, 0.5383, 0.5388, 0.5388, 0.5397,\n", " 0.5434, 0.5010])\n", "targets[:20]: \t\ttensor([0.0280, 0.1301, 0.2970, 0.2207, 0.2648, 0.3342, 0.2873, 0.3362, 0.6023,\n", " 0.5031, 0.5335, 0.6247, 0.5454, 0.4926, 0.4442, 0.5030, 0.5472, 0.5128,\n", " 0.5702, 0.5017])\n" ] }, { "data": { "image/png": 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p5yJctxpxc0sl5GBwwvvHQgTOt1+Hl8nnciTbsSPluZ+NI+U8Drb9OiaSSGzbPKKM39GKJElEIvEjKpLgc/gobN9G12c+iTk8BDDF+B0r+PZraeHbMZ+54BSL9Pzjl0j/3x+Ag2O/jpn4r2/8lgb+ffCZK3IwiByOIGv64R7KYcd/bpYW/v3wmUzPUJYntw2TzJSojwfZsCqBEokgh8IH7ZjHjAPn4+NzZGBnMqjxOMHO5XR87BP+j6WPj8+Spmcoyz2PdRMOqDQEIJ8rcO/TRS7+82uJNccP2nGPiSVUHx+fIwOjp5s9n/hbMn96FPAjHT4+PkufJ7cNEw6oRHSF5b/7OWv/9D+EdYWnto8c1OP6ETgfH58lg9bSSvzsVxJeu+5wD8XHx8dnTiQzJepiAZAkxo5bj6sFCAc1kpnSQT2u78AtQZ599hnuuecODMMkGo3ykY987HAPycfnoFLq2oPe0oocCND01r843MPxWQC+/fI51mgMStiDfUgt7aTXnA5AoWRRHz+4hVe+A3eYeOaZp/ibv/l/tLcvo1Qq0djYyE03fYGGhgZOO+10Nm7cgG273HjjRygUCoTDC0+E7O7u4gtf+AzpdJqamho++cnP0tHROWGb/v4+Pv7xj1b/zuWy5PN5fv3r3+13H+l0ir//+0/T27sXXddpb+/gb/7mE9TW1i543D5HN04ux95/+jKxzWfS/I53Hu7h+MyBpWq/KhiGwTe+8S888cRj6LrOSSedysc+9ncA/PGPD/GDH3wbIUAIl2uvfQ/nn3/BjLbvvvseWPDYfY4eJhcrbFzbyEkv3kdx61aev+x6gtEwBcOmYNicd9r8+7nPB9+BO0xs376VV77yXD7zmS8ghOD//b/384tf/Bfve98N1W0efvj/WL585aIYP4B/+qcv8cY3Xs3FF1/KvffezVe+8kW+/vXvTNimtbWNH/3oP6t/f+1r/4zj2LPuQ5Ik3vrWd7BhwyYAvvWtr/Gd73yDj3/804sydp+jFyUapfmd7yK0es3hHorPHFmq9qvCt7/9dXRd5+c/vw1JkhgdTQKehtvf//2n+bd/+z6rVq3h5Zd3cP317+Lcc181q+3z8RlfrFAXC5AvWdzzWDevvfgKoqdvJiRFqo7deae10dEUO6jj8R24GZjOy17Mm7Ft21ZWrvQa2UqSRFvbMjRNq75/553/S29vL9df/8FFOd7Y2Cjbt2/lq1/9FgCvec3FfPWr/8jY2NiMUTLLsrjvvl/zz//8zTnto+K8AZx00sn86le/XJSx+xwdTH6mTtfTNDfWEFq1itjGTbPvwGfOHMv2q1AocM89d3HbbXdXi2Dq6uqr78uyTC6XA7woW319A/KkvrqTbZ+PT89Qlp/dt51swSIW1lgWU1g2/DK9HSfz9JDDleedxYpDPCbfgZuGmbzsS87oXDQjuH37Vs4//wIAdu/eRU9PF+95z/sBL8T/3e/+G2effQ5f+coXefe7r5/WyfrkJ/+WvXv3Trv/7373hwQC+9bfBwcHaWhoQlEUABRFoaGhkaGhwRkduP/7vwdpaGhibTmhfK77cF2XX/3ql5xzznnzvSw+RylTnqmiyeiv/hOrJsKaT9/kV5suIse6/ert3Us8XsPNN3+fp556glAoxHXXvZ/TTjsdSZL43Oe+xMc//v8RDIYoFAp85Sv/OuX4k22fz7FN5ZnKFkyiQQ3LdlGfeZyWnscoNLbTb9UclnH5Dtw0VEuCg96MsvL/J7cNL4oBNIwS3d1dfO973+Lf/u1rDA0N8c///PWqIXrlK8/l/PPPn7UVzec//48LHsv+uOuu/+X1r3/DvD/31a9+hXA4xFVX/flBGJXPkciUZyqks/P8PycU1DnOd94WlWPdfjmOQ19fL8cdt5YPfODDvPjiC3zsY3/NLbf8ikAgyE9+8iO+9KV/5tRTT+e5557h05/+OD/96a0TlnoP1Pb5HJ1UnqlYWMeyXTRVZkfnBlKNncRCddQHtdl3chDwHbhpqJYEjyMcUBetJHjHjh3EYjF++tNbAfjGN/6FH/7we3zjG9+d137mM4Ntbm5mZGQIx3FQFAXHcRgZGaapqXnaz4+MDPPMM0/xqU99bl77+OY3/5W9e7v58pe/OmVZwufYpfJMhQe7CPfvYeS081Br6xjMGod7aEcdx7r9amlpRVEULrroYsBL50gkEvT0dAOQTA5z6qmnA3DqqacTCoXo6trNCSecBExv+3yObSrPVEdcJfHIPbyw6izUQJgetY4Vh6BYYSZ8B24a6uNB8iWrOnMFKBj2opUEb9++tWosAN7ylr/gqqsu228+2nTMZwZbW1vHmjXH89vf3svFF1/Kb397L8cdt3bG49199x2cddY51NQk5ryP7373W2zbtoWvfOVr6Lrf/shnH5VnqqVrK9G+nYye+AqyrnzQy+yPRY51+5VIJNiwYROPP/4nzjjjFXR3dzE2Nkp7ewemaTA0NER39x46O1ewZ89ukskk7e3Lqp+fzvb5HNtUnqkGc4yO0Z0MthzHTtFKLKzvNzXhYOei+iGSadi4tpGCYZMvWQghyJcsCobNxrWNi7L/HTu2TTCADQ2NnHTSKfzxjw8uyv5n4m/+5hP84he38OY3v5Ff/OIW/uZvPl5976Mf/RBbt75U/fvuu++cdglhpn3s2rWTn/zkZkZGhnnf+67lne9864SSfJ9jmw3HN1AwbHad8mp2XfJOsq68qM+Uzz58+wUf/ejH+Y//+CHveMc1fOYzn+CTn/wcsViM+voGPvrRG/nkJz/GX/7lW7jppk/wiU/cRDy+L4dpJtvnc+xSsV8j8VZefuMHCZxwEita47ztouP367zd81g3+ZI1IRe1Zyi7aOOShBBi0fa2BEgmc7juxFMaGOiipWX5vPZzsD3n2VBVedYckiOFyedyIPdjKdDYGGN4ePEevsPJoTyXwvZtjPzyVrjmWp7qLS3qMyXLEvX10UUa6eHHt19Lj5nO5Ui0Y0eLDTuU5+EUi/R982s4Z57PM1LznJ+p2x/aNSUSXvn7ynO9Cu6F2i9/CXUGOppih9Tg+fgctTgOwrJorwvTuerw5Ioca/j2y8dnkXBdhG1TH9G4cuOqOX/sYOeigu/A+fj4HCScQgElHCZ8wol0fvImJL+oxcfH5wjBLZWQdB0lEqHjY5+Yt/062Lmo4OfA+fj4HARKu3ex+8a/If/C8wC+8+bj43PEIGybvV/9JwZ/fDNwYPbrYOeigu/A+fj4HAS05hYip55KYFnH4R6Kj4+Pz7yQVJXoaacTOfW0A95HR1OMS87oJBLUGM0aRILaooppwzG0hCqE8NXelwBHWc2MzySM3l70lhaUcJjWd7/3cA/nqMG3X0sL344dnTjFIm4uh9bYSN2ll83pM5MLhtobwvSOFCb8DV5O3JPbhgEWzYk7JiJwqqqTz2f8h+4wI4Qgn8+gqr5G3NGINTZG9xc/x4jfA3dR8e3X0sK3Y0cvAz/4Lnv/+R9xLWtO20+WChkYzXPrAzsZHC1M+/diS4kcExG42tpGxsaGyeVSh3soc0aWZVz36CjDH38uqqpTW+trfx2NaLW1NF7zFqJllXufxcG3X4eX6c7Ft2NHJw1XXoU1MoSsza011uS2damcSVBXGcsZNNeFp/y92G3tjgkHTlFUGhpaD/cw5sXRotcDR9e5+EyluGM7SiyO3tJC4rxXAYdfh+xowrdfh5ej6Vx8puIUixReeoHYxs0EOjoIdHTMyX71DGV5escwCAgFVVrrwhQNh5CuUDRsgCl/w+JKiRwTS6g+Pj4HB2HbDPz79xn86Y+rrx0KBXIfHx+fxWDs13fR/73vYI14+WlzsV+VbVRFRlEkLNtlZ18GWYKi6RAKeLGxUECZ8DcsrpTIMRGB8/HxOThIqkrbh/4fSjhSfW3yssJclw38qJ2Pj/8cHGrqLr+CyCmnojV4S+JzsV+VbZY3x3i5N42mSriOSypnYdkuDTVBUtkSiajOaKZEe0MEIQQFw6Zg2Jx32uIImvsROB8fn3lT2L6N1O9/B0CgrR01kai+l8yUCAcmzg1t2+XpHSP8+10vcftDu6ZE4/yonY+P/xwcKpxikaFbfo5rGMiaRui446vvTWe/LMvh6R3DVfvVPZQjHFCJR3TWtNfguC6pvAkCTl5Zi64pbOlOEdJVrn7VaprrwgdFSsSPwPn4+Myb9B8ewNizh/g55yBrE6vxJiuQZ/Im23pShALqhB+l8YZsrlG7ydGJs05uOap6ofoc2xxo9NpnfhR3bCf9+/uJnnY64XUnTHhvsv1K5wy2701PsF8jqSIBVaa5Lkw8ohPUVeriQcJBlfbGGO2N+/qebj6hhc3lfVfs128e71kU++U7cD4+PvOm5Z3vwikWpjhv4CmQ3/NYN+Al7HYNetGDzqYokiRN+6NU6RuYyZv0JfMUDZugrhAJ7dt/JToRHmdIH3y2j5OPbz7Yp+vjc0g4FP0zfSB66mms+NJX0GprJ7zeM5Qlkzd5cc8o0ZBGR2OEnuE8AMubY1X71dYQpnckTzSsEQ6oZAsWsgRt9ZGqDSuULCRJri6BV+yX6whGsyV29aXZM5jlCwuwX74D5+OzxFkqOTGF7dsY+/VdtL73/cjBIGosPu12FQXyJ7cN0z2UI5kuEdRl+kcLSJJEPKJP+VGqjwcZHC3QO5JHU2VCuko6bzCQLPK1XzzD8uY4mbw5JTqhKL64rc/Rw6Hon3moWSr2yykW6f/uv1F/+RWEVq+Z1nmrOFghXWF4rEjfcA5JlqiJBOhL5hFCUBMN0FwbxrAcIkGNZKZELKxRG/Ucby8nTsYVUCiafPO25zlxeS2SJOE6omrjYqGF6wj6OXA+PkuYpZQT46TTWKOjuMbs0YCOphjtDWFGUkVcITAsl3zR4uXeNJm8OeVHaePaRnpHvJmuqkjkShbZguVVcZUc8iWLF/eMYtsT9biCuj8H9Tl6OBT9Mw8lS8l+CaOEnUxip9PTvv/ktuGqg6UoMtGwhiOgZLrkiia5osXOvgy9Q1le2D3KaMYE4LWbO3jbRccjKxJdg1nPfhVMhkaLGKaNZbns2JvixT2j9Cc9501TZZAgqCsLOiff+vn4LGGWQk6Ma5rIuk5s8xlE129AUmc3Gz1DWe54uAuAuliAZNYglTOpiWh0DWZpqQ9PqMTqaIpRXxOgWHLIFExSOQMJsByXdN5kXVAjGtLoHspxSnTfElPJtCcf2sfniGV89LoSsTrvtLYjNv/tYNuvuUT3XNNE0jTURC3Lb/rcjParazBDz2AO03ZRZIl80UKWJVRZwrRd0jmTUEBh6940sZBGe32YJ7cN8dsn9hILe2kdQ2NFLNvBtAUSoAgJ23FJ5RwSMZ3RjEFbw76KfcdZWHcV34Hz8VnCHO6cmOLOl+n7t2/Q9oEPEVq1ek7OG3gG2nEF0ZCKJEnUS5DJW+SKNooiT1uJtbw5zuBogYJhoyoyuipjOQLbtknnDDoaI2zpTpEvWYQDKgXDRtMWNoP18VlqdDTFjliHbTIH035NlxM7uTjKNQz2/stXCB2/lsarrp7RfvUMZUmmDUqmAwhyxXKk3xWoCuiqiqbKZPImuqrQ3hBh73CeXNFClqFQsknnTVzXxXG8j0oS5dUHh6CuoMoybllKJBRQsB2B5SysW4nvwPn4LGEOd06MVl9PcOUqtLr6eX0umSkRC2lYjoumSgR1laCmki2arD9u+hyYjWsb+f4dL3nHLTtvjuMiyxJP7xihviZIZ2OkmndSqeLy8fFZmhxM+zWX6J6k6wRXriS4YuWUz4+P3g2nitRGdUZSRUrWRKfKdiCgCZoSIXqGHeriAXYPZMnmTWRZIqAplEyHgK5QMgUuAqmcmluJsBUMGwEsb4qQLTnkijaxkEZzbWhB18B34Hx8ljCTKzoXWwhyJsyhIbTGRtRELe03fHjen6+PB7Fsh76RAuDltRUNB0WWZ8znGb+MajsuhmniCoEmy9iOS9GwscP6hGUSWfaLGHx8lioH037tL7rnFIsIy0KNx2l689umfHZy9G5XX4a8BDMtaBqmQ8GwkZHIlyzMcU5epU2WKksIAYosIUtg2QIBaIqEQCAE6LrG5RuW0TtSIJkp0ZjwHTgfn6OWw5ETYw4O0PW5m6i/7ArqXnfpAe3DM9xF2hrCpLIm2aKFIktcfvby/Y59eXO8OmN/9uURUnkTx3EJ6CrrOmtRFMnXxPLxOUKYbL80RUZX5KoO2kIqUmeM7sUC9P/bN3FyGTo/+Rn2JgtT8uQmR+9iYY1CycayXTRFwhWCyuqmBFRS1TqbI4zlTBwXTNlBCG8DCTAsB1cIrzJUlpElz4ETQiArMus6EyRiAXpHClx57iq8zRY2AfUdOB+fJc6hzonRmpqpf/3lxM86+4D3Md5wa6rCuuW1czLW42fsriuojerYjmBNew3xiI4QwtfE8vE5gqjYr/FRr3hAnTZnbT7sL7pX13QZTj7P3mRh2jy5kVQRCancp1QhFlQpFG1cAQoCRfEcMCRwbM97a0iE6B/JIQGm5ZRz3VyCmoLlOJRMB1kGTZERAkwBAU1GVxWOXxanvTG66PbLd+B8fHwAr2BBa2hArUlQd+llC97fgTie4x0/JJAkiTXtceIRTzPpSNfE8vE5VlnsitTJ0b3GoMQr64revpq87gr3P7RryjGzBZOB0SI1EZ1QQMGyXQZTJXRNQlMkbEcguS4BTQZJwnEc6uMB4iGVHSUb1xE0JoLYjks6Z2G7LrYDqgLBgIaqeOpskmQT0BVOW11PTblyfrHtl+/A+fj44BoGfd/6OsHVa2j/wIcO2XFnkgHoaIpVZ9iKIh2URtA+Pj6HjoVUpE62E695xQqimjxhkjj0nz8h/X8PYZ90HGpNTfWYigRbh3IUDS/aZhi255wBtiPQFJl8ySKZMVnXmWDvcI58yca0XSTJW149aWU9fck8NRGddM4kU7BoSoQomQ5G3kZTZcIBlVBQRZYk1rTXIITgpa4xVFU+aPbLd+B8fHyQAwHaPvAhtPqGg3qc8YZYlSVSOZOgrlRbyzy1fZjLz17O5hNajjpNLB+fY5kDrUidTi7kVw+8zKtOa51gC+r/7E1ETt9Qdd7AKyyo9GEO6jKW7TKUKtGYCLKqrabats+yXSJBnZVtNdTXhOhL5skWTPJFm9PXNBCP6GzpGqNoWJiWQ75kky14uXCSBLIQZIsW+ZJNPKLRl8zT0RTlpBV1E6rmF9t++Q6cj88xTGH7NpxshtjGzYRWrzmox5psiF/YPeqV4itef8FYSKdg2NzxcBct9ZHq7Np32Hx8jnwOtCJ1uqVXIXnFTG0xldRvf0PdpZehhEJETjxpwmclaV+RgIQECGRZomjaVectFFApGjZ1sQDpnEH/aIGi4RAL6bguqKpMOmeQK1oYZeFwWQK3UrIqwCn/IYBMzkQIQX1N8IDz++bKIWultXv3bq655houvvhirrnmGvbs2TNlm2QyyXve8x4uv/xyLrnkEj7zmc9g277Suo/PwWL07jtJ/u//ICrqkweR8YZYkqSqkKVpudXWMqGAguMKLwfOx8fnqKESUY8ENUazBpGgNicHJ5kpEQ5MjDWFQ15UK//s0yTv/F9Ke3ZP+1nLcVnbkUBTZYqmt9TZ2RQmk7colGwQgv5kgbGsydBYnq1dKSzbJajLFMryIN2DWZ7dmaRk2DgCbBdkmarmiABURUZRZBRFwhECgXTQnTc4hBG4m266ibe+9a1cccUV/M///A+f/vSn+Y//+I8J23znO99h9erVfO9738OyLN761rfym9/8hksvPTApAx+fY4H5NovuGcpy31O9dPenaTz1Uk5fHkdS5t7R4ECbU0/OgQkFFEYzLoq8bx5pO4JY2Tgv5Fg+Pj4Hh56hLL97ci+7+jOAxOq2OK/e0D6n53K6iPpsz/h0S6/9wzl6hrJ8qVdCW/NGrN8OsW67xQUbl1UrXn/35F6e35nEdb1o2Oo2r5L9iW1DaKrMSLqIabnIkqfVlis6GJYgoMtUYltNiSBDYyUMy2Fi1ysJSRaI8Zq/AjRNQVVkOpv3nWfl/LqHchRKXn/n5c1xNq5tZHlLfJ5XfyKHxIFLJpO89NJL3HzzzQBcdtll/P3f/z2jo6PU1dVVt5MkiXw+j+u6mKaJZVk0NzcfiiH6+ByRzKWdzOTtH7n7YZb3v0TdWa8nawnu3ZLiklh8Tgb48S0D3PFwF47rEg1p2LY7ZymAyYa4tS5M70i+qpXk5ZVY6KonrPT4lgGe2Zmc87n5+PgcXHqGsvzywZ0MjRUJ6SoCwdbuMcayBm88f9W8n8vJ9mtwtMC3bnseSQJdU1ndFueE5Qme2ZkkWzBJZU0yYxleuesBdrduIqfFEWoEK2PwyEsDbOka4/zTW9nWk2ZorEg0qJIumAyMFhgeK6LpCpmcia5KWOM8MlVVMG0bWYZUzqSlPszy5hi9IzkcIVBlGV11kSQJy3ZxXUFQUyhZnnQIgOu6GCaEgiqFkkXPUBaAex7rxnUEw2MFJEmiULQJaHnueazIG165kvr66AHfj0PiwPX399Pc3IxSnuUrikJTUxP9/f0THLj3v//9fPCDH+Scc86hWCzytre9jY0bN87rWAu5GEuNxsaj50fqaDmXpXYe9z3VS0MiTDTsyWxEo5ArmGzdm2HDSVNzS+57qpdmK0U02Udcg2g8Sq5g8shLQ2zdm2ForEBTbZizTmllZVvNhM/u7kvz68d60FSZmlAAy3IZTBXpbInNeLzxvOYVK/jVAy8jJIlwyMtjWd4cZyxXIlOwKBk2sZCKpqm01Ef49WM9dLbEaCo/07Od29GAb7+WJv65eNz3VC8l0yUeCaCX+xDrmkPRdA7ouRxvv7oG0rywZ5SS4aCpEo0JhR29afKGw8mr6vj9U73YjkvEKdJaHCaSH2W0rsbTaZO8tlVjWYP//WMXdfFAdYyyKjM0WsQSArvkNag3y9pulVw2w3LQNZmAplATC7LxBK9F38t9GSQJauMBRrNgmjaK7LXXshyXaEjFdsFxXQK6Qk00gKYqtNRHeODZfgK6QkMizO6+DJGQjq4pmJZDwXBZ2RZlZ3+Ok48/8CDVkipiuOeee1i7di0//vGPyefzXHfdddxzzz1ccsklc95HMpnDdWdqiHHk0NgYY3g4e7iHsSgcLeeyFM+juz/tRafyxr4XhaC7Pz1lrMK2ve3XbsI67Uxyhgt5g3S2xJbuFKeuriccUBlK5vj5vVumRLp+++geDNMhGlKxLC9nTsJbznBsd9ZrE9VkXnVaK09uG2bvQIb6eJBrLlgNwM/u244sSUTDGq11YWoiOjtMh/7hHImyczrducmydFQ5Pb79WnocjedyoKkJ3f1piiWLYEDBtPYlgRUMa1qbM5f91cUC9A1meP7lJJblosieM5bKmcTCGiOpAn98rsSaljDhcJCnd8DPT/xz0oaLMGwUWcJxvFZViurl1g6nvK4PICiUvObxris84V5dIV/yepMKPAfOtQVawJMTqYkGyOVKDI0VSecMSqaN47hEgypFCQqGgyJ7ebut9RGKpk0yXUKSJDRFZkVzlJqITr5k8eLOJKevqSedKxHSVUzLRghBOmeD69I3nJn3PRzPIXHgWltbGRwcxHEcFEXBcRyGhoZobW2dsN1Pf/pTvvjFLyLLMrFYjAsuuIA//elP83LgfHyOJeZaml/csZ2BH/6A1le+iTFDIRoNguE5fT3DeaIhbVaBzckN6sFTHc8WTU5YPjdxypmqShsTIdZ2JCZUjcVCGtmiOWE7X8jXx2dhzDftYjz18SB9I3lPP61sA3JFk4Lh0D2Y4/aHds0rT7Viv/pHC4hxExdFkVFkiZLhIEtg5Auc+MKvyKw8iVDkePIlDdkysWwBrqg6Y7bjabe5AsayBlrJJpM3UWQJXZWRZQlFkdBULwpXOaRUFg1HQH08QM9wnpFUkc6mCN1DeQzLxbRd4hGdWFinrSFMOKBh2A7hgIpwUygVEeCyDfMKLzz9t1BA9dp0qTK2IwgFFAqGTXNdeL63bwKHpAq1vr6eE044gTvvvBOAO++8kxNOOGHC8inAsmXL+MMf/gCAaZo88sgjHHfccYdiiD4+RyQb1zZSMGzyJaucR2ZRMOwpDeOHDImUEqY7ZbGtO0XvcLa6fa5o0dk0MYo1ncBmfTyIrsqMpEv0DecZThVJ5439NqifK/XxYLXqq0IipqPI8qzn5uPjM3cmV4NHghrhgDqnyu+NaxuJhTVPO81yyeZNRrMGqiyxqjVWdQYr+V9z2V/B8HJfKw6hKLegkiUwbQdNVdADOiMiyPY0lEwbWZKqDpfjep+R8JZEFVlCuJAr2ViW5wDaTtkBC2sYZrmH6XiE17rvuGUJ2hqidDZFWduZYHV7guPb48iShOtCybCpiWj0jRTY0jXGQLKAbbuEgqoXgVNl+pJ5wJtsrmqNUzBsaqMBb+m05F23RNSTTDplVf18bt0UJCGmnEqVX/ziF3PaiaqqXHnllfvdZufOndx4441kMhni8Thf/vKXWbVqFddddx0f+tCHOOWUU+ju7uamm25iZGQEx3E488wz+bu/+ztUde6BQn8JYulxKM/lYFYtLtV7sr9ztlNj9JtqdcYdDqgMjhUYSpVIRAN0NkW9GWpZi61CvmThOIJ4RK/uN6BJ/P7pPmRJwnZcSqaLwOWys5bzulesXPA5jB9jRSPq9NX19I4UZryfS2UJdbFspW+/lh6H+lwOtg37hx/9ibpYYEK0WwjBaNbgXa8/cU7jq1ShpnMm0bDG8csS1XZ3lRWBStP42c6jZyjLz+7bzmimRL7kTeJ0VcFxXVTbpK0+gqVqZAtWtXAiX7JxbBdHCNJ5CwBNhYCm4rgCw3Bw8eQ+JDwNuFhYRddkxnIWtuUyvoBU1yQ0WSYSUgnoGvU1QToaI9VrlM4Z9CcLDKeLRIIa7Q0RekdyaIqC5bg0J4IMlpdtLcehrSFC30iBhkSImrDnKKfy5rRVqAuxX/t14E488cQ5FRG88MILPP300wc8iMXEN4BLj0N1LjM5AYtVtXik3ROjdy/dX/x7eje+lt6Ok4kEtQlClaGAwtsuOh5gynUbSZUQCBoTIWzbpXsox+BogXBAIRLScYUnA5KI6rTURbjy3FULHu+B/HAtFQdusWylb7+WHod6Anqwbdj3b3t2StpF5e/5Psf/ftdL0zqDPcN5QgFlzudROW/XEfQlc4xlTVxH8BcDv0FWFLa/+q3YjmD3QIZU1gQkamM6177+BP7zvu30jnhRLxmwXEHJcJAkiIS8c5QlQdFwsR2XcNALCBmm4y2pyt7SZ0BXUCSJ2ngAy3Zpb4hMWOLMlyx6hnJ0NEWJBDW2do9h2Z4bqKkyrXXhslSIpzfX3hChqTa033NfqP3ab2grEAjwk5/8ZNadbN68+YAH4OOzWMynWfKxoC+mt7SSeNUFPCJ3EAuopHMGO/syaKpMLKIxlilVc18mt6yyoy6qKuM4ovoZWfJK7x1XsKbd01QSQsypl+FcOJK7Lvi20mcxOBAbNp2+2P6eowPtiDAdM+XgjmVKpGUvUh8KqLTVRwgHVH7/VO+EqP743scVG6SqMuuP895LdIe4/6k+wkGNTN7EdSEaUjEdwVCqyPfveInmRJCTVtQxljPoHc5VuyKAt6RquwKjIhYONNR42m71NQHyJQfTchCI8pKtS2dTlKJp0zuSJxrWJlyjcHm5GaCtPsLLvWlURaJYslFVmZb6MAFVmbCisb97uFD2mwP3q1/9ak47mevygY/PwWRaxe5pcrkqs718yZqQxDvXvI2lTmnPbpxiEUlRaLz6GuJNDRQMm/7RApoqe4m0tiAW1qu5Lx1NMa48dxXvev2JXHnuKmxXEA6o9CXz1c9omozjiil5Hn5RgW8rfRaH+dqwwdECw2MFCiWbZMpgYDQ/qy070I4I0zFdDu5IqkQ6byCEQAKGxoo8tmWIl3aP8szLwzPa3YoNeucFK7mkQ6KjKUZs0xlwwqlV++W6LtmiXe6W4MmY7B0pUDS9PDMhqBYrSBLlnDMH23axbAfHFYzlTGQZiqZLXSyA6wqvzZYk0ZgIUhMN0Fwbpr4mMOUadTZFq7m68YjOmvYaL/ooUd3Gctw53cPFYL8RuBUrVsxpJ8uXL1+Msfj4LIi5VmTOZ5Z7pOEU8uz9l68QPW09Le+6Dtg3484WLKJBrxpKACuaYzMalsq1LBo2Id0zE6GAim1bnmxAyaoWFRzIzP1ow7eVPovBfG1Yz1AOXVPQVK9Reypn0tEUndWWLVa0e3zkbHz0PlMIYpg22aKNLHtVn8lMCU1TsG0XKShh2y4DyQI/uHML649rqEbjhv/zp+SefZqV//AVlHBkgv0yzH0t/+JhnYCmkHVN6mJB+pL5cpWpTDQk4bgCy3Yxqt0WZAKaFxV0LBfLdnFCGgFdIRxU0VWFFS2x6jVf3hyfdkl5fPRSUSRa6sMTHODJ9zCdM+geymE7Yt5VurOx4CrUSmWpj8/hZq4VmXOd5R6JKOEIrde9j4arrq6+VjGysbBGrmShqTLrVtQRj+gzRtAq11JVZEzbwbJdZEni+GVxvMmqvKCZ+7GIbyt9ZmO+Nqxo2GW9M7ylPMNZsC17fMsA//CzJ7nxu4/wDz97kse3DOx3++mi9x2NEbIFL1KlSF4RgeMKasIa/aOFajoHAoRwJ0TjGq66mpZ3vxclHKnu/5IzOtFVuSr4LZdz7jwxXU/aqDERYsNxDcRCGpGQhusKHMfLUXOFtzwaDWvUxwNomoIiS4SDKu0NEXRVoa0hTLys3zZTtftcopcb1zYykirx/K4kj77YzxPbh8nmzQOq0p2NBevAfec73+Gyyy5bjLH4+CyI6WaD553WNsXBmOss91BTyWnpGsx4hjio0dkUndOMrbB9G7gu4XUnEDnl1CnvdzTFeNtFx1cTpGuiAUZG8zNG0CrX8vdP9fLinlGiIY1VrTE0TUEPqL7jdgD4ttJnNuZrw2bSFztQW/b4lgFufWAnQV2lJqxRKNnc+sBOADaXuxPsj56hLHuHciQzJUqmg4TA1VQ0VSYS0tA1haLhVNM5AMJBjZjs0tT1NE/Gg1x53mqiicSUfddEdUbSJU9CBBhJl4iGNFa0xKrnmy9ZrG6Ls2cgi+0Iyv4bEngVqzmT2liAeESjsTbMh646tTru2a55hblEL0W5033J8Ca+siJVJVtg8VZ7FuzA+bNKn6XEXB6uxUziXSwqOS2O45JMGSBBvmgRUOVZRTaFEIzcegvCden8u08jyfKMRRqVH4eRlFcOP95QTfeZd1yybsLriUmf8Zk7vq08ujhYhVDzsWG10QB7h3NeNaSAxkRwQbbs/qd6CepqtVKz8v/7n+qd1oEbfw1UWaI/WSBftHBdryjALbeZimgqnU1R9g7nCQVUiiUbp5zPFg4olJ54luNf/j+eSbQDq6fsezhVRFMkYmGNZLqEYXmVn4riOUeVaNkvH9xJtmAxlvVEygO6jK55XRgs28VyXIqmw3HtNRMqTKe75gd6f5/cNkxjIsSKljhP7xghqHvOdV8yTzyiL+pqz5wduMHBQYLBIDU1+/ojptNpSqWS33De54hirrPcQ8n4nBZNk6s5LWM5Y9acFkmSaLvhwyBE1Xnbn9J6R1NsijTCXD5zoBwLFb/j8W3l0c9CuhksBuNtmGG71SrUlrrIgp6vVM6kJqxNeC2kK6Ry5pRtx18DRYJndybJFS1CuuKlZ5Qs8iWn2vi9JhrAsgWJqM7WnhS5gkVNVCca1NgWOJmuQBPN9V53pse3DHDHw104riAW0hgczWM53mdb6kPkSw4lw8Fx3Oo17xnKesUIeEurQrjIkuJFJUsOQd1z5AKaPMHpm47J5/b8riSPvDjASSvqePWG9v1e32SmRF0s4F27gOJFSBWZYrn4YTFXe+acA/f+97+fgYGJa+EDAwPccMMNizIQH59DyeS8jcPtUOzLaXFQlX1tqoqGPeOMrbB9G8O3/hdCCNSaGtTyssOBKK0vRJ19fxztFb/T4dvKo5+D9bzMh4oN+9BVp3Lj2zby4TedXrVlPUNZbn9oF/9+10vc/tCuOT9viahOcVyhAEDRdEhE9SnbVq6Bbbvs6s9i215LBMN2yJdsErEAbQ0hgppKyXKJBDXeeP4q/vJ1J7CuI0FdQHDert8TMLIgSYyF6z0NuaEsdzzcBXiSIZbjUrRcXFdQslxCAY22hih18QA10UDVdj+5bZiGRJBTVtV7Wm0hHUWRsWxBbUxHkiQEEAvrszrak88NIBJU6RrMzmq/xneVaa0LY9kuBcMmqCuL3k1mzhG4PXv2sHbt2gmvrV27ll27di3KQHx8jmX25bSUZ2yqhFXWUJo8Y6tEtKKPPkDzwA72rDmD3hzVCFf3UI6OxsiE/c8Wtq/MGicI/eoKoaCyoPNazIrf6SJ5y1viCxrfwcC3lUc/46MsFZZKIdT+ooONjft/5i7c0F7NeQvpCkXToWTaXH721OrpyjXYNpRCU2V0XcGwHGxbIFybIduhLhagvuxUVSo6e4ay7OrPECukaEnuYWtsBemaTjRZYld/BumpXhzXJRbSQQJNlVDK3V9MywGBp90mBOFxecyVZdyt3WOk8yaW5WK7Drbj6VmalkMsrHHhLBG06c5NU2UQUDTtCdJL0zE+RSce8fqm9o0UkCSJnqEc4aBWdfQXar/mHIGrq6ujq6trwmtdXV0kpkk29PHxmR+V6rNEVMeyXAolG9NyqI0GJszYeoay3POnLvIli+wZF/Kns97CrY/2MzCarxrrkVSRobHihP3PFravjwcZHCuwsy9T1liSKRg2ybSxoGjZYlX8zhTJ6y/r0S0lfFt59DNd796lUAgF8Lsn9zKQLLC9J822nhS27c45Orj5hBauftVqwkGVdMEiHFS5+lWraamPTInoaYrMC7tH2TucJ5UzkAHH9RrEW47AtFxGMgbquOXKiv1SFZlCfSu/Ov0tbNFaMC2HfLkn6rM7R8otqfY1uwoGFBTFy2dLF0zGsgaGaVMoWVX7pCky23pSWLZLTVgnEdMRSBimg+O6NCQCrGyN88zO5Kw2rXJ/x6+IVCbUk+3X5GgnMKFStaUuwuVnL/faczVF6WiMLJr9mrMDd9VVV/HBD36Q3//+97z88sv87ne/40Mf+hBXX3317B/28fHZL5Wclpa6CPWJAOGgSmNtmOa6iRpDW/7wBJse/Ak1ThFJlhkxIKirpHJmdSmnrSFM70h+Xk3gN65tpG+kAHiG0Ha8Kqr2hsiCloUW64dupiWr53clD3hsBwvfVh79zCb3caBLmAulZyjLS11jICCoe3m0O/syWJYz66SpMubndo2yrrOWD/zZydz4to201EemTJ5ue3AXvSM5ioaNrsjYtksqZyDw5EwkPOkOy3LJFazqMZ5+fi8bHr6FDeZebEeQNGUc14uogVc0YVouRcMT67VsF8N0KBo2humQKxhkcgaaKhHUNRJRvbqkOb4rqEBU9fFa6kJcsGEZ649rorkuPCdndp+MkoRtewUQ+ZKnQ/fEtiGGU0V6hrIzTiyBCSk6vSOFg2K/5ryE+p73vAdVVfnyl7/MwMAALS0tXH311fzVX/3Vggbg43MsMlNi/2yh/VTBpklRoKyD5AnteqX5FZprwxiWQySozblIo6MpRkMiRL5oUjRtQgGV5c0xYmGNrsEMtz+064CKEBar4nemJatUzpjXfg4Fvq08+tlfIdThLHB4ctsw0XL/T0nyBHQBeobznLKqfsbP7W/M06VB7CpkMEzbE9ItC+zaLqjyvqXXYLm3aLa4b1/JnMEyRSEY0lnTUMPDLwyAJACZ+niQgK6AK0jlTVrqwnQNZqt6cqFA2dmzBZm8ydqOBC11XjTryW3D2K7g+GU1DIwVq72eIwEFxxFs7R6rvtZSGyI5Kc9vMpX7+7sn9/JS11h1CdVxBYokoykS37/jJSzb663a2RRFCs4sE3Kw7NecHThZlnn3u9/Nu9/97gUd0MfnWGOys9beEOaZnUlcRzCaLbGrL81T24e5/OzlM2otObkcSjSKvnI1L7Z0EAl5ScWhgEqhZFfL/WH/KuL7o7MpOkUfb2A0TzJt0FBjHdCP0WJV/M6k3TdeCmCp4NvKY4OZJlyHo9NLxcY8+tJgeUJnEwlpaIpcjhDuPwK/vzF3D+XIF01KplPta5ovmqRzJqGghiJLBAMKuaJdXj712lzpmoLjuMhIRGWHJ18aoLYuzpbz3kwkpCOqzouELO/TTtN1haClMJYzcQXomlztsADevhVFJlv0InuVJc2KjVjXWVs9rye2DnpLwSGtGpHcvjfN2o7ErNe0oynGX77uBHqGsvzsvu1ky0vKsaDKYMqLZhZKFuGAys6+DM21IbJFi0LJQpLkCRPdg2W/5qUDt2vXLrZu3UqhUJjw+pve9KYFDcLH52hlupntHQ93URvVGcuZXmP5kNcR4Y6Hu2ipj0wx8qWuPez9py/Tcu272bjWE+NF8gxHbTTAaKZEW0MYIcSCNO2mi5b1jRRob4gs6MdoMdr2zBTJ219U4XDi28pjl0Nd4DDextRENIqGgyRJOK6L7bioisxJK+rmLH0xfszdQzlGUsXq35bt8nJvmmzBwhEgAEWRkIREuSEEqiyjKV7fZNsVNMQ0Tnjov8nrETo/cIPXFquYp2+4gKpIlEwXRZUZy5rEQi6u8CpF1yyrYXtPilTOQFMUHCGwbM/Jc10xRZZj49pGfvngTnYVMti2i6rKlEyHoOYNzFvY9T4vlVcw5kJHU4zGRIi1HQkkySuS0FTvHDN5EyRwhWB7T5qGRBDXFRQMk2/e9nxVduRg2a85O3Df+c53+Na3vsW6desIBvflr0iS5BslH58ZmG5m67iCvSN5EtFAVY08VJ7BTucY6S2tRDdtJrhyNR2JiRGt5rowG45voHeksGBNu+miZQ2JEE21oQnbHY5qu5kiea31kdk/fIjxbeWxzaHu9DLexrQ3RHm5N42mygQ1lY5mr/n6qze0z3nMmbxJXzJPtmBiWC61EZ2hdIlU1qhqVJq2QFM9l8hxBIblUE1Bk7wonKrIBFQZIck8H16OiMbpxEvw/+FdW0jlDCRJQtcUJFnCdV0KhsPy5hiSLBEOqIQCKtmChSs8YWBF9vJzvRy4fbIclQlrRQeuEs0TSKxsiZIt2l66SUClsykxoUBiLoy/PkXD8aJ5jkttLODlxxUtBN51SOVMElEdXVOqsiOXnNF5UOzXnB24H//4x9x6662sW7duQQf08TmWmG5mGwtpjGUNGmv21RDZjidYOd4xMnp60FtbkQMBWv7y2urr00W0Ni/SeCfv+/aHdi2ZtmOL1YD7YOPbymObQ93pZbyNiUd01rTX0DuSI5U3WTfHzimVMecKFnuHc0iShCLJ2LZF32iBSFDFdQWm7WLbFWdKIl+0sF2BqkiEAgpCeJNUw7JJqC5qMUsp2MCO1lNpawxzz2PdLGsIM5wqIUmCgKaiyFCyvFyygKbyxvNX8eS2YfIli7b6CCOpEtmCiRACRZEJ6p44bySkT+gmc/tDu2hIBFnesu9cn9+VZDRrToh05UsWNUFtussw6/UBL8evUpi1pt0T635i6xCyJFEqa+ZFw/oU2ZGDoTc6ZwcuGAyyatX8cmp8fA4HS0n5f7rZeCKm0z/qyXSEAgq241U5NSaCVcfITqfp/ocvUHPuuRgXvOGwnc9SbDu21PFt5bHJeLujK5722GjWmHdUfL72a7roWa5okYjqs352/LECqsKu4bS3hBlSaa0L8/zuURzXwRXQkPAi8amsQdGwvV6jQiAEWLbAth1iEa83qe24rH74VzTlBrnl5GuIRIK83JshX7T400sumiIR1FVcIbAsQSysEdCUsgivN97bHtzFcLpIrmgiEAgkZAkCmsI7Lj2Rte01E85lvCNbuQ75okmuaNNUXkk4UPs1fgUgFPQcuPaGCLGw5sk/xQK0NYTpTxYJ6t7EfCbZkfHX3XZdrn/T+nmNZTySGF97ux9uv/12nnrqKW644QYaGhomvCfLc1YjOegkkzlcd06ntKSZ3OroSOZQnsv4fJDxDsd8KsBmMqAzncf47TVFJlswGUqVAEFTTRDbhYZEcMJ4ljWEeeTFoWqrmETMUw0fP87Mow+Taujk3q3pKeezrCHMsztHq+H601bXYVhizkZ/PvdkKTnEk5Flifr66OEexgQWYit9+7X0mMu5LIbdOdD9VD7jOoK9wzlsx1uK1FWJUECbUBw1/lzGH8uyHHqG8/QlC9RGNQRQLDlepEkINFWmoymG5biMpIq4gG27GNa+pUgJqK8JIAGG5RIvpqg107wc6cCwHAK6gm07WOMKQMenoikynLyqgc6mKO0NYf74wgDdg7lqHp+uypy4og5FkWiqj3LRpGXhymqB44jqMjICDMtGlmUaEiE6m6KLYr9mKkwbSBa8NDsJLNtlTXsNiuJVp44XMq5c96baMG993YkHPI45O3CV5YDxyX9CCCRJYsuWLQc8gMXGN4BLj0N5LtMt+VX+nktV5v4M6IaT2qacx/jtbdvlxd2j5EoWtdEAQV2laNpoqoQsydWWNBduaGfzCS3TOkYN6X7kYIhAR8eM57OzN8Xu/ix18SAh3RO2TOdMjmuvYWVbfE5G/2j5fi1FB24httK3X0uPuZzLQu3OdPvJ5E1292dIpkvIssSG4xtn7MNZqZQczRgYpkMsrBItF0cBXHf5iVMmoZVj2WWtOE2VGRorVIsgKlWblu1llMkSqKqMLEnoqkQ0pDE4VqzmvrkCopLNimw3L0ZXoaoSsiRh2V5hgqZI2E4lO20qsgTr19STiAfZ1p2aENGSyvvRVJm1HQkKlsvbLjxuyjW457FuBpKF6vNm2S6r2+KoqjzvezFfeoayVdmRaEijsymKqspTbPH4exwNa7zlkgN34Oa8hHr//fcf8EF8fA4Vc6kA6xnK8vunetnZlwEEq1rjXLBxGR1Nsf2W0284aWrYffz2W7vHsF2BpioYtkssImPYEqMZk6baEGee0ETBsHlmZ7JabTreGAvXpevrN6PEYiz7248jSdK059OfLFeFlaVDbEegKjJ9owVWtdccEtkCn5nxbeWxx2JVnlb2k8mbvNQ1Sr5oo8oSjvC0zMayBm88fxUDyTz3P9VbjcBfuKGdxkQIIUQ5yX9qcRTAfU/10t2fntBy77m9SbJFC+F6VZ6uAE32NM9URca0vZCZpsooMhQNh5IJIKEpEo7r2SBZgtNHX+Ks5LP0djZgBmsxLc95A28bqZw7VxEKlyWq77sCXtgzxlknteC4LqmsOaG1YKU3dMGwaZpm0lZZ5vzBnS+BgFBQobMpSk00gBBiTvfiQFcb9i2JCk5aUefdB1dQM00O4nTflQNlzg5ce7sXrnRdl5GRERoaGpbU0qnP4WcpLLXNVgHWM5Tltgd3MThWIKgrSHjtV8ZyBledv3rehnj89kXDxnFdVNlTJq+8JkkV4zWz0COAJMu0ffD/IQcC1ejNdOdTNO1qaTx4Sxm66iXQ7m/M4+9PZ2sN65bFZ70/093TyviX4pLqUsC3lUcmC7Ffi1F52jOUZThVZFdfhkLJIl+ykJCwZMnrSxxQyRRM7vjjbvYMZAnqKjVhjULJ5tYHdtKUCNE/UkCSQVMVYiENWZaIhTwx7uF0kYZEeELLPcd2GE6V0FQJVZFxhV2t43QcQJFQZXBcz6ETwou+Wc4+GQ/H9RwzWZZ4tO5kdoVaSQfi6K43yUznrfIeIRxQylIgZTmQsvMmlf8zLZeXukYJagrZosXxy2rKE20oGTZF0+G5nUk26io9Q9kp96ejKcb64xonRDG3do+RLZjEwnr1M9NN4k9cUcszO5PzFmCeLBXlrYA4M36u8l2xbZfBVHGaPc6dOVuVXC7H3/7t33Lqqady3nnnceqpp/Kxj32MbPboCJP7LIyZWoocqhYyFWZrcfPktmEyBZNQQPVarWhytVT9yW3D8279NH77UED1ytxdgVqeAVuW59CFAvuawk92rgrbtzF6768B0JubUcf1zJzufFTZ6wlYQS2X9Qf1fa9NHvPk+5MrmLPen+nu6W0P7uKXD+487Pd5KePbyiOP2ezXbK2xZrM7czn+bQ/uIle0GM2USOctbMdbehfl6k/XFVi2w/aeNEFdJRxUPbmNoIosS+zuzyIkQEg4jstIukShZJOI6RQNh3BAJRrWJ7Tc2zOYQ5E9l82T6pA8XTdJIhbR0VVv+VLTJFTFO1YkpKEqIAQoiozmmFw88hg1qoumawyGGj0dOsdFliVC5aR+VfY04iq9RccjyYDk/d+0XGxXoMgSqiqzqjWGYdqMZg3CQZUTl9eiKTK3PbiLH/96y5R7UrkXg6MFduxNUSjZKJJcbbv1+JYBbntwF1u7x1DKY9rWk+KXD+7Ccdwp7a5ma7s1U5u/mT63cW0jw6mi16fWmp+cyWTm7MB9/vOfp1gscscdd/Dcc89xxx13UCwW+fznP7+gAfgcHcz3S3ywqITRK42EI0FtwkwomSlh2Q6asu+r7/W7c0lmSvM2xOO3b60Lo8oSlu0QUGUsy8VxXUqmTTrnzQTTOWOKc9X/+z/Q+5v7ufl/npny4zDt+Zy5DMcVFEo2olzCbzsubXXhGcc8+f5Ew/qs92e6e5opmGQL1mG/z0sZ31YeeezPfu3uS886OZ3N7szG75/qZXCsQEBTCGgyZZ8KV3jLoAFNIZ030VQF2xVICEbSJQaSBUbSJXJ5E8t2CagyhuVg2i6q4onrKopMuHw+42muDaMqErUxHct2EUBdTC/bE0FQlZHKIrWO4zlWJdPGtF1CQZ22xjCdTVGW2WOcmHqZmnQ/puV4HROEAMnTiFMVhXhIpTERomS76JrCiuZw9RwVCYTrfSSgerbYsl0uP3s5jiPYPZAjV7Sprwly/LIENdEAluMyOFageyg35Z5U7sVYzsAVXhRwdXuclroI4YDK/U/1TjuJLxg2qaw54RqNn2zP5MQnM6Up13Z/qzYdTTHqYkFCARXbXZgDN+cl1Iceeojf/va3hEJeKfHKlSv50pe+xEUXXbSgAfgcHRxq9fH9sT+9sPp4kL6RPJbjVvNEvHJ4l+FUkd883jOtBADAf967tZo/Mr536XiBxpNX1VerUAuGRSigIkmeEbYsl209KZpqQ5x3fhtCeFVjv2t7BfH2TSTisWnD9tOdT0NNiLsf7aY3WUCVJVa1RGlIhGaULTiQ+5PMlFBlqdxH0BPBzJcsAqoyYbvFus9LYQl+MfBt5ZHH/p6PR57vn1NrrIXoFO7syxDUlbJN8goEckXLK2iUJIQrMGyHeDhG0bAZSRsEdAVVkTAth4LpIpdTNXTN64IQ1LyWU6evruf+p3p55MVBJElClgQ10QC10QCNiRAdTVFWtrr0j3oFDHVxyev5qcqotudMypIniltxrmoiAVY2xyhaDl3NK/h5zTWkhU7AhZLloCrekqw2qRJ2/DOeLdqMZk1cvGpUXZVRFBlVkTlxeS0t9RGe2Znk+I4atnenUBSJl3vTrGmvYSRjENQ9CaZM3mTPQJZkpsSLu0ZZf1wDF2xcVu2ekC1Y9CXz7Or3rnEyYxAOKIQD+5a7VUXCdQV9yTxF0+uZ2loXRlW9Hq376xVbWRJ1nPLnDRtVkVnePPN3wXJcTl5ZRyyiH9D3pTruuW4YCAQYHR2t5ncAjI2NoesLG4DP0cGhVh8/UDaubaRrIMvgWAEhFCQkMgUT23HoaIyNy2HYVzlUeXjH54+Md7JmMtzjy9orD3YooFIXC1Kf6mPvf9zO0+svJxzUCQQ9Re65FiC01Edoa4ywZlnNnOQGDuT+aIrM1u4xQgGVkK6WFcdttOjEwP1i3OfD2QB8sfFt5ZHH/p6PobHCvCIsB4ZAKi+IqaqM6wrCAYWS6SKEFwVrrg3zxvNX8b//t5tUzsBxPR22fNFL4RCCqtitwPXkNmqC3PFwF7miRbZgEtAUFFlGCIPRTIlXr29j70gB1xHlAggHRZa54lzP4br9oV0MjOYZGC2QTBueHIgqU6M5nPjH/+ax+lNYsXotA6MKat5EuIKwIqGUl3ZjYZ23XXT8lMloz1CWbMEE8pQMB0kG4QoCmkJ7Y4QLNi6bEBUNBdVqMUNfMo/lCCQkJMlla3eKXNFCkb0WW5Wc5rpYkKGxIr0jeTRVJqR7dtKyXFxdmTCJz5edZSE8OZPJk+39FbdtXNs4Ia9alb1ii9Fsado8vfHft0PmwL3pTW/i2muv5Z3vfCdtbW309fXxox/9iD//8z9f0AB8jg6WiuDrbFGcjqYYbzx/1bgEVpdISKW1rqbaWHiyE1V5eKNhnXzemLOTVZnVS5JEvPygCuFF9tyiiZPLkUrliDVO7Ic3lx+HuTbMrlyPSk/DtoYwzbVhcgVz1vszXmGoZNqkCxaW4zCaNRgcLSxIGPNAz+dIwLeVRx77s19b92YYSuYO6uR0VWucbT0pDMsrSvL01zxdtZNX1U+YnAUDKictr2VHb5q84VQlPlyB13Be93KjRtIlHAc0TcZ1XKRypwBZcpEkwbrltRiW4PTV9dzxcFdZk1InEdN5ZmcSgKd3DJcrOlU2rY0D0J8sUEql0a0SDQHQa0P0JQs0JYJe8VW5A8HpaxoYzRrTPr9PbhumMRGiNhpgz0CWsayBK0MkpHLV+avpaIrxm8d7UCTYOpQjnfPsVSykYtkS4aBOpmQQ0BRKllN1xFRVquY010YD9I7kAbAdh7GsgWm5RIIKpZKN64rqJD6dtwgHFFa2xskUrAmT7cnXobUuTE00ULXTHU0xElG9HAgQhAIync0JVFWe0X5Vvm+VQpADZc4O3PXXX09TUxN33nknQ0NDNDU18e53v9vv7ecDzNyr8lD++M41itPRFOMdl+xrc/Tvd7203+XFuS4/TnYeVVmiYNgTDH8pV6A+HiF62olETjmV+B/3HFDkcq5yKZXr0dEYIaDK9I7kMSyHE1c1sun4hv3eH9sVHL+shq7BHCNpA12TaU6EKBiOtx/bpbMpuij3eSktwS8U31YeeezPftXWRvj5vZ5+38GanF6wcRl9SU9IVxIQ1LxiKMN2sW13gg2rjwfJ6wotJRvLdukdziMQaJKEwGtLFQooaKqCrEhIkqBoOsiyFxkTeFIjQU2pPl9rOxNevll/hq1dKSzb5dkdI9WenlZZK+64phAdTREiy2s59f1fZHfZfo2X+6h0INifHatOboMSp63xnvvK5LZynqossa0nRSigEo9oKDKk8xbRkMaqZTX0DWUZTpVwHAdV9prd10S8HL6S4WC7gvqaAGMZg5G0gaYpNNQEUGSZsbxBe0OkLLjuEg1prOv08usqd1UIQXfZhqqKjBCieh0q2nLVzjmu4OSVdVO0H/eXB3fJGZ1s60kt6HszZweu0ojZN0I+M3G4e1XON4pTjU4NZukbybO8OVaNlI03PpVwd3Sc9NBMVZ7jncdUzmsBQ8Iz/FJfF6f84VbMK/+C2x/al2OWypnVbeb64zCXJdHJ16O5Lkw0rBEJarz14nWzipNWjgGga96yTqZok4jorFlWs6jCmEfKEvxc8G3lkclM9mtlW81Bn5x2NMVob4hiOwK77AC11UeqKv7jj1WJ3mQLFl6zAU+/TVO9Pp1e4YP30x4LaQylikiy1+ZdkSVv24BCz3CeU1bVV+3Qlu4xL0VCkXFcF8eFkuliO4JISCMguaz+3X9iNLQSfeM1/M8f99A9lKN/OI/jOhQMr4AhpCs014bY1p0iFFDY2j1GOKhN6IIwl+d9vDNUaVLvvQ61sSArmyLc/1Qv2bwJEtRFAwR0z5GsOFf18SCZfJJELEDJ9BrNy+Xl3fbGKB9446nAvnSX8RQMm6Lh0FATYnlzrNzdwdOj6x7K0VIfrtrpA7FfHU0xlrfE5/M1mcKsVai/+93v+PSnPz3te5/+9Kd58MEHFzQAH5/FYj7VQONlA1a2eInBlSrRyVWclUrTXLmh8nRVnr97ci8DyQIv7h7ljy8M8MKuJJmCiV5WAB/NGij1TbBiDc9nlGpFm6rKCES1aGKu1WtzqZadb3XUdMcYSZUYHCvgut4SjO24Xh/EctXuYrFQGYalgG8rj146mmJcee4q3vX6Ew9KU3LYl9i+/rhG1nXWEo/o0z6vleiNrsoMp0sENMXrVoBUzYdrrg2zrrOWREzHtLzeo25Z5FeSJMIBhVzRor0hzHCqyGMvDTKSLlEyHTJFE9MWOK4gVzRRFAlNlTFcGKhpJ3HKSTyzM+k1hQ9r2K5D0XIJajKK7C3TDo2VqIloXjuuks3wWIHB0UK1UnQuz7vluKztSKCpMum8QbZoEw4o5IsWDz3Tyx0Pd7G8KUIkqGGYDqmcQS5vUjRsYmGNjWsb2bi2kVTOIJU1cMtCwlY5qtk9lKsea6bxVKp34xGdNe01aKqM5TjYjphgpw+X/ZrVgbv55pt5wxveMO17b3jDG/j3f//3RR+Uj8+BoCkyL+we5ekdw2ztHiOTN2ecBY2PTiViQdZ2JAgFVHb1Z6c4UR1NMU5fXc/uvgx/2jJEz1CO01fva7rcM5Tlpa4xDMshV7SwbK8XYcmw6RrMsb7W4drXrePyi07m5bP+DLUmMUGuoDERIh7R5/XjMBfZgvlq2k13jERULzedBkWRqI8HiIQ0uodyixodW6gMw1LAt5U+C2E+9quChFftGQ4oXl9RSUKWJc4+uZlXb2hHUWQiQa8C3rY9PTnwnLjlzVGe2ZlEUyRKlotbFusdr2xh2oLMaIYVIYe1y+uQL3w9XbWrqrazf7RAPBKgKRGiodaLSNXXBAmHVEYzBkPpIgOjntRJ92C2Ks0yV/ulqjLrOmupiQbKjeO9KF9NVMe0HP74wiBNtUEaE0EcR5DKm7TXh6t5dB1NMeJhr8+0Wy7waKgJomsKhXERt5nG09kUrdrQeERnXWctaztrWX/cxPSTw2W/Zl1C3blzJ5s2bZr2vY0bN/Lyyy8v+qB8fOZLz1CW0WzJSz7VVUzLYWv3GM21Yc47f+pyZDJTqibIFg2vbLyjMYIjmLIs2DOU5ZmdSVa2xVnZEp3SDuvJbcNEQxpjWQNVkZFlrwy/aDq0awbFb/4jo5e+nvo3XDmtNEdrXXjWfLrpJDVmW7LeX2L27r40v310z6ySHbYrOHVVHbv6s2iqXNbME+RK1qLPLg/3EvxC8W2lz4EyH/s1fvVAVSBbMHFdCOoybfUhbFfwzM4kl9RHOH11Pdv2VFr8SeWG8vs0JMMBld7hXNVmTcelvX+goSfPs6+9lvNOa5tQXLB3OE9QU9BUyBZsBpIFioaNKzxHUOBVdToODKWKtGVL1Y4x87FfxZJNvug5XDURnZJhkymYWLagN1nglJV1nH5coLqMOX6/iZiO4wg0Ta5q3DmOmCCuvr/xzLU473DYr1kduFKpRC6XIxqd2nssn89TKh15ScY+Rx/jq5oqekahgEoiqk/7UI1PkA3qMrmixZMjeaJBjdsf2jXBoZlcheo4goFkgR/c+RLrj2ukeyhHZ1OUwdECekWiXAhMW1C/soXdxnmsOe9VABimzTNdqbLukYJTLntf11lbHdvjWwbGVYVpWLbDPY8V59TSZbLTN13uDsCvHngZGTGrZEclt2N1W7x6XVVF4sTltUe0s3Uw8G2lz1yY7jmdbL/SORPDchgYLVRFssfbI9cRGJYLkoymguN4uWpjORNZkhhIFvj9U73EIzo1sQDBgFKNsgnFwXZctvSkWdZgM5IqEdIV8iV72kbz/1d3Ggm3SHNZBqdUstjak/ZEfl2XfNEpy3qAFvY0NMfL0wrh5enJssTLfVnO309+7+Rrc/rqenpHClVR4Pp4AFEWMbYdF1WWME2nWlgQj+hTJsPLm+MEtDypnFmdrDcmgrTURWa9V0uhOG9/zOrAnXjiidx7771cddVVU9677777OOGEEw7KwHx85sP4qqaa6MSqpukYnyBrmA7pnInreu2o8iWLXz64k7pYEMtx6R7MsrLFe2AzeZOXe9NeOxhBtadgQJVpTIRI571S8pbiEEo8gaarGOtfiZpI0DOUpXswhxBek2jHdUnnHIK6UpXs6BnKcsfDXQBEQyqW49I3UqCtIbxfSY39VeBOjije/tAuoiENqXzM/RV7VGbB4YDK2o5EdQZ6wcZl87o/xwK+rfSZjZme06Lh0NEYIWObGKZDtmChqzJCuFMmWMlMidFsiVhYI1uwcFxvWdR2AMuhtS5MybD505ZBQgEVy3KojQVoCOs4rmBorIhwBYrkpVRYtouuyWiajFlu7aS7Jh3FIfbEOkhGG8lpCm2KxG0Peh0IXNcTDJaQyZme46eVxXAnO4GVwJ6CoGTOnBc23bV5ZmeSS87oZOPaRr5/x0u4AgrlSJwsySiqhF4WQO4fLUyoDK3g2bAitdEAQpTIFkwKJZuNx89tBWEprwzMmgP33ve+ly9+8YvcfPPN9Pb2Ypomvb293HzzzXzxi1/k+uuvPxTj9PHZL/PN9xqfIJvKmWiqTGMiiCs8HaahsSJdg1lUWSJTsHh86zCPvdjPrv40Wrk/YCioVnsK9o7kaakLEwmqJEIKF3f9jlf2/N+UPqyKItFQE/TENgVoqkwkqGKXrdyT24ZxXNerWpW85GFNlUllzf0WDcynlVkyUyIc0ia8NlNxw9GQm3ao8G3lkc9sPU8XykzPaaFkMThWYGdfhlTeRFMlHCEoml7C/fhnuT4eJFe0iAY16mKeLIblUBWiTeVNxnJeOodtOxiWQ+9IgYFknmTGm9Cqqkw0rJHOm7hCUCw5yONcr1eOPsefDTxA2Mzjul4VaqWVnmm7NNV6XUYMy61+yhUCyxEok3qdSpQ7OQiJ5trQjLZjfzasoynG5WcvB6BoOmiKRE1EQwiv8laVJbKF6QsHKjnMvSN5ckWbWEinrSHMMzuTR3wP51kjcOeeey5f+MIX+PKXv8w//uM/Vl9vbW3l85//POecc85BHaCPz1yYr5BwZWlwXWdtNe+kosy9ZyBL0bBJ5QxG0t7ygm05JNMlTMshEdGRFZnOJm+prLk2jGE5NNeFMWyXQsnimU1X0NjezCXrJ/ZhjYY0bEfQUOM5lkIIr89f2dGsbDNeJVxVJLJFi3XLa6c5E6qfm6uOWn08SKFoMd7M7s/ZXcoz0KWEbyuPbKaLAP3ywZ08smWIbM5YlPZuMz2noYBC30gB8DoSVBYIYmGvUKC1LsyfXhrk6R3DmJZLrmDiuJ7uWTSkUjIdFMnLNyuULBwHDNOtivyC5/gI4aDKElpQxXEFIV2hZFhYgGELgqpEyRY8VHc6O8PLyKphJBeyeZPe4RyW7SCXq9ErfUYlw+uPivCWcscLgMsSaKpUbfH1+lcsn/XapHPGvjQYXSEU9HLVNp/QQkt9hB/etYXRrIEEJCI6qiKTK1nEwvqMk8vekQJrOxMTZD7yJeuIFAofz5x04C655BIuueQSdu3aRSqVIpFIsGrV4ug/+RybLHbfy/nmKmxc28gvH9zJrkKGdM4kJUyvkKEpyt6hPLIMlF2coukQi2iUTIEsSRRMh9NWJ6pLtQXDZnlznNc2W1hyhppXTv9DXR8PYttuVR1cU2SKhoMiS9VZ41y2mWnfc9Uham8I8+vH9mKYnihmXSyIrEiHvGvG0YhvK49cJusmViLxJdNlbUfNlKXMA7FhMz2ny5vjSFKO0XSRkukg8AoMVFkikzcZSRcxLJemQAglIFEyJFJZ01tCdbzlUCRvWdF0nGpUrNLH1C03i5clCVWViYV1bMclU7AIBjRCgOqYrB94mkcbT8eWdXrkFmSJsvgvbO9JE4toxMIamYJV3b9S9hAFXtHT+DVUV4Bleykjrz+rk80ntEy5Jvv0OHPs7kvjOBAOebnJlYn4+JZUNVGdoulgmF4hA0i0N0QmtOyazHjHueoglmyQ2O99W+r9mecs5Av4hshnUThYfS/nGymqiEOqihd+N2ybYo+NJAmE8FTLPf0kKJquJ0AZ03mpa8zTbxOCgmEznCrixATP/PQ2ovkxEitPorNtarSsEiVsb4gwmi2RLZoosszlZy+foCc02zbTMdcIZKWitrM5ysBInmzRomjkZ92/z/zwbeWRx+ToWP9ooRqZryzpAdWlzAOxYeMnjrbt4ghP3qOtMcpYpkShZNOYCJLJm7gCkmkDSQbbcdEU2atiV2V0TcG0vd6hRceqVpHaZQ0QCc+PUhVPZxLhTT6XNUUZzZYwbU/mCLyoGQiWpfayIfkiO8NtJGvb0ZR9OXGW7WDiYjoONWEdWZYQrku24O0joEpIkoxhec6jIoEse8ummiZx2VnLOXlVA7c/tGuCMzT+Oq5qjfHEtmFcVxDQZSoZXu0NkQltDUO6iiJX8pglLNtlz0CG3z25lws2Lttv71G73ElBU2UURUKSpBnv25HQn3leDpyPz2KwGH0vJ8+M2hvC9I4U5jxTenLbMA2JILWxAC/bLoosky4Y5Io2sgzRkAZSpbRewrIdljVFsSybk1bUEQlqVUkQCQlFkRh61RvpyRfIPNXHJao67fF1RWbXUAaQWNtRy6s3tE/REzp9dT33P9VLOm+RiOpcuKF92pnreOYagaxc+6b6KIly14l8yaJ3pMDmOV15H5+jk8nRMS/yDdHgvobjlbSEhdgwCQnDckhlSxiWi64pGIbJ0FiBoumiyhDQFGQZXOEibAGSXG2F5bqCXMlGlgQBTceyHCyn4miJccfxnDOn/JJpuwyNFdAUCV1VvNxfRcZ2XISAl2OdfG/5lWS0KFLRJhrSvOpVw8FyIKBJRAIqqiKRzHiRr1BAoS4W8Po7u4JYWCMa0ryJYclGkqAuFmRXX5a9I4UpzlBAVSZcx1hYo2jYJDMlArqKpsqMZksYtnd++wo4AuiqzWjWIBTwqvm7h3IzOliVCe5AsoCmeI6h7QjWtMdRFGnCfav8tjy9YxhVkVneHJviwPsOnM8xy0L7Xk6eGQ2M5nn4hX5Wt9XQVBua00ypMoZtPSmEEJQsh5CugvAaI5u2SyKik85bKLLXVFpTZdI5e8J+f/OL37P6uUd4+LgLydteC5vaqJi2qXxlzKevaahGyKY7t2d2JuloirK2Q52iObc/5hKBPJp6jvr4LCaTo9iqIlE0bNat3CcLU0lLONDn6MltwwR1BVmS0DUVXYWSZbO1OzOuGACMcmXoictr2dGbQQi8HlKSVG5cL0DymsY7QuB6QbYJCKg6b+A5dCXTRgnqNMQDOK6gkM5xRc/veaj+dAYDddh6tBq9yxUtlHJkT5ElYmENw3QJ6t5SrO0KSobDsFvEtgUCgVvuRyoEBAMqEpAr2jzzcpLm2iCtdWF6SnZVjqhQsjnjhKbqGOMRHdtxyJdcXGFTKAlS2RK6qvL4lgHq40F29aWpqwkxkraqy7t6Oc9ufNHDeCoT3B/cuQUhXMJBrdo6cXzP0vF22gtcCl7uTbOmvWbGzhiHk1mrUH18FpuFdgiYXK2UypkEdZWxnDFrBebkMRTL/ymyZxyDAQW53DdQVSUSMQ3H9TTdrElNpQGSe/aijg6RHE6RL3rttvYO5ya0aZluzDONcT7VpAfCQq+9j8/RyuSK686mKM21YTRFJp0zeH5Xkud2JknnDDRFnvNzNL6y9ekdw/Qlc2iq11vYFaJabFBOY0MAQV1BVxVGcyahoJcPZjsujiOqS54Skpf/Jk8v3zEZVZEIl6OJQ+kSl5+9HLlUoM5IETNzlBs5VKtZhdj3b0UW5Es2AU0iW27XJeF1biiZXqsuTZGxbJfRTAnHcSkZNrmiRdGwAUGmYLGlO0WuYBLUZRCQKZgMjRWrY4wFVcayVrlxvOOdrwuqAnc83EV7QxhFlikULSzLQQiwLAfTdknnDHoGc3QNZma8v+uPa2BtZ221Vdnk+zbe/oaC+5QA+pL5/d7jw8W8InDZbJbdu3eTz+cnvH7WWWct6qB8liaLldA534rRyWN4escwCAgFVWJBlYGkV72VyZu01oWpiQZmnSlVxqAqXt6Gqii4QlAb07Ftl1zBYihVoq0+wuZ1CVTV266Ca1n0jpV4VF/Bo6uXoQY0XCHIFm1C+sQ2LTD3yNfBjpBVzjtXMKGcwzfXa+8zd3xbufQ4kO4mPUNZHtkyxEtdY0RDGid0erZgOFX0cmgTM9uwnqEsv3tyb/WznU1RVEWmbyRHQPWEdW1nnwzHeCfOsj1dkFzR4sTltZRMh/7RPKmsCXjLkgDZiiNT3ockeQ5XxRGsoKvenmXJ27fsKrTURyiE4vxw+RXYkoI3h5UoZ80RCqgEdAXDdJDLTqIrQJa9dl2uEFVHNBjUCGoymYJF0XC87GLJK16oVKWWTE9cvWS5aKpLOm+CK3ipawyAptoQozkTWRZI5diSLIOieLJNjivoHSlw+dnL+fVjPV6E0XFxhUASEomYPm3Rw3hm++0Zb39b68Jevpwik8qWeH5Xsno/Ztr/oWbODtxtt93G5z73OcLhMMHgPg9UkiTuv//+gzI4n6XDYiZ0Hqi6dWUMFaOQK5jsHcpVw+iKIlcVuacTdKwY8K7BTNXIuK7wwv/l8vrRjIGERFBXiKoamiqzqz9LKOAZvCe3DdOQHqD/+99m2yvfRECVMITqzVIlcPFyZ1oblAnHHU4V2dWXIRbWqk7m4FiBVM7k3+96qfqDMj4PJ5M36UvmyRZMYmF9UYxG5dpv3Zuhuz+95JTFjwZ8W7n0OFD71dEUY+veDKeuricS1Lwoz1CuKrSbiOqMmsaU56hyvIFkgWg5d2pnX4Z42JMRcl0HTZUwJ2VRVAoAHBdCAW8J9YKNy7jtwV0oskxQlzFtQcl0iARVDMOZ4KhVllEFoMpQTh3DdgWyJCgaNkHJ4bIdv6Hr1u2EAmu8z9sukgzC9Y6tazKb1jZSEw3QO5xje08Kw3KwbBdZ8qJi3n/eAXIFCzWqs+G4Bp7cPoIsS56ciXCRJQlJ8nLOJASG6WCU22k11obIl2x6R/IYttdNorUuwmjW8LraSN4ypmm7NNR4ecdXnruKE9Y0cdv92/nTlkE0VaE2Wu53arsTih6mu5/7++0Zb39rogFWt8XZ0ZsiV3SIhKg68EulmGHODtxXv/pVvva1r3H++ecf0IF2797NjTfeWC2t//KXv8yKFSumbHf33Xfz7W9/GyG8hrs333wzDQ0NB3RMn8VjMQoPxnMg2mKVMSxvjvFyb9prbCx780VPE0lFU2S6h3K01Iers6rHtwxw96PdDIwVywm8MuGghhBeK6mxrEKm4PUVDOsqwaDCaKZEPKwT0F2Curc00DWQJR3VUE9ox61v5pl+A8NSqssMtustUyiyxPLmOLDPkNdGA+SLFoWSzc7eDDVRjcGxIqvbaib8oJy+up5ndibJFSz2DueQJAlF8n4oFstodDTF2HBSG8PDR7aI5VJlobbSZ/FZiP0aGvOS79M5o1rBGA2q5EoWpjM1rWL88WxHENTlaueXgTGvI0Aqb1YrPMejyl6ES9MUOpujXLBxGS/sGvEcKNNBSF7HA/B6eaqKhOlMv3jqjIvECeE5XK7rsrwtTkBu5emChhVwUWQJSZOxbU9/TpG9llU1UU+TbXCsiFZegTDLHqGmTIzyifKSbslyCGgSJdPTiZMAIQlE+VTzJQchBOGgSn08iCxL1NcE6WiKEglq1ZaEYznT08Is9y6V8CJslUn5yrYa3nHJOlJ5k3zRpGR6ucvLm2PEwtp+Vyz299szJQ9SlZElmVNW1dFcF55yj48YB85xnAUJUd5000289a1v5YorruB//ud/+PSnP81//Md/TNjm+eef55vf/CY//vGPaWxsJJvNouv6DHv0OZQsheT3arssSWJNew1PbR9G4PX3S0R1sgULIUzC47oFPL5lgP/87XaKhicwWbAEeVGp8pLYM5jDLRsKgILpEAlpZfkAURXT1VQJLZeiqCUYcIP8/qTLsJMFYopNOm9VxXkrDaHHd1+o5lQE1GpErX+0yHHtNVWjUPlB6R0pcMkZnfzsvu24AmIhtRqxOxqEJ48FFmorfRafA7FflYj9rt40CIHjutXOKJbtEgvrMybNJzMlFMmr8B7LOOi6QjToCe42J0JoqsxIuoQie9Es8JY/bRckV3DKqgRvOGclA8k8//t/e3Ar66sCTNtzCoums9+8NwkIBhWkstSGapeIBnUu3NTBIzvDDCQLREybTN5CkiSa6wIIIWFaDo2JMPmSRX+ygGk7FE2HxkSIsaxBsVyVWtGekySJkKagaTJ7+rNoikoJE0UG2wHXqejFAQg0VSqPyUGWPUH0yr147eYO7nmsmxXNUXYPZCmZLooi09kcQVHkKVqYnU3RKbp6+ZK13zy1/S2lTxeha0iEqp0n5vrdOVTMuYjhuuuu49vf/jauO3XWMBvJZJKXXnqJyy67DIDLLruMl156idHR0Qnb/ehHP+Laa6+lsdG7SbFYjEAgMGV/PoeeQ5n8PlM7m/FjiEd0WurDhAMaiiIT1FWa60LURANVpwvgrke7sGyBEMLTJSq/PpIpUTRsbMelZDkgQSysEwqo5Eo2uqZg296SAQKCmSSXPPlz1g2+UHXKOpuiyLJMTVT3SuvT3gM9XlMtmSl5FU3lMa/rrGXT2iZURZ7RKHQ0xWhMhNi0tpF1nbVVweClYjR89s9CbKXPwWGu9qtie772i2f4/h0vMTha4PjOBEXDZnisVLUJlu3SVh+Z9pnsGcrSM5Tl8a3DGJbjtZiyXc9hK1eOappMOORN6hTZy02T8RwdXZc9GSM8+2W7lQrPffbLMF1KhoOuzfwT7gqojQZob4jQ3hDmz4f+wFV7f0vvcL66kqFU7JcsM1bOr7vq/FW88fxVRIIaqbyJZbvUxQJEQhqqIhMNqV6kTvHGGdIVbAGhgIIkS6xo9WxfUFPRy7bYFV5hRiIaIBLScVyXkuWyui1OTTRQvRcVB2plWw1rliVY1hRlVVuM1e2JGeVBCoZNvuQVPuRL07fTGn9v7nmsm3zJmrDyMb6lVkdTjCvPXcW7Xn8iV567is6m6JIt/JpzBO5HP/oRIyMj/OAHPyCRSEx474EHHtjvZ/v7+2lubkZRvLwgRVFoamqiv7+furq66nY7d+5k2bJlvO1tb6NQKHDRRRdx/fXXT2g8Phv19dHZNzpCaGxcOpGW17xiBb964GWEJBEOaRSKFi4Sr3nFijmNs7Exxu6+NI8838/QWIGm2jBnndLKyraaCdvt7kvzwLP9REMay1riFIoWDzzbz5+9KjJlDC31EXqH89REdcIhDctyUVVBZ4uXt7LhpDZGMwYBXQG8pFtFdnFdgeN4ffskPEMklw2SokjeUgXQWBshEtLJlyz0uiYG1p1JfPMZ9JsODXURZEkiFNLZO5RD172S+b9+y4YJ59TZWkOuYBIJ74sk5womTbVhkOUpr3e21tDYGJvxc5X3F4Ol9P06mliIrfTt18FhLvZrvO2xHa9P8WCqSG0ixOlrm3jshQGSWZNIUEPXZEYyBi7lZ9xyeeT5fnb1phkczWParpdOochYwi23xpKoiQVoqY8wmCxQHw94XV8kCAY1SqaN64KuKjy1Y4SuoTwDyQISXiRrPAJPSiSgqeRL+96sVJKCt2xq2oJ0Mk8ooNG/9kza60LkLRdVUxgcyyPwtOPCQZVgQJ1gvzac1EYkspWHnumlJqpTMh1sx6sOVcrVmR3NcUzLQdcUVrbFeWFXkhXLEvQm8wyNetWlnuguyLJMIhbkuM4EW3YncV1oaYpNuReNjV6Kx2xUtq2tjez7XamPctYprQDc91TvlN+a+57qpSERJlq2q9GoZ1crvxcH+t05XMzZgfvKV75yMMcBeEsP27Zt4+abb8Y0Td797nfT1tbGlVdeOed9JJM5XHe2guqlT2NjbEnlKEU1mVed1sqT24bZO5ChPh7kVae1EtXkWcfZ2BjjqRf7qknE4YDKUDLHz+/dMmVW9dtH9yAjkIRgcDhXXXL85n9nedtFx08Zw7LGCK4QZHIGoYDKiuYYsZBGd3+a4eEssuQtH2iqRNHwDKkrKs2VvWURVfbaOJu2A+XcS9d1CWoygcFuClqMvIiwffXZXLZ2GZltw4yM5j1Drkisao1Vw/iTr8e6ZXHueaybQtGcUPV07inNPLMzSaFoYlkOPcN5ckWLk1bU8dSLfTN+btPxDYvyvVhq368DRZalJef0LMRW+vbr4DAX+zXe9mTyZlm6Q7B3MMeq1hgrW6O8sHuMgCYT0hVS2RIDyRzNiQA/v3cL4YDKaLqAbbtkchbxsPfcmrYDltc3NF+0iOhexWcmbyLwVgfyRavqeBmmjRCQTBcx7Zm/C7YjKJr7nLdKz1PKrbMAnGKB1mKSAamVP8n1vPfsU9j1fD8v7hwjFFCJBFSEa5LOmwgh+O2jeyYsKa5bFuehp/cyMlYgX7KREF5LLtkrRhgazYMAVZMYHPUie7v3jlUdPE2VKRo2QohywZiDLku01IYZyxnz/i2pMP77FdVkLtrQXn2vEmVzHcFotsRLu0Z46Om9XH72crr7017kLW/s25kQ1d+LA/3uHCgLtV9zduDOOOOMAz5Ia2srg4ODOI6Doig4jsPQ0BCtra0Ttmtra+OSSy5B13V0XefCCy/kueeem5cD53PwWEhT87kmEVdyVTJ5k5d70+WEYY1sYV/V2JXn7mtTdPtDu/abA3F8Rw3P7x5FkhSCukzJ9DSUwgGV9oYIfSMFQgGFgC6TLdiYtiARU2mti+AaJc598S4GapbxpxMvpqK0NB8ZlP1VPbXUR/jdk3vZ0p0iGtI4cXktirKvtcuBVOr6HH4WYit9Dh6z2a/xeXKhgOJN/BSZfFkSaDRr0lATJBxUKRo24aBnQ57dOVpNwi+ZDuGASk6zyJZsZEkioCqejSh5jtmOvSlUxXMOg5qKLEO+ZGM7ouqEKYq0zyGbAVWWcB2vc0Mlj04ACl41PMBZvX9ibWYXPzvxzVhqkGdfHpnQbL5oWoxmDeyyo/j8riR7BjJcdf7q6vW6/Ozl/PQ3O3BdbxlU0yQKhguSy1iuRFDXaAgHOa49Ssl02NmXRgivECJdsEB4ciI1UW/pNF+ykBVpv71LF8KT24ZxHUHvSB5NlYmFPHmROx7uYk17DQXDnrFn9Ez5cQv57TuYzEsHbsuWLTzxxBOMjY1N+BJ8+MMf3u/n6uvrOeGEE7jzzju54ooruPPOOznhhBMmLJ+Clxv34IMPcsUVV2DbNo8++igXX3zxfIboc5iZ7gFobIzNOYm4Usbdl8wjhCCdNzFNLwnYdaZ2OJjNmbr8lSvJ5C0GU0VMyyUUVFmRCPG21x5fLXK44+EubEfQlAiRiOkYpstwuohlCx47+XWE29o4pbEeIXktV648d1W13dVwqogkScTDGj+7bzuhgMLy5viUxNiZStprooGqRMF4KsdZikbDZ3YO1FYeqxzspuFz2X99PFiugDRI5wwKhkM4oNBQTujPFS1O6EyQiO3LfRJCsHsgy9oO76c0FFDLBQ4q/SNFdE2mZDpeEYIrCGoKBcPBsi1CAZl4RGckVUKWpX1tEySvUh5JmiAFUkGRvWXWjqYooaDCWNZgcLSI43qVn8647X9fdzrbop3I4QhxXWFb1xht9WHa6kPsGcyTzpnViG+hZLN3OI8qw/8+tIsPXHUaAJtPaOHhFwcolhwyBZN8UdAQDxAJavSPFogE1WpXg0ryyI7eNJYjaK4N0VpXD0D3UA7bEUSC2oIno7v70vz20T3T3s9Ku61KBNC7Lwq5ol3uX+1FLSf/XhwJvU8nM+cihltuuYW3vOUtPProo3z/+99n+/bt3HzzzXR3d8/p85/5zGf46U9/ysUXX8xPf/pTPvvZzwJewu/zzz8PwOtf/3rq6+u59NJLufLKK1mzZg1vetObDuC0fA4HMyWI7u5LzzmJuJKUOpopkc4ZWLaLJEsENXnaDgeT1dMdR6ArMr95vIfbH9oFwNteezznn9bGhuMbOf+0tqrzBp5xuu7yE9m8ronG2hDhgEZibC9NI3uIBlX6Y21sHfNUvsMhrzy90u4qEdWJBDRURWJgtEA6Z5BMGQyM5qckxs7E+CKHCn6xwpHNQm3lscZcEssPxf7bG8Ls7EtTKNnEwzohXSFTMJFlryPKictr0TRlwmcKhk0iqldtW1t9BMt2vSbuqoxpec6bl/QvYTleAQQITEtw+ppGNq9rorUuXE3vcF2XouFQNGw0RSKgyeiajKpAQJMJaAqqKpOI6SxvjvOu159IZ3OUgK4iA5prcubYCyAERSXIzmAbQ6kiI6kSw2MFeoaydA3mqYno3pIoVP+TEJiWyzM7R/naL56pFpHVRoNYjkuhZKMoEpqqlLXlPM3MvmSeTN5ka/cYvSM5NEWmKRGio8lbHuweypErWqxqjc3bOZ9c1Pb4lgF+9cDLM97P+niQXNGq9jwFb7k5FtKwXTHh9yIyTrHgYHfBORjMOQL3gx/8gB/84Ads2rSJzZs3861vfYsHH3yQu+++e06fX716NbfeeuuU17///e9X/y3LMh//+Mf5+Mc/Ptdh+SwhZlomfeT5/jkvO1Ycsm3dKVwhEVS9qqegrlIo2VM6HFQ+09EUmzCDigfUCTOo8cuu+6NvJMerdz6CZJT4TeMKNM17RPpHC8RjQerjwep59gzl0DSZgmmjqQqWIwgHZVI5k46m6LQNkifPGCc30IalU+Hkc2As1FYeayy2xuSB7r93pMCqtjipnEnRcEjEAqxojbG6o46LNrRX7QtMtGEXbmjnmZ1JwGvG3t4QoXckjyxLhIIaJcP22vOVc2sd1yWgKVXJoZpogNPWBEjnSqTzNiChKN72juMSDem01YfpGsziuKDIEp1N+2Q1Oppi1ER0hlIlCgJOzHVxXvJpukItDAQ9DVXHhZLl0Nkco2Ra5EoWQV1hfLqlLHuSIG65j1ax5JAvWdz24C4KhkUmb1bz2fJFi1hEpzYWQELyIpYl24t6KQqa6mnDpXMGXYO5CV0sKnqXvSOFWSOu00XF7ni4i1XtNcQiOumcQf9ogWzB4mf3bedtFx3PxrWNPLV9mIJhEwoo5cILl8ZEsFrpOt2xloJU1nyZswOXTCbZtGkT4Dlaruty/vnn8zd/8zcHbXA+RxaTH4BM3qR3JMdLXWPk801THtqZwujeAxYhmTLQNBlVkapyHqGAMmX7CgfyQzDZQOzqS3PfytfSXKNhFkBILqoskS14SyivOq2V3zzeQ10sQNFwvERnuyw4Wa48KxrOhAd/f6H5hbQV81ma+LZyfhzsH875tLFrrg3TUhepvpbKlnj0hf5q15KZbFilS0syU6K5LsylZy3ntj/sJJkyEEJQMr18OARIAlRFJqBJ5EtW9bnXNZV4RPaKEBwXWZFRAyormqO0N8VQNa9F3+Q0DS+ilqM2qlMsWTwfP46+UCMjWmLC+SkSHN9Zy4u7kp79Mh2vcKIsuIsA09rXlzVT8CpudxUyGJbXL9proQUSEkXDZmVLjN6RPCXLJVReSbAct9oNp2coNyVFJFswuePhLtZ2JmZdqpzOppcMiy17RpEliYJhEwtrRIMq2YJZ3c/lZy/njoe7yBVtYiGNxkRwWh258RysyfTBTA+YswPX0tLC3r17WbZsGStWrOD++++ntrYWTdNm/7DPMcHkNlAv96YBr29fvmTxzM7knPMJljfHCWj56mw4FFBoTAQnGNfJHMgPQcVANKb7iO9+kVjrmRQMmaSrsqY9MqGV1Z+9ag1RTa6eZyXRWS0Le2qql5gcCigzNkiGiY7lleeu8osVjjJ8Wzk/DnYUeq77n7xdOmewfW+aeCRQdTRmsmHTRXUqNkxRJAbHilUtyoCu0t4Y4ZUnt0xwBtsao8RDKgNjxarNa6kNkSk3j48EPe3JyQ7Ak9uGSWiCzVt/w4ONG+gjOMV5UxVorvdEw/MlC7Pc37QpEWI4VfS6NFRS8ABFgXzRJpM3sWyHXMGmviZIMKAwljWRJQnbcRjNGjTVhhgcK+I4glBQobMpSk00gBCCVM6s5gdWSGVNHNed00R7uqBAyXIBt6oxly14qzLjhZWvPHfVBKd6Lo7TwZhMH+y8ujk7cO9+97urOm3vf//7+fCHP4xlWfzd3/3dggfhc3RQeQCyBZOX96Y9FW1ZorMlNu9lEW9fRTrKKt2Vh2mxZ1AVAxEa2ktkYA+dx7+SLSWDbMEi1qHRoXgijhVxyeHhbPU8E1GdvuECqiJRKHkJz5blheonN0hWJNg6lJtgmJNlCYClWuHkc2D4tnJ+HOwo9Fz3P347y3J4btcoJdMmEva6vMQjnnbYTDZscqSlvSHMcLrImmU1LG+OVqWCljd7FavP7RqlPh7ktZs76GiKcftDuxgYzU/YZ+9wnqFUicHRArGQhmU73PNYcYIDkMyUWB22aUz30lyzil6ppfr5SoVqOKDSGA+ytWuMoCZ7UiVI2K5LUFfIl5wJVa+6qhALe51jvPQQg1TOyzGW8CRBFFnGdlyuOn81T24bntb2VvIDJ0TgilZVqLjCTBPtyTa9L5knElQpWS5muUrYdgXZgsXxyxIT9jNfu3qgPbr3x8FOD5izA/fGN76x+u/zzz+fxx57DMuyiERmjoj4HFt0NMVY1hDmnj/tpVBOwA0HNfpGCmiyRDyiz3lZ5EAepvn+EPQMZRkey7OrL02s5kSWvfo0ojVRluFVoo1mpzapnjw2w3IoGg610QACb4m3pS4yYbanyhLbelKEAmq1r+r2vWnWdiTmdmFnGPvBrNo7WsZ0OPBt5fw4GD+cB7L/jqYYp6+u565HuxgcK+K6UB/TUSSJl3vTrGmvmbHP5nSRlmd2JlnWEObZnaOkciaJqM6rTm9l70iBkukwmi2xqy/NU9uHufzs5bQ3hHn4hX6CuurpzOUMkukS4YCCYdpeC75kgc7mSDWx/sktg3QP5lCVMKOvupadQyXkglWNprnCi6bZjqB/rEAkpCOhEnEEoYCKabvU12jomoFtgyM8XbpEVCcS1LzVh5CO4ziYlldQYTlguy5r2mKsbKupXsfZ8gMrepfpnIksQe9QlmzJpmg4qIpEZ9NUPbTJNj1bMNEUheaGMNu7UhQNG1kGRZbZ2ZeZcT9z4WDYr4OdHjAvGZGxsTEefPBBhoeHue666xgbGyObzdLS0jL7h32OenqGsjzy4pDXmkX1mhAblkvYdekfLaCq8ryWRQ7mDKpnKMvDv36Yi5++l/vWvI6cHGdb0WKZI89Jo2g+YxvfSUQa1wJ6Ph1GJo99qZW7L8UxHU58WzmR2X4cFyMKPVuPy9n2X6kuV2SZtroIYzmDbNEmEvF6Ivcl83Qo0Wlt2HSRlmzB5JEXh1jbmaC1zqV7KMddj3QTCapelWNIm6BR1lIXQtcUMnmTTL5cuSqBYQlkhWqKRveQ1+lhdCTNaX/6JYHOk3lIXc7OvFsWBJZQJK/HqlePIOG4opyPJxELa5y4oo54REcIUZ6oNlQjXeMLA2Jhnda6MJoqece1XDRNJhTQGMuZvKm8IjIfvcsTlteweyDLi11j1MUC6JpC0bBJ5Ux6hrIzTpaTmRKxsI6mSGRyNnWxAGM508vbEy6O42DZVPdTuS9zccgOlv062OkBc3bgHnvsMT74wQ9y8skn89RTT3HdddfR1dXFD3/4Q77zne8symB8liZznZk8uW0Yx3WJhXQUWWI066ldFwwby3YPSXL+XH8Intw2jBYKIYUjLGtL0F2UyRYsxnLGrM7bfGdqluOytiNB/2iBomETCqh0NiWwnAPrlXmww/JHy5gOF76tnMihcO73dwyY2w/5k9uGcRyXZLqEEKIs6yEYS5eojwfIFswJNmy8HegezLKyJQbl/N++ZJ7+kTxCeLl0A6NFNFVGkiBTsJAlKJlexamqeA7WaMagqTZIS10Yy3EZHC0gkBBCoJTXNzUFSqbz/7P351GSXeWZL/w7c8wROU+VmTVINUgqDSUECJDFPBojBmNfoO223Vyu3b5gvHrgW7422HS3TXfb17TdGLfB2KwGu5sLCEuyQBgQFhYgWSWhoSbVoMqsnDMyImM4EWf+/tgRJyNyHquySvGsBaqKijhnnxNnP/Hud7/v85AtWAx3ZiASwzWiSB7MFaxQCkSWFpwZfD8gUCQ0Teii9bXHGM+WmzJWjZmuVFxHVeWwdOShx0fZ35+mMx2tcZhHVFeIRpQVg/D6vXno8VE6UhEkSWpqZpidt3DcgFLFpTem05mOkC1YfO7+E9x2fdeKwffodJG/uO+EEHiP6uG9VFWFquNz8/4OFEXie8fHsFxv3c/cTvHXTpcHrDuA+0//6T/xx3/8x9x5553ccccdANxyyy08/fTT2zKQFnYnNkK+2UKVRFTD8XwMXRGOCqaNWXXZ0xXfNdkYt1AQqe3eAV54878kJkkchnA1ulbwttb9WBzgaTWP1cNDbeFxylWHdGRzRe27sd19N47pSqHFlc24HMH9Suf43vExckWLgimK8cdny1ycLPKKm3qWyFhcnCqQzVvIskQQiAx5YLsUKzYVWyy8bj3QsUSyqD1pMD5b5syleQY6XaZyIlgLENpvp0fnScU0NFVG1xSqtmgC8HwPSZIwq35o7zeeNfF94Vwg1wJIWZbwa5prjhdgBA6KD9FYhOde9k6ePj9H0ayGRvcBQhNYQkiDBAFoikw6pjGdM7Edj6iuoshQLNucupgnX7ZQJYnxmTK5ooXteuiqTHa+SjKiYuoK6YRBOiHm+OKsUiOW48jnXpjjhuEF/vMD6G2LUnU8+jvinB2bR1VEl+5qvzGD3Uk6M1Ec16dQtpAlib6OGBFdpWK7YVbxqbNZDg6m1/3M7RR/7XR5wLoDuLGxMe68805gYetH0zQ8z1vtYy1c5dgI+XakIriuz9isKMQ1NIV4RCOdkLfdNmWz9QrW6Agjf/Cf2PfStzKhH9pwanvx/XBdn8msyWe+/izphI6EsMXp74zR0yYU3OeKVbF1mlle/Xuj17EbteN245iuFFpc2YzLEdyvdI7HT00DwiEhZojF5ehMka/9o8lN+9ubFmG5ogUSpOIauaINvo/l+UiIzFdEk/mnZyfD7kbfCxidLlGxXFzXZ75kM5uvIMsSiizj+QHpuEbFFqK88ahG1FDJl+xQAw6CsKjCD0DyfVQFPM/H8oTIriQLGRIh9RHwzhceoiprfCt4M5oqUzRtJElCloImXbf6H1VVwtAVdE1BUxWihorr+cg1zTfLdjn1whw+InD1/QBdlQkCiXzRYjpXITlfQVUUIZWkyiRjGj9199JMZEcqIkTPF/1mJKIaI9MljiYWrMrMqrAjE40SNceEiLJmsDXUnSCQJKQg4NRIDsf1cbwFGRMhqhxsSCB9J/lrJ5vU1u3EcODAAR555JGm1x599FEOHjy47YNqYfdgI04Btx/qQlYkBjrjqIpEsWID8HOv33zwtliFe3S6uCXldq23j/QrX8V1rzyGabmUq44Qpqw6a3a5QvP9mC9ZnBsvYFmiwNisuozNlrFdj/FZk0JZ6Ch1ZaJhUXCj+jewqeuou1VsdOw7id04piuFFlc2Y70uLIux3Nzf6DnMqiv8O1Whr6apMp4nhGgXK+5bjl/brpRpS+hUHY8gEMK5HWkDQ1eZzlX47hOXGJkucWmmhOOKQKhgOrieh1uT43B9n6ghU666eJ4vtlVnyswXrYXIqobFdvWev/CaBER1DVmSsB2fihPwZPI6nkldR7nqMDtv4dbSbYos/FMbS2slJHRFIaIrVG2PVFznpn3t3Ha9mJeW4+P7PrYnBuJ6IggUQh0Brh9gaDJz83ZtrEF43Pp3tJjDTlzM4TjNi5XBrjilihPyQyahU7Vd2hKGEGcPwHF9+tqF1Mlqwdbth7rCY/W2RalYLhXLpa89FvLO/r7Uhp65q5W/pKDRqG8VPPXUU3zoQx/i1a9+NQ8++CD33HMP3/3ud/nMZz7DzTffvNPjXDey2VLo7XY1o6sryczM9ljJbAX3PnI+9Aes12+1JQx62mPcfqhrSfYIltabHLuxf1PX0piKb8xc6YqMqspLDOzjEW2J40J9dVgdeYFo/wDHbuxfcauzURhzues6danAD54aC+tGJuZMHNcnVxS1Jz1tUcZny+iqQjqho6kyh4fawq3ZX3nbDUvu7eJV30rXsdy92UrH1E48X1eiC1WWJTo6Ntd1tlPYCldei/xVn8ee55Mv2hQrDoos8fZXDHPHkeWbOlaa+yuVYaz0/vNj88QMDT8IKFYcXNenYrtoisxLj/Qwni1TsUSQVyg77O1Nhlw3NVdBVSTiUZ22pJAQcRy/FpypwurPCyiZDn7gh00DRq3WTNcUZAJyJZEhS0Q0HM/DrHqhWO7ib1qW6s0KIvOnqgo37m3j3IUptFKBGaMNRRbBmusH+LUyWmGxJfIxpuXiejU9Nxl0TcHQFWzbww8CdF3l+oEUZ0bnkSRRU2c7PoYuU7XFAXVNbOHqqoIiQ9XxecNLBsNxNvLWYg575rzoOj26v6Pp/a7rk04YTTIrY7MmTz4/G3Lq4i3alXiw5PihF6pac7lwPL+Jrzfy/NSfoauNv9YdwAFMTU3xd3/3d4yPj9PX18fP/MzP7LquqmuRAK8kHj85yVcePhe2tldsj6rt8prb+rk0a65rgmz2WlYKcM6M5tnflwqbAmRJFPoWKw5dmSixiMZQd4KBzhhPncuSxOaWBz5LdvAIJ29+w5qTePGPjef5pBM6h/d2UChWOT2aBwRZRw2F6VyVzrRBxFCZzVewXZ++jhhV2+e26ztXJKPPP3CC9qTR1I26UrC3GjZDPLvl+doqdmMAB5vnymuVvx4/Ocl9j17E830SUY32ZARZkVaci5tZ3Cw3D777xCV+cjYrMiuIejLXC1BlibZkhFhERVNEwb7j+PR0RAWHGCrfOz5GQEBvRzxsIrAdD88X8350uoSqyJgVBx/BB1BzNah1kEZ0Fdf3uXFvO8WKw6WZMpbt4vlCXNddZlddkuqBoEREV0nGdF7y3DcZLE/w58PvxFVEPV0QBLi1bdaYoYoaMiSqtsj6aaqM4wYEiKxaJqGRSUaYypp4NbssXRW1YxIBsiyHTgyGJuMHwhqsarlEdJVX3dwXjrHOU8ASDpsvWZy4mOPmAx3rCp42+mws93yt93nYDTXYjdgqf21IRqSnp4cPfvCDmz5ZC1cfFvsDxiIq/TVto8HuxI4WJq9U1+K4fqirJgGz81Uc1xNEXHUpVxwMVeb4mRn6O2NE2tOMv/IdVLoGiMnqqmOsd6KNz5q1TieVybkK5azJgT1tpBMGh4fauDhVFBpNEZWuTAS5RvBRQ8XxnNBOq56KX67raDvqLlryHbsTLa5sxtisyaGhzJKAbDuLyperNepui1Aw7TDjRRAgI2oTTcslFdfCbvDhvgQxQyMeEVpvHekIxYqNUusEcDyfqu1xeKiN8Ww5NEcPJGGPVQ+7FRlcH9wAypZLe0JnoGvhR3qqtvC03eUD9Xog6LgB8aiEpsr8Y+cx2uI5bFlDkcRWp6qI6zA0mUxCiA37gY8f+FDrzIwaEvmSTRCA4wTEIzqdGZ9cwabquPhBQERXRM1dLXgT5/ZRFRm1xmt9HdGmMU7lzNpvgsv4bJnhnmQodKyqMjfubV+4j2vIOT11Lkt/ZyxcMFesMm9/xfCW+evFIJC+ZgC3HmP53//939+WwbSw+7CcP2AQBLwwWVpikbLdhckrBTiGpmDVaiyKFUd0jQGSLBGLiAAvV7LoyI8hWQa0H6E4fFiMMQjIFqorrs6yhSr5oi1MmWuFtXXivzRdYn9fMqwjGZkuEotoIuCbEdupAHt7EuRKdliQuxJ5bUeLeUu+Y/egxZUrY6MB2XYtbn743HRtHovtRkmWSEVVTMuDIKBq+0QNYf+UiuvMFa2wNKRcdalaYsfB9300VaGnLcZrjg3wtX88h1lRiGsylu0JZ4OguQNUqXVVmpZHoWyTiuv0d8SZmxem86LLdPmxa4pEWvU4MHWCuQO3UTaS5JQ4BKJGDgLqKkQ9bUI/rjMTYbgnxXeeuIRU24qFhYCwVHWZmjOJGQq97VFmC1UGuxOMz5TIWa7QnUN0rroeuLWAdW9vEkWRQ9/WqZzJ+fECB/rT9LZFOXNpnlMjOQ4NZprkR9Yr51Tnr/pvTLnqMDZrcse6v+kXL9YM4L7+9a+zb98+Xvva17a8/K5ibKTeq3HirUSky1mkLCbY+rHLtkdcVzacwl4pwMkkddIxnclcBcvxiWgyvqpQT+Jrikyl6vDakUdxkJm56XBY2SscIuQVs1aqLDGeLYvOM00hGdWQZQlJEgTWeK11Q+lGR4b69u171nGt29Fi3pLv2D1oceXK2GhAttzcn8lX8JIBn3/gxKo1t/X5U9eljEe0UI7D8wP8QJiyd2aiS+q0GrkhHdOYViWyBYtERKEnEyUZN/j6IxcYnS7jeR6KrQAi816xPWE1VdNgUxWJeEQlX3L451PT9HXGaE9GGOhK4FMkXxSNAXXeCrtGZYmooXLz3CluH3uMr6f7yCQ7yBWs2pZoMybnKkR04RV9+6EuvlW7b7YrBG4b3+/5PnNFt+YtHeXddx/gS98+Q4Doh9VVGUNTKFZs0nGDm/a1Y1ous/kqruszZ1vkSzYH+tP01BoODg1KjEyXOD9R5LbrOzfEYS3+2hrWDOD+9E//lG984xt84xvf4HWvex333HMPt9122+UYWwvbhJW22W490MFT57Krbr+tFEQ1WqQslz1qPGdne5zZufKmtvZ0RebMaB6QONCfCgOectUJddUaGwlACOdGIxon7nwXE7kqQ5a7pAliuazVd5+4FG43SBL4XsDsfBVDk5FlGU0RdSeLr7VQtpkr2EBAX625Y3GjxMh0CbPqEDWUMPCrp/h3s9J3C+tHiytXxkazzYsXN6osISGhKBJBIPHM+SyPPD2Bpkjs7UuGkj2NHFPXpZQkNzQ8lxFZt0RUJRXTw6zSYm7IFy3OjM4TEGBoYovx4nSJvnafuaKFpkiUK76Q7vCFU4KqCFFeVZaxHA/L9qnaIkizHI9c0aJiebz9FcOMzSb57vFL2K6HhKjLq5ejiyAz4LH0DZyWu5h047R7ARFNxnG9sEauDs/z8HyZ0ekSf/vt0yiyhOV4KLJEo1a4RC3r54vGi7tv6eN7x8e4NFOGQGyZaqpMseJgaAquFyBJEp4XMDNf4cJEgXTCwHZcetsWtlTTCYObatnLtZqvFqPFX1vDupsY5ufneeCBB/jGN75BPp/nHe94Bx/4wAdIpVI7PcYN4VotAt4KVioIHp0uNdWx1V9fXCi8mexd4znjcYNy2Vp3h2X9nCt1EcFCh5HjeJy5NI/temiyQn9lkt78JeZuezWyKnPrgY4lgp0PPT66bPPAU2dnOTiYEeR9aZ7AD1AUYXnTlY7S15WgWLaaVv//86HTjM+aSBKosoyswJ6uBO+++0A4Tt8LuDRTEucLoL8rhqLI21KnVr9PvhcwV6xSqjgosrxqhx+0mhh2ElvhymuZv7ZSVF7nE88LODs2j6bKzBWq+D6kEzoH+lOkE0YTx9Q76Mdmy/hBEG6HKrLMB954fajntpgbFAmePCsWp6oiBDPmy3YY1MQida9SGz8IRPOAJGGoCqWKg+v7BH5zh6ksQURXiEVU2lMRXndsgK9+/zzzpo3rCCHfANB9m9fN/jP/2HGMshJBU6gJ+YrALmoodKajjE6XCGrHDQLIJA0cx8dyPA4NpjlzKU8QSLieH27tJiIqSBK6KpGIabQlIozNlMiXLdG1KoGuSMiKjIxEZybCvr4UJy7OUTJFqUpnOsJMvkpEV7hpX/u6u0ZXQiPP171SSxWHG/e285pjA1tqYrgacNmaGNLpNO973/v4mZ/5GT7zmc/wp3/6pxw7doyXv/zlmz55C5cHK6Wp8yV7XXVsm8kSbTU1vlpt1+2HujBUhTOjInCDAE0R9R7DuRfYMz+CkXw9tx3dw2B3ckktxUqrPpDCcyaimuhyrbogwbvu3r9EDuWvHzzJeNZElsXK2w+ErtJsvhKaTccMldHpUk1EUxjZ50s2g92JbalTqxtwiy6ugGRUJ5PUeepclt6OeKsO7gqgxZXLYyvZ5jqfnB7Nh/Wpvi8yRJoqMzFnkk4YTRwz0Bnj+JkZ0ZXpBygStKci4eJmOV25jlSEZ85nCfwAXVNAWtBGc71aoBUEZAsWmiqhyQq97TEqtsvBwQwnLswxO1/FDfXSBOqft2yP0ekif/3NM0Kw1/VFB2vtfV1WnsPFi5xODDOaHECWZYJAdKwCqIocdpXCQr0dgCQFeEHA3r4Uhq5wfqJIvmghyWIno78rzv6BDI7jMjpdYma+QsX2QhHgIADbDdDwkWrnGc8K/1NFWZBGySR08iWbkekSN8X1LdlD1TOtjV6pNwy3oShSqxlrHVhXAOf7Po888gj33nsvjz/+OHfffTd/9Vd/xUtf+tKdHl8L24Ct1LGthLW6H7eaGl8pALw4VWBmvkLMUNnfl6xJekgc3JNC01Wm+t/ArTd3cWy4Z8Vjr7Sdc6A/Fd6Pum1M/RqWI5HzEwUIAjRVCWUDCCRKphv+iLQnDaGfp4uppipS2KG6XXUeG+3wa2Hn0OLKnUGdTxrnkqzIQitNkZgv2ZwaydUM2DUePzm5pLuxUXtuceb6/Pg8x8/McOeN3SKLrUh4vo8kSVi2iypLtWybIiy2CIStla6GLgAxQ8ULxJzPFiw8v7lezfcDihVX+JTKAZ4v9MvwA6K6MIifVnv47N53YqkRNFksSl3Px3FFkDdfdiiUnaZ7I0k1eRMvIBERfNbfmSDwA05ZHhVbeFGPTBaYyJr0tkeJR3UmZsuoioTjSjVJEzHeIIB4RMVxA3xf6LdJskQiKu57PCI6d11PSIl0pCIc3JNu8j7dSHZ1sDspdPKiGq7nMzFn0t8RJ2asrhjQwjoCuE996lP8/d//PQcPHuSee+7hD/7gDzAMY62PtbCLsNk6ttWwVvdj4zmjtVqTjazSFgeA8yWLkekS2XnR3j/ck2RiziRqqHTOj3PoO99g9s3vh4jO8ZEyQ8MrH3ul5gFgg12hEqoiiwLphu1YnyAMVMtVR0iLuH5NoFNsg2xnnUerEHh3oMWVO4c6n6iKLOrGJImIphAEAfMlm4rtoalCfLYtYXDfoxfp7xTd84u7G3uni3zp22eYK1hYtkcyppKMisXsD5+bZrgnwXjWJF+0wsy55wU4rk9Ek8Kt3CAAOSpeH+5JhoviUsUWHZ2K8DCtZ8tqTeri7yKVhxdAxLf52ZHv88K+23lC7cZ0I2Lbs67zFrCkcaERAWBWXVRFpr89ymy+SlG3OTNWEJIiiM5Szweldr8GOhOi1hcp9H5VVbFdbGgK+/vTNUFjD1WViRkKkVrg7HiiMeTo/g7uuWv/pqWMGuuDT4/kyMR1kjEdx/U5OzbPgf5Ui8PWwJoB3Be+8AWGhoYol8t8+ctf5stf/vKS93zpS1/akcG1sD1YrdtxcR3IejuI1goa6uf83vExHntuEtf12N+3/nrJxgCwXucGotsrCEQdjON6pOMGmhSA5yEFPrHIyoHLempwNtIVeqA/xbPns1Rsr1YrI+xukjGtSQ28LWGEtjsE0JWJbHjLYbWxtwqBdwdaXLlzaOST516YIxHVODSYxnJ8TryQw9CEhFB/R5xUXGdkWnR5NsofNWbwi6YtvGklKFZcVFUmaiiUKiIDP9ybZLArwVyxythMGdfz0VWJUtUN69UChDTHcE8SRZHCRfFXv38+NKCXlrFbkFiQDwmC2p89F892MOIKruthuQFOTSeu/vG67Ejj4VRZ/F2WJW7al6EtFWUmX2FqrhLKGsUiKrqm4NU8TpVaM1Y8olEwbRQZbEdk3xQJEjENWZF4/xuE9dtXv3+O6VwFx/EJCKjaHj1tsaYu4LWkjBbzV11kPWaolCs2qiJTqDhomhwGiiPTpaYu4RaWYs0A7sWqW3StYaX6k5Ve36y8yOKgwXI9btrfAb6PabnrrmtoDDqfrIn2DvckGc+Wa9ksCcc0cSIaE6kBZu/4OQ7HkphVB1WWuPeR80ussNazStxInc5rjg2QK1riB6FsEwQBqZjOu+7eFx6jfg2W64ddqL3t8Q1tMay1wt0OPbkWto4WV24fVuKfX3jz4aZ/62mPYbk+g13xpqakRFRjrljl1EiOiuURNUTtVsXy6ExHScaE8K2uKnhBQKkimhvq4ryN8zaT9KjaDpbtgSshE6DpMomohml5XJotM9CdDBd7PzwxjaGbzBWE1ZYs+TRag4aCv55DIClYis7fDr0FXVeJSkKrLh1ThI9qsPCBBotT0byACOg6MxEyCYM93bUFcgZyRZvB7jiXpktYjodZFb6giizR1xHD9QPedfc+vvr986L7VpWRZFFPeHBPpqmB4N13H+C7T1wSJSNIHB5qa/r3tRbzy/HXfY9eZKAzTjyiUbU92hI62YJFoSw6YAmgVHV2vRfplcaGrLSuBlzLXVxweexB1uNDuJ731DvHujsSlMvCdmUz3UqNllN1A/kec4afevY+vn/gdYxnBjm4J42mKczkK0i1DqrV/FPrW7KuF3Db9Z2btp/ayvex3s+ux1Zoo+NodXHtTlzr/AXLP6uwoOemKTJzxWpoabWWFdNy8+PC+Dxnx+dJxXRsx8NyRDaqKxPh5gOdFE2HJ86IRiNVBscLyCSMcNu1Pq/ufeQ8gSRRLFk8+uwkQeAjyzIRXaGnPRZ6o/7uL78svLa/fOAk2fkqsizRljSwHY/JuUpzV2rg8b6xh5jTUvx9zyvD+jJVkUnGVGwnoFwR9l+O6y9xbah3nwaIoCweUYgaGoYuIwUwU7BIRFSm81UkarJItUNoqkR/R5xfftuRpvu+Gf767hOXePL5WfxAlI3s7U0u2w28+Pt5/NQ0iajKkeF2To3kcFxf2BeWbNIJI/RG/cW3HFn23C3+Elg1A/eDH/yAV73qVWse5J/+6Z945StfuelBtLA+jE4X+er3z1E0RWHp+GyZFyYLvPvuA9saxK0nJb4eEdrN1GYtR+6N2b50wuBAf4qLF6u8kNzDXKwdXZOZN22Ge1J4yZr0x6KxnxnNc+t1nQBhECh03fwt2U9ttrNuI3Uj67mPLwbbmN2MFytXbnThsOB7GZCMajiux9e+XyQgoCsTpT1p8OyFOSqWS1vCQIpIuK7PZNbkc/efXLLYGp0uht6biagQ0VZVmVzJZqAjzlS+ShAERHQVVZWYK9hM5yr0tMcY7IpzdqyA6Yv61HRcQ1HkpqxPtlBlT28KqWY5JUkKiizM5gGCBZOukJ9zxSoVy8UHiqYTviNqKDiuL+riUDgb38OclkKVoWq5oU9rOq6ztzeG5/s890IurPurQ5YJZUokRHNEgETBtKEsXk3HtbBholZuB4gt0sAXtmB1vtmo9EfjtU7nKhiaTK5ocWm6xEy+wv6+FMmEHu4ALMdfyahGsSI08vo74pwdEyUyve0xBnsSmJbLa2/fs+FxvdiwagD34Q9/mOPHj695kI9+9KM89thj2zaoFpZidLrI5x84wUyuiq4rpGMiMJnOVfjuE5dWXKlsBusNvNYKGuqBV6K2wGjMet37yPklZL+W4DBAW2UOy4tTClTG7rqHo22xcIVe13FKxpaOHaSww3RizgxtZmIRbU37qa06SiyHjVhgtWrcdj9ejFy52iKkqyu55L3fOz7GYyenUWSJTFJ0Mo7Pmvi+j6Gr7O0VW4Cu5xPVVSbmTIAVF1uwUBZxZCjD6EyZExdz3Li3nY60gVl16UxHGiylAnzfYmy2DAhtt7akjml56KrEfNlZsgjtSEUwKw4S0JY0mMqZ+L4IpqZzFRQZju4XC8MnTs8wm68IBwSaxXbrSCoesl0mqyT5UdtR4csKwuZLEppy5arLhYkC1dq+q99guyVs/UQWTVNknJrOmyJLoo4tCNBUGUNXCRBuDLLvLwRxtXSc5wcrdnmuJyh/4vQMxZo2XLnqE6k1atmux8WpEh+45fpVa3QzSdE0Uq6KruFMXOfiVEm43UjwulU04K4kLscO2EawagBnmiavfvWrVz1AEATYNbXpFnYGdaKcK1jommidz5Vs2pMGUV2t1SZsH7YrYLj9UBdf/f45RqfLzJcsShUHQ5c5uq9j2YxTPajxvIDTo3kqltBvGp8pk0nqlKdnue3H/xNv4Cj+La8JC5Qbg5+Vxl6XCAGoVF0836dYcYkZCqdGcvS1x5bNDK7XUWKjE3sj2clWjdvux4uRK1dbhBy7ceHZrM+hyayJJAXIsky+5NCWFDVXs3lLGMbXEDVU7Jo13WqLLSDki8lcBdfzSUTFvw/3pHj81BTJqB4e1/UC2pMRohGFXMnCD4SLwOGhWLjtt9iDc6AzxoOPjWLZHkHgi8xbAJom4/kiIjoynAHEnC6ZLp4XLPE4DRDNWD8z8Y+0Veb43PA7cCUFVRXZvABwPAhsD8v2kGQRcCWjGn4g5Gm82kH9ml6b63n4NcmP+r9LQGc6gh8EpBMGVdvDdiQcT6jNBQF4XkCuYJEvVqnaXvgdPXF6hotTBbLzFgOdcbrboivuDGQLwlqrYrkosoQsy+g1nctETAu7fZc7pmm5KIpcc6QwuThVIF+2uWFvW/jvu1HHcrPdtjuJVQO4L37xi+s6iNww+VrYftSJsi5VIcviv8WKQyqm0VzeunVsZ8Ag1cZmVsVqTVOEIuVyGae6Zc658QKaKgLVfNEiCGBvbxI13cs587WMZYboaYs1nace/LzxjsFlx15fsT9xegbb8ymZDumETiKi4bg+p0fzoTVXI+r33nV9njuXZSZnYjke58cKvOyGng01SDRiI0HydnimtrCzeDFy5XoXIeEc8nwMTalllCRKFZeOlEEAqOrCfenviHNqJCf8RasuiiK2Fod7kkvO0cgXUV3Fdj2ee2GO99y9n+OyMFaPGsJBwXF9ujIRetvjZAtVDg1mmhofFo99dLrIU+eyDPUmmZgpMTFroiky6YSBpiphY0Q96OtIRfAJcFeoYfQCeLT7NqLleTRdRZNkvFp2rA5ZEsGZ7Iu6Nc8Pav8TWba6fAlQy7yB53o4LPw2OK5HIqbT3xFndr6CW/UJgqDJVst2fY6fydKeMvjrB0+SL9l0ZiJUqiKgG5stEzVUUnE9/A4XZybHZ8s4ji8Ej2vHtF0fyXT48ckpXpgs0JWJMtSdxNAUxmbLWK7PUHci5K87EHWGnWlnXbsRVxIb2TW5XFg1gGuJT+4O1ImyLWkwO18Vq9iaeGPVlpcNPLaC7QoYnjg9Q2cmQndHgh885RCtiV4up5oOCyrodaX1+bKNosgMmFMULznsObyfwqFj5KdLJFcQIF5t7KHyem1FO1+2KZZtZEVGlSWW6+fJFqooEpyfKCLLwtBeQqLo2EzOlfnmYxUMVdnwxN6MN+RuIrMWmvFi5Mr1LkLq/BU1VHw/oGA6yBK4bkDF8ogbGsmYFvqSKopET1uMTELn/EQBSZK4biAVBhON52jkC8v2yJdtPM/nO8fHuPPGbn743DSliksyqtGViYQ1bnU/5XhEY75kMTFnhiLAo9NFBruT4Q92d0eCTEynYDpULRez6tLbIQKkZEwLOez2Q1386LlJShW36fp132afOcHzyWHczl4u6m0otXo3ryF6E7ubwhVBlkBRZBw3wPN9IrqMZfvIioQciNq7IBCcVXUDJET2TZEl5ooW6bjGxakiQSB0TBqDtzqEfpzD2bF5PD+gLWlQsb0w4B3PlknF9ZCnG3cZ1Jr7TCCB43rCqsz20FWZeFTFtD2mc5WwjrG3PU4ypi/bwHa16FjuxnGu20rrxY4rufddJ8q9vUmqtkfV8bAcD1VV6GmL8ZpjA9t+zu0IGBof+KihUq4IMd96R1hbwqCnfSGTdvuhLn743CSJiFbbbvJQpIDXjf4j5Zkkc4f3i67XiBZuhy4X/Cw39tHpIl/7/nkKpk2+VMX1AnADDE1GUUCRJPK1TtlGNAaVpYoQywSQFSW0xDozOs+t1zXrFa01sVtZtRYuN7abw9a7CKnzV71YPRXTKFUc/NqC6V1371uqR3n3wqLrm4+N1EzsgyXnqPNF1XLJFiyQoD0hJEIuzZrhNt1y1/zNx0YomjbjMyZIhCLA9ex5I3+NzwoRcdcTwdXEbJl8yWJfbyrksMHuJHff2sdXHr7QdP0vzz3Hy3LP8cVkFwf39PHk87PYrtCOtBtiPWFSH6DUNOQyCYPDQ5nw/Z4foNbq10TctxD8BbXPtycNXN/nhakyve0xju5r4+nzcxTKdk16RKo1PYiAUVFkiqZDe8pgPFsOGy00RaZS41jTctEUuWmXQWQ2VYa741yYLOF5AVFdJRXXRJeuFoR1jHW/1JU4sSMVYWrOrAkHi+Mu/m3YDdiNtcitAG4duNJ733WijBkqhwbT6zb8vdKoT8wLkyVm56uCRGSQkbg4WeS8V2CgS0zSOrHeMNzGyHSJqu2j6woRTeYHN76NIBJlL2LCDHUnwlX0eoOf7x0fYyonnBvEKle8rqqyqLuoulQsb8nn6kFlPCK2ZwgEebbVNKVEg0SwKUuyVlathcuFneCw9S5CGvnrQH+K5y/lsV2fVEznuoF0WOu0kk7l4nPUbZuyhSpRQ8F2PYqmg6bKpOIaiiwTVWVihsrYrLlsl2X9uF/69hm8wCcZ1UMR4LoNXf0He65U4KmzWbHoQ9SglSouxYrLTK5Kd1uUQtnmyHCGS7MmXWmDuYJF7e38qPMWLqX2EOnuZmS6REc6QntC58JUCdv1w+PqmoIiS0iSaOSIRcQWZkc6wmTWFN2nsKS+TpZE5s3QFZJxnaBm+VUXwTVqx3W9gIguYTlCf0SRxWertsdMvoIiy1y/J81UTogARw0ldNDRFXnpLkNG/PfnXneQz91/kiDwiUU0+jviNQ9Vr4lTV+LEgc4Yjz47QURXieoKZtVlrlDl2MHOdT2Hlwu7sRa5FcCtA1d677uJxGyPo/s7Vl0975ZOmfrETMZ0At9HlsFxAhQlQJZkVEUiW7CYmjPDH5PX3r6Hbz42QndhAqM8xT+q+5lSUxzqyzTZcW00+Dk3XiCiC1scRZbxfY8gALPiMJ2rYDsejqeH2yd1DHYnuXFvOxeniqLKRJJoT+iink8VNTb7+1KrZgRbaOFKY6c4bD3zsJG/Lk4VkGWZm/d3hAXrG2kKWhyIOq7H+fECkiyqbefmLSRZ4uBAal1Z8K5MdMVauDfeMcjXvn+e8xOFMMiqI2j4ryzBqZEcJy/mGO5NcMPeds6dn+bGkcf4ftvN+IpOqWuQfe0xzk8UGOyKUayKBV8iqlIs21Rtj972WOh0kIrp9HXEmCtaDHUnGM8K31LLWVrm4QfgewG+5TKZNdFVifZUNPz3dELH8/2aP6vI1InLlbAcHz/wqdqgyD6j0yW6M1HyZZt4VGx5/tQt/Tz0+CgpozlcqN+nwe4kt13fuSQ7Va9jXC5z2oixWZP9/SnyJVssiiMq/Z2xJQ0lVxq7cddk0wHc6OgosiwzMLD923e7DasZqy9W/N+pL3O9ActmVto7FfDVJ6Zp+UzlKsiSRMQQNi6GrqJIErbrkStZDHYneOL0DPfctZ83v3SIic99E316jCOvP0qg6jiej+QF6Iq8KcNksXkgtj8NXUGWoGK71JrKyCR0DF1d9l695tgA33xshMHeJOdG86Ko2AtCS6zGBondMrFb2D3YDVx5pTmszl/rKVhfjcOeOD2D54lAo+6wEFFl5opCwFtTJRKGxlS+iqLKTVZay6EjFWFyrhwGD3KtpktRFJ44PYMq01SnthiSBJbrk47rzOQq5Is20Q6VAWuKo3OnuJAcZETvRZIkXpgqUq46PH1uDkWRURVRu+f7omPUtJyGACbetLtyaiRPvmShKqKLdLkR+b7I3Lke9KgLAWlfe4xC2aYzbWA5PpYlPFjFtfpEdQUfkcUrmsK7+YNvvyH8Xh56fJSZfAXH9ZruZ2NGbXF2qrGOsW54vxInZgtVetpiTccOgmDX1cDB7ts1WXcA95u/+Zt84AMf4NixY3z1q1/ld3/3d5Flmd/6rd/iZ3/2Z3dyjFccy+19T+VMsvMWnWlnUwa+O0WYG11p7+T2cH1iJhIRbMdlfLaMBJSrHp4nulINTaFiuUt8VPf8u48wOjJNcqwaFs2OzQoNuc2IGO/vS3F6NA+SSiKikrU9CISCuVjJ+6Gn4eJ7VV95nbpUoFCyV7TE2k0Tu4Urh93IldvFYVvlr/UUgi/HYSXT4UvfPsNMvoLrBSRjGvGISqniMJ0TjUYRXQUJLNtDkSXGZ03edufeVcfTuH0nETCdtwgI2NeT4JnzWUanS6sGcATifFpKRpLE9ZmWi58a4m9ufC/TjobrCP06ANv2cH1wfR/b8TF0sahs12RcX3QpJ6MaptX8XRwZbuPHJ6aIRBQsx8Oy/TCIqzstBICmygx2JciX7bAppF4m0p6MUHF8coUKQRBwcbKEocsYhkoiqhLRhXxLveGh8XfBdX3OjQux3Z4G7c3GuuMl2am717eI3Y21ZVcL1h3A/fCHP+QP/uAPAPirv/orvvCFL5BKpfjX//pfX/MB3HJ73+OzZujlBuvbkrgctXQb7ZTZ7NbKeoi8Uci3vyPO2HSZiu1CLYXv+wGO6yFLElM5E+XSBX70W/8fU697L/19GZ46lwvv1ROnp5krWiSiGr4fULFcLk0H/LeZp3nl0b41f0hee/seciVLKKMHIuOWLVjEImJbdbgnSapWP7LcvRrsTnLsxv5rwr6lhZ3FbuTK7eCw7eCv9fxYL+awQtnm0kwpFLL1/UDUvCmy0DGTRODTnjIoVhxsx8N2ffo6EyuO6/GTk3zn+BiXZsooEmiKTLnqYugKqiJxKVshGdOWbJ0uhqKILFauWEX1bF537nv8sPNmpowuArSwXq1UcdFVCbehGzQAqrZ4YWS6TH9nnERUDQWO+ztj4XfxmmMDnHwhh2m7YvtTApmFejhVlWlPGmiqzL7+FKMzZeIRbUkw1WhB9fG//DGqLKNpCxIuYiPaX/K7UG8oyJUsNFVZNqO22ezUbqwtu1qw7gDOcRx0XWdqaop8Ps/tt98OwOzs7I4NbrdgudVFZyZKd1u06X1r1VxsVx3KasHTRlczm7W7Wg+R1ydmybRJxjRiEYWq46FKgnXqAp3lqsP5cYeXyzYxq0i1bHLfo/P0d8bC6yhVXCSgUCtWrq+K86XmGrqV7uNgd5J3332g6b4VynaT7dZa96qFFtaDrXDlQ4+PcHGyuO3Z+e3gsO3gr9sPdYm6MrOA43poqkIqpnPwpnS4lTuTr+C6fhg0jGfLSJJEMirqqexaFFQ0HWzXQ0Loyhm6gqELI/RixWaoe3mPycdPTvKVh88Jey1Zwg8C8mWbmKHSkYoI7TTXZ3Z+7S08zwMXn5l5i5RXJemWiNoVvAX9YISQB0v8TBvhB+B5PpKkotW2P/NFG00VGmuD3Unedfe+mg2Z0LH0gwDL8YnoCu0pA0WWw7rcoe4E99y1P/Qr/dz9JwCJGw90cOeRbga7k027EmpNa69iuxwazCz7u9DdFkVVZX7lbTeseV82gt1YW3a1YN0B3JEjR/jzP/9zxsbGQsXxqakpEolrx0h6NSxeXSxn0LvWj/926MisFTxtdDWzmfT1eom8cetxZGKeiKFyS3ecUtVjvmTjeKJd3a5UObivC7m9j/M33IIhK3iTU+SLdlNdhO+L2givXokbCFHLkxdzdKQjfO/4GL/w5sMrjnvxd1i/l+u9V9uN1Qq1d0MTSgubw1a4smK525qdX+1Z2iiHbZcOVr2CKxT5thwefXaKzkwkbEyob9d1t0UpmjaKJNNXC+hkWaJquVQdr9aYJOF6IhOmKhIVy0ORmz1NG/Gd42NEdJVYRMx5zw+QpIBy1SUV96nYHq7nr7p1qiki8PN8UH0XV1IoqzG+MPh2fKlZrLku2bF6Lg+m8hU0TRaBpSJRrDgcHl7Q+LzjSG84fsvxcRyfnoxOoeIym68iyxL7epMhhzX6lUZ1lYCAZ87OMjFd4l1372/alahaXrjV+trb9zTp5NWx+NlY7dnaKIftttqyqwXrlgX/j//xP3LmzBksy+I3fuM3AHjyySd5+9vfvlNj29W4/VBX6OUWBEHYIbkSaUDNV89qFnncaManMXiSJJE9qnvawULQFI9ozBUt4hFt1R+BzVxHtlCtyWcIzJcsRqdK/OjEFPc+cp7HT05y7yPn+fwDJ3ji9Ax3Hu3jV952A7dd30VbKsrhoTZedkMPrzrax8tSJr9w+isMu8LrFFmsOBNRjWLFCc/RljRw/aC29SqEeF1PaCaBCOyee2FuQax3HWi8V6MzZUanS5g1CYGNHAcEYdWv+d5Hzq/5+XrwWK421x89fnJy2dc3Op4Wrhy2wpV1mZvF83ozWOkZqz9LG53728VfXZkoR/d3cNvBLo7u78D1AgqmHXJab3uc/f0pciWLuaJFMqbT3yXsrvraY8iSRCyiMdAZ4/qBNIosM9QdR1PkUET37a8YXpHz8iWbqF7nGSEuHPg+tuMxkS0L2yyW2mE1oj1tCDFy2ef/GP82r8s/KfxPpeV/UmVp2Zeb4PuQLVhULbcWhEpN38XodJF/enYSx/WJGyoRXWF2voqM8FD1/YCz4wXsmj1W3a80aqhomoyuKcQjGgXTDhfb7777AEf3d9CRiQr3G0l8bqAztuTZmM1XmS9ZfP6BE/z1gyf52vfPL/tsrfXctbB9WHcGbmhoiD/8wz9seu3Nb34zb37zm7d9UFcDNpP23Y69/vWsgjeymtnMdTRm7eZLFufGhRdrOq4xNWfy6LMT7O9P0dMWo1x1+PrDZ3n1LX3LXr+rJah0DVDQEjTsOoiCW6scFuL2tEeZnDNrekY+vr+gfaSqMpIkkYhqG96Orr93Zr5CR0q4Q2w0+7GZ2qCVspjfOT7GYHdiV9m1tLAxbBdXblXlfa1M+Ubm/uh0kULZ5rkX5khENQa74miasiH+Gp0u8uTzMxBANKKGGbXsfFVk41U51GLraYthuwEdqQilis34rImERHdblP7OGOOzJvGoTk97jGMHO0Ox3sPDbWtmezIJHbPqEouIwv14xGc2X0VVZHoyUcZmy7hLJSFDSIBl+6KGF5lxo5PZeLf4N2l5E/u6g4KzSk2dLIFtu0xkPdpTEd71U/uaruO7T1xibKaM6wf4no/l+kiSRCSioimiljcIAuaKFt98bASz6uC6PhFDCY+haTJm1W5qGIOl/PfUuSy3HugI76umyAQEqKpMylB59sIcFculLWmECw5o9qjdLg5bLpvX1XV1cOFO76ZsSEbkBz/4ASdPnsQ0zabXP/KRj2zbgK4mbDTtux17/Vvt2FnpgVo8htUevMZAbCK78CykYjrPj81TtTzOjhWI6irphEEgSU0SIU+cnqE8PkG8r5fbf+oG+KkbeOaxEWK1YM20XGRFalJR722PM/zKBD98bpqK5TBfctBrxbcRXaiH7+9LbuoHb6u1PZv5/EqBeL5kc2hweb2lFq4ebAdXbrUec7sWe40LlLrQ9smRPDcMt214kaMqIshwXJ+TF3O4no9ZdfADeGGiyNhMmcNDGVRFZjYvgorFXpqZuM6B/tSKnqON51yOw153bICvPHwOgKiuUK66qKrYfvQC0RChBMKjdPEZJMDQJBTHwvA9qrLODwdeLvayLG/ZrJ0kgaqI7tKYITMxVw3daBqh1yzBAqCnPUpvR7MEyqmRHFXLQ9PE+6q2RwBk8xaDPYlabZzYWgbwfB9VlXG9IKyrcxw/bEKoYyX+ahRB/usHT1I0HXJFG0kKyM5bSBI8fT7L0X3tS6wRt8tyaqXFcVtbnIS27g3EVY+/UwHW5WhaXHcA93u/93s8+OCDvOxlLyMaja79gWscm/3it7rXv54s3mLPOkmScDwfVZZC0+LVHqjHT06GxbKJqIbr+k3vawxE82WbdFwjFdOZylWwbQ9Dk7Btj3PjBQ70p+jtTnJpshBef09gcvFvv0jHT7+D9u4DACsGtr2L7nM9qPvxiSksx8PQFBJRjb72GKoqk2kIbNeLrdb2bObzKwXimYS+KVeHFnYPtsKVFctdU/h0vVhrsbdeDlv8A380YYTHXS+X1Y8x3JPk7Ng8miphVt1wS1au2UhVbZdnL8whyxIxQ+HsJVEL5wcBmiqjymC5wgElZahMzpV59NkJDvSn6W6LUq46fO3751EViZHpUpgtbOS6xlqyfMlGAnrbIlyaMWseo6K2drnwsN45+s6J7xD3q/zP4behKJrQqVym0E1CZOQs28NxPJS2KN1tEfJFm2KDZ6osgeP5eL4why+azpKa3orlIcmiBhCEDZbrClmS7HwF0xId/YYmLBuKpktn2qBoOhCIGjjHC+isfdd1rMVfo9NFTlzMkYhogE+2YGM7wve0kedVVQ6fre2SBVkpuPzhMxO8YYsWkjsdYF0OA4B1B3APPPAA9957L319fdty4qsZV9Jaa60sXuPYFAmeuZDFskUgZjkessyyae/Gz9/36EUAklEhoDs2W2agM77sg2doYoU3O19FU2X0WjZMr7keTMyZpJIRNEVeEAxNGtz86jeRuvMV4TnXo7peT+2/+aVDTfY8W21A2GpWczOfXykQf92xAZ46l13yequl/urBVrgyaqhczBW3pRNvtcXeRjhsO5oX6scQxvRpxrNloZcWgFoPSiQJyQfL8YnqMvGIKMnw/ABdE/Ie4zNlbrm+I5xr+ZJNRFfJlSx62mN4XsBUzqRqe2QSoijj/ESRA/2psKZwsDtJb0ecw0NtoUn7VG0LVVNkHLfWwLBSgk+S+EHbTbSrHgPdKcazJv4iU/q6N6mmStiOqKfzA5jIVpBrvquGKuHXtlX9QHzIAzzfYzpnki/ZTc4w0YhKqWzj+wGyJMzkq7Y4X9X2asFigO8HWI5YDA50JgiCgPMTBUDi6HWdYRdqHWvx1xOnZ0hExb+Vqx6qIiPpIiiNRzU0RWZkukRvRyzkqe1qDlvp2ZvOmSt8Yv3Y6QBru5p+VsO6A7hMJkMyeXXsO+80doO11mq1VXWl8kvTJWFMrMrYrofr+gQEnJ+Y59brxApsORFNzw9IRIUoZl3qY65YRa39uZH89/UmOXNpnvmSTVcmQlQXwrzJmIZaU/aezJpYtkMqN0Z3WwcFS+E70UO82VZgDdX1le5z43bsZrajF2cp8yUbMpsjnM3UNq4WiC8x9m611F9V2ApXvvGOoaZgYCtY7Rm795Hz6+aw7RBabTxGXW/x3FgBmQBFkUIZjaC2s+h6AWOzZZEJC8Dx3DAwOnspH3anVywv5BwQkiMRXaFgOuiqQq3RlYk5M5THWBy8XpgQZR+qIrJofi0Dtzihpvs2fdVZRmP9XIr1MqvKRMt2zZQ+IBbRwgYIxw2QEK4JixEE4AbQkTaoWC5exW3aelUVCccNAK8pC3d4MMOzF+ZwfZGpdP2AWtxb2yaViegKfi37dv2eDI7nN8l+NOrA1bEWf2ULVYa6E5wbL2A7HlrNRFVRJNJxHcfzkCS5KfjfLlmQlZ697o6tq1/sdIB1OQSK1x3A/dIv/RL/5t/8Gz70oQ/R2dlsMjs4OLjm5y9cuMDHPvYx8vk8mUyGT33qU+zdu3fZ954/f553vvOdvO997+Pf//t/v94hXjZcjsh6s7g4VSCbt9A0GbeW1ne9AMnxMHQVx/HJF+3w/ablNmXHRqZK6IvqJjRFplixOTIsHrzvHR9jMmviej5RQ2WgM07V9pgrWPR3xenriFEwHYqmTTImzJjdSsB1D3yN6Vg339nzWvwgYGymxP6+9Io/JGvd581uRy8mcNNyCRD2WGvZviyHzdY2rjT+Vkv91Y2tcuV2YjnpnHsfOc+PTkyRjmsMdCZIxUWmaiUO247mq8XHGJkuCfkPV1hV+bXALfQYDQgdARYjV3QYmy4y0J0kaijkixaW4/Hwk2OYlouhKUjAdL6C74sAUarZRrlewJe+fQZdkcnOi67KuYJFgMiEyQDy8sm3u7NPcnPhLJ8dfhdlNUrF8bFcC79mDK+pInsnmhhEVm21Kq18yUZVZGSp2aDecQMUGVIxLWwQgwUx8ux8lZzjYagKvicaGVxf1BVKBEQjGoYmmrvS6ygpWYu/6oHIdQNpnj6fxbaF5EhPW4xbrutcdjt9uzhspWfvzqNb3wnc6QDrcggUrzuA+8QnPgHAww8/3PS6JEmcPHlyzc9//OMf533vex/veMc7+MY3vsHv/M7v8MUvfnHJ+zzP4+Mf/zivf/3r1zu0y47dbP1RsbyFzFlDS5TnQzKqMWNVkGUprLOZzVcJEM4Gc8UquWIV1/WFIKYk4dc012K6ykBnjL9+8CQ/OjlNRJPJJIQF1VSuwoG+JCMzZQa7E8QMlXTCDX1CH/jRCJOzZc723k1eiYviWkVmJlehULa55UDzj1z9h2Q773Njxm0mXyGT0JuDxoz4b71od6NoBV0t1LFVrtwpNC5cMnFRa3l2bJ7rBtKkan9fbm6t9gO/3jq6xcdwvYAb97ZxfqLAfMlZWnMmBUs6OjVNJvBFHde5iSL9XQk0RSJXFPZXsiSkNMpVkY3TFNGQ4FREUFWpOGiaytiMs5BhW3QOP/y/pfh+x22ciQ9SVhfqGusf9XwRiGqqRNXyw+zdSnV0IBbWASIAa3xngMgCViwPoza40eki3zs+xtRchVzREgGjJmFaQk1PUYSosONBQhJb8RsJFlbjr8ZylZv2tnGmVpe4tzcZSs/sVInHSs/evv70ll1xdjrAuhwCxesO4E6dOrXpk2SzWU6cOMEXvvAFAH76p3+aT37yk8zNzdHe3t703v/xP/4Hr371qzFNc0kH127Bbrf+mC9Z5EsWBIIkZBk0WUaWJaKGQiK6YDDsJnyqtsfYbBmtZscyPlumWhaimAQL4rlfrRUHRzUFr9auXrdvmSvZ3DDc1mzfcks/3oWz6GdOk9MGsKJdgjFdIZyp6+LxG5kucTSxkGmr/5Bs131enHE7Pz6PWXHDLtn68XdDBrWFqx9b4cqdRGNJQl9HLMzujM2WUJRmAdj1dqqvVP6wnMxD4zHqIsK3xAweOzmF7fh4ta1LqIt2L3xWqjU5eLVtw3LFYWS6iOMFxCMK5apHEBBuZ4IIZhrTeBUnIJAWOkWD8P9Whu7b3JE/yQ/bjmLLOhdjzdzTOEZFlgh8iWhEpVoTCAZo3EUN6+OAqKHg+z6231w3Ry24rDdpPX5ykkefnWIqZxI1FEoV4aVVKDmoqkTgCycKEIFjvuRw474OXnv7nm0JFpoCEdtjoDPO7HyVkyN5Mgmd1x0b2NHF604tji9HgLXTC/sNyYgAjI+PMzU1RW9v77qLdCcmJujp6UFRhB6Noih0d3czMTHRFMCdOnWKH/zgB3zxi1/kM5/5zEaHdtlwOb745bDWand0uhjqG9muj+f6eBIoihQKSe7pSvKuu/eHq+fP3X+C+ZKNLEuk4lrNp1SwSBAENaNkiaol1MklWSIT1zAtwUoF0yYTNyhVnWUJ4/h/v5/bigWe3NOL7wc16xqwHZ/epIHl+JQqTqj31hikbdd9XlxLl4wJHaiJOTMM4HZLBrWFaweb4crtRiNnjEyV2N8n5k46YXCgP8VE1iRfFiK6jQXo9aBsas7kL+47QUfaYLgn1cQ5q9WoHrtx6SKrPpaR6RK5QpX5skVbMkJH2iBfcnBcr1YzFizdPg3ArQVmsiSaHsyqS6HsUKi4EIgAanE2a/H2ZN17dDU0fua68iVeMfc0L0T7GIt2L/teVQHHFYvAaFKhYosOzdfc1s8/Pj1JNl8JPVDDLeL6dSgKMUNCUxWm81WCQCy4FUUmGdfY25vkO8fHcFxRrqKpsiiFcUXAqwQQMVQs20OWZOIRUbv8i285suZ1bgT1QKQetF+3Jx3ydb3pqi75dDW5x1ztOyfrDuCmp6f5zd/8TZ566ikymQz5fJ5bbrmFP/qjP6Knp2fLA3Ech9/+7d/m93//98NAbzPo2IbixvWgqyu5LElt9znquDA+z8M/mSAR1djTm8KsONz/wxE60hEcz6e7LUauWGX/njQjk0XSqoKmyeTmK5Qtj8PD7ewfSHPn0T729afD40UNjVzRQpUk5suOWAEiVoeuJwI4JPBr68bADyhVRSFrxXKxbKH/dvvhnmXvx+k730lXXKH3osnYdEkEcYqEoshEDI2IAfsH0rSlIkznTLo7EuEYt+s+l22PzvZ4bZUK+/dkOHkhKwqgYzpmxcFH4vUv37umQOTVIiC5HlxL17KbsBWu3E7+WswZM/kqZ8cLHL2uk7ZkhHjcIJWMkIjpvO9NolD+y986RWcmRiKmkytWhb2TKuN6EEgSD/9kgne+Os6+/vSSeQUQjenM5ivA8vzl+cL43fF8XA/GZkrCWSUIUBUZXZNx3ABVgVhEpVSx8TxqzQXiWPXasvPjBZGpq50j8AKkhpSY2FqU8FfxIF0OASDLIgt4IrmfcaOTvJ5a8r6ILgMSiizRltLIJCNkCxU6UlHe9sq9DHQnefz0LIM9SUoVm2zBwvfrQZ/oPlBliYrjM9SbQlJkKlUH2/Xp7Yhz/WCGdMJg5MlLGJpCPKohIdGWijCTM4XtvC805iRDojMTxfd84lF91bm9lXn/7eNj4fMBkEjA2EyRBx8b5eiBzvC3qfE52Sm0+AukIFhON3opfu3Xfo3+/n5+8zd/k1gshmma/NEf/RGXLl3is5/97KqfzWazvOlNb+LHP/4xiqLgeR4ve9nLeOihh8IM3Pj4OO985zuJx0V3UaFQIAgC3vrWt/LJT35y3ReUzZa2rYvrSmJxt9Bi38JC2ebUSI6ooXLTvnZMy+Xpc1luGG5DkiTGs2UqlktEV4hHdT787pubjl8/nucFPHFGqGfLkqihC4IATRNCkZoiynDr3aySJFTIEzENiYAAoTv0s68+EHZPOuefZ3j8OXr/5S9z/OwcgSQxPlXg1EieqiO0ilRZrD51TeaWA528ZgfT8Mt5Pk7NmeRKFl2Z6LpXjMt1cG0VV8r3dCeu5UpAlqXLtmhbL7bGldvHX4uf+7GZEqdG80gBtKV0fB9s1+fGve3h/Pv8AydCuY9TI7nQX7Rq+9x2/ULB+j137W86/nzJ4sJkgbmChSxJvOKW/ia5ivp7R6dLlCsOBdPB8/ywe7Jqu4Coza3b44FYSLYlDcZnTbGglIX4btX2VmxyaNymlGqB2Eag+zZvm3qURzpvY1ZbOQCRqOnXIdwdbj7QyZHhTJiJujRdolyxMS1P1McpMrbr4/tCZkmRhWyJ0MTziEUUYhGN4R5xz8azZYqmHRrWG5oSqgKUKg65QhXb9UXDQs2MHuBtdw7xlpfvW3bMW533jc9HHU89P8NcUWRTo4YSanJupaZ4LbT4S2DdGbgnnniCT3/602iaIINYLMa/+3f/jrvuumvNz3Z0dHDkyBHuv/9+3vGOd3D//fdz5MiRpu3T/v5+fvzjH4d//5M/+RNM09yVXahXAos7Muvt8q4XhJpuiajG82PzJKIaFcslaqi0JQx6apY1yx2vaDokIioz81UgQEIiGdOo2F647Von1bqNlSKL7QrbCejKGPS0RznxQo6nzmWJGSp7rBzG9BjfffR5brxhkMfPzDKWNUnHdaSKg1l18XzoiCncsLcdRZF2VEdvuVo6WZF4/xsOXtH0+ZXUE2xh57AVrtxONHJGoWwzlauQimrMl2xm8xayLHF4KN00/xobhyqWqBN1PJ9ozY6pXivaaK2lqRKViiusnYBEQmsyTR/sTjJSC2bGZk1830dTZDxf2NobmowfKLhu3f0gQJHFvzuuT2c6Sq5o4wdCD851Vw7eQHSESpJUq5lbuo26FhJulT5rlg57nryRwQ+W90Wt16y1JXQSMZ2nz8/y1NlZDg1miOgKUzkTRZZJxlRyRYeK7YbZSl1VkCVwPZ+OdBRZdjm4J43t+VQsl0szJSRJQpFk+toNRqbL5FwLWQZVEQHb9YMZejIRfnRiRliRKRKGpvDwUxPM5K0dWRQvbiwrlG2m8xUimkpEF12458YLwhXHXsWPrIVtwboDuHQ6zblz5zh8eEEZ+vz586RSS1PLy+ETn/gEH/vYx/jMZz5DKpXiU5/6FAAf/OAH+fCHP8zRo0c3OPRrE/WMTNn2iOtKmJFZPHEqlosqy0SNhUb1toTGcy+UkSWJqK5gVl3mClWOHexccp6OVISpOZOx2TKRmhTIXNHCsl00RWGgP84LU0XKFZcA0BURtFXtAEOThSdhzbcwCAJBXANJYhGN3JGXkr/+NjRX1EV0ZqKiDsTz6e+MY9liRRqLLDQRwM7p6G2llq4xQ5ZMGFRMG9cPtiVbdjn1BBdn+l7/8r3bYkXTwlJslSu3C42cMZ4VTUqaKmO5PumadEix4jLQtfDcNS52IroSOiUMdYssgWm5qLLUZK311LkspYpLxFBIxTSqjo/jupQqDn/3gwskYzqnR3JCKFeWKDsBvu+JcgpVxg8gqquYvoPvB7geyLLgGd+HS9NlVAUsG2zHw3ZWDwxURaY9ZdDbHuWZC7maptraUPDxkJnTU3xh3ztp60iyR5WZK9pUqo5o4IpoJKOiUcusukiShI9EEIhMn+cH5EpW7f6pIgj1IBHTcFwf2/Fq26iS2DauSTYloxquH/Dmlw7xpW+fwQ8gGRV+sSXTxnE9XDdAliX8wMXQdF55Uy9jsyYvvaEbtxY41f1QL06JxeFiP9NIRKNYtjbNX4sXwxeniiiKTCyiIElSKD01OlPm6P6ODR17LTRxcdygWnVwPP+qqrnbbqx7C/V//+//zR/90R/xnve8h/7+fsbHx/na177GRz7yEX7u535up8e5blzNW6iNGZnO9jizc+VQigMWiotjNTPhQtkmHlUJAtFdWjKFvlsipov6LkMhk9DpbY8vSWWPThf5i/tOAAv1bo7r098ZC7szT4/keGGygCzVPPoCsB2X6/akOTCQCY9VrjrknjvF3RcfZvS1P4+dESLBdWPlSEQjpslh2v3J52eJaDIF0yadMFbd6r2SaPw+XNfn7HgBz/M5uCcdGnlvJVu23HZE/Z41im9uFY3XUc9A+ki8+pa+q570duMW6la4cjv5q/F7Pz2SQ1MUHM/HcX1ScVFPVbFdbru+q+m5a2w2mM1X6O+M0dMWCxuMDFVBURacXJ58foZcQWT0AsRCT1VlSmWLAImOdITADyhUxLZpXSutfpmyBIYuMlISErbnE6npmCUiIgOoqwqlikOp4uD7PsvFcHU5jY5klMPDGVw/YC5vcmasuVZuOei+w3vH/4FTib081X6EiKHS3xmHQOx2BEGAoal0ZiJYtselmVKY3VNrZSayLKEq0JaM1K5LCjvb4xGVuYKF6wcoNRcFVZVJxXVkSaK/MxbydCMvzJcsjp+ZrV1gQGc6iuP6DHTG6WmPhVnW06P5sMyFACq2S39HnLHZMoeGMriuz+nRPKoqs783uSX+am6MKdKdjjCVF048YpvYo1x1+fV3Hd0RX1HH8Tg/WcR1fQ4NZlBVectcfKVw2bZQ3/ve9zI4OMj999/P6dOn6e7u5g//8A+58847N33yFprRmJGRF1ldLXUeMJgrVPB9laguY1ZdsgWLm/a2MdDwEAdBsKw8xmB3UiiBVz0qtgj2hrqFqOdc0QprXNIJnXzJDgNCXZHJl+0lXaPdA51Y0yl8faGTs97ZGY8bTGdL4fXIEozNlrFdH8vxScU0KpZHxao0WcdcaTR+H6dGcsQjGo7rMZmrcHioLXzPZsd7ufQEl8v0BZJ02ZxDXmzYLVzZmHmWJBkkROfpnInj+kBA1BA/AY3PXWNn3uLM7U/d0s9Dj4+SjC1kzqOGSkERtV7xiCqyRL6oj9U1hZLp0NseQ9NkCmWHIHBDM3cRtAlbJlURAWBEX2hic/2ARFQjHtVJJ3TOjxeQkFE9n0qto7RRgqMzFaG3M84vvuUIo9NFvvTtM6LIXwXb9leSeMOVFApqnKIaE1k1vy5F4hM1NGzHxXZF1JgrVsO6OmGR5SNLErbjI0tKeE9FoKxTMm2KpoOmykQVCUmWKZsOhg6JiEYmqaMocuhP2sgLE3ML/qyeF5CdryLLEuPZUug9Wq46VCyv1lRRH7PKXLGK5wchf4kuVmXL/NXYkfqlb59hfK6Crsp4vo/rCY3PG/e2byu3NHHxdImYoeEoHhNz5rZw8dWKDcmI3Hnnna2AbQexEeeBex85jyLL5EoWFUtIh6TiGqcvzfP8mNB3aksa9LbHlq2BAxjuSVGuOriuz8ScyfmJIqoihdsl2UKViNbcEdyeMlA1Ua/y1OgsUdukf28f+246wJPR9xKTVWINIsFuwmcqX2Vsukh/Z4yIplCsiMJcQ5NrAaZFIqIx3JdY9yRczT91u5oCGr+PiuWSShgEgS/Ektm6dtzl0hNc9rmKalyaLKzwiRa2it3ClXXOqD9rqirT2xYNxViHujOrirEuJ7OweOHR3xEnO18h8P0w0PADUYeWiWvMl8VWV0RXMTSFqblKuIWoyOK/dfmPiiW2X+uQJdjfl2SoO8Hth7r40rfPhA4vc4UqBVPwlyxJZBI60YhGOqbx+MlJ7nv0ItN5E9cVosDIoCsSjhOE2TjdtwEJR9b4u96fEnpzXoDreeSLFpIkYegy82WPwA/IzleE5lzD/fB9QBLX7HoBpYqD63qUqx4RXaEtYYRacHXR5PNj84zPCQkXJJq01AY6Y9z36EU8P6BsimM5HsQMkfn0fZjNV9nfl174XhVJyKxIYidluCfJ6dEcyajYKq/XM2qqTKEsdmpW4q/1cGg9I9aWMChXnNA7tr8rhqLIvGaLRvOL0czFnnAPCYLQQu3FquO5agD3Z3/2Z/zqr/4qAJ/+9KdXfN9HPvKR7R3VixR1YnRdn/MTReZLVlNA1YhsoUp3WzQMzgplm5mcKYI5Q0OSAqZzFebL9rI1cCACiK9+/xzTuQpRXUWRxUSvmyirssTp0TxRY6FA9cyleQY6Ytiezy1JhyPf+TIj0ht4yr1xSb1FgKjx2NsdB99nbLaM43qk47VgIhBNEZoqYxjCmmU9k3Cl4v9bD3SEjRTb0RTQ+EMVNYQNmesFYUH3VrNll0tPcNlMX8Vp6d5tI3Y7Vy4WYz00mEGSJBzPJ13TgFvvc7d44aEoEnu6kqhqmXJFFOqn4zpFU0hnaKq88PwF1AI26MlEidSyVQRwcZkFhR8IM3pJFtuJyaiQPXJcn+HuBM+PzVOu2f65nk+xbDORNTl5MRdaLUjSglm8UxMC9gKQCXjP+PcIJPib/jeCJCEjDOUlSXBsKqYRi0RIGCoVx2WuIIIfWRKlJ44X4DgisxePKMiSaCRQJLFF6roBxapLR8oIbcuE0LqNocm85FA3puXy6LNTnHghR75skZ23aEvoVB2PYtnG9YQdmKLISCB8rb2A06N50gmDWw90cEKROXExRyKqcaA/Jd4vy2SSIoCLGkI7Dklalb/W21jVmBGLGmrYMZsv2TvSHNbMxQqO64eZxpWu5cWAVQO4ycnJZf/cws6gMaBKxvUwoBrPlvniN081FWwu/lEez5ZxfTFRNU2u2WHJxAyFsVmTO5Y532B3kvZkhKLphL6me3vF5BfbLgu1WVKDtfNsweK6PRHkZC+5Qy/B2XuImKwyNmuGtXb3PnI+rJORJYme9hiJmMaZ0Xlu2tce1mz4fkDBtJmdt3j2wtyywepirFT8/53jYwx2J7atKaDxh6qvPRbWwA12pbfNQuZyCEkul+mr18C1sD24Grhyo8/aao4MSxYedy+IAHuez1SuiqEp2K7oWi+ZDkXTASAV11BlGaVWO1Yo2+SKFlaDUG/dRsuv/c+2vRpneMKGL19hfKZMQEBEE8eSJAlZkSjV5qamCl052wEpCEKZDT+oy39IHM8cFtuqNa7TNBlDkuhIG0zOVYQ/qiyEdtNJg+mcie8HVB2Ridc1WSxuPeHnqukyQ92JsDmrLp3SyEsTcyZIkIzqSJKE5wVM5UwKph3KNuVKNtcNpBnoTPDjE1PIsqipq1gOjhfQltBRFYly1eGpc1ne/NIhXnv7nvA7SUc03v6KYZ46l6Vcdehrj4n75/nsX8UCa7XGqvp/F4tCp+J62Mw2V7SWzdZtdVekkcN626JhDdxaGeRrHetuYrhacDU3MQB88ZunuDhVBElCUySSUY2x2XKT3ttsvooqw8hMmURUY6g7wXMXssyXHDRNJmKoJKIqEU3FtByGe1MrFsWvVkgPoMoSE3NmKEvS1x5j4plTKN09lHxR79HfEScZ05qK7xuPG48blMtW2K16cDCD5wWcuDgXrthlWQSfPW2xUHpgJaw05h+fnOZlR7q3tSlgJ7tQLyeu1S7U3djEsBXsFv5arvFlPYXi9boo0/LElp8El2ZNPE/4Kx/ck6Fqe0zOlZgr2MLIfpnLVYQFTK2WDiGRoSt4XoChK3RnokxkTSq2S2fawHYDsZUqS9iOi6oo+H6Arsm4foDt+Fi2J7Qs7SqdToExo3lnQpZEMCLLIoOYna+SimtkklHKFRtNlZmaMylVHCGj1NDdqqsSqiKTievIisyB/hTphEEQBIxMF4lFtPBe/vPpGWQJrt+TIRXXOTWSw3YWpFEiuhzq4x0eauMnZ2fJzldpSxmUq26TJtzhobYmbb7lvo/6vF9PF+pK3Lr4Gp69MEfFcjk81Ca2M2HZcWz2OVrp2brWulAvWxPDS1/6Uh577LElr99555388Ic/3PQAWmiG4/nctK+dRCJCuWyJ4lNdFTZWDau1qKFyZCjD6EyZp89nsR0fVRPdop4fkCvaJKM+hq4um1oOO82miozPlhnuSYYTsTEdXa46YZEowMzUHG89+yDjc3t56qY34bg+Z8fmw66oOlYq0N/fl8K0XGKGSkRTqFgenufTloqwvy8dZv9Wm4wrHTuTEIbc29kU0Ji1uJrFIxdnX67ma9ntuBa4crMSN4PdSboyUfb0pqiYQmy8Mx0Rzi22S097jHNjefJlUdTvWMtLgghHrYUAyfMDShVR72S7oqHKcsRW4tRcVfiK1urvHC8gk1AJAMcTzi91CZWutMGRpx7h+vIonx1+F1VFD88hath8UhEdx/VJxETRf7kmIVIfh3BAEMf1awGoHwQk4xpGbUuvbtNnWm5oQRYGHzGNtoQR8u1iSSjH9UOnG4Ce9ihVW2Qeq5aL5/l4XsCRYcHLq9V/bXTer8StFcujMx0NXx/qTnB6NM/FqWKYWNhoRm+zzRPruY4XC9a9BHccZ9nX/I3KXLewKjpSkVB7CWrOCA3dYo0CvplkhKP7O8gkDGKGgoQwea5P8vmyI2yuat1NddRXReWqw77eJBXL5dRIjvmSFaajbz/Uxe2HujAtl3LVIQgCsR0w7/GTm9/Ck8Mvx/H8kNjGZstN52n8rF/77GxekIxZ21YoVV362mPccbibW6/rIhXX11WMuty4TMvldccGln198fW30MJO4lrgymyhSsxoXt+vt1C8IxXBrIh7ULFEd2ljvdJEtoIiSezpTqCs4xeo0UYLhD/9dK5CUHsxAMy6V7MkhHzNqk13OgKBcI4hCEhHVS7NmDzccYy/67kLVzcwNDkULJcQ2m2JiEZ/Z4zOdJRUTEdTRC1bqeJQtUUDg18znFcVmVRttyOiqTWJlIBK1W3in8HuJPfctZ9fedsNvP8NB5Fr259BzUKsYgsu7GuP4bg+Zk1aqVx1sGwh+wKgynIodlzHdtZ/rcSt9exbHemEwcE9aVzPZ65oEY9oy2bVtvIctbA21szAve9970OSJGzb5v3vf3/Tv01OTnLbbbft2OBejKjv9ZdMGwKxyqtYLnt7hQjocgK+ZlWIZralIpRMofjtOj6ZhLHsdmTTqiiicWhQYmS6xKmRPOmETiyihaKe9VoX5/zzdMU0splOEtffTH/Zrm2tesLEOaI0naexTmY2X8HzgrCpYag7KVZsVZe2pNEk5rseMlqt+L9u57WTTQEttLAcriWu3IrEze2Hunj4JxPIBEJQ3HJxXGEH9eTzMxRMm2goFdJsPt/8t5rhe62D1G1I1vk+SEoQuizIknjNlwKE25TEbKGKqkpokkxMchi8+CSzbTdgGTHOq1HwhDxH3Z5qb2+S3vYY5ycKVLIeB/pTHBnOcGGqzI+em8Cpdc5bdTtAdUEfU5ICckWr5t/qYWjCSmox/9R3PsyqQ3a+GlpnzRWrqKpMzFDp74wxPmsSj+rEIxpuwqczEwkty86Ni2aPsdkSipLc1vqvlbj1idMzS54HTVO47fquVe2yLpdU0osVawZwP/uzP0sQBDzzzDO85z3vCV+XJImOjg5e/vKX7+gArxWst5CzPoFOXSowMjHPUHeCfMlGUaSF1ZrlMtSTCT9jOR6qqpCICjstEEFdLKIue47FshLphMFgEHByJM9gd4KYoTZ1H73jVfsYfeR/Eng+5t3/grLlkk4YTYW6jRO08VoGu5N0dSX5r198jKLpkCvaoV9ef2eMsdkyiZi2YRmNlQqyL0dTQAstLIfdzJUbLSRf3PgynaswNlumI21w7yPnV/38YHeSd746zj/86AWiEYX5so3reUiSgiotdFLWa9K8BsulevAm1QzfPTcQXaG1jFf9PZIEmiyDKuE4HpIk4foBcUOtCY57WI7HjXvbmCvadJ7+Ca+afpzxeC+jSiZskvB8SMZUYhGV3nbRXX9wMEPMUJnKmdz36EUGupMoEpRd4R7h+aCpAYaqiOtwPIIANFUmHlGJ6Ao9bbEl96ixHixcxFpuKLlR/3562+O87c694Wc//8AJUrUsVjphCC2/rJAgWS5I3CpW4tDNSB5dLqmkFyvW3cRw7tw5Dhw4sNPj2TJ2SxFwI+qaRJ7vk4hqtCcjyIq0aiFnyfH5hx+9QLZQRZWlsOVfU2TmilW6MtFwQjxxehpDVYlF1dDU2HF8OjIGH3nPrUuOvZy5+zPnswBN9ieNRaluoQC+z4StbLgoteT4fPLzPyIR0VBVKXR92N+XZN60Ge5JXXYz983gWqq7uFauZTc2MWyFK//mmye4OFlsmvNbnRdbaUioOzKMz5RQa9IUqiqTjGm8++4Da/LXyHSJ50fzVGs+oNGISntCYzJXRVMVCHxKlWZdNRAZNUUBxxUNDWIBW+tORQRfqbjOvp4EZ8YKVC1XBHWaQqUq/izLErqi0N0eZWSyQJedZ9pogwZXhgBoT+kMdiVpSxph53xjpgtgfLYsGrJqtXe2G6AqIvunqTKeF6DKEqqmcHBPmkzSWFLQvxzvrtaAsNXPLYetzPvNdpNupzZnHS3+Elh3E8Pf/M3f8Na3vpVjx46Frx0/fpwHH3yQ3/qt39r0AK51jE4Xue/Ri4BoG3c8nwsTwrPuc/ef5LbrO5ddqdW3IBQJRqZLlCoON+5tX7Ja60hFuPW6LkzLaXJM6MpE6G2PLzum5VZFpYrDDcNtTe/rzI+jnTtB8KpfQ635OA7ChrXLfvjMRJgZXM4vb6Mk1EILuxlb4cqK5aJIcHo0D8ChwcyW9Qy30pAw2J3krx88yaQsYehigViuOEznKnzm68/ysht6VuQv07Q5dylHuSoybD4BVculoqns7Ukwnq1g15oMZEm4LtT12hRZIqKraLLQ+/J8IfqryBKeH+D5QsR1Zr5KdzrChakiqrSQjZOkAN2zee3Yj3k+eheqqjBFG5oshZXfTk3gNxnVedfd+5scJibmTDRVxvcDLs2UCWp1Z1XXJxHTkS0XyxHlI7IkkcnoRGpaa8WKQ39nfEmd11pC7Stho1msnQiYYPO7G61dkZ3DupsY7r//fm666aam12666Sbuv//+bR/UtYQnTs/g+TXxVwk836dcC5iCwA/JeXS62PSZRFQLBX1BeOnVDYqBsCD2nrv285pjA1RtTwg1BiK7VbW9sHh/dLrIvY+c5/MPnODeR84DIgiLR7SwAPXGve2oavPjoI+epWvmAr5pbukeTOdMhroTQnzRFcvfIBBFwa0GgxauNWyFK6OGymSuQtRQiRoqE3Mm8VoBeV2La6PYaiH5+YlCqOJvOR7FmvSPWXVW5a+LU0VKlebtUdsNmMlXmMxVa4vXbl5xUy/JuI7nLzSeCmX/gM5MhGRclIZ4fhDaZ3WkDHRV6MEVKw6HBlLomlIzlReabMlKgf3lS/iTl8T5AxG0Oe6CJlxPW4S2VERYCzY0kNW742fnq/i+EAomoMZhHnqNK28/2EVvRyzUtBM1y96ydV6LG9RgYzW/jXy9UjDf2KDWKMTb+P20cO1g3Rk4SRI1WI3wPO+q6qy6EsgWqiSjmtj+VCVKFdGE4Pk+sYi27Go4W6iypzfF6YtzYft73aDY9wK+9O0zdGWi4eoK6kK7C91J9b+vpqy9nF4PQExXMG2Pmevv5MB73oESjy9530bcDrrbYkxnS1w3kGY8WxaNGDvgl9dCC7sBW+XKuq+lhLQtVkEbLSRfnMGxHR/FEHxSqrjIsiggCwJpVf6aKwgtyXqjQR0+UDRtTl3M05E2mJmvkJ23wn+vb5E6ns9MroIHdKYjtXvjomkK0YhKmy4yXpbtkis7mFW31vQQ4AUSk5EOPjv8LmxFRwuCpnEEtcaH+bJD4JcZnS42ZbqiuiIEdwFdE40KqhLgBQgbwFrNsarK9HfEOTs2Hw5eVaRlM2RbqQdbbxZrO2U7Wtj9WHcG7iUveQl//Md/HJKQ7/v8yZ/8CS95yUt2bHDXAjpSETJJPcw+ua6P5wtNt/4OERgtJud6G369BR8EmcmSxKWZEkWzeXX1veNjdGaEpMixg90c3d9BZybCd5+4xJe+fYYXJoqcvTTPT87NcmY0z2TW5HvHx5rGWV/ldc2PM3jvnzM7MolpuRy/WFqyuq4ThCRJK2YHGrN+uUKV2XwVRZE4NJjh4GCG3o7YtvvltdDCbsBWuTJqCJmg7bIKWkkaYrns93IZHAIRcIlgyaNSdShVHCRJuCisxF9BzTKrQRM2RERXCAg4NzZPNm8tfQNQtX0s18dzfabnTIqm8Nx0XI/5krC0cj2PfMmmVHGIGSqqa/Pzo9/ixtIFACxFDwPCiL7g62xocq1e2MfxPb53fKwp0xWNiAYFCRH42a6PH0DcUFBlGUOXedMdezAtF0WRONAvSkxKVYeh7sSyC9qNZNI2i5Zsx4sL687A/dZv/RYf+tCHeNWrXkV/fz8TExN0dXXx2c9+difHd9VDrLoq9HfGyBdt/CAgCODwUHpZ4dz6Zx7+ycQSg2KlVticjKph8ARwZnSeW6/raDqv43icHMkLEUtFZna+ChJ0JIU6+HMvzDE6XVwi/eHv7WDmxyp9XQm0THJJhm09dRyLs3TIwhfV84QrQkveo4VrGVvhyqfPZalaLuWqsILaDqugjXjuLpfBGe5LcHGyhOV4WLaLJElENLGl+sTpGQxdoT1lhHxy+6Eu7v/hyIJ+2zI9ZX4AVdtDUWQs1wvtswias3WyLLKZdZcCRZYIfChXXSzbo2i6oUdoJqkzY1XxZAW31u0KohnC9QL0hkZ5x/NRZQldVUjF9LBhoZ7pGp0u8t+/9gwVy0NRQXUDXD+gXPVIxjR+9tUHuONIb1O28uj+jjXrzXa6Hqwl2/HiwroDuN7eXr7+9a/zk5/8hMnJSfr6+rj55puR5avfjmerWK1otJE8NVWhryNGvmSHNivLpdHrbfhf/YfTTQbFZy7lUSSZvgbHA7HaCpY4EIzWbLY0VWY6VxF6RUCp6pKO6ySiWlNa3SuVUBIJnqwkKL/hF3G9gPOj+TAL+N0nLvGLbzmyLoJY8iMQ0+nKRDfVNdVCC1cbtsKVsYhKdr6CIkvs6Yrj+sGGzeaXQ/2zdZ6qZ8zrwcqCx2WRfb1JaJjfPW0xLMejYnlYCQPbreuh+fhBQNV28T2N//q3T6EqEsmYhqoowo6qYC2xyopowvC9XHVJRFQRyMlSrY5M2F7VO05TcZ1SxUFVwKvVrQVBgCRL5EsWtiMcE1KKWOjqsSj/u//1BJIUBnASoqmhantIgCSDpsihm4J4Z/P29hOnZ9jbl+TiRAnTdolHVVFbBwz3Cr3J+v3bTQvRlmzHiwvrDuAAZFm+qsQoLwfWUxO2eJIvDviWI+d9/Wl+8S1Hmv3fYjqZhL5E+LZuTwVLu0olSeLSdAldVZAlQY51CY961qx68QUu/ddP0fNL/4psIYoiwfmJIpoqjJpdN+DExdySOpGVCGKz3VYttHCtYLNced1Amt622KZlIlbCSjx164EOnjqXDV8fny1z5tI8hwalkGfqdlDZQpVDgxkKZZunzs7WpDtE2mx0piRkT1yEaX0ABwdTDPUkeeb8nHAoqI3FdgNc38b3aubxmkzF9nF9L9RnC4CYoaCrctj4JNVEfSUk/FoWrjMdYW9XnGM/+l/kAoPvX/d6ShUJRSLM2gEQCHsuGaEpV69RdP2Aiu1yaDDTdL+yhSo9bTEm50wcX8Z1fTRVQVMlujLRXVtTtpFsawtXP1YN4N7ylrfw4IMPAnD33Xc3Gdw24uGHH972gV1ubLb1ejNFoxtZtTW+t7E+ZbGeU/2c9fHfuLc91DTqykSZL9s4boCuKxzoT6GqMpnaWPXePpJ3vJTo/gOoUxP85FwW1/PRNYVEVEWR5TBjd89d+9ckiFYav4UXG7abKzez4FmNw1biqe8cH2OwOxH+fbgnyamRHCPTJW6K600LtLoaP4huzlhE/HwUKw5SAEgBsiyHWa6z40XuvKGH3o4oM7kK1VoHqR9A4IutTbPqoqtyreYvaLQ/pWp5VKyKyMZRE971hNm7LINa2/40XZ/c8I1UtBiSJBoOXF9oxxmajCKL5jECkOR6s5fQb5OA7rYor719T9O9rHNYEEj0dcRxXNHlr9XcEhZ/Nzsl3bEZ7LasYAs7h1UDuE9+8pPhn//Lf/kvOz6YK4XNdFbWsdVs00Ym/lqrq+VUv0FYxJwezYMuRCZVVca0XF7V7eM7DrJh0PMLv8TodJF8yaZqC1L1vIBswSJuqBwZbguvaS2CWJylK5l2K43fwjWN7ebKjS541uKwlXgqX7I5NLjwM5CK6xwazHB+orhsveo3HxthMmsKfggCPM8n8GsbkIHoAPWDQLwWBLwwWURCCoOfqKFQtYVvaURXSUY1JEnUw+mqFJrFu34t0GOpvZaonZMwfAd3Oo++b4jy0ZczMl1Cyle4YbiNc+PzqIqQbtJVCVmWiCgyiiKRjOsUTQddlelIR5YIEo9OF5kvWZy4mMNx6/6q4hqGe5JLvpv6vfe9gLlilfPj8xw/M8PbXzHMHUd61/+lt9DCBrFqANfYNfXSl750xwdzpbCV1uutZJsaSVeVJZ45n+WHz01yw3Ab7379IRLa0pqZ9a6uFgd7h4faCAJRiJuJaNy1P4H1R59k9lV30f1/vD+83s5MhHwpRrZQxbZdAkCVPSq2u6Iw8FrnHupL85KDna1VYQvXLLaLK4OATTUtrMVhjTw1X7KYqHV1up7PdK5CT0NdrarK3HZ955Lt2/q8/tz9J9E1STgoLIqwHK/Z13QqV0GS6tuXAa7nE49qJCLC8spyfTIxHUNXmS/b+L6oqROicEKSxfMDlJpVFrVTGZrM60b/kZ7yFF+L/Dy/9s6bufeR83SkhANCseKQLVSxLI9yNSCiKvgE6IpCIqIh1zKk73/DwRXtro4MZTg7Ns90rkJ7yuD6gTTKMhIhT5yewfcCxmbLaKpMMioyl/c9epHejniL91rYMawawH36059e10E+8pGPbMtgrhS2kkXbStFonXQ9L+DcuHBnSEQ0RqZLfP3hs7z6lr4tFy6v9vnCv/gFYodvCP9evw+d6Qgz+QqGrqDKQvfo/HiB2w+uX3S38dzXiu1JCy2shO3iyvmytSl/y7U4bKAzxn2PXqRqu1i2T9RQ0FSZ/vYY58aFhll3W3RN/hrsTnLb9Z2Uqw7PX8pTKDsgCe265WC7HpoiEzfUcPsyGdWElhwQjagUTYdEVMV1fVRFZP4VWQ6zbwDJmMZc0UYCZFnEd48P3EGyPEfJlZbcgzqHRXSFiu0h13xVo7pCxXaJGgrxqL7kHjcFwhGNlxyOkC/bTGbLKzaUZAtV5orVBc1OhBRMqeLu2lq5Fq4NrBrATU5Ohn+2LIuHHnqIm266iYGBAcbHx3nmmWd44xvfuOOD3GlsJYu21rbmalukF6cKVKoek3MmsiyRimsYmkLV9pd0iW4XKs+fQY5EMQYHSb38FU3/Vr8PxYpDJmlQtT1sxyNiqBzoTzM2a3LHto6mhRauDWwXV773Nddvyst5NQ4bnS7y1LksA51xzlzKY3seVtkjEVGIRzV622LkShaqKi9b07qYwwY6Yzx1LotZFTppiry03i+ii0BM12QSUR0CoZEmSZAvWUQNle62KK+8qZf7Hr0Ydrs7ntDIVGQJTZUp18R56xrIum9zuHyJE6kD5KIpZuQ4iZruWeM9qHOYEPf1URSZdFwnHtU4PNS25F7VsVwg3NeVwPN8fuVtNyx5f/2858fnSUb18DXXC0hGtVbjVgs7ilUDuN///d8P//zRj36UP/zDP+RNb3pT+NpDDz3EN7/5zZ0b3WXCVluvV8p0rVaXAoTq40FN1TxXtElGVRIxnVhU49JkYckxt4LA95n66y+gJJME//LXOX5mtimwrN+HoumQiKgYmoLj+lw3kCYZa5FRCy2shCvBlY2BlSpL5Es2ZJZyWGNW6YWpIq7r10TXZEqmTdF00DSFw0Nty/qaLuawp85l2dMZ49lzc3i+qG2ruxxIEHaS1gO7umhvqRpgOwGyDLde18lrb98Tnuu+Ry8KzTfXJxFVqdgehqbgugEBAbbrEY+oHJt8mpfP/oSJSCfzcgY/8InoypIO+UrVhUB4piaiimiQgND+ayV+XzYQrjirLuZvP9TF8TMzmJa7IMLs+nRlIq3GrRZ2FFKw2PNlBdx+++089thjKMqCmrXnebz0pS/liSee2LEBbhTZbGlTK9id6CK695HzS8ig8e9TcyZjs+WaL+qCkfOxg52kkhGkINiyjMDi67qtEyRN51un5okZ6rLdrF/69hmKpk0yptPfEScV17cka3CtbKFeK9cB1861yLJER0fiSg+jCVvhyvXyV2NgVZ/DM/kK7ckIjuc3cdjnHzhBe9JAkiR+8MwEjuujKhK24yPLEr4vMmXHDgrHhlsPdDA2a5ItVJnJV8gk9Kb613LVYXS6RCahMz5rUqw4VKrCJcEPhJadrsqUKw6KIpOMasSjGpoih3JHH3z7Dctm+UamS5i1TlcQi9tC2UFRJFzXo1S26KrMMhntRtcUorrC3r4kiiI3deP/+OQUJdMhndBJRDRKFZv5skMypjXVAy/m+uXuq4+0ZjnL4ycnue/Ri3i+yLxlkno4pt20hXqtzPtr5Tq2yl/r1oEbHh7mS1/6Er/wC78QvvblL3+ZoaGhTZ98N2E5rbZ7Hzm/pYBurbqU7rYoUUPlwkSBmbwQ240YKqoqU6o4vPqWvi1dU51UOvLj7LFmmbzx5Tw0L2Oo9ooFz/fctZ/3v+FgE4ltVQm+hRZeTLgcXLls00KGpkVWncNGpoqMz5YZ7kmiKRKOI7b4PN9HlhVkSUJXZbH1aNrc9+hFDg1laE8anB+fx6y4RHU11IVb6F7NENVVnnx+Fk1VCFwPhQCjJgsiSRJRTUHXlHB7FKC/M7akPGS5XYzR6SLfOz5GPjvGHRP/zFODLyOTijIj95CMaLSnDPraY6QTBpNz5SaP6D0dMcYwIQiYna9gu+LcbXENy/WIGSqpGrc1dusuVxLz+pfvXbahrBF3HOmltyO+a6REWnhxYN0B3H/4D/+BX//1X+dzn/scPT09TE1Noaoqf/Inf7KT47si2IqsSCPWqq0rVx1ScZ1bruukULa5OFUUXVoRbQlpbDRDODpd5L5HLwJwKH+Wzvw4z3Yeoacnzbnp8hLrrcbAsiUG2UILm8fl4Mq1FoeNHLavN8mZS/OcGslhaAoBoukgqBmvRw2VRFRwVL5o4/l+yFnJmI5ZdZmYM5uEfTMJ0WmZThj0dsSET6rjUbW9mk+y2E4tmDZm1cVxfQxdaLJNzJpcnBRSHStlwb77xCVOXMxhuz77nRyH555nsusA8f03cubSPImoyuGhNgDmSxbjMyZe4HNoUFiPjcyU6UjqjGUrBH6ApslEdYWL02WSCWNVxYHFweTibM9KXNzSX2vhcmPdAdwNN9zAt771LX7yk58wPT1NV1cXt956K5q2tBD0asdWZEUasVZtXf3fXNdnZLoUuifcfqiLff3pkDQ2E1A+cXoGz/NIxgyeOvRqdNdClgzyRZvlrLdMy0VT5C1nHa9W7CYhzhaublwOrlyraUGUQYgtw772GIcGM7XtSRddVdjbmyRftMmXrdAk/tRIjrmiRVtyoRi/vyPO85fyFE2nyfrvdccGeOpcFoBkVOXUyDy+H9CViZCMqcwWbPb2JNA1mYrl4Tg+VcsjHtWwXY9SxeX0aJ6De9JMzZn8xX0n6EgbtCUizBWrog7XUJmxqpzVuinf/gGcSJzCnEkyqlGs2OEYJ+ZMkCAZFfpu49kylarLhbJNRzoSBqeO6yNLDvmi3bQlvFHdzu1Y3O8EWhz24sOmjUzvuOMOHMfBNM3tHM+uQLZQrXmMLmAzyuj1TFY8ojFXFPIAi1P1nidsqgCODGVQVZlvPjbChVprPzQHlHUT+5ihhn6GddS3TD7/wAkuPfE0bz51P1RNkGRsLYqqSBQrTmi9JZTGA8pVh9m8aIUvV50mYhqdvvrrDNZCo8NFXY/vT7/2DH/94MkXxfW3sLPYCa68/VDXkjlsWi4DnbFaI5JNIqLiuH5o1H7Tvnau25Pmg2+/gd72uGgsMB1ihkIqqmFWXYqmTb5o8eTzM5waEby0pytRk/FY4LA7jvSG/DUyXSaT0OnMGDiez8hUmT3dcXraYwx0ivoe03axHbGNWaqIDF7UULk4VeLCRIH5ks3ZSwV+8Mw4z56fY2pijlf85F4GS+MgScx6GqoiUbE8UV8my+G114PLVEzj7Ng8juvTnjJwvYB80aJqiQxg/fVixWm6lxsRTV4vF19u1Dlsas5kOmfy+Kkp/uK+Ezx+cnLtD7dw1WLdGbjTp0/zq7/6q+i6ztTUFG9961t5/PHH+frXv84f//Ef7+AQLz+20wpqtbT6YHeSVFzn5gMdS1raf/jMBG84NgCsT6du8cowGzioThWraiFrETRFrIQVWQptYxpXa27CR63VwcBC1vF7x8dIxfVrelW3mh7fblldt3D14HJw5UplDvVnORnTQ/cDEFmqukxIIye1JQ3yJZtCWbileJ5PvmTTpSnYjsepkRw9bbElgrf1MSzHX4+fmqZa04VLxXWuG0jzxOlpbK9mRRURAVBAwHiuApLwJzWrbniMwPPQnCqKY+G6vmg6cIX5vKLIvP0Vw2GjRTKm0ZYwyJWsJi22RFTFDyBbsDB0Ba32Wd8LltgRrre+d7f6PLfEhF+cWHcG7hOf+AQf/vCH+eY3v4mqirjvjjvu2FUdqNuFlVa3tx9av5DterFStm86t7Ba70hFwu6tOhYHlHXiTiiieFg7fBP33/RunEiilnkTWw5vf8VwSOD33LWfX3nbDdxz135cP1gyDtf1ee6FuWs+K1f/Dsaz5fAHQFUlXC/YFavrFq4uXC6uXDyH65ZZMUOlvyMeZp1UWaJoLuWwumF7f0ccVZGRJYl4VGS6imWHqu0RNVQyiaWCt3WMTJcYmSqGGbtC2SYZ1Zgvi6Dw1EiOc+PzyIpMW9Lg8FAb6biO4/mh3IYqy0L/TQY9cJAIqCoRvrz3bZxJDKHUAq9S1WGoOxFmAOvX/v43HERWJIqmjVqzvHJcn+v6U8R0Fc/3ScU0YoaG7wekEhqeFyzZFVkP1sPFVwJLxIQlISbs+UGLv65hrDsDd/bsWd7xjncAhEbNsVgMy7J2ZmRXEJeziH+lbF93Q2vxenTqsoUqA9VZhr77t1x69Xugdy/XD7ZxYbJId1uMI8OrZ8+WG8fIdIlEVNtyLeBuR/3aK5botgPRpRc1lF2xum7h6sKV5Mr6s1zPfI1ny6Ek0OJApf7e+sLF9wMkCaIRjXRcR1NlDg1mmCuKcS8n6Dubr4jrM8R27dmxeTJxnVLV5dRIjoiuoMoyqixRqthMzZn0tceENzOiicLzfTw/QA983jv+D0zo7Xyn+2VEDA3TcnG8gJcc6uQ1xwaW5Z3B7iS3Hujg9EiefKlM1FDZ15NgoDvJxJxJRFfxfIgaMkM9mXCnYTOSSFvVDN0ptMSEX5xYdwA3MDDAs88+y9GjR8PXnn766WtGRmQxLldH0UqEcOfRBQmRxQGlKksYqsJDj4+G25odqQjzQZJy317slOgw1TSF267vWhdRLTeOUsXhyFCm6X3XYkBTv3ZVkbFdD0kSq/ih7sSuWF23cHXhSnJl4zxOxjQGlUSo8biYzxaEu23hD1rLXmUSWq3kwm1qjFhcvH/foxdJxzXmyw6OJ7TlHBfyZZu9/UkmZsq1hZDMTfvaqdguuZJFVybKnq440/kKfhDg2D6aIuFLCiOxPqb0dhFQBhCP6tx2nQjenjg908R5jV2rT53Lcv1AmkszJSRJYipXpep45Eo2qZhO1FBCyZF8scqTo/lNlYXs1g79lpjwixPrDuA+8pGP8KEPfYif//mfx3Ec/vzP/5y//du/5ZOf/OROju+y4Up18KxECI1dqPX3DXYnm4g0GTOYmjP5X//7n3AznZiWR+7oW+iJxjA3qN223DhuGG5DVZt32a/FgKZ+7d87PsZzL8yRiGrs70uiqvKuWF23cHVhp7hyNY5q/DdDVXBdnznbWjXAqD/39Y7VVFzDsoS4r+16eH7A6ZE8HWmDUyM5Mgm9KRvv+T6OG9CTiXBhqkTV9ohoCh1pg6ihcdO+9jADCZAKdDRV4Y13DPLNx0a4fk+GvT1JTp6ZxC6XmFfifL/9VjRVoiNlEATQ0xbjyHBm1c7PxsaCaK0UYm6+wvyUTVvSQFflsJmjJxNhLGsS3UIX6W6UCxnsTvL2Vwxz36MXKVVcklGNrkwERZF3pPSnhd2BdTsxADz33HN85StfYXx8nN7eXt773vdy00037eT4NozNODEsp7690qr1cmElpelGd4dC2Wbi/CV++qm/5ezgMSZv/inGZst0pA2Ge1JbDkK3675cTarZq/1IXk3XsRaulWvZjU4MsHmuXIm/VpuLwJbmaeOxHcdjdKZMvmShKwrDfQl62mL88+lpFEnmwEAq1IN78vlpZvNVUXOrKaRjGvWh33SgE8dplipqLNFo5LCBh75Ewirw5eveSSCLYKteL1fPvK3kaHPPXfubnCbqOHlxjlLF5dBghrNj86IuLIBcySKiC9uwVFxfcqzlcDXNlbUSEVfTtayGa+U6LosTg+d5vOlNb+Lv//7v+cQnPrHpk10JrCeztl26b5cDjV1Q49kyTizFc/vv5FxmP0faYyRi2qbrOxZjt24X7CR24+q6hasHO8GVC7puzfZ2QFigvhZ/rcaDTfPc9ji6v4NC2UZRpBUFfedLFmbVw/UCIrqMVOv2jEdV9vWmQIKZfIXzZgHXFR3uyZjGT93dz9f+8RyVqkfF9ihXHbKDL6GdKpl0jNuu72wKqEanRYMEAUQjargN2ljKUa/l87xAaMBZLrmCRUc60lQLaFYdbNfnlgMd4f2r37trpSykxV8vLqwrgFMUBUVRsCwLXdfX/sAuwXpFF3dra/hyqJNVZ2ECed5BS7Vzuu/msHV+u8fdIoQWWlg/tpsr6xxWNB0SES1sFLhuIE0ytlCgvl5XhpV4cPE8//wDJwgCiVMjOSqWiyxJ2I7YVg2CgJHpEpoqk4rr+EGA5wVoqkxUV+luizI9byEhMmLCRh4kJCazZbLzFpprs7cyyZP08ILSTjaqkjCUprHXx60qMkEQhNugB/pToSQKiPqvr33/PFM5M2yaCCQoVkQnbCquh37Oo9Olq7YspCXU28JirFtG5Bd+4Rf4jd/4DR577DFGRkYYHR0N/7dbsV7Rxe1uDW8U1L33kfPbKrtx+6EuKmaV/u9/jZe/8Ijo0nJ9+tpjWx53Cy20sHVsJ1cu6LppuH4QykSMZ8vhXF+LvxbzoOv6TGZNPnf/yRX5ybJd/vn0DOOzZcoVF8fz8XwfXZWZK1q4XsChwQwd6QiZmp1WdyaKX3NrKFccOjMRju7v4NjBbo7u76AzE+E7x8cY6Ixz9NI/8/LnHqQtqOD7AUXTXcJh9XEP9yRxPREEaoosHCUaJFEGu5OhMLDng6bJHN6TRlcVLk4Vm6SgXnds4LJJRG0nGsXGr2VJpxY2hnU3MdQLcP/pn/6p6XVJkjh58uT2jmqbsN7M2na2hu+01cpgd5I33bmf54Kf42JZgqowh66vMFtF9y20cGWxnVxZ57C+9ljoqCB03exlbfmW469GHpwvWUKsWpEJAn9ZfhqdLjIyVSIIAlRFxvN95kseEV1YcP3iW46EtbiN46q/37RcYhF1WX3LUqXJOQAAJ7BJREFUfMnm0GCG0stez8Nn9zJHlKrtomtKU0D1U7f089Djo2FtW+M2qCTJS/jU9YMlTRPxqMaFySJzxeZmjsWm81dDWcjVVObTwuXDugO4U6dO7eQ4dgTrdVTYzlqvnZxo5pnTODMzDL7yVQze80pgaVr9aiCjFlq4lrGdXFnnsHTC4EB/iok5s+Zx2qzrthp/NfLgxJy5UG4R0ZblpydOz6AoEp3pCKWqi1tzdIhHVNxal0J90RszVPb3JRmdKVOuuty4t53XHBvg1KUC09lSE/dWS2VeOfXPFIdfi69qTCT76VZlLMfDrLqcHMlzw3BbeF2N427cBo1HtCUctxzXrySjdDWWhVxNZT4tXD6sGcBVKhX+7M/+jDNnznDjjTfyoQ996Kqpg9tIZm27JvVOTrT8d76NPTFO6mUvR6opvF+NZNRCC9citpsrR6eLjM+WOPFCniDw0VQFXZOJ6CqvWyRquxoPNPJgpeqiKMJlZLhHvH8xP2ULVRJRDdcL6EyLxW4QBJQqC4vf5RofGmuy2tri/M23TobHNy2X6KVzHLz0FD8Zup5zUgeqIrJlsiRxy4GOUGC3foyN8PduFdjdLmynvWML1w7WDOB+7/d+j2effZa77rqLb33rW+TzeX77t3/7coxty7gSXZQ7OdF6/9X/iW9WwuCthRZa2D3YTq4cnS6GhfkxQ2a+7GE7Do4r09ce46lz2XV7XDbyIBK1LclU2Im5mJ86UhFc12dstgzQ5KPcWCu2WtC4rz+9hHtvv+f19L77LmKewXP3n6h1lioMdSdIJwyCIGgKJDfC39d6x/y1HqC2sDmsGQk88sgjfO1rX6O7u5t/8S/+Be9///uvmgAOLn+GarsnmnnmNPnvfJvef/V/Ims6cvrqyH620MKLDdvJlU+cnqFg2kQNlXnXJxbVqFd3Wa5Pd60Za6Pis3V+UhSJoNZwsJif6u8Z6IwzV6xSrNgoshz6KK8Xg91J+pMqk3/xWTrueReR7iSQZBC47fqudZe3XM0Cu9uFaz1AbWFzWDOAM02T7u5uAPr6+iiVSps60YULF/jYxz5GPp8nk8nwqU99ir179za957//9//O3//936MoCqqq8tGPfpS77rprU+e7UtjuiebMzGBPjOOblVbw1kILuxjbxZUgtjEd1yNmaLiuL2Q0PB/PC5j0A3rbomRtb0PHrNfLmlWH7HyVWERjqDsR8lNjPa2uyEiqRLe6to/yavDNMvbEBG52FoaGw9d3KqN0LUttXMsBagubw5oBnOd5/OhHP6Ju2OC6btPfAe688841T/Txj3+c973vfbzjHe/gG9/4Br/zO7/DF7/4xab33HzzzfzyL/8y0WiUU6dO8YEPfIAf/OAHRCLr33586PERDg1mruiDvh0TzXccANKvfFVTzVsLLbSwO7EdXFnnr45UhPHZMo7nAwFV20OSEP+TJc5cmufQYGbdY2vsjh/qToYBUz3AWdw9v1U3mjp/aR2d7P3kf1rCXzuRUdppBYAWWthtWNNK67Wvfe3qB5AkvvOd76z6nmw2y5ve9CZ+/OMfoygKnufxspe9jIceeoj29vZlPxMEAS95yUt44IEH6O3tXeMyFvCX33iayTnzqp60leefZ+rzf07vr/3fRBpWrRvFblmNXiu2J9fKdcC1cy27yUprO7iyzl+3Hujg0WenmMqZlEwHs+oSIAK4evF/X0ecX37bkaY5vdKcb7Tgq6NcdXBdn3TC4MnnZ1EVKaxHq//7Zlxd/GqVqT/5f9GP3EjHT//Mhj7biI3y10rXuFVnmmtlrsC1cy3XynXsuJXWd7/73U0fvI6JiQl6enpQFKG0rSgK3d3dTExMrBjA3XvvvQwNDW0oeAOI1vwAF9eH7JZgZj1Q2zLEhgZRU+lNH6O1Gm2hhcuL7eDKOn+NzZq86+79fO/4GD98bhIkCALxP8cLMDSZUsVumtOrzfnluuMdx+PkSJ6bD3QQBD4ESuh0sNiuaiOQdJ3Y0CBy3+a3QzfDXy2pjRZebNiV+3KPPfYYn/70p/nLv/zLDX82FjXobJeYzVfo6hIT/cL4PA//ZIJEVGNPbwqz4vDwTyZ456vj7OvffJC03ahOT2N0dSF1Jen/+P+zpWN9+/gYnZkYiZiom0skoGTanLpU4NiNl79zqf5dXO24Vq4Drq1ruVYQixqomsUzF+Yo2x593Um6xwvMFap4fgAiD4fn+XiBRGcmFs7p1eb8UF+akmkTjy3U0Z4aydOWjNDdkWAqV8V2PDRNIVu06e9JUTJthvrS635OXNMk8Dy0ZJruX//VTV3/hfF5fvjMBD96dgJNkUkPREgkIuvir+WucaPXsBKupblyrVzLtXIdW8FlCeD6+vqYmprC87xwC3V6epq+vr4l733yySf5t//23/KZz3yG/fs3nvY2KxazcybxiBamWP/hRy8gEyAFARXTRgJkAv7hRy9si+n7dsCeGOfif/hdOt/5Htpe/4Ytp4hHJubFyrVsLbwYBIxMzF/21PO1ku6+Vq4Drp1r2U1bqNuB6WyJn5yZEZk4TWY6W2ImX4EgwAsCFEkiQPiROo4Hvh/O6dXm/BvvGOSbj41gVuywaSBXrHLDcBvlskVnyuDs2DyqIlGpOkxnhV3VSw52rus5CYKAS3/4nwksi8H/3/9Dd096w89XY9bNcVzwFZ49NxtmBNfir8N7UkuucSPXsBKulbkC1861XCvXsVX+WrcX6lbQ0dHBkSNHuP/++wG4//77OXLkyJLt06effpqPfvSj/Lf/9t+48cYbN3WuSkNxbh3ZQnVZW5fdlFrXenppe+ObSb7kjm053nb7u7bQQgs7j7r22lB3IvRvVhUJVZFQZBnPD5AkiYiuoshS05xebc7XmwbiEY25okU8onHj3vbQ2D0V17luIC2sqCThILORcgtJkmh/01toe/NbkeTN/aw0utjEIhq261GsODz5/CynRnJM5yqr8tdy19gqGWnhWsZl20L9xCc+wcc+9jE+85nPkEql+NSnPgXABz/4QT784Q9z9OhRfvd3f5dqtcrv/M7vhJ/7z//5P3Po0KF1nydqqEsm7W5Wsa6cP4fe3YOSSND5jndu23Fbwo8ttHD1wfV8Du5Jh40EAO1Jg5l8hfZUhELZRpIkfN8nEdWa5vRac35xd3w941V/v6JI9HbENhT0eJUK9qVRotcfJH705i1de2MNWyqmMTpdQpElZEnCrLrMFaocO9i56jFaUhstvJhw2QK4AwcO8JWvfGXJ63/xF38R/vmrX/3qls/zxjuG8P3mxtrdGsz41Srj/+2PiR4+Qv//9WvbeuyW8GMLLVx9uHFfO1NzZtNrvR0xqraHoSnEIxqW46HIMgcH23hNg6XWRuf8dnDE7Ff+F4Uf/4j9f/BfUJJb45bGhXbBdGhLGpSrLoEfEIuo9HfGGJs12Z49ihZauPqxK5sYthu7NZiRIxH6/q9fQ+9dWgu4HWitRlto4erC0f0dXJgoAAuLTUWReffd+xmbNdfsot/onN8qR3S+6z0kjt2+5eANmhfaZtXBUBXkqBTWwC222mqhhRc7XhQBHOyuYMY8cxq/WiVx8y3EDh+50sNpoYUWdgn6OuIrLjZ3S+bJq1SYf/i7tL3pLSiJBPGbjm7LcRsX2pIkg8RCAwO7p+ylhRZ2C140Adx2YDu05IIgIPuNr+ObZeI3Hd10wW8LLbRwbWInF5vbwWGlf36M2Xu/RuzwESL7treLf7Fnq6rKK3q2ttDCix2tAG6d2Iow7mLSPPbeX2IgY7SCtxZaaOGyYbMctjToO8bwx6/H6N+5YGq3lr208P9v797Doqzz/48/58DAwIAcFJhRKTFD84joankMTVFR0Tx8a3PXr5W1pZZtbelVloe1vPr23S3XzZ9tpa1ttfZL3JDUrFx1w1MX/cz1QCKKymEEROQ4zMz9+4NlYgRkMJlhxvfjuri6hrmZ+/2B5uVn7vtzEO2JdOBc1HCKO+D477U7PlyrPjQjy/LpfeEE2f3HsvOHqrrQdEvlQghxYxlWn18GtY1+mTs423sUO664J7/a07AXIdojuQTkohtdS64+NMOv5BNUeI4Qtc2x1ZcQQrhLcxmWay4ndd8Z3t1+nNR9Zzhv/mmB1Pr8CrVWElRwjrCqEskvIdoJuQLnoubWktOqVaTuO9PsmJLiK5WEh+gp7jOMkrjBKH46AmU2lRDCzZrKsLP5ZZwtKKewpJJgvR+1Vhs7DlU5bqvW51dNQCQ/Tl/Y5vnlTXtWC+FpcgXORQlxnaissVJRXYuiKFRU13KptIrScgsV1bVOY0rqP8FWZp3iF99sxFZc92lV8avbo09mUwkh3O3aDCssqSQ7rwy9vwaDXkutzU5eUSU2m53vTl3CXl1FwrefEHj8CND2+VV/u7a5PBVCOJMOnIua2qYlPDiAjqEBBAX4Oba9aXh7QaPXow8NocKmcur4XbvVlxBC3GznzVedbo0CThl2ubwGvb+GUIM/KpUKP60aP62a0quWuitsGg2GiFAq1Tq35FfDMXpN5akQwpncQm2FawfVvrv9OMGB/k7HBPprKbtUDMTi3zWG7i++hP+lcplNJYRw2c+9lXi9GacpI+qW/nh3+3EURaHWZsfvP3uiajUqqq9W0LGzAbWfju7PPOO2/Gq4lVa99rZntRDtiXTgfoamxpQoBRcZ/PVmyjpbCRlyNyqVSmZTCSFc1rDzpVWr+OFMMRn/LuCu28JITOjiUpa4MuM0IiQAq9XOxaIKAPw0aqqra7kvK53o8nCU0c+5Nb+cttKqsJBXXMHVSgvBgTrOm69KhgpxDZ+7hbrrcG6Ts6naQlPj4kr8O6AbfDeBcbLDghCidfKLK/jwyyzO5pdx+sIVTuReBsAQ4EeuudzlMWGuzJpPiOuEWqOic8cgtBoVV6ssKCo1hmEjiEpMRKVS3dzGtaA+TwtLKvnxQimV1VY0KjWhBp2MhROiCT53Ba6qxtrqhXZvVMPFJqtzz2IwdWbk3bF0jezfJucTQvi2f/6/PK5W1mII8OPSlWpqrXb0Oi06PzXVFrtjTFhLmdbcrPmIkACn27P+Wg2KRsFo0BAVbKHP3Xd57EpXfZ5++GUWdgWC9VqM4YF0MNTluSvtFuJW4nNX4PT+WrcOgO0aGczk+EgGHdjC0PPfSsAIIW6YXqchONAPq13Bblfw06i5WlWL1aag99e4PCasqbsDlTVWOncMdJrpqdGosNjsjMjdz53ffETnEM9+pu8aGUynUD2D4jrRMybMsQ+qjIUTojGfuwLXkLve9BqDgaj/fgR9bPc2P5cQwncF6OquOmXnlaFSq7Db7VgtUOtvJybS0GgJj+YmOzS3FVVzY+NO9BjO6FEjUAfoPdLuhq539VAI8ROf7sC19Zu+MusUKo0Gffc7CB6Y0GbnEULcGqotVjoY/OluCuFswVXMl6vw12mINQaj1aqdNnRvaW/TpiYf7Dp83jHTU22pISTnGEqPeAot/hj63+X29jalfiN7qPsQLhvZC9E0n7uFWlVjdct6a4rdzqWPPuTSJ39DUZQ2OYcQ4tZSZbFRUV1LSJCOO7p04M6uoXQ3BpNTcJWs81fw12ocx97IumkRIQFU1lgBCMv6DuPBL7BfKmhXV7eaWnOzLccyC+GtfO4KnN5fy7nLV9t8vTWVWo1p4dOo1Gq3z9YSQvimUf1NZBwrcNz2vLNPB77PLubOrqGOq1H1V9luZN20hle3lLuGUhTehZKAMJLa2cLisvSSEC3zuQ7cuMEx2O1td0WsMusUlcf/TcTUafiFh7fZeYQQtx5jRJBjoV2A1H1nml3P7UbGipmCtSRe2M/x7vdgLtcQYYohSfYbFcIr+dwt1LZW8X0m5UcOY6+WGVFCiLZ1vfXcmtufuazC0uxamJa8iyj//p5Eo8LDk+4iZUSsdN6E8FLSgXNR/Ti3jjNn03XJi2j0np+tJYTwbQ3HrNWrv8p27Vgxq9WOChUajarRZvD1+aXvfgfdXn2doD79PNEcIcRNJB04F1RmneL8qyuxlpWhUqnQBAV5uiQhxC2gufXc6idndY0MJmVELA9PuosOBn86hgY0mtSQ+cMFLrz+GuXfZwJ1yx4JIbyfdOBcYbejWG1gt3u6EiHELaQ1MzKbu91aUlaFYrOh2KyNfkYI4b18bhLDzWSrrEQTGEhgz17EvPgyKrX0d4UQ7uXqjMxrJzWoai1U1CqEdgyl6/NLJb+E8DHyjm5Gdc4Zcl54jopjRwEk/IQQ7VrD261YrZi++IDwPamcKyxj27/OymbwQvgY6ZU0wy8qmqD+/fHvEuPpUoQQokUNb7fmllSTHRxDZcydxEQGO01oEEL4hlvmFmpzewZeq+biBXTRRjSBgRgfnu+BSoUQojFXMswUrCWqp4F0oCLkXgKaWD9Olg0Rwjf43BW4XYcbf8qs3zOworq20fT6hmovXyZ39UqKtv5fd5YshBAA/P2bH5tcv83VDCt4dwMX/mcNJZfLm10/TgjhG3zuClxVg61m6j9pNtwzEJr/NOoXFkbk7AcJ6tff5St2Qghxs3QI8sd8ufKGM6xjynRqL5kJLzdQUFJBabmFqhoben8NoQYd0eGyBJIQvsLnrsDp/bWNNnS+3mrmAFU/ZmEpyAegw8hR5Fs0Ln3aFUKIm0mloslN6a+XYbaqKq4eOQSAf5euGOIT6NwxkDN5ZVRWWwnwU1NZbeVMXhmdOwa6tT1CiLbjcx04aHyr4HqrmStWKwXvvUPh5g8czzX8tNtwQcyGgSqEEG2lNRl2eUc6+e/8H2ov/ZRPF4sq6W7qQGCAlupaG4EBWrqbOnCxqNJtbRBCtC2fu4UKjTd0TojrxI5DuUBdMFbWWKmssTKyvwmVVotp4WI0gT99Mi0uqyY82N/pNWX8iBDCXVrKsMLLleQVVdIxVM/+iH4MeLg7fp06OY4vLqsmMkxPVPhPuaYoimSYED7E567AVf2nc1a/1Qw0vZr5fZ0sBB87CIC/yYQ2NNRx/PU+7QohRFtRFBptlwXXLBFivsqlglJGFRwiJtSPilo7X5r9nIZ4SIYJ4ft8rgOn99c2udVMwz0DU0bE4n/sMKVff4XdYmn0Gi3tPyiEEG3h+9NFnDeXM6B7RLMZdltUCPH6q3Q+9z2BxXlNDvGQDBPC9/ncLdRxg2Ow25UWj4v+9TxsVZWodbpGz9V/2m04C3Vkf5PMQhVCtKkBd3TEfLmS77OLiY4IanbP0/Db4zgdtQhrYN3z1w7xkAwTwvf5XAfueiqzTlGSvh3T40+gDghAGxzS7LGu7j8ohBA3S/0sVGi8RIitqor89evoEjOYIl00QYE/PdfU7VHJMCF8m8/dQr0eW9kVrJdLsNfIQF4hRPvV1KQppaYa6+US4sI1cntUCHFrXIGzWyyodTqCB/0CQ3wCKo3G0yUJIUSzGl5Rs1ssqPz80IaGcdvLK1FpNCRds9C43B4V4tbj8x24quzT5P15LaYnFqLvfod03oQQ7VbDWagj+5uw19Rw4X9fR9/jTjrNmOXIL7k9KoTw+Q6cX0RHArrF4hfR0dOlCCHEdV2pqFvmqP6KmqIoBHSLJaBbrKdLE0K0Mz7bgbMUFuIXGYk2NJTOC57ydDlCCNGiWff2wG5XsFVVYb1yBW2HDkT+14OeLksI0Q755CQGi9nMuRXLuLwj3dOlCCFEqyiKQv7bf+LCH/4HxWbzdDlCiHbKbVfgcnJyeOGFFygtLSU0NJQ1a9Zw++23Ox1js9lYtWoV+/btQ6VSMX/+fGbOnNnqc/l16kRE8lRC7r7nJlUvhBDuoVKpCE+egq28XMbsCiGa5bYrcC+//DIPPvggO3fu5MEHH2TZsmWNjvn888/Jzc1l165dfPLJJ6xdu5YLFy606jy2srK6AJww0Wl7LCGEaO+qfvwRgMA74wgemODhaoQQ7ZlbrsAVFxdz/Phx3n//fQCSk5NZuXIlJSUlhIeHO45LT09n5syZqNVqwsPDGTt2LDt27OCRRx5x+VxXvt5FeMqMm94GT1CrVZ4u4abxlbb4SjvAN9riC21o6HJaKlGPPIbmOouMewtf+ttIW9ofX2jHz22DWzpw+fn5REVFofnP7QCNRkNkZCT5+flOHbj8/HxMJpPjsdFopKCgoFXn6vHw3JtSc3sQEWHwdAk3ja+0xVfaAb7VFl/Rd9VyT5dw0/jS/1/SlvbHV9rxc/jkJAYhhBBCCF/mlg6c0WiksLAQ239mVNlsNsxmM0ajsdFxeXl5jsf5+flER0e7o0QhhBBCCK/hlg5cREQEvXr1Ii0tDYC0tDR69erldPsUICkpiS1btmC32ykpKWH37t2MHz/eHSUKIYQQQngNlaIoijtOlJ2dzQsvvEBZWRkhISGsWbOG2NhYHn30URYtWkTfvn2x2WysWLGCf/3rXwA8+uijzJ492x3lCSGEEEJ4Dbd14IQQQgghxM0hkxiEEEIIIbyMdOCEEEIIIbyMdOCEEEIIIbyMdOCEEEIIIbyM13XgcnJymD17NuPHj2f27NmcPXu20TE2m43ly5czduxY7rvvPrZs2eL+Ql3gSlvWrVvHpEmTmDJlCtOnT2ffvn3uL9QFrrSl3pkzZ+jfvz9r1qxxX4EucrUd6enpTJ48meTkZCZPnkxRUZF7C3WBK20pLi5m/vz5TJ48maSkJF555RWsVqv7i72ONWvWkJiYSFxcHFlZWU0e4y3vefCdDJP8an/5Bb6TYb6SX9CGGaZ4mTlz5iipqamKoihKamqqMmfOnEbHbN26VZk3b55is9mU4uJiZcSIEcr58+fdXWqLXGnL3r17lcrKSkVRFOXEiRNKQkKCUlVV5dY6XeFKWxRFUaxWq/LQQw8pzzzzjPLaa6+5s0SXuNKOo0ePKhMmTFDMZrOiKIpSVlamVFdXu7VOV7jSllWrVjn+DhaLRZkxY4ayfft2t9bZksOHDyt5eXnKvffeq5w6darJY7zlPa8ovpNhkl/tL78UxXcyzFfyS1HaLsO86gpccXExx48fJzk5GYDk5GSOHz9OSUmJ03Hp6enMnDkTtVpNeHg4Y8eOZceOHZ4ouVmutmXEiBHo9XoA4uLiUBSF0tJSd5d7Xa62BWDDhg2MHj2a22+/3c1VtszVdmzcuJF58+bRqVMnAIKDg/H393d7vdfjaltUKhUVFRXY7XYsFgu1tbVERUV5ouRmDRo0qNGuLdfyhvc8+E6GSX7d7uYqXeMrGeZL+QVtl2Fe1YHLz88nKioKjUYDgEajITIykvz8/EbHmUwmx2Oj0UhBQYFba22Jq21pKDU1lZiYmHa3vZirbTl58iT79+9n7ty5HqiyZa62Izs7m/Pnz/PLX/6SadOm8ec//xmlnS2n6GpbnnjiCXJychg+fLjjKyEhwRMl/yze8J4H38kwya/2yVcy7FbLL7ix97xXdeBuZYcOHeLNN9/kjTfe8HQpN6S2tpaXXnqJ5cuXO96U3spms3Hq1Cnef/99/vrXv7J37162bdvm6bJuyI4dO4iLi2P//v3s3buXI0eOtKsrPcI3SH61L76SYbd6fnlVB85oNFJYWIjNZgPq/ic0m82NLk0ajUby8vIcj/Pz89vdpz5X2wKQmZnJc889x7p164iNjXV3qS1ypS2XLl0iNzeX+fPnk5iYyKZNm/j73//OSy+95KmyG3H1b2IymUhKSkKn02EwGBgzZgxHjx71RMnNcrUtmzdvZsqUKajVaoKDg0lMTOTgwYOeKPln8Yb3PPhOhkl+tb/8At/JsFstv+DG3vNe1YGLiIigV69epKWlAZCWlkavXr0IDw93Oi4pKYktW7Zgt9spKSlh9+7djB8/3hMlN8vVthw9epTFixfz1ltv0bt3b0+U2iJX2mIymTh48CBff/01X3/9Nb/+9a+ZNWsWK1eu9FTZjbj6N0lOTmb//v0oikJtbS0HDhygZ8+enii5Wa62pUuXLuzduxcAi8VCRkYGPXr0cHu9P5c3vOfBdzJM8qv95Rf4TobdavkFN/iev7lzLdre6dOnlRkzZijjxo1TZsyYoWRnZyuKoiiPPPKIcvToUUVR6mYKLVu2TBkzZowyZswY5eOPP/Zkyc1ypS3Tp09XhgwZokyZMsXxdfLkSU+W3SRX2tLQW2+91S5ncbnSDpvNpqxevVpJSkpSJk6cqKxevVqx2WyeLLtJrrTl3Llzyty5c5Xk5GRlwoQJyiuvvKLU1tZ6suxGVq5cqYwYMULp1auXcs899ygTJ05UFMU73/OK4jsZJvnV/vJLUXwnw3wlvxSl7TJMNrMXQgghhPAyXnULVQghhBBCSAdOCCGEEMLrSAdOCCGEEMLLSAdOCCGEEMLLSAdOCCGEEMLLSAdOCCGEEMLLSAdO3DRr167l2Wefdes54+LiGDBgAH/4wx/cel5vNHbsWPr06eP2v5EQ3kDyq32T/GpMOnDtVHx8vOOrZ8+e9OvXz/H4H//4R5ucMzMzkwEDBlBeXt7ouZSUFDZv3twm5/25tm3bxuLFiwG4cOECcXFxTJs2zemYkpIS+vTpQ2JioidKJCcnh0WLFjFkyBASEhKYPHky77//vmOrGHfYvXs3jz32mNvOJ25dkl+uk/xyjeRXY9KBa6cyMzMdXyaTifXr1zseT5kyxXGc1Wq9aeeMj48nKiqKXbt2OX0/KyuL06dPM2nSpJt2rrZWWVlJVlaW43FaWhqdO3f2SC25ubnMmjULo9HI559/znfffcebb77JsWPHqKio8EhNQrQlya+fR/JLuEI6cF7m4MGDjBw5kg0bNjBs2DCWLFnCZ599xgMPPOB0XFxcHOfOnQPq9ohbs2YNo0eP5p577mHZsmVUV1c3+frTpk0jNTXV6XupqamMHj2asLAwVq1axahRoxg4cCDTp0/nyJEj162zocTERL799lsA7HY7GzZsYOzYsQwZMoSnnnqK0tJSAGpqanj22WcZMmQIgwYN4v7776eoqKhVv6epU6eydetWpzakpKQ4HVNYWMjChQsZOnQoiYmJfPDBB47njh49yuzZsxk0aBDDhw9nxYoVWCwWx/NxcXF89NFHjBs3jsGDB7N8+XKa29TkrbfeIj4+niVLlhAZGQlAbGwsb7zxBiEhIQB89dVXTJo0iUGDBjFnzhyys7Odfm/vvvsukydPJiEhgaeffpqamhrH87t372bq1KkMHDiQsWPHOvYGFKK9kfxyjeSXcIV04LxQUVERV65c4ZtvvnFpM+XXX3+dnJwcUlNT2bVrF2azmXXr1jV57NSpU/nuu+/Iy8sD6oIqLS3NER59+/YlNTWVQ4cOkZyczFNPPeX0ZnTVBx98wO7du9m8eTP79u2jQ4cOrFixAoCtW7dSXl7Onj17OHjwIMuXLycgIKBVrz9lyhTS09Ox2WxkZ2dTUVFB//79Hc/b7XZ+85vfEBcXx969e9m0aRObNm1i3759AKjVapYsWcKBAwf4+OOPycjI4G9/+5vTOfbs2cOnn37Ktm3b+OKLLxw/e62MjIzrbkqck5PDb3/7W5YuXUpGRgYjR47k8ccfdwrcL774gr/85S989dVXnDp1is8++wyoC+rnn3+e3/3udxw5coQPP/zQY5/UhXCF5FfLJL+EK6QD54XUajWLFi1Cp9O1GAyKorBlyxaWLl1KaGgoBoOBxx57jO3btzd5vNFoZPDgwY5xKhkZGdTU1DBq1CigLiDDwsLQarXMmzcPi8VCTk5Oq9vwySefsHjxYqKjo9HpdCxYsICdO3ditVrRarWUlpZy7tw5NBoNffr0wWAwtOr1o6Oj6datG99++y1bt25t9On1hx9+oKSkhAULFqDT6ejatSuzZs0iPT0dgD59+jBgwAC0Wi1dunRh9uzZHD582Ok1Hn30UUJCQjCZTAwZMoSTJ082WUtpaSmdOnVqttb09HRGjRrFsGHD8PPz4+GHH6a6uprMzEzHMXPmzCEqKorQ0FDuvfdeTpw4AcCnn37K/fffz7Bhw1Cr1URFRdG9e/dW/a6EcCfJr5ZJfglXaD1dgGi9sLAw/P39XTq2pKSEqqoqpk+f7vieoijY7fZmfyYlJYX169fz+OOPs23bNiZPnoyfnx8A7733Hlu2bMFsNqNSqSgvL+fy5cutbkNeXh5PPvkkavVPnyHUajXFxcVMnTqVgoICnnnmGcrKypgyZQqLFy921OCqlJQUtm7dSmZmJps3b3bckgG4ePEiZrOZQYMGOb5ns9kcj3Nycnjttdc4duwYVVVV2Gw2evfu7fT6DUNNr9c3Ox4kNDSUS5cuNVun2WzGZDI5/R6MRiOFhYXNnstsNgOQn5/v+MdJCG8g+eUayS/REunAeSGVSuX0WK/XO40JafhmCwsLIyAggO3btxMVFeXS648bN47ly5dz4MABvvzyS8fYiiNHjvDOO++wceNGevTogVqtZvDgwU2Onbi2JpvNRklJieNxdHQ0q1evJiEhockaFixYwIIFC7hw4QLz58+nW7duzJw506X6G7ZjxYoV9O7dm86dOzsFoNFopEuXLo0GPNd75ZVXuOuuu3jjjTcwGAxs3LiRnTt3tur89e6++2527drF/fff3+TzkZGRTgOWFUUhPz/fpb+X0WgkNzf3huoSwhMkv1wj+SVaIrdQfUDPnj358ccfOXHiBDU1Naxdu9bxnFqtZubMmaxevZri4mKgbvBrc+MdAAIDA0lKSmLp0qWYTCb69u0LQEVFBRqNhvDwcKxWK3/605+anLIP0K1bN2pqatizZw+1tbW8/fbbTmMiHnjgAf74xz9y8eJFoO6T9u7duwE4cOAAp06dwmazYTAY0Gq1aDSaVv9eAgMD2bRpE7///e8bPdevXz8MBgMbNmyguroam81GVlYWR48edbQ1KCiIoKAgsrOz+eijj1p9/nqLFi0iMzOTNWvWOP5xOnfuHM8++yxlZWVMmDCBf/7zn2RkZFBbW8t7772HTqcjPj6+xdeeMWMGn332GRkZGdjtdgoLC50GEAvR3kl+Nd8OyS9xPdKB8wHdunXjySefZO7cuYwbN67Rp8LnnnuO2267jVmzZjFw4EDmzp3b4riPlJQULl68yNSpUx3fGz58OCNHjmT8+PEkJibi7++P0Whs8ueDg4N5+eWXefHFFxk5ciR6vZ7o6GjH87/61a9ITExk3rx5xMfHM2vWLEf4FBUVsWjRIhISEpg4cSK/+MUvnJYeaI2+ffsSExPT6PsajYa3336bkydPMmbMGIYOHcqLL77oCPTnn3+etLQ0Bg4cyEsvvcTEiRNv6PwAMTExfPzxx1y8eJHk5GQSEhJYuHAhffr0ISgoiNjYWF5//XVWrlzJ0KFD+eabb1i/fj06na7F1+7Xrx+vvvqq42rAQw895BjAvWzZMpYtW3bDdQvhDpJfzZP8kvy6HpXS3NxhIbxA37590el0zJkzh6efftrT5bRr48ePx2w2k5SUxKuvvurpcoS45Ul+uU7yqzHpwAkhhBBCeBm5hSqEEEII4WWkAyeEEEII4WWkAyeEEEII4WWkAyeEEEII4WWkAyeEEEII4WWkAyeEEEII4WWkAyeEEEII4WX+P2OkSucSqRh5AAAAAElFTkSuQmCC\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Test\n", "with torch.no_grad():\n", " data, targets_val = next(iter(dataloaders['all_val']))\n", " model_input = data.to(device)# TODO: Turn the 28 by 28 image tensors into a 784 dimensional tensor.\n", " predicted_val = model(model_input).squeeze()\n", "# _, predicted = torch.max(out, 1)\n", " print('predicted.shape: {}'.format(predicted_val.shape))\n", " print('predicted[:20]: \\t{}'.format(predicted_val[:20]))\n", " print('targets[:20]: \\t\\t{}'.format(targets_val[:20]))\n", " \n", "# Test\n", "with torch.no_grad():\n", " data, targets = next(iter(dataloaders['test']))\n", " model_input = data.to(device)# TODO: Turn the 28 by 28 image tensors into a 784 dimensional tensor.\n", " predicted = model(model_input).squeeze()\n", " print('predicted.shape: {}'.format(predicted.shape))\n", " print('predicted[:20]: \\t{}'.format(predicted[:20]))\n", " print('targets[:20]: \\t\\t{}'.format(targets[:20]))\n", " \n", "#Time for a real test\n", "path_to_save = './plots'\n", "if not os.path.exists(path_to_save):\n", " os.makedirs(path_to_save)\n", " \n", "fig, (ax1,ax2) = plt.subplots(1,2, sharey=True)\n", "# test_predictions = model(normed_test_data).flatten()\n", "r = r2_score(targets_val, predicted_val.cpu())\n", "ax1.scatter(targets_val, predicted_val.cpu(),alpha=0.5, label='$R^2$ = %.3f' % (r))\n", "ax1.legend(loc=\"upper left\")\n", "ax1.set_xlabel('True Values [Mean Conc.]')\n", "ax1.set_ylabel('Predictions [Mean Conc.]')\n", "ax1.axis('equal')\n", "ax1.axis('square')\n", "ax1.set_xlim([0,1])\n", "ax1.set_ylim([0,1])\n", "_ = ax1.plot([-100, 100], [-100, 100], 'r:')\n", "ax1.set_title('Test dataset')\n", "fig.set_figheight(30)\n", "fig.set_figwidth(10)\n", "#plt.show()\n", "#plt.close('all')\n", "\n", "#Whole dataset\n", "# dataset_predictions = model.predict(normed_dataset).flatten()\n", "r = r2_score(targets, predicted.cpu())\n", "ax2.scatter(targets, predicted.cpu(), alpha=0.5, label='$R^2$ = %.3f' % (r))\n", "ax2.legend(loc=\"upper left\")\n", "ax2.set_xlabel('True Values [Mean Conc.]')\n", "ax2.set_ylabel('Predictions [Mean Conc.]')\n", "ax2.axis('equal')\n", "ax2.axis('square')\n", "ax2.set_xlim([0,1])\n", "ax2.set_ylim([0,1])\n", "_ = ax2.plot([-100, 100], [-100, 100], 'r:')\n", "ax2.set_title('Whole dataset')\n", "# plt.show()\n", "fig.savefig(os.path.join(path_to_save, 'FFNet_LONGER_R2Score_' + config_str + '.png'), bbox_inches='tight')\n", "# #plt.close('all')" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "lr: 0.01, momentum: 0.9, weight_decay: 0.001, dampening: 0, nesterov: False \n", "FeedforwardNeuralNetModel(\n", " (fc1): Linear(in_features=47, out_features=64, bias=True)\n", " (relu): ReLU()\n", " (fc2): Linear(in_features=64, out_features=1, bias=True)\n", ")\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: 1000\n", " EPOCH 0. Progress: 0.0%. \n", " Training R^2: 0.3907. Test R^2: 0.3027. Loss Train: 0.0637. Loss Val: 0.0418. Time: 2.5571\n", " EPOCH 10. Progress: 1.0%. \n", " Training R^2: 0.5283. Test R^2: 0.4687. Loss Train: 0.0272. Loss Val: 0.0335. Time: 2.3194\n", " EPOCH 20. Progress: 2.0%. \n", " Training R^2: 0.5861. Test R^2: 0.5017. Loss Train: 0.0250. Loss Val: 0.0318. Time: 2.1796\n", " EPOCH 30. Progress: 3.0%. \n", " Training R^2: 0.5841. Test R^2: 0.5459. Loss Train: 0.0249. Loss Val: 0.0283. Time: 2.1632\n", " EPOCH 40. Progress: 4.0%. \n", " Training R^2: 0.4285. Test R^2: 0.3964. Loss Train: 0.0226. Loss Val: 0.0375. Time: 2.2345\n", " EPOCH 50. Progress: 5.0%. \n", " Training R^2: 0.6103. Test R^2: 0.6069. Loss Train: 0.0199. Loss Val: 0.0246. Time: 2.3856\n", " EPOCH 60. Progress: 6.0%. \n", " Training R^2: 0.5282. Test R^2: 0.4812. Loss Train: 0.0202. Loss Val: 0.0336. Time: 2.1624\n", " EPOCH 70. Progress: 7.000000000000001%. \n", " Training R^2: 0.6683. Test R^2: 0.6814. Loss Train: 0.0209. Loss Val: 0.0201. Time: 2.6237\n", " EPOCH 80. Progress: 8.0%. \n", " Training R^2: 0.6456. Test R^2: 0.6339. Loss Train: 0.0203. Loss Val: 0.0246. Time: 2.3192\n", " EPOCH 90. Progress: 9.0%. \n", " Training R^2: 0.6576. Test R^2: 0.6848. Loss Train: 0.0209. Loss Val: 0.0205. Time: 2.1755\n", " EPOCH 100. Progress: 10.0%. \n", " Training R^2: 0.6762. Test R^2: 0.5351. Loss Train: 0.0177. Loss Val: 0.0203. Time: 2.4397\n", " EPOCH 110. Progress: 11.0%. \n", " Training R^2: 0.7007. Test R^2: 0.6804. Loss Train: 0.0166. Loss Val: 0.0192. Time: 2.8877\n", " EPOCH 120. Progress: 12.0%. \n", " Training R^2: 0.6851. Test R^2: 0.6832. Loss Train: 0.0173. Loss Val: 0.0206. Time: 2.3207\n", " EPOCH 130. Progress: 13.0%. \n", " Training R^2: 0.6630. Test R^2: 0.6563. Loss Train: 0.0166. Loss Val: 0.0217. Time: 2.2040\n", " EPOCH 140. Progress: 14.000000000000002%. \n", " Training R^2: 0.7237. Test R^2: 0.7122. Loss Train: 0.0164. Loss Val: 0.0175. Time: 2.1338\n", " EPOCH 150. Progress: 15.0%. \n", " Training R^2: 0.6712. Test R^2: 0.6499. Loss Train: 0.0159. Loss Val: 0.0213. Time: 2.4244\n", " EPOCH 160. Progress: 16.0%. \n", " Training R^2: 0.6859. Test R^2: 0.6410. Loss Train: 0.0169. Loss Val: 0.0220. Time: 2.7059\n", " EPOCH 170. Progress: 17.0%. \n", " Training R^2: 0.7282. Test R^2: 0.7186. Loss Train: 0.0156. Loss Val: 0.0162. Time: 2.3983\n", " EPOCH 180. Progress: 18.0%. \n", " Training R^2: 0.7141. Test R^2: 0.7137. Loss Train: 0.0157. Loss Val: 0.0175. Time: 2.2243\n", " EPOCH 190. Progress: 19.0%. \n", " Training R^2: 0.7243. Test R^2: 0.7229. Loss Train: 0.0166. Loss Val: 0.0168. Time: 2.1938\n", " EPOCH 200. Progress: 20.0%. \n", " Training R^2: 0.7037. Test R^2: 0.7038. Loss Train: 0.0148. Loss Val: 0.0176. Time: 2.2693\n", " EPOCH 210. Progress: 21.0%. \n", " Training R^2: 0.6899. Test R^2: 0.7147. Loss Train: 0.0153. Loss Val: 0.0189. Time: 2.3512\n", " EPOCH 220. Progress: 22.0%. \n", " Training R^2: 0.6460. Test R^2: 0.6535. Loss Train: 0.0149. Loss Val: 0.0215. Time: 2.2150\n", " EPOCH 230. Progress: 23.0%. \n", " Training R^2: 0.7394. Test R^2: 0.7456. Loss Train: 0.0146. Loss Val: 0.0150. Time: 2.2148\n", " EPOCH 240. Progress: 24.0%. \n", " Training R^2: 0.7453. Test R^2: 0.7390. Loss Train: 0.0157. Loss Val: 0.0165. Time: 2.1932\n", " EPOCH 250. Progress: 25.0%. \n", " Training R^2: 0.7065. Test R^2: 0.7047. Loss Train: 0.0151. Loss Val: 0.0182. Time: 2.4854\n", " EPOCH 260. Progress: 26.0%. \n", " Training R^2: 0.7158. Test R^2: 0.7538. Loss Train: 0.0146. Loss Val: 0.0166. Time: 2.2082\n", " EPOCH 270. Progress: 27.0%. \n", " Training R^2: 0.6339. Test R^2: 0.5646. Loss Train: 0.0154. Loss Val: 0.0268. Time: 2.1861\n", " EPOCH 280. Progress: 28.000000000000004%. \n", " Training R^2: 0.7504. Test R^2: 0.7543. Loss Train: 0.0144. Loss Val: 0.0151. Time: 2.1763\n", " EPOCH 290. Progress: 28.999999999999996%. \n", " Training R^2: 0.7155. Test R^2: 0.7553. Loss Train: 0.0146. Loss Val: 0.0163. Time: 2.2144\n", " EPOCH 300. Progress: 30.0%. \n", " Training R^2: 0.7428. Test R^2: 0.7277. Loss Train: 0.0176. Loss Val: 0.0160. Time: 2.4493\n", " EPOCH 310. Progress: 31.0%. \n", " Training R^2: 0.7452. Test R^2: 0.7370. Loss Train: 0.0150. Loss Val: 0.0156. Time: 2.2905\n", " EPOCH 320. Progress: 32.0%. \n", " Training R^2: 0.7173. Test R^2: 0.7602. Loss Train: 0.0147. Loss Val: 0.0158. Time: 2.3167\n", " EPOCH 330. Progress: 33.0%. \n", " Training R^2: 0.6786. Test R^2: 0.6614. Loss Train: 0.0157. Loss Val: 0.0223. Time: 3.1340\n", " EPOCH 340. Progress: 34.0%. \n", " Training R^2: 0.7275. Test R^2: 0.6948. Loss Train: 0.0148. Loss Val: 0.0184. Time: 2.9551\n", " EPOCH 350. Progress: 35.0%. \n", " Training R^2: 0.7568. Test R^2: 0.7569. Loss Train: 0.0134. Loss Val: 0.0146. Time: 3.0766\n", " EPOCH 360. Progress: 36.0%. \n", " Training R^2: 0.7079. Test R^2: 0.7480. Loss Train: 0.0138. Loss Val: 0.0161. Time: 2.3230\n", " EPOCH 370. Progress: 37.0%. \n", " Training R^2: 0.7385. Test R^2: 0.7483. Loss Train: 0.0145. Loss Val: 0.0157. Time: 2.2408\n", " EPOCH 380. Progress: 38.0%. \n", " Training R^2: 0.7519. Test R^2: 0.7619. Loss Train: 0.0141. Loss Val: 0.0150. Time: 2.3781\n", " EPOCH 390. Progress: 39.0%. \n", " Training R^2: 0.7424. Test R^2: 0.7667. Loss Train: 0.0131. Loss Val: 0.0144. Time: 2.3558\n", " EPOCH 400. Progress: 40.0%. \n", " Training R^2: 0.7519. Test R^2: 0.7712. Loss Train: 0.0151. Loss Val: 0.0152. Time: 2.5086\n", " EPOCH 410. Progress: 41.0%. \n", " Training R^2: 0.7430. Test R^2: 0.7316. Loss Train: 0.0137. Loss Val: 0.0171. Time: 2.6166\n", " EPOCH 420. Progress: 42.0%. \n", " Training R^2: 0.7765. Test R^2: 0.7671. Loss Train: 0.0136. Loss Val: 0.0131. Time: 2.5005\n", " EPOCH 430. Progress: 43.0%. \n", " Training R^2: 0.6761. Test R^2: 0.6853. Loss Train: 0.0134. Loss Val: 0.0200. Time: 2.5180\n", " EPOCH 440. Progress: 44.0%. \n", " Training R^2: 0.7145. Test R^2: 0.7455. Loss Train: 0.0132. Loss Val: 0.0175. Time: 2.4153\n", " EPOCH 450. Progress: 45.0%. \n", " Training R^2: 0.7104. Test R^2: 0.7209. Loss Train: 0.0126. Loss Val: 0.0177. Time: 2.2806\n", " EPOCH 460. Progress: 46.0%. \n", " Training R^2: 0.7438. Test R^2: 0.7513. Loss Train: 0.0130. Loss Val: 0.0158. Time: 3.4491\n", " EPOCH 470. Progress: 47.0%. \n", " Training R^2: 0.7741. Test R^2: 0.7920. Loss Train: 0.0130. Loss Val: 0.0132. Time: 2.5159\n", " EPOCH 480. Progress: 48.0%. \n", " Training R^2: 0.7337. Test R^2: 0.7877. Loss Train: 0.0125. Loss Val: 0.0143. Time: 2.3047\n", " EPOCH 490. Progress: 49.0%. \n", " Training R^2: 0.7610. Test R^2: 0.7817. Loss Train: 0.0142. Loss Val: 0.0146. Time: 2.2825\n", " EPOCH 500. Progress: 50.0%. \n", " Training R^2: 0.7630. Test R^2: 0.7784. Loss Train: 0.0148. Loss Val: 0.0143. Time: 2.2451\n", " EPOCH 510. Progress: 51.0%. \n", " Training R^2: 0.7710. Test R^2: 0.8033. Loss Train: 0.0140. Loss Val: 0.0141. Time: 2.2595\n", " EPOCH 520. Progress: 52.0%. \n", " Training R^2: 0.7628. Test R^2: 0.7718. Loss Train: 0.0135. Loss Val: 0.0136. Time: 2.2565\n", " EPOCH 530. Progress: 53.0%. \n", " Training R^2: 0.7754. Test R^2: 0.7837. Loss Train: 0.0122. Loss Val: 0.0124. Time: 2.5370\n", " EPOCH 540. Progress: 54.0%. \n", " Training R^2: 0.7569. Test R^2: 0.7522. Loss Train: 0.0132. Loss Val: 0.0148. Time: 2.2073\n", " EPOCH 550. Progress: 55.00000000000001%. \n", " Training R^2: 0.7675. Test R^2: 0.7798. Loss Train: 0.0126. Loss Val: 0.0137. Time: 2.1677\n", " EPOCH 560. Progress: 56.00000000000001%. \n", " Training R^2: 0.7860. Test R^2: 0.7938. Loss Train: 0.0153. Loss Val: 0.0124. Time: 2.1945\n", " EPOCH 570. Progress: 56.99999999999999%. \n", " Training R^2: 0.7332. Test R^2: 0.7530. Loss Train: 0.0124. Loss Val: 0.0154. Time: 2.3476\n", " EPOCH 580. Progress: 57.99999999999999%. \n", " Training R^2: 0.7587. Test R^2: 0.7810. Loss Train: 0.0127. Loss Val: 0.0129. Time: 2.3162\n", " EPOCH 590. Progress: 59.0%. \n", " Training R^2: 0.7203. Test R^2: 0.7146. Loss Train: 0.0123. Loss Val: 0.0175. Time: 2.4659\n", " EPOCH 600. Progress: 60.0%. \n", " Training R^2: 0.7534. Test R^2: 0.7553. Loss Train: 0.0121. Loss Val: 0.0144. Time: 2.3201\n", " EPOCH 610. Progress: 61.0%. \n", " Training R^2: 0.7489. Test R^2: 0.7627. Loss Train: 0.0134. Loss Val: 0.0145. Time: 2.7255\n", " EPOCH 620. Progress: 62.0%. \n", " Training R^2: 0.6548. Test R^2: 0.6529. Loss Train: 0.0144. Loss Val: 0.0227. Time: 2.6257\n", " EPOCH 630. Progress: 63.0%. \n", " Training R^2: 0.7746. Test R^2: 0.8111. Loss Train: 0.0119. Loss Val: 0.0120. Time: 2.8682\n", " EPOCH 640. Progress: 64.0%. \n", " Training R^2: 0.6986. Test R^2: 0.7465. Loss Train: 0.0123. Loss Val: 0.0163. Time: 2.2709\n", " EPOCH 650. Progress: 65.0%. \n", " Training R^2: 0.7670. Test R^2: 0.7824. Loss Train: 0.0127. Loss Val: 0.0138. Time: 2.5673\n", " EPOCH 660. Progress: 66.0%. \n", " Training R^2: 0.7683. Test R^2: 0.7935. Loss Train: 0.0127. Loss Val: 0.0118. Time: 2.6559\n", " EPOCH 670. Progress: 67.0%. \n", " Training R^2: 0.7814. Test R^2: 0.8140. Loss Train: 0.0121. Loss Val: 0.0119. Time: 2.1893\n", " EPOCH 680. Progress: 68.0%. \n", " Training R^2: 0.7712. Test R^2: 0.7824. Loss Train: 0.0122. Loss Val: 0.0140. Time: 2.5471\n", " EPOCH 690. Progress: 69.0%. \n", " Training R^2: 0.7881. Test R^2: 0.8145. Loss Train: 0.0128. Loss Val: 0.0117. Time: 1.9975\n", " EPOCH 700. Progress: 70.0%. \n", " Training R^2: 0.7613. Test R^2: 0.8018. Loss Train: 0.0121. Loss Val: 0.0128. Time: 1.9574\n", " EPOCH 710. Progress: 71.0%. \n", " Training R^2: 0.7207. Test R^2: 0.7138. Loss Train: 0.0117. Loss Val: 0.0183. Time: 1.9471\n", " EPOCH 720. Progress: 72.0%. \n", " Training R^2: 0.7946. Test R^2: 0.8287. Loss Train: 0.0124. Loss Val: 0.0111. Time: 1.9482\n", " EPOCH 730. Progress: 73.0%. \n", " Training R^2: 0.7411. Test R^2: 0.7795. Loss Train: 0.0119. Loss Val: 0.0142. Time: 2.0103\n", " EPOCH 740. Progress: 74.0%. \n", " Training R^2: 0.7652. Test R^2: 0.7693. Loss Train: 0.0127. Loss Val: 0.0142. Time: 1.8568\n", " EPOCH 750. Progress: 75.0%. \n", " Training R^2: 0.7594. Test R^2: 0.8003. Loss Train: 0.0115. Loss Val: 0.0125. Time: 2.1871\n", " EPOCH 760. Progress: 76.0%. \n", " Training R^2: 0.7727. Test R^2: 0.8022. Loss Train: 0.0120. Loss Val: 0.0126. Time: 2.1412\n", " EPOCH 770. Progress: 77.0%. \n", " Training R^2: 0.7260. Test R^2: 0.6854. Loss Train: 0.0122. Loss Val: 0.0194. Time: 1.9576\n", " EPOCH 780. Progress: 78.0%. \n", " Training R^2: 0.7772. Test R^2: 0.8020. Loss Train: 0.0119. Loss Val: 0.0131. Time: 1.9466\n", " EPOCH 790. Progress: 79.0%. \n", " Training R^2: 0.7488. Test R^2: 0.8251. Loss Train: 0.0121. Loss Val: 0.0112. Time: 1.9324\n", " EPOCH 800. Progress: 80.0%. \n", " Training R^2: 0.7789. Test R^2: 0.8018. Loss Train: 0.0122. Loss Val: 0.0127. Time: 1.9675\n", " EPOCH 810. Progress: 81.0%. \n", " Training R^2: 0.7686. Test R^2: 0.7813. Loss Train: 0.0129. Loss Val: 0.0134. Time: 1.8761\n", " EPOCH 820. Progress: 82.0%. \n", " Training R^2: 0.7578. Test R^2: 0.8056. Loss Train: 0.0123. Loss Val: 0.0126. Time: 1.9206\n", " EPOCH 830. Progress: 83.0%. \n", " Training R^2: 0.7945. Test R^2: 0.7955. Loss Train: 0.0119. Loss Val: 0.0113. Time: 1.9568\n", " EPOCH 840. Progress: 84.0%. \n", " Training R^2: 0.7344. Test R^2: 0.7341. Loss Train: 0.0123. Loss Val: 0.0160. Time: 2.0485\n", " EPOCH 850. Progress: 85.0%. \n", " Training R^2: 0.7771. Test R^2: 0.8063. Loss Train: 0.0126. Loss Val: 0.0123. Time: 1.8801\n", " EPOCH 860. Progress: 86.0%. \n", " Training R^2: 0.7941. Test R^2: 0.8254. Loss Train: 0.0117. Loss Val: 0.0107. Time: 1.9319\n", " EPOCH 870. Progress: 87.0%. \n", " Training R^2: 0.7810. Test R^2: 0.8012. Loss Train: 0.0121. Loss Val: 0.0118. Time: 1.9670\n", " EPOCH 880. Progress: 88.0%. \n", " Training R^2: 0.7466. Test R^2: 0.8268. Loss Train: 0.0124. Loss Val: 0.0113. Time: 1.9424\n", " EPOCH 890. Progress: 89.0%. \n", " Training R^2: 0.7937. Test R^2: 0.8166. Loss Train: 0.0122. Loss Val: 0.0115. Time: 1.8968\n", " EPOCH 900. Progress: 90.0%. \n", " Training R^2: 0.8011. Test R^2: 0.8291. Loss Train: 0.0114. Loss Val: 0.0120. Time: 1.9333\n", " EPOCH 910. Progress: 91.0%. \n", " Training R^2: 0.7764. Test R^2: 0.7922. Loss Train: 0.0115. Loss Val: 0.0134. Time: 1.9312\n", " EPOCH 920. Progress: 92.0%. \n", " Training R^2: 0.7935. Test R^2: 0.8062. Loss Train: 0.0132. Loss Val: 0.0119. Time: 2.3265\n", " EPOCH 930. Progress: 93.0%. \n", " Training R^2: 0.7865. Test R^2: 0.8079. Loss Train: 0.0118. Loss Val: 0.0131. Time: 2.8770\n", " EPOCH 940. Progress: 94.0%. \n", " Training R^2: 0.7785. Test R^2: 0.8259. Loss Train: 0.0120. Loss Val: 0.0107. Time: 2.8656\n", " EPOCH 950. Progress: 95.0%. \n", " Training R^2: 0.7886. Test R^2: 0.8126. Loss Train: 0.0113. Loss Val: 0.0115. Time: 2.2849\n", " EPOCH 960. Progress: 96.0%. \n", " Training R^2: 0.7984. Test R^2: 0.8284. Loss Train: 0.0122. Loss Val: 0.0108. Time: 2.2682\n", " EPOCH 970. Progress: 97.0%. \n", " Training R^2: 0.7796. Test R^2: 0.8180. Loss Train: 0.0129. Loss Val: 0.0113. Time: 2.4791\n", " EPOCH 980. Progress: 98.0%. \n", " Training R^2: 0.7381. Test R^2: 0.7013. Loss Train: 0.0133. Loss Val: 0.0175. Time: 2.6938\n", " EPOCH 990. Progress: 99.0%. \n", " Training R^2: 0.7749. Test R^2: 0.8163. Loss Train: 0.0122. Loss Val: 0.0115. Time: 2.3152\n", " EPOCH 1000. Progress: 100.0%. \n", " Training R^2: 0.7629. Test R^2: 0.7712. Loss Train: 0.0131. Loss Val: 0.0133. Time: 2.2270\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "saving ./plots/LONGER_ADAM_FFNet_ReLU_lr0.01_momentum0.9_wdecay0.001_dampening0_nesterovFalse_HidDim64.png\n", "\n", "lr: 0.001, momentum: 0.9, weight_decay: 0.001, dampening: 0, nesterov: False \n", "FeedforwardNeuralNetModel(\n", " (fc1): Linear(in_features=47, out_features=64, bias=True)\n", " (relu): ReLU()\n", " (fc2): Linear(in_features=64, out_features=1, bias=True)\n", ")\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: 1000\n", " EPOCH 0. Progress: 0.0%. \n", " Training R^2: 0.2028. Test R^2: 0.1560. Loss Train: 0.0692. Loss Val: 0.0486. Time: 2.3983\n", " EPOCH 10. Progress: 1.0%. \n", " Training R^2: 0.4759. Test R^2: 0.4325. Loss Train: 0.0263. Loss Val: 0.0361. Time: 2.0657\n", " EPOCH 20. Progress: 2.0%. \n", " Training R^2: 0.5427. Test R^2: 0.4802. Loss Train: 0.0245. Loss Val: 0.0329. Time: 2.7306\n", " EPOCH 30. Progress: 3.0%. \n", " Training R^2: 0.5391. Test R^2: 0.5306. Loss Train: 0.0224. Loss Val: 0.0307. Time: 2.2783\n", " EPOCH 40. Progress: 4.0%. \n", " Training R^2: 0.6295. Test R^2: 0.5802. Loss Train: 0.0204. Loss Val: 0.0273. Time: 2.8154\n", " EPOCH 50. Progress: 5.0%. \n", " Training R^2: 0.6422. Test R^2: 0.6217. Loss Train: 0.0190. Loss Val: 0.0242. Time: 2.1834\n", " EPOCH 60. Progress: 6.0%. \n", " Training R^2: 0.6406. Test R^2: 0.6399. Loss Train: 0.0178. Loss Val: 0.0236. Time: 2.6537\n", " EPOCH 70. Progress: 7.000000000000001%. \n", " Training R^2: 0.6753. Test R^2: 0.6471. Loss Train: 0.0180. Loss Val: 0.0239. Time: 2.2396\n", " EPOCH 80. Progress: 8.0%. \n", " Training R^2: 0.7032. Test R^2: 0.6909. Loss Train: 0.0166. Loss Val: 0.0203. Time: 2.2092\n", " EPOCH 90. Progress: 9.0%. \n", " Training R^2: 0.6927. Test R^2: 0.6677. Loss Train: 0.0153. Loss Val: 0.0215. Time: 2.2108\n", " EPOCH 100. Progress: 10.0%. \n", " Training R^2: 0.7271. Test R^2: 0.7260. Loss Train: 0.0143. Loss Val: 0.0179. Time: 2.1933\n", " EPOCH 110. Progress: 11.0%. \n", " Training R^2: 0.7351. Test R^2: 0.7490. Loss Train: 0.0140. Loss Val: 0.0169. Time: 2.1348\n", " EPOCH 120. Progress: 12.0%. \n", " Training R^2: 0.7628. Test R^2: 0.7371. Loss Train: 0.0132. Loss Val: 0.0157. Time: 2.1435\n", " EPOCH 130. Progress: 13.0%. \n", " Training R^2: 0.7598. Test R^2: 0.7347. Loss Train: 0.0139. Loss Val: 0.0153. Time: 2.3784\n", " EPOCH 140. Progress: 14.000000000000002%. \n", " Training R^2: 0.7580. Test R^2: 0.7632. Loss Train: 0.0127. Loss Val: 0.0153. Time: 2.1633\n", " EPOCH 150. Progress: 15.0%. \n", " Training R^2: 0.7713. Test R^2: 0.7808. Loss Train: 0.0130. Loss Val: 0.0147. Time: 2.8185\n", " EPOCH 160. Progress: 16.0%. \n", " Training R^2: 0.7694. Test R^2: 0.7524. Loss Train: 0.0119. Loss Val: 0.0157. Time: 2.2254\n", " EPOCH 170. Progress: 17.0%. \n", " Training R^2: 0.7840. Test R^2: 0.7902. Loss Train: 0.0122. Loss Val: 0.0133. Time: 2.1863\n", " EPOCH 180. Progress: 18.0%. \n", " Training R^2: 0.7839. Test R^2: 0.8062. Loss Train: 0.0114. Loss Val: 0.0125. Time: 2.1960\n", " EPOCH 190. Progress: 19.0%. \n", " Training R^2: 0.8026. Test R^2: 0.8171. Loss Train: 0.0114. Loss Val: 0.0120. Time: 2.8994\n", " EPOCH 200. Progress: 20.0%. \n", " Training R^2: 0.8004. Test R^2: 0.8059. Loss Train: 0.0108. Loss Val: 0.0127. Time: 2.1921\n", " EPOCH 210. Progress: 21.0%. \n", " Training R^2: 0.7806. Test R^2: 0.7829. Loss Train: 0.0110. Loss Val: 0.0130. Time: 5.2012\n", " EPOCH 220. Progress: 22.0%. \n", " Training R^2: 0.8110. Test R^2: 0.8164. Loss Train: 0.0107. Loss Val: 0.0114. Time: 2.1437\n", " EPOCH 230. Progress: 23.0%. \n", " Training R^2: 0.8206. Test R^2: 0.8211. Loss Train: 0.0102. Loss Val: 0.0120. Time: 2.1805\n", " EPOCH 240. Progress: 24.0%. \n", " Training R^2: 0.8222. Test R^2: 0.8289. Loss Train: 0.0098. Loss Val: 0.0115. Time: 2.2140\n", " EPOCH 250. Progress: 25.0%. \n", " Training R^2: 0.8244. Test R^2: 0.8094. Loss Train: 0.0102. Loss Val: 0.0112. Time: 2.3777\n", " EPOCH 260. Progress: 26.0%. \n", " Training R^2: 0.8277. Test R^2: 0.8291. Loss Train: 0.0096. Loss Val: 0.0107. Time: 2.9095\n", " EPOCH 270. Progress: 27.0%. \n", " Training R^2: 0.8364. Test R^2: 0.8429. Loss Train: 0.0095. Loss Val: 0.0100. Time: 2.1717\n", " EPOCH 280. Progress: 28.000000000000004%. \n", " Training R^2: 0.8072. Test R^2: 0.8402. Loss Train: 0.0093. Loss Val: 0.0097. Time: 7.8986\n", " EPOCH 290. Progress: 28.999999999999996%. \n", " Training R^2: 0.8334. Test R^2: 0.8464. Loss Train: 0.0092. Loss Val: 0.0100. Time: 2.7538\n", " EPOCH 300. Progress: 30.0%. \n", " Training R^2: 0.7824. Test R^2: 0.7868. Loss Train: 0.0096. Loss Val: 0.0129. Time: 2.2980\n", " EPOCH 310. Progress: 31.0%. \n", " Training R^2: 0.8343. Test R^2: 0.8366. Loss Train: 0.0090. Loss Val: 0.0100. Time: 2.1131\n", " EPOCH 320. Progress: 32.0%. \n", " Training R^2: 0.8383. Test R^2: 0.8348. Loss Train: 0.0090. Loss Val: 0.0102. Time: 2.2003\n", " EPOCH 330. Progress: 33.0%. \n", " Training R^2: 0.8388. Test R^2: 0.8457. Loss Train: 0.0088. Loss Val: 0.0095. Time: 2.4573\n", " EPOCH 340. Progress: 34.0%. \n", " Training R^2: 0.8498. Test R^2: 0.8529. Loss Train: 0.0084. Loss Val: 0.0089. Time: 2.7186\n", " EPOCH 350. Progress: 35.0%. \n", " Training R^2: 0.8288. Test R^2: 0.8138. Loss Train: 0.0089. Loss Val: 0.0115. Time: 2.4576\n", " EPOCH 360. Progress: 36.0%. \n", " Training R^2: 0.8576. Test R^2: 0.8621. Loss Train: 0.0084. Loss Val: 0.0087. Time: 3.0517\n", " EPOCH 370. Progress: 37.0%. \n", " Training R^2: 0.8561. Test R^2: 0.8660. Loss Train: 0.0081. Loss Val: 0.0082. Time: 2.6274\n", " EPOCH 380. Progress: 38.0%. \n", " Training R^2: 0.8491. Test R^2: 0.8655. Loss Train: 0.0079. Loss Val: 0.0086. Time: 2.2206\n", " EPOCH 390. Progress: 39.0%. \n", " Training R^2: 0.8490. Test R^2: 0.8699. Loss Train: 0.0080. Loss Val: 0.0086. Time: 2.3447\n", " EPOCH 400. Progress: 40.0%. \n", " Training R^2: 0.8618. Test R^2: 0.8707. Loss Train: 0.0075. Loss Val: 0.0084. Time: 2.2453\n", " EPOCH 410. Progress: 41.0%. \n", " Training R^2: 0.8660. Test R^2: 0.8819. Loss Train: 0.0075. Loss Val: 0.0088. Time: 2.1638\n", " EPOCH 420. Progress: 42.0%. \n", " Training R^2: 0.8731. Test R^2: 0.8894. Loss Train: 0.0076. Loss Val: 0.0074. Time: 2.2099\n", " EPOCH 430. Progress: 43.0%. \n", " Training R^2: 0.8611. Test R^2: 0.8809. Loss Train: 0.0074. Loss Val: 0.0089. Time: 2.2619\n", " EPOCH 440. Progress: 44.0%. \n", " Training R^2: 0.8699. Test R^2: 0.8724. Loss Train: 0.0070. Loss Val: 0.0081. Time: 2.0963\n", " EPOCH 450. Progress: 45.0%. \n", " Training R^2: 0.8672. Test R^2: 0.8861. Loss Train: 0.0073. Loss Val: 0.0074. Time: 2.6645\n", " EPOCH 460. Progress: 46.0%. \n", " Training R^2: 0.8652. Test R^2: 0.8532. Loss Train: 0.0067. Loss Val: 0.0089. Time: 2.2519\n", " EPOCH 470. Progress: 47.0%. \n", " Training R^2: 0.8743. Test R^2: 0.8826. Loss Train: 0.0076. Loss Val: 0.0069. Time: 2.6056\n", " EPOCH 480. Progress: 48.0%. \n", " Training R^2: 0.8699. Test R^2: 0.8694. Loss Train: 0.0072. Loss Val: 0.0081. Time: 2.4231\n", " EPOCH 490. Progress: 49.0%. \n", " Training R^2: 0.8383. Test R^2: 0.8550. Loss Train: 0.0077. Loss Val: 0.0085. Time: 2.3354\n", " EPOCH 500. Progress: 50.0%. \n", " Training R^2: 0.8849. Test R^2: 0.8842. Loss Train: 0.0065. Loss Val: 0.0069. Time: 2.0191\n", " EPOCH 510. Progress: 51.0%. \n", " Training R^2: 0.8795. Test R^2: 0.8905. Loss Train: 0.0068. Loss Val: 0.0068. Time: 2.4990\n", " EPOCH 520. Progress: 52.0%. \n", " Training R^2: 0.8657. Test R^2: 0.8653. Loss Train: 0.0071. Loss Val: 0.0076. Time: 2.5995\n", " EPOCH 530. Progress: 53.0%. \n", " Training R^2: 0.8777. Test R^2: 0.8941. Loss Train: 0.0066. Loss Val: 0.0070. Time: 2.3459\n", " EPOCH 540. Progress: 54.0%. \n", " Training R^2: 0.8790. Test R^2: 0.8877. Loss Train: 0.0064. Loss Val: 0.0071. Time: 2.9594\n", " EPOCH 550. Progress: 55.00000000000001%. \n", " Training R^2: 0.8903. Test R^2: 0.8929. Loss Train: 0.0062. Loss Val: 0.0065. Time: 2.1561\n", " EPOCH 560. Progress: 56.00000000000001%. \n", " Training R^2: 0.8719. Test R^2: 0.8808. Loss Train: 0.0061. Loss Val: 0.0076. Time: 2.5248\n", " EPOCH 570. Progress: 56.99999999999999%. \n", " Training R^2: 0.8848. Test R^2: 0.8927. Loss Train: 0.0064. Loss Val: 0.0063. Time: 2.1494\n", " EPOCH 580. Progress: 57.99999999999999%. \n", " Training R^2: 0.8837. Test R^2: 0.8893. Loss Train: 0.0063. Loss Val: 0.0070. Time: 2.2030\n", " EPOCH 590. Progress: 59.0%. \n", " Training R^2: 0.8776. Test R^2: 0.9042. Loss Train: 0.0072. Loss Val: 0.0060. Time: 2.2955\n", " EPOCH 600. Progress: 60.0%. \n", " Training R^2: 0.8867. Test R^2: 0.8880. Loss Train: 0.0065. Loss Val: 0.0071. Time: 2.8852\n", " EPOCH 610. Progress: 61.0%. \n", " Training R^2: 0.8930. Test R^2: 0.8890. Loss Train: 0.0065. Loss Val: 0.0068. Time: 2.9015\n", " EPOCH 620. Progress: 62.0%. \n", " Training R^2: 0.8952. Test R^2: 0.9084. Loss Train: 0.0059. Loss Val: 0.0056. Time: 2.4803\n", " EPOCH 630. Progress: 63.0%. \n", " Training R^2: 0.8945. Test R^2: 0.9036. Loss Train: 0.0061. Loss Val: 0.0063. Time: 2.2777\n", " EPOCH 640. Progress: 64.0%. \n", " Training R^2: 0.8942. Test R^2: 0.8980. Loss Train: 0.0065. Loss Val: 0.0058. Time: 2.2503\n", " EPOCH 650. Progress: 65.0%. \n", " Training R^2: 0.9067. Test R^2: 0.9076. Loss Train: 0.0056. Loss Val: 0.0056. Time: 2.1684\n", " EPOCH 660. Progress: 66.0%. \n", " Training R^2: 0.8991. Test R^2: 0.8998. Loss Train: 0.0060. Loss Val: 0.0065. Time: 2.2702\n", " EPOCH 670. Progress: 67.0%. \n", " Training R^2: 0.8743. Test R^2: 0.8937. Loss Train: 0.0073. Loss Val: 0.0069. Time: 2.4311\n", " EPOCH 680. Progress: 68.0%. \n", " Training R^2: 0.8516. Test R^2: 0.8870. Loss Train: 0.0062. Loss Val: 0.0069. Time: 2.2977\n", " EPOCH 690. Progress: 69.0%. \n", " Training R^2: 0.8600. Test R^2: 0.8760. Loss Train: 0.0056. Loss Val: 0.0075. Time: 2.1302\n", " EPOCH 700. Progress: 70.0%. \n", " Training R^2: 0.9006. Test R^2: 0.9012. Loss Train: 0.0060. Loss Val: 0.0061. Time: 2.1501\n", " EPOCH 710. Progress: 71.0%. \n", " Training R^2: 0.9048. Test R^2: 0.9060. Loss Train: 0.0059. Loss Val: 0.0055. Time: 2.1127\n", " EPOCH 720. Progress: 72.0%. \n", " Training R^2: 0.9020. Test R^2: 0.8869. Loss Train: 0.0055. Loss Val: 0.0055. Time: 2.5087\n", " EPOCH 730. Progress: 73.0%. \n", " Training R^2: 0.8889. Test R^2: 0.9134. Loss Train: 0.0054. Loss Val: 0.0054. Time: 2.6028\n", " EPOCH 740. Progress: 74.0%. \n", " Training R^2: 0.9135. Test R^2: 0.9221. Loss Train: 0.0050. Loss Val: 0.0051. Time: 2.3192\n", " EPOCH 750. Progress: 75.0%. \n", " Training R^2: 0.9123. Test R^2: 0.9141. Loss Train: 0.0060. Loss Val: 0.0053. Time: 2.2378\n", " EPOCH 760. Progress: 76.0%. \n", " Training R^2: 0.9118. Test R^2: 0.9186. Loss Train: 0.0058. Loss Val: 0.0054. Time: 2.2381\n", " EPOCH 770. Progress: 77.0%. \n", " Training R^2: 0.9103. Test R^2: 0.9151. Loss Train: 0.0051. Loss Val: 0.0056. Time: 2.1798\n", " EPOCH 780. Progress: 78.0%. \n", " Training R^2: 0.8842. Test R^2: 0.8948. Loss Train: 0.0051. Loss Val: 0.0067. Time: 2.1070\n", " EPOCH 790. Progress: 79.0%. \n", " Training R^2: 0.9094. Test R^2: 0.9133. Loss Train: 0.0055. Loss Val: 0.0053. Time: 2.2978\n", " EPOCH 800. Progress: 80.0%. \n", " Training R^2: 0.9154. Test R^2: 0.9182. Loss Train: 0.0049. Loss Val: 0.0050. Time: 2.4846\n", " EPOCH 810. Progress: 81.0%. \n", " Training R^2: 0.9108. Test R^2: 0.9168. Loss Train: 0.0058. Loss Val: 0.0053. Time: 2.7128\n", " EPOCH 820. Progress: 82.0%. \n", " Training R^2: 0.9180. Test R^2: 0.9214. Loss Train: 0.0049. Loss Val: 0.0050. Time: 2.9778\n", " EPOCH 830. Progress: 83.0%. \n", " Training R^2: 0.8779. Test R^2: 0.9026. Loss Train: 0.0051. Loss Val: 0.0062. Time: 2.0989\n", " EPOCH 840. Progress: 84.0%. \n", " Training R^2: 0.9098. Test R^2: 0.9109. Loss Train: 0.0054. Loss Val: 0.0054. Time: 2.3217\n", " EPOCH 850. Progress: 85.0%. \n", " Training R^2: 0.9130. Test R^2: 0.9193. Loss Train: 0.0045. Loss Val: 0.0052. Time: 2.8466\n", " EPOCH 860. Progress: 86.0%. \n", " Training R^2: 0.9044. Test R^2: 0.9118. Loss Train: 0.0048. Loss Val: 0.0051. Time: 2.6478\n", " EPOCH 870. Progress: 87.0%. \n", " Training R^2: 0.9165. Test R^2: 0.9212. Loss Train: 0.0052. Loss Val: 0.0045. Time: 3.0131\n", " EPOCH 880. Progress: 88.0%. \n", " Training R^2: 0.9131. Test R^2: 0.9275. Loss Train: 0.0049. Loss Val: 0.0047. Time: 2.3367\n", " EPOCH 890. Progress: 89.0%. \n", " Training R^2: 0.8942. Test R^2: 0.8939. Loss Train: 0.0050. Loss Val: 0.0062. Time: 2.5193\n", " EPOCH 900. Progress: 90.0%. \n", " Training R^2: 0.9157. Test R^2: 0.9114. Loss Train: 0.0046. Loss Val: 0.0053. Time: 2.2089\n", " EPOCH 910. Progress: 91.0%. \n", " Training R^2: 0.9176. Test R^2: 0.9082. Loss Train: 0.0052. Loss Val: 0.0050. Time: 2.2816\n", " EPOCH 920. Progress: 92.0%. \n", " Training R^2: 0.9207. Test R^2: 0.9222. Loss Train: 0.0052. Loss Val: 0.0048. Time: 2.5538\n", " EPOCH 930. Progress: 93.0%. \n", " Training R^2: 0.9163. Test R^2: 0.9321. Loss Train: 0.0051. Loss Val: 0.0044. Time: 2.2491\n", " EPOCH 940. Progress: 94.0%. \n", " Training R^2: 0.9143. Test R^2: 0.8993. Loss Train: 0.0046. Loss Val: 0.0052. Time: 2.6589\n", " EPOCH 950. Progress: 95.0%. \n", " Training R^2: 0.9080. Test R^2: 0.9124. Loss Train: 0.0054. Loss Val: 0.0055. Time: 2.3593\n", " EPOCH 960. Progress: 96.0%. \n", " Training R^2: 0.9220. Test R^2: 0.9357. Loss Train: 0.0046. Loss Val: 0.0043. Time: 2.2051\n", " EPOCH 970. Progress: 97.0%. \n", " Training R^2: 0.9123. Test R^2: 0.9244. Loss Train: 0.0044. Loss Val: 0.0049. Time: 2.2814\n", " EPOCH 980. Progress: 98.0%. \n", " Training R^2: 0.9086. Test R^2: 0.9219. Loss Train: 0.0049. Loss Val: 0.0050. Time: 2.3907\n", " EPOCH 990. Progress: 99.0%. \n", " Training R^2: 0.9198. Test R^2: 0.9207. Loss Train: 0.0051. Loss Val: 0.0048. Time: 2.3489\n", " EPOCH 1000. Progress: 100.0%. \n", " Training R^2: 0.9296. Test R^2: 0.9287. Loss Train: 0.0047. Loss Val: 0.0042. Time: 2.2467\n" ] }, { "data": { "image/png": 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HcO4uvuFyfjIPICrzB9JX/YfzdafPTvkR54q/YK+ykWuJpw+HWfrdIdatXANAGu2psmv07xpV6zniErtxwhmJbc8ajOO72GJLpE9i9aQKXAldfFQQ25X+YA1kTXAyhMYQFuyawig00Mrjtwzk3ht6MaDrmelMrBYTkZ1d67DHHf0CDZXIQeNcNW5tOqJYqzeDmmJd60qjmggbfjMxtTSVBviZsVpMNe6rT1ITVyupihNCnF9mZiaxsbGYTK6HtclkIiYmhszMTI9lBXv37s2KFSvo27cvJ06cYMeOHbRvX7dmlroO0oiO9u0ajvVF4m442Rv+h2Lxx9CdmA59A127ER0dgm6r4FjqNoL6XEV4dATW+J9jSxpA9id/xZmyht329gy65Zd07xjJ1y8/S5/0tRgbNaKvvx/FbKl2naP7N2AYFsJu/QvtlFyy3v8TT40xU7A1jarIdhzIB4tZZdTgDvj71ZyGjBjQnq93dqZ9yXYUYKeSxK/6t8NkctU9nft5d+0QwfqDDn71x3fY9MwaBnRv41EmOjqEpG7Vk8AxY4dQ/u5CQtVy8iP70q1jQrUyZ9PD+pK+JoiwK5KJuEDZ2tTn34okceeSqjghRD2bNWsWzz33HMnJycTHx3PFFVdgNtft8SujU5uuRhnlmbEP7fhurIOnoJhdk2tr2YdRw+Pcy0gZhu5qFozqgFGcQ3nKBqz9rsWoKqVk5zdEXHUL+WUGB7/5kninnfkbLBxau5wgfzPBgVZCTZNIqNpNedwQrowKoqLMhmXYL/ly1ftcv+c70nMzCJjwiMdACMPQCSrYT6qSwBWhIZTpQSjBUURn/kC4JZcfCvuwoegkPTtEUFpSSW2fWnSIld16F5LZTpoeR9uOnSgoKHftq+Hzjg71o6DExpY9WRSV2kiICvTqv0mov5ljaiydOU5Q33FeHRM4/UUcJutF/TeX0alCCNFExMXFkZ2djaZpmEyulSlycnKq9XWLjIzkr389M1BjxowZJCYm+jpc4SN6WQGKf7A7ubpUhq0cx/7vsSSNdjfx2X/6Eu3EHpxZBwkYez/27UtxHPgeLaYnYclPoCgK9u1Lsf+4FDU83pVoqRYsfSdgVJXhOLCOwq3/Y8GhBIae+J48cxjtkvrRJ9Sf4jIbZZUO7A6d/NAR3HpNd3csvbtE8Ya9P/FdujAw9wsqvniOgOt+jxrsahrV89MJ1MspCOkGgKKqWLoPx/7jF5iAnRWx5DmrmDC0w3nv2WJWiU9oz4cZIzlmD+fa4bU3vQIkxLgSnm93nASga/twrz5bRVEI6jOak8d20KN79TVPazzG7HfhQj4iSVwtZGCDaE1mzLgDh8OB0+ng+PF0Ond2JRjdu/fgqafmeHWOH3/chtPpZOjQ+p+VvKmKiooiKSmJ5cuXk5yczPLly0lKSvJoSgUoLCwkJCQEs9nMxo0bOXjwIC+//HIjRS0akvPEXipX/R1Tm84ETHocxeTZ3KhXlqD4BaGo3vWX0ktyqPzfi64+bKoJa9/xGJoTLesganRn9Lw0yt9/FMOAQ462dM/ZjzN1G6bIBOw/fYkprieGvRwt6yCWPuNRA8MgMAzi+1C4/mN+boDVomEZchO/HNjzgvEE+JnpnhDOVzmBXHndo1SueomKpX8mcNLjqGFtsaX+BIAR19t9jKX7COw/fgFmKxFdesHBQvonnj8pA+jTOZL/HukMQN8u5y/f/lQSt2VfDgF+Jtq1Of+i9mdLvHIsXDnW6/JNiSRx51CkPVW0QgsWvAtAZmYG99xzOwsX/rfO59ixYzuVlZWtKokDmDt3LrNmzeK1114jNDSU+fPnA67atocffpi+ffuya9cu/vznP6OqKhEREbz++usEBDTc3FGicTgzD1C56iWUgDC07ENUffc2/lffe2aqjtJcyj/6A35DbsTab6L7OEPXUdTq4wyPpqQQsuEV/CwqamA4zuO7sPYdj5abCk471v7XoQSGU7Xtc9493pGdpZH8PvRL4jf8F3N4LIZq4e2SEQSERdC9WzH9+vTHH9B0nY8K+9LNUUaHTgnEJ3bF0n2E1/fZPzGKD9YepiiwHxGTZ1H55f9R+fXrBE55mqq0n8hwRtE2PtZdXg2NwdxxIFgDuO2KPgwfUEIbL+ZOcyVuh+gcF0Jo0PlrNUMDrYQFWykus9M9IbLBR4U2FZLE1UIq4oQvOQ5uwHHg+wY5t6XHKCzdh9f5uI0b17No0dvYbHYsFgsPPfQ7+vTpS3p6Gn/+87xT65pqXHvtZC6/fBhLl36Kruts27aFsWPHc/vtd9b/zTRBiYmJLFmypNr2BQsWuF9fddVVXHXVVb4MS/iYVniSypV/Rw2JImDykzj2fYt926fYw9riN9g1J6Bt++eg2XFm7HMncY6jW6n6/h2CbpyHGhrtcc7yn1YRrDn5a/kN/Kp7DpEZGzEcVWgZ+wAwxfdE9Q9hqV8yO4pPMGNyL5b8L4/fmFeiVRbyneUq9mZr+BcUsqHcTkTKTzz8835s3JvFhpMWht7yGzp1DK/zvfbr2oYP1h5m55F8xg7uiN/IX1K15jVsW5ZgLjxGiqMvV8V49ucKmPCI+/WFatVOi4kIYFD36POOYj1bQnQwxWUFdG0X5v3NNHOSxJ3jAlPfCNEqnDx5goUL3+LFF18hKCiYo0eP8OijD/Ppp1/y6acfM2zYcO688x4ASkpKCA0NJTn5RiorK3nwwd80bvBC+Jiha1R99zaKaibg+sdRA0KxDpyMXpyNfftnqGExqJEdcB78AUxm9OwjGIaBoig4034EewW2rZ8QMPY+j/OGl6dxQm2HJaIt/00p4cFQJ9rJfWiZ+9FC27Hom5OczC3jaEYJYwa354rebVm/uwc/FGXQL8rO50cS+OXErlw1oB3Hskp5+ZNd/Pk/23FqOmMHt2fMZQkX1Tm/bWQgMREB7DqSz9jB7bF0GYqz8xYcu1aiAIf0BG6qh1UKFEXhwRv7el2+fUwwe1IL6NpekrhWT/rECV+ydB9+UbVlDWXz5o2cPHmCmTPvdW/TNI2CgnwGDBjIq6++hMPhYNCgyxg06LJGjFSIxleyfSV6zhH8r74XNSgCXTdQFPAfdSeVZXlUffsWamR7sPhjHTgJ+5YlGMXZEBaLM2M/umLGeWQTWr8JmKJdfcD0sgLCjWLSwgbz5E2D+dPbDmyGBVPqNrSsQ2x39mBzRjad24Yw8fIO3DDcddxVA9rxr88H8Hm5iXbR/ozs51pNoGPbEGbfcRn/WroXq1ll2piul3TP/RPb8M2Ok9jsGn5WE37Db0fL2E+FzQmRHVEboUZkULdoDp8spmu8JHFCiFbMMAwuv3wYTz/9p2r7Ro8eS58+/diyZROLFy/kyy+/YPbsZxohSiEaj6FrGOWF6MXZlH3zHqaEfpi7DuNEThl/X7KTy5NiuXlMVwKueYjypc+i56VhvexGzB0GYN+yBC3niKuyoKKQ5RWDGBuQQtU3i2kz9Y8oikJF+l7XdWK6YzapJI/qxv6v4uh3eCOKobOzrA0zb+xDn86eTY0Du7UhJNBCaYWDaWO7efQNCwv2Y9atg9y1gJeif9covtp2nL1pBQzqHo0aGIb/hN/w1pJttEsIvaRzX6yu7cN46rbBjXLtxiIrNtRKquJE6zV06BVs3ryRo0ePuLft2+f6R+XEieNERkZx3XWTueuuGaSkuLYHBQVRXl7WKPEK4SuGYeA4soXy9x+l/P1HqVzxf6Ca8B95B+nZZbzw/g6Kymys2pJOWlYJin8wuzrdyldVfXnjUBzbs1SwBKBlH2bjWlc/2KheQ1mnXIZ/0REObnRtq0hPoUK3ENquCwCDurchJzARxdDRDQjtXD2BA9fqBTeO6sL4IQn07hRZbT9wyQkcQPeEcIL8zWw/kOveVhqUwM7yGNpHez8yVFwaqYk7R338cQvR3CUkdGD27Gf4y1+ewWaz4XQ66Nu3P0lJvVm79itWr16JxWJGURQeeeT3AIwadTV/+MNj3Hnn9FY1sEG0bIa9EvvuVTiP70KxBGA4KtFzjqJGdcQ6KBk1OJKorkms2JbH+2v2EuBn4vfThvCPJTv5z6oD3HRVIu98l0e76JGU5dv56Yt9PBASTsT+3TirQrEFBjLhmstxOAaR8fZOAg98iTFsFGrOQQ46Y0ls4xogoCgKvYePgu++IUOP4sZxfWqN+aoB7Rr8czGbVAZ0bcOOQ3k4NR2zSeVErutHXEJM3VYXERdPkrhaSJ840RrFxcXz5ZdfA67auJqmC/nlL+/ml7+8u9r2+Ph2vPNO3acmEaKpchzdim3duxi2MtTYrhiOKtCc+F15G5ZeY1BUlYy8cv7v7d0cOl5E57hQ7k/uTZvwAKaN6cqby1J48aOdxEQE8Pgtg/C3mjiQXkjVtqO0KdhASEAFgR16oSgKVquV3cFXMrpiFY6Ur/GryueodhmXh/u74+nRozP7f+qPOSbRvRB8YxrUI5oNe7LYn15In85RnMhxJXHtoiWJ8xVJ4oQQQogaVG39FPyDCbzu9+4BB2fTDYMFy1IoKLVxz6Qkrujd1t2h//JesazfncmxrFIevqkfgf6uf26TOkXiVIdSuXI9AXoF5vgzE+wqnYeSvWsjsRs/AKAwsBOmc+aP6znttw11u3XWu1MkfhYTPx7IpXenSFLSCogI8SM4oPp6qqJhSBJXC6mIE0KI1svQNbTibL6pTGLzp1n07mTn5qu74mc9s9LCxj1ZHMsu5fe3DqZ3gueISEVReOSm/tgcWrWkxhTT5czruDNJXNf2kaza3I9fmtZThRVT5MUtsO4rVouJvolR/Hgoj6AAC3vTCpl6tSwn50sysOEc0iVO+JIh7faXTD5DUR+0/OPYti/F0HUAKvOzMKFjioijfXQw3/50kr9/9BOVNicANrvGJ98doUt8KFcNrLkPmsWs1lgrpfgHo4a1RfEPQY2Id2/vHB/KTmdnSqyx7LPHE9um6TdLDu4eTUm5nS83HmNU/zgmXmBNVFG/pCauNvIPg2hgZrOV8vISgoJCZUDNRTIMg/LyEsz1tNC4aJ203DTKl7+A4qiAdn3wa5tIRmoqsUBS3yRuGNiXLfuyWbAshb9+sIMhPWNJyyqhqMzOA1P6XtT31zroBgynHUU5U5fiZzGREBvKy6XXUlDm4K6owHq8y4bRLzGKAD8zXeJDuW18D3mW+ZgkceeQPz/hKxER0RQW5lJWVuTza6uqin6qxqE5qSlus9lKRER0LUeI1srQdRz7vsHUtjumqNqbJbWco1Ss+D8qHBAEHD+wn65tEyk4cYxYIKGrq3lwaFIsFrPKm1+k8NE3hwEY1T/+olcHsHS7ssbtXduF89W2UsBEXFTTn6ojwM/M8/deQXCApdWsV9qUSBJXC6mHEw3NZDLTpk1co1w7OjrkopbbaWzNNW7R8PSSXJwn92LuNAjFbKXy69fR0n/C3O1KAq6+t8ZjDHsFlV/9k0rDj78WjeGJsC8oTHclaI6CDKrwIzj4TJI2sFs0r/xmJA6njqYbBPnX/z+h3dqH8dW244Breavm4EKL04uGI0ncuaQqWAghmhXDMKj67t9omQewrV+EEhCKUVmM4heMUZZf4zGarlO+/n0oL+SN0mvp1jOR8sJYrKUZFJba8LflUxXSplrzoNmkYjY1XHfyxFOLt4cHWwnwk3+ixfnJX0gtpEucEEI0D9qJ3WiZByjuNIboMH+07MNYR92N48gmtMwD7nKG00bVnrX8lOfHj/sy+KXfOtZU9ibPEsdvxnWj/PvOBB3bzEdrD3GdqQRLZC+f30tEiB/R4f5E18MC8qLlkyTuHFIPJ4QQzYdh6FRu/pgiQnh2Rzx/f2S0e062zAO7CCsvxNA1FNVE8b4tmLZ8SB+gjx9U+kfT7spbmNOhDSGBVqwdu2JPX8/xgwcID6vAHNc4Iy0fmNIXq0UmjxAX5rO/ktTUVKZNm8aECROYNm0aaWlp1cpomsa8efMYN24c11xzDUuWLPHYv2LFCiZPnsykSZOYPHkyeXl5DRewVMUJIUSTc+6UMrbDW6AgnWVl/XAYKodPFrm22zVW7y1HMXSM8kIActJT0Q3I6HMX1oGTiZr0CEP7tHevfmCOciVt/a3HXO8jGqfPase2Ic1iUINofD6riZszZw7Tp08nOTmZpUuXMnv2bBYtWuRRZtmyZaSnp7N69WqKioqYMmUKw4YNo3379uzevZt//vOfvPvuu0RHR1NaWorV2gCdKaUqTgghmhzDMLBv/QTH4Y0E/fxPKH5B5BVWUPn9Eiqd4SRdNYGf1h5hf3oR/RLbsD+9kDynKxHSy/JRQ9pASTaFejBdrxhZbSUEADWyHQYKA61prvfhbX15i0LUmU9q4vLz80lJSWHSpEkATJo0iZSUFAoKCjzKrVixgqlTp6KqKpGRkYwbN46VK1cCsHDhQu6++26io11TCYSEhODn59dgMUs9nBBCNB32Hcuw/7Qcoyyfwxu+5l+f7+HVt74kXMunJGEEVw/uQJe4UA6ku2rd9qQWUKC7Jss1Sl2tNpbKPIpN4TUmcACK2Q9TeByxphJAQQ2N9cm9CXGxfFITl5mZSWxsLCaTa7kSk8lETEwMmZmZREZGepSLjz8ze3VcXBxZWVkAHDlyhPbt23PrrbdSUVHBNddcw/3331+niQWjoi48+7W1zOZ+HR0d4vW5mxKJ27ckbt9qrnGLi2MYOtnrPiVo/3JS1O6EO3Kw7f+eFHs49yZkY5SaGTz+WgC6d4hgxcZjVNqc7EktoFB31cTZinIwGwbBzkJy/JLOez01KgG9KAMlOBJFJpEWTVyzGdigaRoHDhzgnXfewW63c8899xAfH8+UKVO8Pkd+fhm6fv46ttIKO+DqEtcc56NqrvNoSdy+1RriVlXFqx9uonFphSdRg9ugWKq3rOhFmZR8/W+C8o+w09GJjeHjGRN1gO65X/HizZ2wffkRpk6DUPxcyVrPDuEs/yGNTXuzyC6ooHtCFMWlAfgVZBNQWYI/dvSg808MrUZ1gCObUcOkKVU0fT5pTo2LiyM7OxtN0wBXQpaTk0NcXFy1chkZGe73mZmZtG3r+iLFx8czceJErFYrwcHBjB07ll27dtV7rLJkiBBC+IaWm0bFx09T+dUr1QYsGPZKyj//E/b8EyyxjSTp1id47NbLGDjhelBU7N/9G8NWhqX7cPcxie3CMKkKX/yQBsDYwe0p0INwluRRnn0SuPBgBdOpwQ3SH040Bz5J4qKiokhKSmL58uUALF++nKSkJI+mVICJEyeyZMkSdF2noKCANWvWMGHCBMDVj279+vUYhoHD4WDTpk307NmzwWI2pFecEEI0GENzUPXtv0FR0U7swXlog8d+LTcV7JUsKh3O4GuTiTo1b5oaGI4poS96fjpKQCim9n3cx/hZTHSJD6W4zE5kqB/9EqMo1IJRKwooyT4BQFBM+/PGpbbpCCYzpjad6veGhWgAPptiZO7cuSxevJgJEyawePFi5s2bB8CMGTPYvXs3AMnJybRv357x48dz8803M3PmTBISXGveXX/99URFRXHdddcxZcoUunbtyk033eSr8IUQQtQj+49foBeeIOCamZjadqdq4/s4ywrd+8vSXZP0duozgP5d23gce7r2zdx1GIpq8tjXo0M4AH06R+JnMVFhDsXPXowt7ySaoRAVF8/5qAGhBE2bj7nb8POWE6Ip8FmfuMTExGrzvgEsWLDA/dpkMrmTu3OpqsqTTz7Jk08+2WAxepCKOCGEF1JTU5k1axZFRUWEh4czf/58OnXq5FEmPz+fJ598kszMTBwOB1dccQV//OMfMZubTbfkeqUVnMD+05eYuw/H3HEgalhbyj95mvw1C1GH3wNA+YmDVGqhDOnfudrx5o6DsA6cjKXXmGr7enWMZPkPx+jbxZX46YGRmGwa1sKj5OshdIy48PxranDUJd6hEL4hU0KfQ7rECSHq4vQcmKtWrWL69OnMnj27WpnXX3+dxMREli1bxrJly9i7dy+rV69uhGibBvtPX4LJgt/lv+C/Xx3kg22llLYfTvm+jRj2CgzDwK/4GMf1GBJiqg9OUUxm/Ib8HDUootq+nh0j+MMvBzOouyuJM4W4/j+08gRFaniDrnsqhK/JX3MtpCJOCHEh3s6BqSgK5eXl6LqO3W7H4XAQG9s65yDTy/JxHtmCpedVHC3QWbP9BF9vO8G/dweCruFM34lRmoefXkFVaAdUte6/rBPjw9yD1AIiXZ+zioHNL/J8hwnR7LTOuvzzkIo4IYS3vJ0D84EHHuChhx5ixIgRVFZWcuuttzJ48OA6Xauu06U01fn08nd+Chi0Hf0zPlt5En+riTefHMfS7w5R/NO3cHQ7IVYLABGJvS/5PtoldoaDrtdqRFyDfS5N9fO+EInb9+ozdkniaiFLpwoh6svKlSvp0aMH7777LuXl5cyYMYOVK1cyceJEr8/hzTyXpzXVeQANewVlP67G3GUomaUWvt9xkiFJMThtDkb2i+eHrQlcfmwnJ2wmAgwTke06XfJ9+Pv5U65bCVLtEBzTIJ9LU/28L0Ti9j1vY/d2nktpTq1G6uKEEN7xdg7MxYsXc8MNN6CqKiEhIYwZM4bNmzc3RsiNyrH/e3BUYe0/ka37c7A5NEb1c40WDQ20UtW2P2bDQXDWNo5rbejcLvySrxkdEeBefisopt0ln0+IpkSSuFpJVZwQ4vy8nQOzffv2fP/99wDY7XY2btxIt27dfB5vY3OeTEGNaIepTSfW7cogLiqQxHah7v29hl1JhW7BZGgU+7fDYjad52ze8bOYKFNDcRgqkW3PP9GvEM2NJHHnkNGpQoi68GYOzKeeeort27czefJkpkyZQqdOnbj55psbM+xGUZWVyu6iIGa/tYUjJ0sY2S/eY5WcIX3acUA/tWJCTJd6u25qQB++rupDTERgvZ1TiKZA+sTVQvrECSG84c0cmB06dOCdd97xZViNTq8opuq7f+M//HbU0Bgqi/KxOErJMPoQHelPu+ggRvb3rBmzWkyUxQ+hKCuTyK596y0WpcMAdjs7MLUeavaEaEokiRNCCFHvHPu+QTu+G8eRLfgNnMRPW3bQB7hs2GXc2K9frccNGjWKZRs6cEfi+ZfHqosbR3UheUT1SYOFaO6kObUWUhMnhBAXx9CdOPZ9C4CWsY8qu5OMQ/sASOiZdN5j20YGMmNyL6yW+qs1M5tU/OrxfEI0FZLEnUP6xAkhxKVxpu3AqCiiUI3EnnGA91enEG3koQVGoVilX5oQ9UWSuFpJVZwQQlwMx75vKDeF8nlpP0yGk4z9e0n0L8IvVpo0hahPksSdQ5F54oQQwmtVdifGWf1PtKIMtJMpfFOWSHyvQYDCvQPshOrFqG06Nl6gQrRAMrChFtInTgghzq+w1MaTb2wkKsyfqwe244rebbEc2oiOyk96T54elYTyv474H98IgClKkjgh6pMkceeSijghhPDK3tQC7E4dVVH475pDvL/mEI9FbsHmiOKqYT0JDrBQFd8TPS8NALVNh8YNWIgWRpK4WkhFnBBCnN++YwWEBFqY96uhHM8uY8/Bk8Ttz2FbwGWMG+yaIsQc3wvHrpUoAWGogeGNG7AQLYwkceeQijghhLgwwzBIOVZIUscIVEWhY9sQ2tltVO43uGrC1ZhPTelhatsNFFX6wwnRACSJq4UhneKEEKJGWsFxirb+j4qyTvTseGbEqTNjH6hmTLFd3dsUawB+V0xDjUxojFCFaNEkiTuHzBMnhBDn59j9FdZjPzAuoIxeHUe4t2sZ+zDFdkUxWz3KW/tO8HWIQrQKMsWIEEIIrxm6hvPYDnQUxvnvIYpi13ZbOXpeOqb486/IIISoP5LEVSNVcUIIURst6xBGVSnLbZejqxZsP/wHwzBwZh4ADEzxPRs7RCFaDWlOrYV0iRNCiOqcadsxVDPryjtzWVJb4o9+TuWXL7gemiYrppjExg5RiFZDkrhzSJ84IYSomWEYOFJ/5JiSgKZaaXv5VVgjVRwHN2CUZGNK6Idikn9WhPAV+bbVwpCZ4oQQwoMzNw3K8/mhrDt3XtuTsBB/GHQD1oGT0QtPoASENXaIQrQq0idOCCGEV1K+/xrdUOh2xSiG941zb1cUBVNkAmpAaCNGJ0TrI0lcbaQiTgghPJjzD5NnbsuYK2UEqhBNgSRx55A+cUIIUV1phZ0QvQQjNKaxQxFCnCJJXC2kIk4IIc5IPVlImFpJYGRsY4cihDjFZ0lcamoq06ZNY8KECUybNo20tLRqZTRNY968eYwbN45rrrmGJUuWuPe98sorDBs2jOTkZJKTk5k3b16DxKnIPHFCCFFN5vGTqIpBeNv4xg5FCHGKz0anzpkzh+nTp5OcnMzSpUuZPXs2ixYt8iizbNky0tPTWb16NUVFRUyZMoVhw4bRvn17AKZMmcITTzzhk3hlnjghhDijKOskANYIqYkToqnwSU1cfn4+KSkpTJo0CYBJkyaRkpJCQUGBR7kVK1YwdepUVFUlMjKScePGsXLlSl+EeIZUxAkh6sCbVobHH3/c3YqQnJxMz549+frrr30f7EUyDIOqghwA1OA2jRyNEOI0n9TEZWZmEhsbi8lkAsBkMhETE0NmZiaRkZEe5eLjz1TVx8XFkZWV5X7/5Zdfsn79eqKjo3nooYcYOHBgwwUtVXFCCC9408rwwgsvuF/v37+fO+64g5EjR/o61DoxDIO84iqiwwPILa4iUCvGQEEJjmjs0IQQpzSbyX5/8YtfcN9992GxWNiwYQMPPPAAK1asICLC+wdKVFTwBcsYZyVv0dEhFxVrY5O4fUvi9q2mFPfpVoZ33nkHcLUyPPPMMxQUFHj8QD3bxx9/zOTJk7Farb4Mtc62HcjlX5/v4cEb++Jw6kSqZegB4Shqs/lnQ4gWzyffxri4OLKzs9E0DZPJhKZp5OTkEBcXV61cRkYG/fr1Azxr5qKjo93lhg8fTlxcHIcOHWLo0KFex5GfX4aun7+G7XQSZwC5uaVen7upiI4Okbh9SOL2rbrEraqKVz/cLoW3rQyn2e12li1bxsKFCxs0rvqw60geAAv/t58+nSO53FSOJTT6AkcJIXzJJ0lcVFQUSUlJLF++nOTkZJYvX05SUlK1h9zEiRNZsmQJ48ePp6ioiDVr1vDee+8BkJ2dTWysq0Ptvn37OHnyJJ07d673WBWZKE4I0UDWrFlDfHw8SUl1nyy3rgnppdRYGobBwRPFdE0I51hmCZtSspkUVUFgTM8GrwltSjWtdSFx+1ZzjRvqN3af1YvPnTuXWbNm8dprrxEaGsr8+fMBmDFjBg8//DB9+/YlOTmZnTt3Mn78eABmzpxJQkICAC+++CJ79+5FVVUsFgsvvPCCR+1cfZMucUKIC/G2leG0Tz75hJ///OcXdS1vWhJOu9Sa1uzCCvIKy7mxl0JJ90Q++no/wUYZdktYg9bgtoYa4qZE4vY9b2P3tiXBZ0lcYmKix7xvpy1YsMD92mQy1Tr/2+mkr6EZhsEY/z2YnB18cj0hRPPlbSsDQFZWFtu3b+dvf/tbI0RaN/uOFdLfmk6vfd/jn/xHzFdGo+w3ZGSqEE2MrNhwDqM0l+TAHwkv3t/YoQghmoG5c+eyePFiJkyYwOLFi90/RGfMmMHu3bvd5T777DOuvvpqwsPDGylS7+1LK6RrQDEAetqPjEx0DcJQQiSJE6IpkWFG1bj6xCmG3shxCCGaA29aGQDuv/9+X4V0SXTDYN+xQkaHl4MNHGnbUcPaAqBKEidEkyI1cedSXR+JJHFCiNboZG45ZZUOoikA1YxRnI3z+C5QFJSgmqdNEUI0DknizqVIEieEaL32pRVgwYnVVoil+wgAnGnbUQIjUEzSeCNEUyJJ3LlU06kXMjxVCNH6HDheRM9wGwoGpva9UGO6gGFIU6oQTZAkcedQpCZOCNGKpWaW0DuiEgA1oh3mToMBGdQgRFMkSdy5TvWJQ5I4IUQrU1hqo6jMTgf/MlBMqKGxWDoNAkANkdUahGhqpIPDuaQmTgjRSqVmlgDQhgLU8FgUkxklPA7/Mfdhiu/ZyNEJIc4lSdy53Emc1siBCCGEb6VmlmBSFfwrc1Cjzkx4bul6RSNGJYSojTSnnuvUwAZFBjYIIVqZoxkldIr2xyjJRY1o19jhCCEuQJK4c52qiUOX5lQhROuhGwZpWSX0aWMHDEnihGgGpDn1HIqioBsKCpLECSFaj+yCCiptGolBVQCSxAnRDEhNXA10FBmdKoRoVU4PamirFrlGpobFNm5AQogLkiSuBjqKjE4VQrQqqRmldPPPxy91HWp0J1mdQYhmQL6lNZAkTgjRGpzIKeNfS/cQEx4AuUe4N3A1SkAEAeMeaOzQhBBekCSuBjqq9IkTQrRohmHw3lcHKSqzYzIcPKCuxGkNIWLyk6hBEY0dnhDCC5LE1cAwFDBkihEhRMv148E8Dhwv4rbx3RkZlErVd3YCxs+QBE6IZkT6xNVAmlOFEC2Zw6nz0TeHiG8TxFUD4rHv/w41rC2mOFmVQYjmRJK4GmioksQJIVqs73dmkFtUxS/GdoWik+jZh7EkjUZRlMYOTQhRB5LE1cBA5okTQrRcx7JKCQu20qdzFI5934FqxtJ9RGOHJYSoI0niaqCjyjxxQogWq6zSQWigFcNpw3HoB8xdLkPxD27ssIQQdSRJXA2kT5wQoiUrrbATHGBBy9gP9gqphROimZIkrgauZbdkdKoQomUqrXAQEmhBKzgOgCm6cyNHJIS4GJLE1UBq4oQQLVlppYOQQCt6wQmUoEgUv6DGDkkIcREkiauB9IkTQrRUTk2n0uYkJMCCXnACNbJ9Y4ckhLhIksTVQJcpRoQQXkpNTWXatGlMmDCBadOmkZaWVmO5FStWMHnyZCZNmsTkyZPJy8vzbaCnlFU6AAj1V9GLMjFJEidEsyUrNtRAphgRQnhrzpw5TJ8+neTkZJYuXcrs2bNZtGiRR5ndu3fzz3/+k3fffZfo6GhKS0uxWq2NEm9phSuJi1SKQdekJk6IZkxq4mogfeKEEN7Iz88nJSWFSZMmATBp0iRSUlIoKCjwKLdw4ULuvvtuoqOjAQgJCcHPz8/n8QKUVdgBCHPkAqBGJjRKHEKISyc1cTWQJE4I4Y3MzExiY2MxmUwAmEwmYmJiyMzMJDIy0l3uyJEjtG/fnltvvZWKigquueYa7r///jqtkBAVVbd53KKjQ2rcvv9Eiet8SgGGaiK2WzcUk6VO525ItcXd1EncvtVc44b6jd1nSVxqaiqzZs2iqKiI8PBw5s+fT6dOnTzKaJrGs88+y7p161AUhXvvvZepU6d6lDl69Cg/+9nPmD59Ok888USDxKqjyhQjQoh6o2kaBw4c4J133sFut3PPPfcQHx/PlClTvD5Hfn4Zuu7dcyk6OoTc3NIa953MdiVxet5xTGFtySuoAqq8jqMhnS/upkzi9q3mGjd4H7uqKl79cPNZc+rpfiOrVq1i+vTpzJ49u1qZZcuWkZ6ezurVq/nwww955ZVXOHHihHu/pmnMmTOHcePGNWisUhMnhPBGXFwc2dnZaJoGuJ5ROTk5xMXFeZSLj49n4sSJWK1WgoODGTt2LLt27WqMkCk91ZyqFp+U/nBCNHM+SeK87TeyYsUKpk6diqqqREZGMm7cOFauXOne/+abbzJ69OhqNXj1TTdkihEhxIVFRUWRlJTE8uXLAVi+fDlJSUkeTangeuatX78ewzBwOBxs2rSJnj17NkbIlFY6iPTXMcryJYkTopnzSRJ3vn4j55aLj493v4+LiyMrKwuA/fv3s379eu68884Gj1dGpwohvDV37lwWL17MhAkTWLx4MfPmzQNgxowZ7N69G4Drr7+eqKgorrvuOqZMmULXrl256aabGiXesgoHnQLLAGR6ESGauWYxsMHhcPD000/z/PPPuxPBi+Ftx2CnYkLVK5ttx0mJ27ckbt9qanEnJiayZMmSatsXLFjgfq2qKk8++SRPPvmkL0OrUWmFnR6WYnAgNXFCNHM+SeLO7jdiMplq7TcSFxdHRkYG/fr1A87UzOXm5pKens69994LQElJCYZhUFZWxjPPPON1HN52DK7U/bBouc2y42Rz7fApcftWa4jb247BrU1ppYO2lkLAHyW4TWOHI4S4BD5J4s7uN5KcnFxrv5GJEyeyZMkSxo8fT1FREWvWrOG9994jPj6ezZs3u8u98sorVFRUNNjo1Ar8sGiVGIZRpykAhBCiqSurcBAZVowaFivPNyGaOa/7xG3atInjx48DkJOTwxNPPMGTTz5Jbm6uV8d7028kOTmZ9u3bM378eG6++WZmzpxJQoLvJ6KsMPxQ0cFR6fNrCyF871Kfb82FYRiUVToI1YpQw9o2djhCiEvkdU3cvHnzeOuttwCYP38+AH5+fjz99NO8/vrrFzzem34jJpPJndydz0MPPeRt2BelwnDNpG5UlaNYAxv0WkKIxnepz7fmotLmBN1JgKMINSy2scMRQlwir5O47Oxs4uPjcTqdrF+/nrVr12KxWBg5cmRDxtcoKvEHQMtPRw2NbuRohBANrbU830orHESpZSgYUhMnRAvgdXNqcHAweXl5bN26lcTERIKCggBwOp0NFlxjqTiVxFV99UojRyKE8IXW8nwrrXAQY3Kt2CA1cUI0f17XxN12223cdNNNOBwOnnrqKQB+/PFHunTp0mDBNZbTzakAenGW/GIVooVrLc+30ko70aeTuFBJ4oRo7rxO4u69916uueYaTCYTHTp0ACA2NpZnn322wYJrLFWclcSVSwdgIVq61vJ8K61wEK2WYFiDUPxl+hUhmrs6TTHSuXNn9+tNmzZhMpkYMmRIvQfV2Oyq/5k3zqaxMLQQomG1hudbWaWDWFOp/DAVooXwuk/cbbfdxvbt2wHXGqa/+93v+N3vfteiRm65qSZ+jLkBAMe+7xo5GCFEQ2stz7fSCjsxphJM4ZLECdESeJ3EHTp0iAEDBgCwZMkS/vOf//DRRx/xwQcfNFRsjUZVFHL9XE0qzmM7GjkaIURDay3Pt8qycsLVChnUIEQL4XVzqq7rKIpCeno6hmGQmJgIQHFxcYMF11hMqkIV1sYOQwjhIy39+bb/WCEdYoNRy/MApDlViBbC6yRu8ODB/OlPfyI3N5drrrkGgPT0dCIiIhosuMaiqgo2w+J+b9+5Amv/6xoxIiFEQ2rJz7eTuWW88P4OAvzM9DOfBD+ZXkSIlsLr5tTnn3+e0NBQevTowYMPPgjA0aNH+eUvf9lgwTUWVVXQjTPvbZs/arxghBANriU/33KLXIOz4tsEEqoVAZLECdFSeF0TFxERwe9+9zuPbaNHj67veJoEk6Kg6waWPtfg2PNVY4cjhGhgLfn5VljqSuIemNIXNm6H7DAUi/8FjhJCNAde18Q5HA5efvllxo4dS9++fRk7diwvv/wydru9IeNrFKqqoOkGakS7xg5FCOEDLfn5VlBqQ1UUwoKs+FXmychUIVoQr2vi/u///o9du3Yxb9484uPjycjI4LXXXqOsrMw9w3lLYVIVNE1HUU3ubWXvP4oppisBY+9rxMiEEA2hJT/fCktthIdYXd1EirMwdxrY2CEJIeqJ10ncypUrWbp0qbujb5cuXejVqxfJycnN/iF3LuVUTRxnJXFGaR7O0jyQJE6IFqclP98KS21EhPhh2CswqkpRQqUmToiWwuvmVMMw6rS9OTMpp5I4pfrHo5fkNkJEQoiG1JKfb64kzh+9OBuQQQ1CtCReJ3ETJ07k/vvvZ926dRw5coTvv/+emTNncu211zZkfI1CVV0DG1BrSOLKCxohIiFEQ2qpzzfDMCgstREZ4idJnBAtkNfNqY899hj/+te/+NOf/kROTg6xsbFcd911LaLj77lMqoJDM0AxVd/ZAn6ZCyE8tdTnW6XNic2hEXF2Ehca08hRCSHqi9dJnNVq5ZFHHuGRRx5xb7PZbAwYMIDHH3+8QYJrLKqqoDm0mh92hu77gIQQDaqlPt8KSm0AriQuMwslKBLFLKvRCNFSeN2cWhNFUVpEn5Fzqaf6xJmiEqrvbIH3K4SoriU83wrPTuJKsqUpVYgWxuuauNooilIfcTQpptN94mrUvB/qQgjvefN8S01NZdasWRQVFREeHs78+fPp1KmTR5lXXnmF//73v8TEuGr3Bw0axJw5cxoiZA8eSVxxNpYuQxr8mkII37lgErdx48Za9zkcjnoNpqk4PdkvgLX/ddh3rjizU3c2UlRCiPpWH8+3OXPmMH36dJKTk1m6dCmzZ89m0aJF1cpNmTKFJ5544qJjvRgFJVUoQJjZQZWtXGrihGhhLpjE/eEPfzjv/ri4uHoLpqkwnZXE+V1+s0cSZ2iSxAnRUlzq8y0/P5+UlBTeeecdACZNmsQzzzxDQUEBkZGR9RbnxSostREaZEUtywFAlTnihGhRLpjErV271hdxNCmKciaJq0ZrmbWPQrRGl/p8y8zMJDY2FpPJNZLdZDIRExNDZmZmtSTuyy+/ZP369URHR/PQQw8xcGDdVk6IigquU/no6BDKbRrRkYEEGcVUAFGdu2BtE1Kn8/hadHTTjq82ErdvNde4oX5jv+Q+cS3RefvESU2cEKKOfvGLX3DfffdhsVjYsGEDDzzwACtWrHCvEOGN/Pyy8/TV9RQdHUJubinZBeXEhAdQfOIwKApFzkCU3NKLvY0Gdzru5kbi9q3mGjd4H7uqKl79cLuk0aktlWuy3zNTiShnTTViOGzNfsSaEKJ+xMXFkZ2djaZpAGiaRk5OTrVm2OjoaCwWCwDDhw8nLi6OQ4cONXh8hSU296AGJbgNisnS4NcUQviOJHE1OLtPHICl+wj3a9sPi7Ft+qAxwhJCNDFRUVEkJSWxfPlyAJYvX05SUlK1ptTs7Gz363379nHy5Ek6d+7coLFV2Z1U2JzuJE4GNQjR8khzag1URUHTziRx1v7XofiHYFv/LgCOPV/hP+yWxgpPCNGEzJ07l1mzZvHaa68RGhrK/PnzAZgxYwYPP/wwffv25cUXX2Tv3r2oqorFYuGFF14gOjq6QeM6Pb1IZPCp6UViEhv0ekII35MkrgbqOTVxismMpedV7iROVm0QQpyWmJjIkiVLqm1fsGCB+/XpxM6XTidxUX4OcFRKTZwQLZA0p9bApCro5/R7U1TPj8qQRE4I0YS5J/pVigBZ+F6IlshnSVxqairTpk1jwoQJTJs2jbS0tGplNE1j3rx5jBs3jmuuucbj1+0nn3zC5MmTSU5OZvLkyTVOpllfVNWzObVGDluDXV8IIS5VUZnrGRVkKwBADW95c3oK0dr5rDnVm1nNly1bRnp6OqtXr6aoqIgpU6YwbNgw2rdvz4QJE7jxxhtRFIWysjImT57M0KFD6dmzZ73Heu7o1JroRZmYYrrU+7WFEKI+2B2uZ5halo2mmlGC2zRyREKI+uaTmrjTs5pPmjQJcM1qnpKSQkFBgUe5FStWMHXqVFRVJTIyknHjxrFy5UoAgoOD3esYVlVV4XA4GmzdVlUB3aDaVCLqWUlb1XdvN8i1hRCiPjg1HbNJwSjOQg2LqdYlRAjR/PnkW32+Wc3PLRcfH+9+HxcXR1ZWlvv9119/zfXXX8/VV1/NPffcQ48ePRokXpPqSg7P7RcXmPxHFD/X5HtKiPyqFUI0XU7NwGxS0YsyUcOkKVWIlqhZjU4dO3YsY8eOJSMjg5kzZzJq1Ci6dPG+SdPbZWtCQvwBiIwMxmoxee589F1OLPgtZn9rk132o6nGdSESt29J3C2bU9OxmkAvycXceXBjhyOEaAA+SeLOntXcZDLVOqt5XFwcGRkZ9OvXD6heM3dafHw8ffv25dtvv61TEuftsjVVla71UbOySwjwq/4ROXUFrcrWJJf9aK7LkUjcvtUa4vZ22ZqWyqHpxJjLwNBQw2TheyFaIp80p3o7q/nEiRNZsmQJuq5TUFDAmjVrmDBhAgBHjhxxlysoKGDz5s107969QeK1Wlwfi91Zy+AG1QS61iDXFkKI+uDUdKLVEkBGpgrRUvmsOdWbWc2Tk5PZuXMn48ePB2DmzJkkJCQA8OGHH7JhwwbMZjOGYXDbbbcxYsSIWq93KaxmVxOq3VFzoqaoZknihBBNmtOp085UDCA1cUK0UD5L4ryZ1dxkMjFv3rwaj3/qqacaLLZz+VnPn8RJTZwQoqlzagZtlGIUvxAU/9bbrCxESyZjzmtgNV+4OdXQnT6MSAgh6sap6URSLLVwQrRgksTV4PSI1Fpr4hRVauKEEE2aU9OJNIpQwyWJE6KlkiSuBoGnRqSWVdZc26ZIc6oQoolTnJUEUoEic8QJ0WJJEleDiBA/AApLq2rcbziq0AtO4Mw66MuwhBDCa6GOQgCpiROiBZMkrgbBgRYA/rvmULWltwC0vDQA7Fs/9WVYQgjhtVD99ML3ksQJ0VJJElcD9aw1WUtPTfzrsT8wwvVCmlSFEE1UsO6aFFmVhe+FaLEkibuA3MLKatusQ24EQMs+hG37Ul+HJIQQF6QaTgwAk6WxQxFCNBBJ4mrxz8euBiC7sKLaPkvny9yv7ds/81lMQgjhLUV3omNCOatlQQjRskgSV4v4NsFYzSpLvj2CXkO/uLPV1G9OCCEak2po6IrP5nMXQjQCSeJqYTGr2J06xWV27pn/Dcdzymov7Kh5FKsQQjQW1dDQVVNjhyGEaECSxHlpc0q2x/uAG84sA2bYqze5CiFEY1INDUNq4oRo0SSJO4/rrujofn16Ka7TTLHd3K/1sgKfxSSEEBei6QYmNAxFauKEaMkkiTuPm0Yncv+UPoCrefVsiqIQdPvLAFR+8We0ogyfxyeEaHypqalMmzaNCRMmMG3aNNLS0mote/ToUfr378/8+fMbNCaHU8Ok6Biq1MQJ0ZJJEncBg3tEoyhQaa8+J5waEOp+bd+8xJdhCSGaiDlz5jB9+nRWrVrF9OnTmT17do3lNE1jzpw5jBs3rsFjcjp1zGgY0idOiBZNkrgLUBUFf6uJghIZvCCE8JSfn09KSgqTJk0CYNKkSaSkpFBQUL2LxZtvvsno0aPp1KlTg8flcOqYFU1q4oRo4SSJ80KlTeOHPVnnnUrEeWwHWuFJH0YlhGhsmZmZxMbGYjK5arxMJhMxMTFkZmZ6lNu/fz/r16/nzjvv9ElcDk3HjA6SxAnRosk3vA7++O/NTBvTjX6JUe5tSnAURlk+ABVfPEfw7a8ABoo0YwghAIfDwdNPP83zzz/vTvYuRlRUsNdlM3LLMKNjslqJjg656Gs2huYW72kSt28117ihfmOXJM4LfbtEsftoPpn5FXz2/VGPJC7wukcp/+hJ1xtbORUf/wG9KJOgm59HDY9rpIiFEL4QFxdHdnY2mqZhMpnQNI2cnBzi4s5893Nzc0lPT+fee+8FoKSkBMMwKCsr45lnnvH6Wvn5Zei6dxOLO5wGZkVDM1Ryc0vrdlONKDo6pFnFe5rE7VvNNW7wPnZVVbz64SbNqV64Y2IP9+uIED+PfWp4HOaOA93v9SJXM0r5Z/N8E5wQotFERUWRlJTE8uXLAVi+fDlJSUlERka6y8THx7N582bWrl3L2rVrueOOO7j55pvrlMDVlcMpzalCtAaSxHkhMtSf2XdeRpf4UArLbCz/Ic3jF7Ea1aH6QbKKgxCtwty5c1m8eDETJkxg8eLFzJvn+gE3Y8YMdu/e3SgxOZw6JkUDkyRxQrRk8g33Uqe2oYQFWdlxKI9jWaW0Cffnil5tAVCCIho5OiFEY0lMTGTJkupTDC1YsKDG8g899FBDh4RD0zCjo5gsDX4tIUTjkZq4OggKOPNAtDt092tLz1GNEY4QQtTIeapPnCRxQrRsksTVQaBfzRWXiqISMOkJLP0m+jgiIYSozuE8VRNnlsYWIVoySeLqICzI6n6tnzNnnDk+CXO7Xr4OSQghqnFoOiZFR5U+cUK0aJLE1cHoge3OvKlppL80XQghmgDHqWW3FLM8k4RoySSJq4OAs5pTcworqxdQqn+c9r1r0IuzGjIsIYTw4HA4MSkGqiRxQrRoksTV0bDesQBk5pdX36k5PN4aDhu2DYsp/3AWth+XnnfZLiGEqC+aww6AKq0DQrRoksTV0a+u70W39mGUVznZk5rP/mOF7n2Kv+fsyobtTKJn3/YZen66z+IUQrReTofrB6XJIkmcEC2ZJHF1pKoKhaU2Dp8s5sUPd/LC+zvc+0xtOhH4szlYL/sZAOX//Z3nwVITJ4TwAc1+qiZOmlOFaNF8lsSlpqYybdo0JkyYwLRp00hLS6tWRtM05s2bx7hx47jmmms8JtB89dVXuf7667nhhhu48cYbWbduna9Cr6ZjrOfitaUVdvdrU3RnFGtgzQcqSkOGJYQQAGjumjjrBUoKIZoznyVxc+bMYfr06axatYrp06cze/bsamWWLVtGeno6q1ev5sMPP+SVV17hxIkTAPTr14+PP/6YL774gueee47f/va3VFU1ztJWEy73XGYrLctzMVujqnkuzCuEaBnO9ImTKUaEaMl8ksTl5+eTkpLCpEmTAJg0aRIpKSkUFBR4lFuxYgVTp05FVVUiIyMZN24cK1euBGDkyJEEBAQA0KNHDwzDoKioyBfhV9OuTZDH+yq75vHecNqpScWnc3Ae+wm9rKDG/TUxnDYqV7+CXpZf90CFEK2SrjldL2RggxAtmk+SuMzMTGJjYzGZTACYTCZiYmLIzMysVi4+Pt79Pi4ujqys6tNzfP7553To0IG2bds2bOC1CPAz88CUPu73ZzenAuCw1Xps5ap/VO8rdx7O1O0407Zj21J9bUYhhKiJfqomDqmJE6JFa3bf8C1btvDSSy/x9ttv1/nYqKjgCxc6S3R0SK37JrYJJiIikD+/swUdxaNscYeu5O/75qLPfbbS7ACqAH8/i9fHeFuuqZG4fUvibrl0p6tPnCJJnBAtmk++4XFxcWRnZ6NpGiaTCU3TyMnJIS4urlq5jIwM+vXrB1SvmduxYwePPfYYr732Gl26dKlzHPn5Zei6dyNEo6NDyM09f9+2xNhggvzNrP/pJKP7x2E2uSo2jQ5XEjAhCGfmfhy7VtZ47IXOfZqjxDWpcJXN4dUx3sTdFEncvtUa4lZVpc4/3FqK00kcqiRxQrRkPmlOjYqKIikpieXLlwOwfPlykpKSiIyM9Cg3ceJElixZgq7rFBQUsGbNGiZMmADArl27+O1vf8vLL79M7969fRG2V8qrnKTnlPHyx7vIK67kaEYJiqJg7jgA/yt+0djhCSFaIeN0Eid94oRo0Xw2OnXu3LksXryYCRMmsHjxYubNmwfAjBkz2L17NwDJycm0b9+e8ePHc/PNNzNz5kwSEhIAmDdvHlVVVcyePZvk5GSSk5M5cOCAr8Kv1cBubQDYk1rA4//ayLOLtnnsV2MS6+dCMsWcEMJLxqmBDdKcKkTL5rNveGJiose8b6ctWLDA/dpkMrmTu3N98sknDRbbpbgvuTe//ut3te73H/FLKj6d48OIhBCtnaFJc6oQrYGs2HCJLGbTefeb2nQk+M5/VduuVxTV7UIyT7AQwltSEydEqyBJXD147JaBHu/3HM2npPysaUcsftWOMewVdbuINKcKIbxkuOeJkyROiJZMkrh6kNQxghtHnRkt++JHO1m06kx/PUWp4WM+z1xyQghxSXQZ2CBEayBJXD1J6hjh8f7Hg7n8eDDX/T5o+t889tu2f163C0hzqhDCW6ebU6VPnBAtmiRx9SQ6IqDats++P+p+rQZHeezT0ndiVJVRsXw+WuHJC19AmlOFEF5SdGlOFaI1kCSunoQGWrljYg+PbSfzytl+IIcjGcU1HuNM+xEtYx+29f/BqCrDkCZWIUR9kCROiFZBkrh6dNWAdozs57kKxauf7eHPi7YDEJj8R/yumIb/6BkAVH3vWjrMcNooW/QgFZ/XPL2KEKLpSk1NZdq0aUyYMIFp06aRlpZWrcwnn3zC5MmTSU5OZvLkySxatKhBY1J0p6vyXjn/6HkhRPMmSVw9u+u6pBq3HzxehCm2K9Z+14L1nKZX1fWg1QszGjo8IUQ9mzNnDtOnT2fVqlVMnz6d2bNnVyszYcIEvvjiC5YuXcr777/PO++8w/79+xssJkV3omNCUaQzrRAtmSRxDeDvD42otu0v7/3ofq36n7OAt65d+KTyLBaiycnPzyclJYVJkyYBMGnSJFJSUigoKPAoFxwc7E6oqqqqcDgcDZpgKYaGrkhTqhAtnXzLG0BYkJU3Hr2KXUcKePWz3e7tBSVVhAVbUWMTMXcfifPgOgD03NQLn1QGNgjR5GRmZhIbG4vJ5KpNN5lMxMTEkJmZWW1t6K+//poXX3yR9PR0fv/739OjR4+aTlmrqKhgr8uqhoZhMhMdHXLhwk1Mc4wZJG5fa65xQ/3GLklcA7GYTfTsGO6x7dHXfgDgydsG0W30ryg9tAEMvdZz2Pd/h3bsJwCcRzahD/sFamB4reWFEE3X2LFjGTt2LBkZGcycOZNRo0bRpUuXCx94Sn5+Gbp+4V9zhmGgGq7m1Nzc0ksJ2eeio0OaXcwgcftac40bvI9dVRWvfrhJc2oDCvK38MuJ1X9tP7/Y1bQadOvfMcV79qHTi7Mo++AJ9LJ8bN+/g/PYDvc+x75va7yO4+B6DFt5/QUuhPBKXFwc2dnZaJqrS4SmaeTk5BAXF1frMfHx8fTt25dvv/22QWLSdAMzOoYMahCixZMkroGNHtCOO6/tWW17cbkdNTAMtU1Hj+0VXzyPUZJN+X9/X/1kRvVf4VrBcaq+/TdV3/673mIWQngnKiqKpKQkli9fDsDy5ctJSkqq1pR65MgR9+uCggI2b95M9+7dGyQmh1PHpOgYMtGvEC2eJHE+cFmP6GrbXv54FwBqcBuP7UZlzXPKndpbfZPTtbyOXpqLfef/zqyZKITwiblz57J48WImTJjA4sWLmTfPNVXQjBkz2L3b1Sf2ww8/5Prrryc5OZk777yT2267jREjqg+Aqg9OTceMhqFKTZwQLZ38VPOBQH8LI/vFsW5XpntbamYJAJZeY1Asfuhl+dgvtBRXDTVxp+kFJ7Bt/pCSsCDoNKo+whZCeCExMZElS5ZU275gwQL366eeespn8Tg1A7OiYaiybqoQLZ3UxPnI9HHd6dPFs4nlq63HceoGlh4j8Rs85cInqSGJM+yVHu91W2W1MkKI1sNVE6e7558UQrRcksT5iJ/VxH039AbAbHLND/X+14f4y3s72JtWwOETxZh6jzvvOfSKM02thubAtvkjKlf8n2eh84x2FUK0fO4kTpbcEqLFkyTOhwL9Lbw9awzD+54ZuZaaWcLfPviJ5xZv5+F1bTk8bA67TH1qPN6Ztg0Aw15B2VszsO9cUa2McZ4m13PL2X78Ar0o6yLuRAjRVDmcOmZFAxnYIESLJ0lcI7hpdGKN23VUXvkyle8K42s+0F6FXlF83sSrbOdar2IwbGXYt31Kxbk1eUKIZs2puaYYUaQmTogWT5K4RlDb/HGnHXa2rWWPQfniR6hY8ddaj3WW5HkXhH6q2VVzeFdeCNEsODUdk6KBDGwQosWTJK6RXNU/nikjOte6f3u3BzjqcE1NssnWFY+J2u0V5z23XpKDXpyFlncMZ+aBmgtJ3zkhWqTTfeKkJk6Ilk++5Y1EURQmXt6B9JwyruzTli83pnEitxyH05VcLdpchh/jGO5/gG+qepGrhTA5cMcFzupS/sHjHu8DfzYXJTgSNSD0zEZdOxWId3l85epXsHQfgbnTQK/KCyEah1Nz9YlTzFITJ0RLJzVxjchqMfHgjX0Z1D2ap+8YwuPTPRMkGxbWVvXBQOXrqt4E3/0GARN/U+frVHw2l/L/PIzj0A/ubc70ny54nFZ4EsMwMHQNZ9p2Kle/VOdrCyF8y+GUPnFCtBbyLW9CEuPDat1noJJV7MThl0hE2yTI2lfn81d98yb2PV+h56ae2VhLTZwzYx+Vy+fjN/JOLJ0vc2/XK0vQju/G0n14na8vhGh4rj5xutTECdEKSE1cE/PmY6Nr3feHBZuZ+85WNh+zX/T5PRK4sxj2Sgyn7Uy5osxT5dMwHKcmEDaZKX//Maq+XYBeXnjRMQgXwzDYfiAXTZf+iaL+nF52S5I4IVo+qYlrYswmlbl3DaGg1Ea/LlHc88I31cp8WjEEgAoCuNpvj2ujajrTz60OjPICDM1J2cL7UYIi8bv8Ztegh1PzzRm2Mmw//NdVWHMCp9ZmlYERl2z7gVxe+3wPN41O5LorOjZ2OKKFcDqdmBQDTJLECdHSSRLXBHWIDaFDbAgAfbtEsftoPpf3imVzSjYAVYaV/5YPR0UnPK4dgb1Hs2N3Go4Te7g9eEOdr1e1biHgSuiq1r4OgN/w2wBwpm6r8/mcGfux/fAe1v7XulaW+P4dgu9+E8VsrfO5WrK84ioASisuvmZViHNpTtcPLdUsj3chWjr5ljdxj0zth2EYmFTVncSdpqOy8HAsHD7dP64Lt1P3JM55cH31jRdY+MGZvhNrrzE17qv88gUwdKq+eRMlwNXPz7CVSxJ3DpvDVXPqZ5E1LkX90RyuHwUmaU4VosWTJK6JUxUFFNdaq3PuHMLOI3l8vi6Vfzw0gt+8cm7ypZCnBaNg8OfiKRgoXOF3mGlBm+p8Xe3k3vPut61fhLnzZWgn9mDpdiWGruHY8xXmzpd5NLUa+qnm11P3UBd6cTaO1K34DZhUp+Oc6T9Rueplgu94FcUaUOfr+orNfiqJs0oSJ+qP7nRN4K1a5EeTaDia5qSwMBen0/ctCTk5Knoz7Ut8buyqaiIgIJjg4DCUi/h30mdJXGpqKrNmzaKoqIjw8HDmz59Pp06dPMpomsazzz7LunXrUBSFe++9l6lTpwKwfv16XnzxRQ4ePMjtt9/OE0884avQm4yObUPo2DaEyVd2QlEUfjmxB4tWuibzTYgJ5nhOGc8U3+hxzA+27vxg685LkYvqdC3nsQvPSVex5A8YVaWY4pMwyguwbfoAZ8Z+z0KnV4QwDJxZh1CDI1GDo7yKoWL5fIzyAqxJV6P4BXkdu237F2Do6EUZmGJqXuKsKbA7XUmc1SxJnKg/p5M4qYkTDamwMBd//0CCgtpeVPJxKcxmFaezeSZxZ8duGAaa5qS0tIjCwlwiI2PqfD6fjU6dM2cO06dPZ9WqVUyfPp3Zs2dXK7Ns2TLS09NZvXo1H374Ia+88gonTpwAICEhgWeffZZf/epXvgq5yTr9hRk9oJ1727y7h563c/yrJeNYUj60XuMwqkpdL3QnxqlfY8a5o1ZPJ3G6RuUXf6b8A++Tb6Oi6NSLC7Ttnuv0A6Wux/nY6fBU3z7/RAt3OomTeeJEQ3I67QQFhfo8gWtJFEXBbLYQHh6F3V51UefwSRKXn59PSkoKkya5msUmTZpESkoKBQUFHuVWrFjB1KlTUVWVyMhIxo0bx8qVKwHo2LEjvXr1wiyddT3MvWsIf55xOQA3jU7kn49eXWO5g8541tt68njBL1A6XsZnFZfVWO6i6Bo4a1mD9XSmcrqJ9XTzak1FdQ3b5o/QK0s8jjF0DcfRrWiFGV6Fo5ya+85o4iNo3SnmWQ9BwzCw7Vh+5jMQoo4M7dR3TEanigYmCVz9cP2bdXGVDj7JiDIzM4mNjcVkcjUbmUwmYmJiyMzMJDIy0qNcfHy8+31cXBxZWVm+CLHZOj2K9bSOcaG8PWsMmfnl+FvNhAW5+sV88t0RCkttbErJ5uEdvQAo1f1JNOew3xHP1f576WLJvagYbJs+POtdzYmTcZ7k7TTtxB7sO1dg37mC4F/9++yDqVrzKgAh9y68cEDqqd8mTb3PxKkE9+zHoJZ9GPvWj9GyDxN4EatzXPCSmvNUIh1ywbKieTpdE4fUxAnR4rWqb3lUVHCdykdHN89/6KKjQ6rF/sDNA8kuqGBTylfubdvtXdhu7wLALkcHrgvYwYSA3XW+nkf/uVomAY4MC6DirPhqEhps5dS0wkSFqJSdPjbcn/ILHHu2DKsZDQgP8yfgPOUNQyf9pRlEjrkNa3QHrG07u2vxaqNVllK8eRkRo6ahqCavYzJ0jZPvzCJy1C8I7DYYAD9/V01JSIi/+xyV5RYqAQuOBvn7O7nwSWwnDxLzh0+a9d+3qJ0hzamilZkx4w4cDgdOp4Pjx9Pp3NnVF7p79x489dScCx7/+ecfY7PZmDbt1vOWW7/+O3bu/ImZMx+pl7jrg0++5XFxcWRnZ6NpGiaTCU3TyMnJIS4urlq5jIwM+vXrB1SvmbtU+fll6Lp3VZbR0SHk5pbW27V95Xxxq8D1wzry5cZjNe5fUTmQFZUDudx6mOnBP5DhDCe039Us3lzCPkc74k2FPBG27LzX16tcqVdBVD8i83e5txfkF7tf52Tmg8niroo3DIOyBXehhES7y2TvODNVSkHemWNP35th6LUmXA6HqwauqLCcsqDa/xsaDhtaeRG5y/4JgN/lN2Ptf91576/y69dxHtmELTgBc8cBXv+d6JUl2LOOkv3FKwT/8hXXuSpd/QhLSird53AWV566B2eD/P3ZTh50v25pf9/nUlWlzj/cWgJ3c6oqSZxoHRYseBeAzMwM7rnndhYu/K/HfqfTed6uWFOm3OTVdUaMuIoRI666+EAbgE++5VFRUSQlJbF8+XKSk5NZvnw5SUlJHk2pABMnTmTJkiWMHz+eoqIi1qxZw3vvveeLEFuNG0d1oU/nSKwWE0H+Zg6dKObdlQdwameaHvc6XAMmllYOZv/6EE43vWVo4XxeMZj+1mN8WD6MWedJ6PLt/pz9X/f0Ml4AZW/fy8HI0aj9r2dgt2gwXKM0jdIzzbmOw2emRXEe96wd1HKOUvH5nwiY8BvMHQdUv7g7uXMl7HpJDobTgSmynWc5w3OFCy0/vdb7cR9iP1WfeJ6uIM70n6hc+Q+Cpr+IGuz5N372YIvTvyc8f1c0j0EZoumSPnGiMWzYncn6XZkXLngRRvSLY3jfuAsXPMtNN01m0qRktm/fSnx8O+699wHmzv0D5eXl2O12rrxyOA884KpRe+utN6isrOTBB3/DihXL+OqrlYSEhHL06BFCQoJ59tkXiIpqw4oVy/jhh3U8++wL/PjjNl5++UV69erN3r27AYV5856jU6fOALzxxqusXfsVoaFhDBw4mO3bt/LWW/+p74/Gd6NT586dy+LFi5kwYQKLFy9m3rx5AMyYMYPdu13/SCcnJ9O+fXvGjx/PzTffzMyZM0lISABg27ZtjBo1infeeYcPPviAUaNGsW7dOl+F32IoikKPDhF0jgslJiKQ4X3jeO13o/jLfcPcZfxCInik4Jfsd3gmPWFBfnxT1Zt/lFxHphbBf8pGUKb7sd9R/cu1P9tzoEPV2jc83kfkbueVT04lZzUMilBDz9TK2X7wTOSdp+awq1z1D5xpnlOhOE/sRcs+7Hpzqh9e+QePU/HxHwBwpG3H0JzoZfln/rFzfzjn/zqULrgL7fip2kXVjGHo5K/9D3pZvkc5x77vANDy0qqdwzir86pFq6KPJd2zdrgOHYUNQ6dqw2K0ghNeHyNaAV2aU4UAyMvL45VX3uDJJ2cTHBzC/Pl/5+23F7Nw4X/Zv38fmzb9UONx+/alMHPmIyxe/BGdOnXh448/rLFcauoRpkz5Oe+++wFjxozj3XffAmD9+u/54Yf1LFz4Pm+88Q4nThxvsHv02bc8MTGRJUuWVNu+YMEC92uTyeRO7s512WWX8f333zdYfK2Z2aQSEx7AW09c7W7iLK9ykJFXzn9WHSSvuJJ7JvViUPdo7v7LWvdx2+xd2GbvQrypgJ5hyz3Oma2Fnfea0aZSXopcRNn7KzC17VFtv+Goebi1fc9XHrVUlatf8hjsULni/86cQ/OsaXOeTKFq9StgCQBHJQETzunXcIEkzqN2TDWj56ZRtvFzTGn7CJz8ZE0HYNuxHEuXy8DiX23v4MKVJIQc4Meq/kBC7deqLZyyfBx71+A8toPg6X+7YHnRSkhzqmgEw/vWvbasoU2ceL37ta7rvPbaS+zevQswyM/P59Chg1xxxZXVjuvXrz+xsW0B6N27D1u3bq7x/B06dKR7956nyvVlwwZXxdKOHdsYM2YcAQGuyeavvfZ6Fi58qz5vzU2+5cLt7OHiQf4WurUP50+/8pxbLiYiwL0MWM+OEXy74yQZWiR/K76ODuY8pgZt4eWS8RxxxpKthRJrOv9UGUZpHs7SvGrbnYdrXmXC9sN7mLuPqH4ep61abV/VV6/A+IfPlDndFOpw9TvTCk56lD/foIZq05UoimtqFVxNxXpRJmr4OQ+wqnLsWz/Gse8bAm/4Q7Vz+umueKy2mgeDnI9eXkj5+4/V+bizlb79a0yxXQm8/tLO09p5M5H5q6++yooVKzCZTJjNZn77298ycuTIhgnoVBInNXGitQsMPLNiz4cfvkdpaQlvvrkQPz8/5s//M3a7rcbjrNYzq52oqqsff83l/M4qp7rLGYbhs+lX5Fsu6uQvvx7m8X7C0ARSM0voEBPCopX7eeRED0736/pL8Q0M9TvCLUEb6zUGvTS/+raCEzjTfqy2vWr1y+7XzqPbPPYZpTmehRUF27ZPsfS8qvqqEuc2vRo6xumBGZUllH/0ZLXpT9xNpw5btf53AJWqq9O9xV6CXl6IM3U7alSC57G1cB7delbcF9krwmm74PJq4sJOT2SenJzM0qVLmT17NosWea6Q0q9fP+6++24CAgLYv38/t912G+vXr8ffv3oN7SU7PZ2PJHFCuJWWlhIV1QY/Pz9yc3NYv/47pkz5eYNca9Cgy3jrrTe4+ebpWK1WVq1a0SDXAR/2iRMtU2xEIFf0akt8myAe/Hk/VEVl9IB4OrUNQUdlq61LvV8zI9Oz5k4rzKBi2fMXPM55xLN2z7H/+3Pef4f9xy+o/PpfHtv1iqIzK0+4N2oXbvLUz0rcTs9Zd3ZTsCkQAKu9iMqvXsH2w2KM4uwL3se556lN2QePU/7Zny550mO9qpSKZc+j1zJ9TGvm7UTmI0eOdDet9OjRA8MwKCoqapCYOkSfSgxlYIMQblOn/oLdu3dy113T+etfn2fw4CENdq0RI65i6NBh3HnnLTz88H20a5dAcHDDjJSXn2qi3gQHWPj3E2dWjCitsHPweBHPf+GaEmJYRB5pJSYqdD8eCF0DQKYznAPOOEb77wPwqgm2LZ6TElcseao+bwM9+zClb94JgKnDALT0nzC17+NZpuB4tXViDaedsv884m6uPV17Z9jK0GtoMlZO5WFmZxWG0zU1i+N0beIFk7QLD4YwSnIwyMGx5yusfSdc4Hy1c+z/Di3zAI49X+F3+c0XfZ6WyNuJzM/2+eef06FDB9q2bVuna3k7XcqwPrHkZ0Cb6HBMgc1vTr3mOg9ga4s7J0fFbG68eqCarp2Q0J5Vq74B4PPPv/TY1759O955Z3GN5/r1r+93v77hhmRuuCG5xvdnvx46dChDh54ZdHfu+7vvvoeHHnoEXdd57rk/0bdvP3fMNcWuqupF/beQJE40mJBAK4N7xOC4/kpCA6306hTJweNFtIsOwr/iKpzpu3h5lUKwYmO0/z5WVvbjf5UD+L+I97AqGovLhtPVks0Vfocb7R609J9c/39ij8d2z1UqXMrevtezzMYzcxVpuUdPvXIlX44jW4izubYpupPTTdCnr3cyr5zutcSkl+WjZR06a8v5+17o5/T9A3CWVE8qm4Kqje9j2CoIGN0y10jesmULL730Em+//Xadj/V2nkt7sesHQX5RFUr5BQo3MS1xfs6m7FLi1nW90RahP3sR+aZq7tynycrKwGaz0aNHErfccjtOp15r7Lque/y38HaeS0niRIO7oteZGofuCeGuF/4JmCITmFCeSmGZnc633s6UrBL074/w+JZbGBFbxlZ7CEVtBrAhtye/D1le88mbCfv+M9Ph2ApzsH/9GqGn3iuGo1ptms1e+zJl5R89Bc6zOuTWkMMZZ9XkaVmuCX6dGfvc29Jf+fWZ2FLWYorpQtWGxfiPvKv6fHoNwHnsJwxHFZauV3hsd+xe5XrRTJI4bycyB9ixYwePPfYYr732Gl261H83g9POzBMnj3chGsvzz//VJ9eRb7loVJOHuyZGVBQFi1nl5qu78rORXbBaTNzlLjWE91f3YttPh/l1cl/ivnNNQ/NKyXjMisb9IV83Sux1cmoQhcPuhCWPe+xqV7zTq6WPK7/+F0ZVqWcCB5zO4mzbPsN5bAf+o+72GEihF2dhGAaVy+fXeF7b+jOd8G1bltRpzVbdMEjNKCGx3fmnlDlX5ap/AFRL4pobbycy37VrF7/97W95+eWX6d27d8MGpTkABRRTw15HCNHoJIkTTYqiKFgt1f/xuWV8L24Z3wsAR8gT2CoqOPyxazmulOHPsep/39NWLWaTvRtdzNk8Euqq0cnSwlhRMYBKw0pXS5Z7bdhCLZAIU0W16zQ0C9UnNq6Jv+JAy03FFN3Zvc15pOa5ioyqUvTSXOw/LgWg4rO51co4Dng3x2JdhsUbhk75grsJN0zktulG9M9neX1sSzJ37lxmzZrFa6+9RmhoKPPnu5LlGTNm8PDDD9O3b1/mzZtHVVUVs2fPdh/3wgsv0KNH9TkSL5muoZjMPpviQAjReCSJE82OJT4JC/D2WTlDVPi1hAZZuTfcNQLQcN7IydQ00kuC6G0YRIX588onu91JXMaIpwjfNBvlEkduNpS2pmIqPpsHZis47ZSOn0etXV5t5RecM07POXre/ac5j+3AcXiTRw1ZZm45UXg20QI4U11TtlgVDfL3o5fkulfaMJx29OJsFJO5+vx5Z7GnrMWSNBpFUaudv7nwZiLzTz75xGfxGJoDzDIyVYjWQJI40SJ0Pac5TzH70b5bD9qfte3Nx0ajlvXG0JxcEdkOo9vLlG1bBimr+LByOBP9fiRQsbEuaDxjKla6j1tX1YNItYyDzjh+FuhKXCp0C4Gqd7Vql8RpByBk9ZxLOo1e6X3n5aq1r2NOvNxdk2M/vAlMYM84wNmzmlWtec3juPIPnyBkhqvDfuXKv6Od6oN3ev4829ZPsO9Y5jHxsW39IpSAMCydB6NlnhntqxWcwBR59n894TXNKRP9CtFKyDddtBpmkwphse73in8wISNugRG3ML3KSUbuTbRrF0qyauKzVd0ozM3nzlvGYvkxg+8O5nLgeCEZznDuGurPcxsDKDUC+N21bckurGTowVc8rvVIwS8Z7ZfCz4K2nRuGh+UVA5gU+FND3K4H7Vj1iZDPp2rNq5ja98G2biFxp1q3lbyjOE/sxdy+N3pRVvWDDJ2y/z6KqW1XdwIHUJKaQqCfgn3HMgAq03Z6xpZ1EKMsDzU83r2t4uM/EjhlNqYY1wAAw2nHvnsVxpipdbqP1sjQnCgyR5xoRWbMuAOHw4HT6eD48XQ6d04EoHv3Hjz1lHc/gH/8cRtOp5OhQ5tXP11J4oQAAv3NdE2IcL//2YTB7tfXDEngmiEJrN6STtf2Q4iND+XhrsVEhweQ2DGK3NxSjnT7O3nLXqGXepSXSibw2C0D+dfnFrYWdmFgVDlTdc85i/5afB02w4LNMPskiasrZ+o2d3Pp2fSyPPSSXMo/qrn/m1GWh/Ow5/QlylcvYIye4X6fue8nzp4h7fSIVOtlN3ocl3cineOHchl85VAqPvsTeuEJ8qmCgZLInZcuNXGidVmw4F0AMjMzuOee21m48L8XOKK6HTu2U1lZKUmcEC3V+KEd3K8T4z2bbxPbRZB4n6vT+h9PbXv5kZEUldkIDbSSU3Q9f17wLX3a+XP7IBOPdhrGo6/9wNiBbbEfNrHfEU8/63EOOOJ4o3QML0a+R4o9Houi0c3i5SoOPlD500rU79+p83FHjqRzeuKSto4TNZaxb/vU433upmX0MOeTv38RfpprwrOSrV8SIknc+WkOFOkTJ1q5jRvXs2jR29hsdiwWCw899Dv69OlLenoaf/6za6CRrmtce+1kLr98GEuXfoqu62zbtoWxY8dz++13NvYteEWSOCEaUHiwa4HktpGB/P3RiZhUxd3X7I1HRwNQMORVEp06K3eepLTCyevX9sAoHUDKdxmY/fwpiQ3G0A3Y8BaX+aUCrubau4O/pb81/YIxpDnb0Mnsqh074Yygvfnil89SSzIv6rh2x1fV+ZiOZtcauacTOACHISsFXoihOVGlOVX4mOPgBq9HwdeVpccoLN2He13+5MkTLFz4Fi+++ApBQcEcPXqERx99mE8//ZJPP/2YYcOGc+ed9wBQUlJCaGgoyck3UllZyYMP/qZB7qGhSBInhI+YTTUnIJGhruECU6/udmZjWDR33RDtUS4tcgZa6RHC+ozgd2kFxIcPISuvECUwjGhTGVs2bKHnyGt444u9BFHJuKGdWfLtYaZf25/ffLKTAMXOkB5tKD3xGe39ywnRi/nR1olBfmke10l1tqGz2fsVHU46I2h3CYlhXeRoYdS8mJVwk+ZU0cpt3ryRkydPMHPmmVV0NE2joCCfAQMG8uqrL+FwOBg06DIGDbqsESO9dPJNF6KZ6NQpHnB1/u/TOQqAyIjTE4+Ecc3PfwbAM78a6q7tG9KvI7phcMu47lzZpy2B/hZgCG2iAjm2eR2RSkf+9ularrm8I8MGdcWh+rHt2+NY0t6kvbmQBep09uaZuGNULGkHD1KQX8KvQr4F4JBfb6JCrJhjekHKexxQu7G3NIwbTw3m2GLrwlC/mqc2qdCtFOmBxJuLPLZvrOrKMP/al1mLCGie05D4lOZEMcujXfiWpfvwOtWWNSTDMLj88mE8/fSfqu0bPXosffr0Y8uWTSxevJAvv/yC2bOfaYQo64d804VoYc6d5FVVFMZdluBZRjUR1HUwfYEO9ycTdqrZ1w+4+7oknNrfMJtUfgekZ5fSPjqYkcN688OeLCzdfoHVsDMowLVwmKE5sGnpDLrsRhJsVjieRHHMQHr7+fPVf17jkKMtxdYY7vRfw97KaCY/+Hssmk6IbuBXkU3Fx6d6EaomBg1Ogr2HcWLGNuQOgra+5RF3SIA8si7E0J0o5sDGDkOIRjN06BW8884Cjh49QpcurpGq+/btJSmpNydOHCc+vh3XXTeZ9u0TeO45V6IXFBREXl5uY4Z9UeSJKEQrdzqBO9vZTb8dYs9MMzy87+mJe8/MGKeYLPhf5VrrNDYIiBzrnph44q9/w3jDIDjAgqaPI96mYTapZ87v357gu9/EcFSB5iDYGoiNCoKHTkWx+FMeFoJi8cc/qi3oGuH+OiX1efMtkLX3OEIjw/D9eiRCNA0JCR2YPfsZ/vKXZ7DZbDidDvr27U9SUm/Wrv2K1atXYrG4VjV55JHfAzBq1NX84Q+Pceed05vVwAbFaK7TpF+E/PwydN27242ODiE31/sJUpsKidu3JG7fqkvcqqoQFRXcwBH5jjy/mq7WGHdW1jHatu1YzxF5x2xWcTqb5mo7F1Jb7Od+nt4+v2SolxBCCCFEMyRJnBBCCCFEMyRJnBBCCCFEMyRJnBBCCCHqrBV1qW9QhqEDygXL1USSOCGEEELUidlspby8RBK5S2AYBk6ng6KiPKxW/wsfUAOZYkQIIYQQdRIREU1hYS5lZUU+v7aqquh68xydem7sqmoiICCY4OCw8xxVO0nihBBCCFEnJpOZNm3iLlywATTXKV2g/mOX5lQhhBBCiGZIkjghhBBCiGaoVTWnqmrdRn/UtXxTIXH7lsTtW97G3Vzvrzby/GraJG7faq5xg3exe3t/rWrZLSGEEEKIlkKaU4UQQgghmiFJ4oQQQgghmiFJ4oQQQgghmiFJ4oQQQgghmiFJ4oQQQgghmiFJ4oQQQgghmiFJ4oQQQgghmiFJ4oQQQgghmiFJ4oQQQgghmiFJ4s6RmprKtGnTmDBhAtOmTSMtLa2xQwKgsLCQGTNmMGHCBCZPnsyDDz5IQUEBcP6Ym9L9/POf/6RHjx4cPHjwgrE1hbhtNhtz5sxh/PjxTJ48maeffrpZxP3NN98wZcoUkpOTmTx5MqtXr26Scc+fP58xY8Z4/E1cSpxN4bNvbE31M5DnV1rNJ21g8gxrWE3iGWYID7fffrvx+eefG4ZhGJ9//rlx++23N3JELoWFhcamTZvc7//yl78YTz75pGEY54+5qdzPnj17jF/96lfG6NGjjQMHDlwwtqYQ9zPPPGP8+c9/NnRdNwzDMHJzcy8YW2PHreu6cdlll7k/43379hkDBgwwNE1rcnFv3brVyMjIMK6++mp3vBeKpandQ1PTVD8DeX41TtzyDGtYTeEZJkncWfLy8ozBgwcbTqfTMAzDcDqdxuDBg438/PxGjqy6lStXGnfcccd5Y24q92Oz2Yybb77ZSE9Pd/+xN/W4y8rKjMGDBxtlZWUe25t63LquG0OHDjW2bdtmGIZhbNmyxRg/fnyTjvvsB+DFxtnY99AUNKfPQJ5fDU+eYa3jGWauv4rF5i8zM5PY2FhMJhMAJpOJmJgYMjMziYyMbOToztB1nffff58xY8acN2bDMJrE/bz00kvccMMNJCQkuLc19biPHz9OeHg4//znP9m8eTNBQUE88sgj+Pv7N+m4FUXhH//4Bw888ACBgYGUl5fzxhtvNPnP+7SLjbMp3UNjkedXw2iOzy+QZ1hreYZJn7hm6JlnniEwMJDbbrutsUO5oB07drB7926mT5/e2KHUidPp5Pjx4/Tq1YtPP/2URx99lIceeoiKiorGDu28nE4nb7zxBq+99hrffPMN//rXv/jtb3/b5OMWrYc8v3xDnmGtg9TEnSUuLo7s7Gw0TcNkMqFpGjk5OcTFxTV2aG7z58/n2LFjvP7666iqet6YDcNo9PvZunUrR48eZezYsQBkZWXxq1/9iieffLJJxx0fH4/ZbGbSpEkA9O/fn4iICPz9/Zt03Pv27SMnJ4fBgwcDMHjwYAICAvDz82vScZ92sX/PTekeGos8v+pfc31+gTzDWsszTGrizhIVFUVSUhLLly8HYPny5SQlJTWZpoi///3v7Nmzh1dffRWr1QqcP+amcD/33nsv69evZ+3ataxdu5a2bdvy1ltvcd111zXpuCMjI7n88svZsGED4Bo1lJ+fT6dOnZp03G3btiUrK4ujR48CcOTIEfLy8ujYsWOTjvu0i/17bkr30Fia+mcgzy/f/neQZ1jreIYphmEYDXtLzcuRI0eYNWsWJSUlhIaGMn/+fLp06dLYYXHo0CEmTZpEp06d8Pf3B6B9+/a8+uqr5425qd3PmDFjeP311+nevXuTj/v48eM89dRTFBUVYTab+c1vfsNVV13V5OP+4osvWLBgAYqiAPDwww8zbty4Jhf3s88+y+rVq8nLyyMiIoLw8HC+/PLLi46zKXz2ja2pfgby/GqcuOUZ1vKfYZLECSGEEEI0Q9KcKoQQQgjRDEkSJ4QQQgjRDEkSJ4QQQgjRDEkSJ4QQQgjRDEkSJ4QQQgjRDEkSJ4QQQgjRDEkSJ1q9Hj16cOzYscYOQwgh6kyeX62bLLslmpwxY8aQl5fnXggY4Gc/+xmzZ89uxKiEEOLC5PklfEmSONEkvf7661x55ZWNHYYQQtSZPL+Er0hzqmg2Pv30U37xi1/wzDPPMHjwYCZOnMjGjRvd+7Ozs7nvvvsYOnQo11xzDR999JF7n6ZpvP7664wbN46BAwdy4403kpmZ6d7/ww8/MH78eIYMGcK8efM4vZDJsWPHuO222xg8eDCXX345v/nNb3x2v0KIlkOeX6IhSE2caFZ27drFxIkT2bRpE1999RUPPvggX3/9NeHh4fz+97+na9eurFu3jqNHj3LXXXeRkJDAsGHDeOedd/jyyy9588036dy5MwcOHHCv4Qjw7bff8vHHH1NWVsaNN97I1VdfzahRo3jppZcYPnw4ixYtwuFwsHv37ka8eyFEcybPL1HfpCZONEkzZ87ksssuc//v9K/SyMhI7rjjDiwWC9dddx2dO3fm22+/JTMzk+3bt/Poo4/i5+dHUlISU6dOZenSpQAsWbKERx55hC5duqAoCj179iQiIsJ9vRkzZhAaGkp8fDyXX345+/fvB8BsNpORkUFOTg5+fn5cdtllvv8whBDNijy/hK9IEieapFdffZVt27a5/3fzzTcDEBsbi6Io7nLx8fHk5OSQk5NDWFgYwcHBHvuys7MByMrKokOHDrVeLzo62v06ICCA8vJyAB577DEMw+Cmm27i+uuv5+OPP67X+xRCtDzy/BK+Is2polnJzs7GMAz3gzAzM5MxY8YQExNDcXExZWVl7gdhZmYmsbGxALRt25b09HS6d+9ep+tFR0fz7LPPArBt2zbuuusuhgwZQseOHevxroQQrYE8v0R9k5o40awUFBS4+3f873//48iRI1x11VXExcUxcOBAXnzxRWw2G/v37+fjjz9m8uTJAEydOpWXXnqJtLQ0DMNg//79FBYWXvB6//vf/8jKygIgLCwMRVFQVfnaCCHqTp5for5JTZxoku677z6PeZauvPJKxo4dS79+/Th27BhXXHEFbdq04eWXX3b3DXnxxReZM2cOI0eOJDQ0lIceeojhw4cDcNddd2G327n77rspLCykS5cuvPrqqxeMY/fu3Tz33HOUlZURFRXFH/7wBxISEhrmpoUQLYI8v4SvKMbpschCNHGffvopS5Ys4f3332/sUIQQok7k+SUagtSrCiGEEEI0Q5LECSGEEEI0Q9KcKoQQQgjRDElNnBBCCCFEMyRJnBBCCCFEMyRJnBBCCCFEMyRJnBBCCCFEMyRJnBBCCCFEM/T/13+gAqTvF4AAAAAASUVORK5CYII=\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "saving ./plots/LONGER_ADAM_FFNet_ReLU_lr0.001_momentum0.9_wdecay0.001_dampening0_nesterovFalse_HidDim64.png\n" ] } ], "source": [ "# Trying ADAM\n", "class FeedforwardNeuralNetModel(nn.Module):\n", " def __init__(self, in_dim, hid_dim, out_dim):\n", " super(FeedforwardNeuralNetModel, self).__init__()\n", " # Linear function\n", " self.fc1 = nn.Linear(in_dim, hid_dim) \n", " # Non-linearity\n", " self.relu = nn.ReLU()\n", " # Linear function (readout)\n", " self.fc2 = nn.Linear(hid_dim, out_dim) \n", "\n", " def forward(self, x):\n", " # Linear function\n", " out = self.fc1(x)\n", " # Non-linearity\n", " out = self.relu(out)\n", " # Linear function (readout)\n", " out = self.fc2(out)\n", " return out\n", "\n", "# Best configuration for longer\n", "lr_range = [0.01,0.001]\n", "hid_dim_range = [64]\n", "weight_decay_range = [0.001]\n", "momentum_range = [0.9]\n", "dampening_range = [0]\n", "nesterov_range = [False]\n", "for lr in lr_range:\n", " for hid_dim in hid_dim_range:\n", " print('\\nlr: {}, momentum: {}, weight_decay: {}, dampening: {}, nesterov: {} '.format(lr, momentum, weight_decay, dampening, nesterov))\n", " model = FeedforwardNeuralNetModel(in_dim=47,hid_dim=hid_dim, out_dim=1).to(device)\n", " print(model)\n", " SGD = torch.optim.Adam(model.parameters(), lr = lr) # This is absurdly high.\n", " # initialize the loss function. You don't want to use this one, so change it accordingly\n", " loss_fn = nn.MSELoss().to(device)\n", " config_str = 'LONGER_ADAM_FFNet_ReLU_lr' + str(lr) + '_momentum' + str(momentum) + '_wdecay' + str(weight_decay) + '_dampening' + str(dampening) +'_nesterov' + str(nesterov) + '_HidDim' + str(hid_dim)\n", " train(model,loss_fn, SGD, dataloaders['train'], dataloaders['val'], config_str,num_epochs=1000, verbose=False)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "predicted.shape: torch.Size([335])\n", "predicted[:20]: \ttensor([0.3208, 0.7937, 0.8534, 0.3528, 0.4023, 0.1810, 0.3304, 0.0766, 0.8246,\n", " 0.6655, 0.2339, 0.2251, 0.4263, 0.6891, 0.7257, 0.3355, 0.6974, 0.3572,\n", " 0.8831, 0.2559])\n", "targets[:20]: \t\ttensor([0.8422, 0.7139, 0.8761, 0.5382, 0.2834, 0.1301, 0.3854, 0.2266, 0.8438,\n", " 0.6774, 0.3873, 0.4123, 0.4433, 0.8151, 0.7777, 0.3105, 0.6314, 0.3211,\n", " 0.8954, 0.2512])\n", "predicted.shape: torch.Size([1118])\n", "predicted[:20]: \ttensor([0.0556, 0.1810, 0.2741, 0.1903, 0.2008, 0.2848, 0.2288, 0.3241, 0.5576,\n", " 0.5037, 0.4970, 0.5641, 0.5313, 0.5002, 0.4886, 0.4925, 0.4925, 0.4964,\n", " 0.5011, 0.4783])\n", "targets[:20]: \t\ttensor([0.0280, 0.1301, 0.2970, 0.2207, 0.2648, 0.3342, 0.2873, 0.3362, 0.6023,\n", " 0.5031, 0.5335, 0.6247, 0.5454, 0.4926, 0.4442, 0.5030, 0.5472, 0.5128,\n", " 0.5702, 0.5017])\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Test\n", "with torch.no_grad():\n", " data, targets_val = next(iter(dataloaders['all_val']))\n", " model_input = data.to(device)# TODO: Turn the 28 by 28 image tensors into a 784 dimensional tensor.\n", " predicted_val = model(model_input).squeeze()\n", "# _, predicted = torch.max(out, 1)\n", " print('predicted.shape: {}'.format(predicted_val.shape))\n", " print('predicted[:20]: \\t{}'.format(predicted_val[:20]))\n", " print('targets[:20]: \\t\\t{}'.format(targets_val[:20]))\n", " \n", "# Test\n", "with torch.no_grad():\n", " data, targets = next(iter(dataloaders['test']))\n", " model_input = data.to(device)# TODO: Turn the 28 by 28 image tensors into a 784 dimensional tensor.\n", " predicted = model(model_input).squeeze()\n", " print('predicted.shape: {}'.format(predicted.shape))\n", " print('predicted[:20]: \\t{}'.format(predicted[:20]))\n", " print('targets[:20]: \\t\\t{}'.format(targets[:20]))\n", " \n", "#Time for a real test\n", "path_to_save = './plots'\n", "if not os.path.exists(path_to_save):\n", " os.makedirs(path_to_save)\n", " \n", "fig, (ax1,ax2) = plt.subplots(1,2, sharey=True)\n", "# test_predictions = model(normed_test_data).flatten()\n", "r = r2_score(targets_val, predicted_val.cpu())\n", "ax1.scatter(targets_val, predicted_val.cpu(),alpha=0.5, label='$R^2$ = %.3f' % (r))\n", "ax1.legend(loc=\"upper left\")\n", "ax1.set_xlabel('True Values [Mean Conc.]')\n", "ax1.set_ylabel('Predictions [Mean Conc.]')\n", "ax1.axis('equal')\n", "ax1.axis('square')\n", "ax1.set_xlim([0,1])\n", "ax1.set_ylim([0,1])\n", "_ = ax1.plot([-100, 100], [-100, 100], 'r:')\n", "ax1.set_title('Test dataset')\n", "fig.set_figheight(30)\n", "fig.set_figwidth(10)\n", "#plt.show()\n", "#plt.close('all')\n", "\n", "#Whole dataset\n", "# dataset_predictions = model.predict(normed_dataset).flatten()\n", "r = r2_score(targets, predicted.cpu())\n", "ax2.scatter(targets, predicted.cpu(), alpha=0.5, label='$R^2$ = %.3f' % (r))\n", "ax2.legend(loc=\"upper left\")\n", "ax2.set_xlabel('True Values [Mean Conc.]')\n", "ax2.set_ylabel('Predictions [Mean Conc.]')\n", "ax2.axis('equal')\n", "ax2.axis('square')\n", "ax2.set_xlim([0,1])\n", "ax2.set_ylim([0,1])\n", "_ = ax2.plot([-100, 100], [-100, 100], 'r:')\n", "ax2.set_title('Whole dataset')\n", "# plt.show()\n", "fig.savefig(os.path.join(path_to_save, 'FFNet_ADAM_LONGER_R2Score_' + config_str + '.png'), bbox_inches='tight')\n", "# #plt.close('all')" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "lr: 0.001, momentum: 0.9, weight_decay: 0.001, dampening: 0, nesterov: False \n", "FeedforwardNeuralNetModel(\n", " (fc1): Linear(in_features=47, out_features=64, bias=True)\n", " (drop): Dropout(p=0.2, inplace=False)\n", " (relu): ReLU()\n", " (fc2): Linear(in_features=64, out_features=64, bias=True)\n", " (fc3): Linear(in_features=64, out_features=1, bias=True)\n", ")\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", " Training R^2: -0.1116. Test R^2: -0.0033. Loss Train: 0.1149. Loss Val: 0.0621. Time: 3.7105\n", " EPOCH 10. Progress: 2.0%. \n", " Training R^2: 0.4195. Test R^2: 0.3340. Loss Train: 0.0315. Loss Val: 0.0411. Time: 3.9621\n", " EPOCH 20. Progress: 4.0%. \n", " Training R^2: 0.4887. Test R^2: 0.4492. Loss Train: 0.0295. Loss Val: 0.0366. Time: 2.6475\n", " EPOCH 30. Progress: 6.0%. \n", " Training R^2: 0.5252. Test R^2: 0.4602. Loss Train: 0.0255. Loss Val: 0.0329. Time: 2.6859\n", " EPOCH 40. Progress: 8.0%. \n", " Training R^2: 0.5288. Test R^2: 0.5088. Loss Train: 0.0222. Loss Val: 0.0307. Time: 2.4811\n", " EPOCH 50. Progress: 10.0%. \n", " Training R^2: 0.6128. Test R^2: 0.5821. Loss Train: 0.0221. Loss Val: 0.0285. Time: 3.0067\n", " EPOCH 60. Progress: 12.0%. \n", " Training R^2: 0.6259. Test R^2: 0.5806. Loss Train: 0.0201. Loss Val: 0.0258. Time: 2.9824\n", " EPOCH 70. Progress: 14.000000000000002%. \n", " Training R^2: 0.6326. Test R^2: 0.5524. Loss Train: 0.0203. Loss Val: 0.0254. Time: 2.6254\n", " EPOCH 80. Progress: 16.0%. \n", " Training R^2: 0.6316. Test R^2: 0.5922. Loss Train: 0.0191. Loss Val: 0.0237. Time: 2.5129\n", " EPOCH 90. Progress: 18.0%. \n", " Training R^2: 0.6932. Test R^2: 0.6243. Loss Train: 0.0190. Loss Val: 0.0234. Time: 2.6534\n", " EPOCH 100. Progress: 20.0%. \n", " Training R^2: 0.6969. Test R^2: 0.6793. Loss Train: 0.0180. Loss Val: 0.0214. Time: 2.6627\n", " EPOCH 110. Progress: 22.0%. \n", " Training R^2: 0.6888. Test R^2: 0.6659. Loss Train: 0.0169. Loss Val: 0.0210. Time: 3.4999\n", " EPOCH 120. Progress: 24.0%. \n", " Training R^2: 0.6329. Test R^2: 0.6819. Loss Train: 0.0166. Loss Val: 0.0195. Time: 2.8489\n", " EPOCH 130. Progress: 26.0%. \n", " Training R^2: 0.6987. Test R^2: 0.6835. Loss Train: 0.0159. Loss Val: 0.0195. Time: 2.9676\n", " EPOCH 140. Progress: 28.000000000000004%. \n", " Training R^2: 0.7195. Test R^2: 0.7354. Loss Train: 0.0160. Loss Val: 0.0204. Time: 2.9275\n", " EPOCH 150. Progress: 30.0%. \n", " Training R^2: 0.7237. Test R^2: 0.7208. Loss Train: 0.0162. Loss Val: 0.0169. Time: 3.5524\n", " EPOCH 160. Progress: 32.0%. \n", " Training R^2: 0.7257. Test R^2: 0.7492. Loss Train: 0.0150. Loss Val: 0.0178. Time: 3.0206\n", " EPOCH 170. Progress: 34.0%. \n", " Training R^2: 0.7380. Test R^2: 0.7035. Loss Train: 0.0151. Loss Val: 0.0180. Time: 2.7954\n", " EPOCH 180. Progress: 36.0%. \n", " Training R^2: 0.7294. Test R^2: 0.7261. Loss Train: 0.0161. Loss Val: 0.0159. Time: 2.8494\n", " EPOCH 190. Progress: 38.0%. \n", " Training R^2: 0.7208. Test R^2: 0.7466. Loss Train: 0.0134. Loss Val: 0.0169. Time: 2.8734\n", " EPOCH 200. Progress: 40.0%. \n", " Training R^2: 0.7529. Test R^2: 0.7552. Loss Train: 0.0148. Loss Val: 0.0142. Time: 2.9881\n", " EPOCH 210. Progress: 42.0%. \n", " Training R^2: 0.7380. Test R^2: 0.7536. Loss Train: 0.0139. Loss Val: 0.0154. Time: 2.8477\n", " EPOCH 220. Progress: 44.0%. \n", " Training R^2: 0.7237. Test R^2: 0.7282. Loss Train: 0.0132. Loss Val: 0.0145. Time: 3.3642\n", " EPOCH 230. Progress: 46.0%. \n", " Training R^2: 0.7590. Test R^2: 0.7601. Loss Train: 0.0134. Loss Val: 0.0149. Time: 2.9623\n", " EPOCH 240. Progress: 48.0%. \n", " Training R^2: 0.7489. Test R^2: 0.7782. Loss Train: 0.0136. Loss Val: 0.0144. Time: 3.0059\n", " EPOCH 250. Progress: 50.0%. \n", " Training R^2: 0.7542. Test R^2: 0.7567. Loss Train: 0.0130. Loss Val: 0.0141. Time: 3.2227\n", " EPOCH 260. Progress: 52.0%. \n", " Training R^2: 0.7870. Test R^2: 0.7698. Loss Train: 0.0117. Loss Val: 0.0132. Time: 2.9511\n", " EPOCH 270. Progress: 54.0%. \n", " Training R^2: 0.7815. Test R^2: 0.7898. Loss Train: 0.0122. Loss Val: 0.0137. Time: 2.7537\n", " EPOCH 280. Progress: 56.00000000000001%. \n", " Training R^2: 0.7869. Test R^2: 0.7865. Loss Train: 0.0121. Loss Val: 0.0133. Time: 2.8151\n", " EPOCH 290. Progress: 57.99999999999999%. \n", " Training R^2: 0.7609. Test R^2: 0.7872. Loss Train: 0.0118. Loss Val: 0.0148. Time: 4.0698\n", " EPOCH 300. Progress: 60.0%. \n", " Training R^2: 0.7668. Test R^2: 0.7948. Loss Train: 0.0111. Loss Val: 0.0129. Time: 2.7746\n", " EPOCH 310. Progress: 62.0%. \n", " Training R^2: 0.7591. Test R^2: 0.7990. Loss Train: 0.0119. Loss Val: 0.0133. Time: 3.1289\n", " EPOCH 320. Progress: 64.0%. \n", " Training R^2: 0.7815. Test R^2: 0.7622. Loss Train: 0.0104. Loss Val: 0.0137. Time: 2.6921\n", " EPOCH 330. Progress: 66.0%. \n", " Training R^2: 0.7867. Test R^2: 0.7924. Loss Train: 0.0121. Loss Val: 0.0122. Time: 2.5199\n", " EPOCH 340. Progress: 68.0%. \n", " Training R^2: 0.7781. Test R^2: 0.7896. Loss Train: 0.0113. Loss Val: 0.0145. Time: 3.1129\n", " EPOCH 350. Progress: 70.0%. \n", " Training R^2: 0.7975. Test R^2: 0.8189. Loss Train: 0.0113. Loss Val: 0.0116. Time: 3.3524\n", " EPOCH 360. Progress: 72.0%. \n", " Training R^2: 0.8009. Test R^2: 0.8176. Loss Train: 0.0105. Loss Val: 0.0120. Time: 2.6168\n", " EPOCH 370. Progress: 74.0%. \n", " Training R^2: 0.7851. Test R^2: 0.8166. Loss Train: 0.0100. Loss Val: 0.0113. Time: 2.9490\n", " EPOCH 380. Progress: 76.0%. \n", " Training R^2: 0.7779. Test R^2: 0.7722. Loss Train: 0.0105. Loss Val: 0.0125. Time: 2.7381\n", " EPOCH 390. Progress: 78.0%. \n", " Training R^2: 0.8053. Test R^2: 0.8106. Loss Train: 0.0106. Loss Val: 0.0120. Time: 2.9906\n", " EPOCH 400. Progress: 80.0%. \n", " Training R^2: 0.8104. Test R^2: 0.8018. Loss Train: 0.0119. Loss Val: 0.0122. Time: 2.8241\n", " EPOCH 410. Progress: 82.0%. \n", " Training R^2: 0.8031. Test R^2: 0.8322. Loss Train: 0.0120. Loss Val: 0.0124. Time: 2.8660\n", " EPOCH 420. Progress: 84.0%. \n", " Training R^2: 0.8009. Test R^2: 0.8090. Loss Train: 0.0110. Loss Val: 0.0111. Time: 2.7802\n", " EPOCH 430. Progress: 86.0%. \n", " Training R^2: 0.7885. Test R^2: 0.7977. Loss Train: 0.0115. Loss Val: 0.0136. Time: 2.6376\n", " EPOCH 440. Progress: 88.0%. \n", " Training R^2: 0.8036. Test R^2: 0.7974. Loss Train: 0.0099. Loss Val: 0.0107. Time: 2.7612\n", " EPOCH 450. Progress: 90.0%. \n", " Training R^2: 0.8133. Test R^2: 0.8169. Loss Train: 0.0114. Loss Val: 0.0128. Time: 2.8415\n", " EPOCH 460. Progress: 92.0%. \n", " Training R^2: 0.8185. Test R^2: 0.8351. Loss Train: 0.0112. Loss Val: 0.0101. Time: 2.5918\n", " EPOCH 470. Progress: 94.0%. \n", " Training R^2: 0.8342. Test R^2: 0.8323. Loss Train: 0.0104. Loss Val: 0.0105. Time: 2.7809\n", " EPOCH 480. Progress: 96.0%. \n", " Training R^2: 0.8234. Test R^2: 0.8328. Loss Train: 0.0098. Loss Val: 0.0099. Time: 3.1542\n", " EPOCH 490. Progress: 98.0%. \n", " Training R^2: 0.8127. Test R^2: 0.8577. Loss Train: 0.0106. Loss Val: 0.0114. Time: 2.5527\n", " EPOCH 500. Progress: 100.0%. \n", " Training R^2: 0.8027. Test R^2: 0.8241. Loss Train: 0.0110. Loss Val: 0.0107. Time: 2.7262\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "saving ./plots/lr0.001_3FFNet_2ReLU_Drop_momentum0.9_wdecay0.001_dampening0_nesterovFalse_HidDim64.png\n", "\n", "lr: 0.001, momentum: 0.9, weight_decay: 0.001, dampening: 0, nesterov: False \n", "FeedforwardNeuralNetModel(\n", " (fc1): Linear(in_features=47, out_features=128, bias=True)\n", " (drop): Dropout(p=0.2, inplace=False)\n", " (relu): ReLU()\n", " (fc2): Linear(in_features=128, out_features=128, bias=True)\n", " (fc3): Linear(in_features=128, out_features=1, bias=True)\n", ")\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", " Training R^2: 0.1006. Test R^2: 0.1840. Loss Train: 0.0998. Loss Val: 0.0517. Time: 2.6534\n", " EPOCH 10. Progress: 2.0%. \n", " Training R^2: 0.4191. Test R^2: 0.3644. Loss Train: 0.0283. Loss Val: 0.0408. Time: 2.6442\n", " EPOCH 20. Progress: 4.0%. \n", " Training R^2: 0.5341. Test R^2: 0.5072. Loss Train: 0.0266. Loss Val: 0.0310. Time: 2.4408\n", " EPOCH 30. Progress: 6.0%. \n", " Training R^2: 0.5715. Test R^2: 0.5125. Loss Train: 0.0233. Loss Val: 0.0297. Time: 2.9272\n", " EPOCH 40. Progress: 8.0%. \n", " Training R^2: 0.6088. Test R^2: 0.6030. Loss Train: 0.0214. Loss Val: 0.0256. Time: 2.6961\n", " EPOCH 50. Progress: 10.0%. \n", " Training R^2: 0.6220. Test R^2: 0.6237. Loss Train: 0.0203. Loss Val: 0.0232. Time: 2.7807\n", " EPOCH 60. Progress: 12.0%. \n", " Training R^2: 0.6905. Test R^2: 0.6549. Loss Train: 0.0188. Loss Val: 0.0208. Time: 2.6271\n", " EPOCH 70. Progress: 14.000000000000002%. \n", " Training R^2: 0.6881. Test R^2: 0.6740. Loss Train: 0.0175. Loss Val: 0.0180. Time: 2.8633\n", " EPOCH 80. Progress: 16.0%. \n", " Training R^2: 0.7055. Test R^2: 0.7114. Loss Train: 0.0173. Loss Val: 0.0183. Time: 2.6832\n", " EPOCH 90. Progress: 18.0%. \n", " Training R^2: 0.6565. Test R^2: 0.6804. Loss Train: 0.0156. Loss Val: 0.0210. Time: 2.5049\n", " EPOCH 100. Progress: 20.0%. \n", " Training R^2: 0.7215. Test R^2: 0.7378. Loss Train: 0.0148. Loss Val: 0.0161. Time: 2.4777\n", " EPOCH 110. Progress: 22.0%. \n", " Training R^2: 0.7345. Test R^2: 0.7626. Loss Train: 0.0147. Loss Val: 0.0148. Time: 2.8506\n", " EPOCH 120. Progress: 24.0%. \n", " Training R^2: 0.7580. Test R^2: 0.7546. Loss Train: 0.0129. Loss Val: 0.0150. Time: 2.8920\n", " EPOCH 130. Progress: 26.0%. \n", " Training R^2: 0.7640. Test R^2: 0.7900. Loss Train: 0.0144. Loss Val: 0.0148. Time: 2.6616\n", " EPOCH 140. Progress: 28.000000000000004%. \n", " Training R^2: 0.6779. Test R^2: 0.7368. Loss Train: 0.0135. Loss Val: 0.0162. Time: 3.2868\n", " EPOCH 150. Progress: 30.0%. \n", " Training R^2: 0.7856. Test R^2: 0.8021. Loss Train: 0.0132. Loss Val: 0.0123. Time: 3.0104\n", " EPOCH 160. Progress: 32.0%. \n", " Training R^2: 0.8018. Test R^2: 0.7938. Loss Train: 0.0121. Loss Val: 0.0133. Time: 2.5466\n", " EPOCH 170. Progress: 34.0%. \n", " Training R^2: 0.7795. Test R^2: 0.8494. Loss Train: 0.0122. Loss Val: 0.0118. Time: 3.2550\n", " EPOCH 180. Progress: 36.0%. \n", " Training R^2: 0.7942. Test R^2: 0.8183. Loss Train: 0.0107. Loss Val: 0.0119. Time: 2.9926\n", " EPOCH 190. Progress: 38.0%. \n", " Training R^2: 0.8078. Test R^2: 0.8063. Loss Train: 0.0111. Loss Val: 0.0112. Time: 4.9563\n", " EPOCH 200. Progress: 40.0%. \n", " Training R^2: 0.7939. Test R^2: 0.8080. Loss Train: 0.0121. Loss Val: 0.0113. Time: 2.5114\n", " EPOCH 210. Progress: 42.0%. \n", " Training R^2: 0.7413. Test R^2: 0.6860. Loss Train: 0.0116. Loss Val: 0.0165. Time: 2.8041\n", " EPOCH 220. Progress: 44.0%. \n", " Training R^2: 0.8138. Test R^2: 0.8304. Loss Train: 0.0115. Loss Val: 0.0115. Time: 2.5076\n", " EPOCH 230. Progress: 46.0%. \n", " Training R^2: 0.8340. Test R^2: 0.8408. Loss Train: 0.0104. Loss Val: 0.0111. Time: 2.7833\n", " EPOCH 240. Progress: 48.0%. \n", " Training R^2: 0.7997. Test R^2: 0.8122. Loss Train: 0.0099. Loss Val: 0.0109. Time: 4.3409\n", " EPOCH 250. Progress: 50.0%. \n", " Training R^2: 0.8180. Test R^2: 0.8270. Loss Train: 0.0110. Loss Val: 0.0100. Time: 2.4696\n", " EPOCH 260. Progress: 52.0%. \n", " Training R^2: 0.8192. Test R^2: 0.8465. Loss Train: 0.0094. Loss Val: 0.0097. Time: 3.0370\n", " EPOCH 270. Progress: 54.0%. \n", " Training R^2: 0.8276. Test R^2: 0.8379. Loss Train: 0.0084. Loss Val: 0.0094. Time: 2.6946\n", " EPOCH 280. Progress: 56.00000000000001%. \n", " Training R^2: 0.8436. Test R^2: 0.8470. Loss Train: 0.0090. Loss Val: 0.0092. Time: 2.5102\n", " EPOCH 290. Progress: 57.99999999999999%. \n", " Training R^2: 0.8386. Test R^2: 0.8624. Loss Train: 0.0089. Loss Val: 0.0087. Time: 2.8042\n", " EPOCH 300. Progress: 60.0%. \n", " Training R^2: 0.8205. Test R^2: 0.8548. Loss Train: 0.0090. Loss Val: 0.0095. Time: 3.0341\n", " EPOCH 310. Progress: 62.0%. \n", " Training R^2: 0.8296. Test R^2: 0.8491. Loss Train: 0.0094. Loss Val: 0.0094. Time: 3.0174\n", " EPOCH 320. Progress: 64.0%. \n", " Training R^2: 0.8443. Test R^2: 0.8615. Loss Train: 0.0087. Loss Val: 0.0081. Time: 2.4216\n", " EPOCH 330. Progress: 66.0%. \n", " Training R^2: 0.8441. Test R^2: 0.8643. Loss Train: 0.0088. Loss Val: 0.0092. Time: 2.8313\n", " EPOCH 340. Progress: 68.0%. \n", " Training R^2: 0.8522. Test R^2: 0.8641. Loss Train: 0.0083. Loss Val: 0.0081. Time: 2.2324\n", " EPOCH 350. Progress: 70.0%. \n", " Training R^2: 0.8495. Test R^2: 0.8272. Loss Train: 0.0082. Loss Val: 0.0086. Time: 2.4278\n", " EPOCH 360. Progress: 72.0%. \n", " Training R^2: 0.8395. Test R^2: 0.8802. Loss Train: 0.0080. Loss Val: 0.0088. Time: 2.2403\n", " EPOCH 370. Progress: 74.0%. \n", " Training R^2: 0.8253. Test R^2: 0.8545. Loss Train: 0.0079. Loss Val: 0.0095. Time: 2.3689\n", " EPOCH 380. Progress: 76.0%. \n", " Training R^2: 0.8380. Test R^2: 0.8688. Loss Train: 0.0092. Loss Val: 0.0078. Time: 2.3288\n", " EPOCH 390. Progress: 78.0%. \n", " Training R^2: 0.8439. Test R^2: 0.8692. Loss Train: 0.0076. Loss Val: 0.0073. Time: 2.3230\n", " EPOCH 400. Progress: 80.0%. \n", " Training R^2: 0.8536. Test R^2: 0.8641. Loss Train: 0.0081. Loss Val: 0.0084. Time: 2.2428\n", " EPOCH 410. Progress: 82.0%. \n", " Training R^2: 0.8551. Test R^2: 0.8414. Loss Train: 0.0082. Loss Val: 0.0090. Time: 2.3062\n", " EPOCH 420. Progress: 84.0%. \n", " Training R^2: 0.8601. Test R^2: 0.8843. Loss Train: 0.0081. Loss Val: 0.0078. Time: 2.2651\n", " EPOCH 430. Progress: 86.0%. \n", " Training R^2: 0.8603. Test R^2: 0.8657. Loss Train: 0.0079. Loss Val: 0.0080. Time: 3.3355\n", " EPOCH 440. Progress: 88.0%. \n", " Training R^2: 0.8760. Test R^2: 0.8742. Loss Train: 0.0078. Loss Val: 0.0076. Time: 2.5091\n", " EPOCH 450. Progress: 90.0%. \n", " Training R^2: 0.8422. Test R^2: 0.8567. Loss Train: 0.0083. Loss Val: 0.0077. Time: 2.9181\n", " EPOCH 460. Progress: 92.0%. \n", " Training R^2: 0.8607. Test R^2: 0.8852. Loss Train: 0.0071. Loss Val: 0.0080. Time: 2.8883\n", " EPOCH 470. Progress: 94.0%. \n", " Training R^2: 0.8590. Test R^2: 0.8947. Loss Train: 0.0075. Loss Val: 0.0085. Time: 2.7605\n", " EPOCH 480. Progress: 96.0%. \n", " Training R^2: 0.8684. Test R^2: 0.8668. Loss Train: 0.0085. Loss Val: 0.0081. Time: 2.6697\n", " EPOCH 490. Progress: 98.0%. \n", " Training R^2: 0.8751. Test R^2: 0.8838. Loss Train: 0.0067. Loss Val: 0.0071. Time: 3.1223\n", " EPOCH 500. Progress: 100.0%. \n", " Training R^2: 0.8764. Test R^2: 0.8946. Loss Train: 0.0068. Loss Val: 0.0065. Time: 2.5091\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "saving ./plots/lr0.001_3FFNet_2ReLU_Drop_momentum0.9_wdecay0.001_dampening0_nesterovFalse_HidDim128.png\n", "\n", "lr: 0.01, momentum: 0.9, weight_decay: 0.001, dampening: 0, nesterov: False \n", "FeedforwardNeuralNetModel(\n", " (fc1): Linear(in_features=47, out_features=64, bias=True)\n", " (drop): Dropout(p=0.2, inplace=False)\n", " (relu): ReLU()\n", " (fc2): Linear(in_features=64, out_features=64, bias=True)\n", " (fc3): Linear(in_features=64, out_features=1, bias=True)\n", ")\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: 500\n", " EPOCH 0. Progress: 0.0%. \n", " Training R^2: 0.2998. Test R^2: 0.2777. Loss Train: 0.0643. Loss Val: 0.0466. Time: 2.5134\n", " EPOCH 10. Progress: 2.0%. \n", " Training R^2: 0.3839. Test R^2: 0.2695. Loss Train: 0.0285. Loss Val: 0.0448. Time: 2.4105\n", " EPOCH 20. Progress: 4.0%. \n", " Training R^2: 0.5141. Test R^2: 0.3926. Loss Train: 0.0257. Loss Val: 0.0333. Time: 2.2853\n", " EPOCH 30. Progress: 6.0%. \n", " Training R^2: 0.5089. Test R^2: 0.4398. Loss Train: 0.0310. Loss Val: 0.0376. Time: 2.2108\n", " EPOCH 40. Progress: 8.0%. \n", " Training R^2: 0.5461. Test R^2: 0.4677. Loss Train: 0.0254. Loss Val: 0.0338. Time: 2.2169\n", " EPOCH 50. Progress: 10.0%. \n", " Training R^2: 0.5158. Test R^2: 0.4651. Loss Train: 0.0260. Loss Val: 0.0338. Time: 2.3295\n", " EPOCH 60. Progress: 12.0%. \n", " Training R^2: 0.5175. Test R^2: 0.4284. Loss Train: 0.0277. Loss Val: 0.0356. Time: 2.4124\n", " EPOCH 70. Progress: 14.000000000000002%. \n", " Training R^2: 0.4871. Test R^2: 0.5046. Loss Train: 0.0260. Loss Val: 0.0314. Time: 2.2909\n", " EPOCH 80. Progress: 16.0%. \n", " Training R^2: 0.4907. Test R^2: 0.4062. Loss Train: 0.0264. Loss Val: 0.0349. Time: 2.5873\n", " EPOCH 90. Progress: 18.0%. \n", " Training R^2: 0.5500. Test R^2: 0.5296. Loss Train: 0.0263. Loss Val: 0.0316. Time: 2.6759\n", " EPOCH 100. Progress: 20.0%. \n", " Training R^2: 0.5196. Test R^2: 0.5304. Loss Train: 0.0245. Loss Val: 0.0320. Time: 2.1618\n", " EPOCH 110. Progress: 22.0%. \n", " Training R^2: 0.5000. Test R^2: 0.5130. Loss Train: 0.0256. Loss Val: 0.0318. Time: 2.2579\n", " EPOCH 120. Progress: 24.0%. \n", " Training R^2: 0.5664. Test R^2: 0.5156. Loss Train: 0.0251. Loss Val: 0.0288. Time: 2.3752\n", " EPOCH 130. Progress: 26.0%. \n", " Training R^2: 0.5466. Test R^2: 0.5108. Loss Train: 0.0237. Loss Val: 0.0312. Time: 3.1384\n", " EPOCH 140. Progress: 28.000000000000004%. \n", " Training R^2: 0.5116. Test R^2: 0.5307. Loss Train: 0.0233. Loss Val: 0.0296. Time: 2.8697\n", " EPOCH 150. Progress: 30.0%. \n", " Training R^2: 0.5299. Test R^2: 0.5219. Loss Train: 0.0258. Loss Val: 0.0288. Time: 3.5238\n", " EPOCH 160. Progress: 32.0%. \n", " Training R^2: 0.5890. Test R^2: 0.5202. Loss Train: 0.0226. Loss Val: 0.0265. Time: 2.9057\n", " EPOCH 170. Progress: 34.0%. \n", " Training R^2: 0.5342. Test R^2: 0.5756. Loss Train: 0.0237. Loss Val: 0.0267. Time: 2.3796\n", " EPOCH 180. Progress: 36.0%. \n", " Training R^2: 0.5633. Test R^2: 0.5595. Loss Train: 0.0236. Loss Val: 0.0295. Time: 2.3007\n", " EPOCH 190. Progress: 38.0%. \n", " Training R^2: 0.6085. Test R^2: 0.5920. Loss Train: 0.0224. Loss Val: 0.0285. Time: 3.9387\n", " EPOCH 200. Progress: 40.0%. \n", " Training R^2: 0.5341. Test R^2: 0.4958. Loss Train: 0.0263. Loss Val: 0.0307. Time: 2.5295\n", " EPOCH 210. Progress: 42.0%. \n", " Training R^2: 0.5787. Test R^2: 0.5873. Loss Train: 0.0218. Loss Val: 0.0267. Time: 2.5182\n", " EPOCH 220. Progress: 44.0%. \n", " Training R^2: 0.4903. Test R^2: 0.4651. Loss Train: 0.0236. Loss Val: 0.0370. Time: 3.1504\n", " EPOCH 230. Progress: 46.0%. \n", " Training R^2: 0.5705. Test R^2: 0.5264. Loss Train: 0.0220. Loss Val: 0.0271. Time: 2.7024\n", " EPOCH 240. Progress: 48.0%. \n", " Training R^2: 0.6059. Test R^2: 0.6056. Loss Train: 0.0224. Loss Val: 0.0277. Time: 2.4748\n", " EPOCH 250. Progress: 50.0%. \n", " Training R^2: 0.6056. Test R^2: 0.5032. Loss Train: 0.0241. Loss Val: 0.0265. Time: 2.9292\n", " EPOCH 260. Progress: 52.0%. \n", " Training R^2: 0.5261. Test R^2: 0.5335. Loss Train: 0.0233. Loss Val: 0.0308. Time: 2.0955\n", " EPOCH 270. Progress: 54.0%. \n", " Training R^2: 0.6430. Test R^2: 0.6282. Loss Train: 0.0228. Loss Val: 0.0250. Time: 2.1221\n", " EPOCH 280. Progress: 56.00000000000001%. \n", " Training R^2: 0.6421. Test R^2: 0.6347. Loss Train: 0.0222. Loss Val: 0.0253. Time: 2.0285\n", " EPOCH 290. Progress: 57.99999999999999%. \n", " Training R^2: 0.5530. Test R^2: 0.4413. Loss Train: 0.0223. Loss Val: 0.0317. Time: 2.0783\n", " EPOCH 300. Progress: 60.0%. \n", " Training R^2: 0.6043. Test R^2: 0.5717. Loss Train: 0.0240. Loss Val: 0.0274. Time: 1.9601\n", " EPOCH 310. Progress: 62.0%. \n", " Training R^2: 0.5727. Test R^2: 0.6019. Loss Train: 0.0224. Loss Val: 0.0250. Time: 2.1326\n", " EPOCH 320. Progress: 64.0%. \n", " Training R^2: 0.6096. Test R^2: 0.5911. Loss Train: 0.0213. Loss Val: 0.0237. Time: 2.0559\n", " EPOCH 330. Progress: 66.0%. \n", " Training R^2: 0.5851. Test R^2: 0.5921. Loss Train: 0.0218. Loss Val: 0.0247. Time: 1.9013\n", " EPOCH 340. Progress: 68.0%. \n", " Training R^2: 0.6065. Test R^2: 0.6318. Loss Train: 0.0215. Loss Val: 0.0257. Time: 2.0601\n", " EPOCH 350. Progress: 70.0%. \n", " Training R^2: 0.6115. Test R^2: 0.6292. Loss Train: 0.0225. Loss Val: 0.0235. Time: 2.1107\n", " EPOCH 360. Progress: 72.0%. \n", " Training R^2: 0.5679. Test R^2: 0.5540. Loss Train: 0.0210. Loss Val: 0.0267. Time: 1.9785\n", " EPOCH 370. Progress: 74.0%. \n", " Training R^2: 0.5849. Test R^2: 0.5736. Loss Train: 0.0219. Loss Val: 0.0277. Time: 1.9948\n", " EPOCH 380. Progress: 76.0%. \n", " Training R^2: 0.6078. Test R^2: 0.5650. Loss Train: 0.0211. Loss Val: 0.0259. Time: 2.1080\n", " EPOCH 390. Progress: 78.0%. \n", " Training R^2: 0.5616. Test R^2: 0.4989. Loss Train: 0.0224. Loss Val: 0.0277. Time: 2.2105\n", " EPOCH 400. Progress: 80.0%. \n", " Training R^2: 0.5890. Test R^2: 0.5888. Loss Train: 0.0213. Loss Val: 0.0264. Time: 2.3170\n", " EPOCH 410. Progress: 82.0%. \n", " Training R^2: 0.5709. Test R^2: 0.5842. Loss Train: 0.0227. Loss Val: 0.0261. Time: 3.8046\n", " EPOCH 420. Progress: 84.0%. \n", " Training R^2: 0.6271. Test R^2: 0.6695. Loss Train: 0.0193. Loss Val: 0.0230. Time: 2.8342\n", " EPOCH 430. Progress: 86.0%. \n", " Training R^2: 0.5913. Test R^2: 0.5732. Loss Train: 0.0222. Loss Val: 0.0228. Time: 2.0817\n", " EPOCH 440. Progress: 88.0%. \n", " Training R^2: 0.5846. Test R^2: 0.6495. Loss Train: 0.0202. Loss Val: 0.0256. Time: 2.2084\n", " EPOCH 450. Progress: 90.0%. \n", " Training R^2: 0.5689. Test R^2: 0.5825. Loss Train: 0.0217. Loss Val: 0.0224. Time: 2.0139\n", " EPOCH 460. Progress: 92.0%. \n", " Training R^2: 0.6162. Test R^2: 0.5900. Loss Train: 0.0208. Loss Val: 0.0261. Time: 2.0481\n", " EPOCH 470. Progress: 94.0%. \n", " Training R^2: 0.6126. Test R^2: 0.6053. Loss Train: 0.0209. Loss Val: 0.0300. Time: 2.1299\n", " EPOCH 480. Progress: 96.0%. \n", " Training R^2: 0.6341. Test R^2: 0.6465. Loss Train: 0.0208. Loss Val: 0.0225. Time: 2.2350\n", " EPOCH 490. Progress: 98.0%. \n", " Training R^2: 0.5622. Test R^2: 0.5931. Loss Train: 0.0238. Loss Val: 0.0257. Time: 2.1152\n", " EPOCH 500. Progress: 100.0%. \n", " Training R^2: 0.6081. Test R^2: 0.5442. Loss Train: 0.0231. Loss Val: 0.0280. Time: 2.0840\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "saving ./plots/lr0.01_3FFNet_2ReLU_Drop_momentum0.9_wdecay0.001_dampening0_nesterovFalse_HidDim64.png\n", "\n", "lr: 0.01, momentum: 0.9, weight_decay: 0.001, dampening: 0, nesterov: False \n", "FeedforwardNeuralNetModel(\n", " (fc1): Linear(in_features=47, out_features=128, bias=True)\n", " (drop): Dropout(p=0.2, inplace=False)\n", " (relu): ReLU()\n", " (fc2): Linear(in_features=128, out_features=128, bias=True)\n", " (fc3): Linear(in_features=128, out_features=1, bias=True)\n", ")\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: 500\n", " EPOCH 0. Progress: 0.0%. \n", " Training R^2: 0.3500. Test R^2: 0.2949. Loss Train: 0.0595. Loss Val: 0.0439. Time: 1.9680\n", " EPOCH 10. Progress: 2.0%. \n", " Training R^2: 0.4798. Test R^2: 0.4110. Loss Train: 0.0307. Loss Val: 0.0393. Time: 2.0753\n", " EPOCH 20. Progress: 4.0%. \n", " Training R^2: 0.5034. Test R^2: 0.4412. Loss Train: 0.0277. Loss Val: 0.0351. Time: 2.0036\n", " EPOCH 30. Progress: 6.0%. \n", " Training R^2: 0.4865. Test R^2: 0.4305. Loss Train: 0.0260. Loss Val: 0.0345. Time: 2.0027\n", " EPOCH 40. Progress: 8.0%. \n", " Training R^2: 0.5258. Test R^2: 0.4015. Loss Train: 0.0267. Loss Val: 0.0349. Time: 2.0814\n", " EPOCH 50. Progress: 10.0%. \n", " Training R^2: 0.5195. Test R^2: 0.4381. Loss Train: 0.0278. Loss Val: 0.0341. Time: 2.3265\n", " EPOCH 60. Progress: 12.0%. \n", " Training R^2: 0.5268. Test R^2: 0.4469. Loss Train: 0.0276. Loss Val: 0.0320. Time: 2.3129\n", " EPOCH 70. Progress: 14.000000000000002%. \n", " Training R^2: 0.5594. Test R^2: 0.4859. Loss Train: 0.0252. Loss Val: 0.0318. Time: 2.7331\n", " EPOCH 80. Progress: 16.0%. \n", " Training R^2: 0.4903. Test R^2: 0.4379. Loss Train: 0.0282. Loss Val: 0.0321. Time: 2.3010\n", " EPOCH 90. Progress: 18.0%. \n", " Training R^2: 0.5962. Test R^2: 0.5093. Loss Train: 0.0269. Loss Val: 0.0277. Time: 2.2278\n", " EPOCH 100. Progress: 20.0%. \n", " Training R^2: 0.5259. Test R^2: 0.5340. Loss Train: 0.0259. Loss Val: 0.0298. Time: 2.5875\n", " EPOCH 110. Progress: 22.0%. \n", " Training R^2: 0.5319. Test R^2: 0.4856. Loss Train: 0.0232. Loss Val: 0.0296. Time: 2.3185\n", " EPOCH 120. Progress: 24.0%. \n", " Training R^2: 0.4807. Test R^2: 0.4518. Loss Train: 0.0237. Loss Val: 0.0376. Time: 2.7557\n", " EPOCH 130. Progress: 26.0%. \n", " Training R^2: 0.5671. Test R^2: 0.5112. Loss Train: 0.0233. Loss Val: 0.0319. Time: 2.0406\n", " EPOCH 140. Progress: 28.000000000000004%. \n", " Training R^2: 0.5711. Test R^2: 0.5372. Loss Train: 0.0236. Loss Val: 0.0286. Time: 2.0366\n", " EPOCH 150. Progress: 30.0%. \n", " Training R^2: 0.5868. Test R^2: 0.5373. Loss Train: 0.0244. Loss Val: 0.0269. Time: 2.0286\n", " EPOCH 160. Progress: 32.0%. \n", " Training R^2: 0.5343. Test R^2: 0.5735. Loss Train: 0.0233. Loss Val: 0.0254. Time: 2.0422\n", " EPOCH 170. Progress: 34.0%. \n", " Training R^2: 0.6077. Test R^2: 0.5212. Loss Train: 0.0224. Loss Val: 0.0267. Time: 2.4437\n", " EPOCH 180. Progress: 36.0%. \n", " Training R^2: 0.5493. Test R^2: 0.5407. Loss Train: 0.0235. Loss Val: 0.0273. Time: 2.1331\n", " EPOCH 190. Progress: 38.0%. \n", " Training R^2: 0.5637. Test R^2: 0.5632. Loss Train: 0.0225. Loss Val: 0.0247. Time: 2.1259\n", " EPOCH 200. Progress: 40.0%. \n", " Training R^2: 0.5241. Test R^2: 0.4822. Loss Train: 0.0235. Loss Val: 0.0334. Time: 1.8994\n", " EPOCH 210. Progress: 42.0%. \n", " Training R^2: 0.5772. Test R^2: 0.5895. Loss Train: 0.0223. Loss Val: 0.0265. Time: 2.5330\n", " EPOCH 220. Progress: 44.0%. \n", " Training R^2: 0.5771. Test R^2: 0.5445. Loss Train: 0.0233. Loss Val: 0.0283. Time: 1.9817\n", " EPOCH 230. Progress: 46.0%. \n", " Training R^2: 0.5913. Test R^2: 0.6073. Loss Train: 0.0248. Loss Val: 0.0274. Time: 1.9013\n", " EPOCH 240. Progress: 48.0%. \n", " Training R^2: 0.5661. Test R^2: 0.5790. Loss Train: 0.0231. Loss Val: 0.0256. Time: 2.0631\n", " EPOCH 250. Progress: 50.0%. \n", " Training R^2: 0.5597. Test R^2: 0.5917. Loss Train: 0.0219. Loss Val: 0.0281. Time: 2.0783\n", " EPOCH 260. Progress: 52.0%. \n", " Training R^2: 0.6261. Test R^2: 0.6122. Loss Train: 0.0212. Loss Val: 0.0266. Time: 2.0385\n", " EPOCH 270. Progress: 54.0%. \n", " Training R^2: 0.5363. Test R^2: 0.4878. Loss Train: 0.0241. Loss Val: 0.0287. Time: 2.4742\n", " EPOCH 280. Progress: 56.00000000000001%. \n", " Training R^2: 0.5605. Test R^2: 0.5216. Loss Train: 0.0228. Loss Val: 0.0308. Time: 2.0517\n", " EPOCH 290. Progress: 57.99999999999999%. \n", " Training R^2: 0.5725. Test R^2: 0.5658. Loss Train: 0.0215. Loss Val: 0.0254. Time: 2.1625\n", " EPOCH 300. Progress: 60.0%. \n", " Training R^2: 0.5841. Test R^2: 0.5739. Loss Train: 0.0227. Loss Val: 0.0239. Time: 2.0216\n", " EPOCH 310. Progress: 62.0%. \n", " Training R^2: 0.5970. Test R^2: 0.5840. Loss Train: 0.0258. Loss Val: 0.0264. Time: 1.9824\n", " EPOCH 320. Progress: 64.0%. \n", " Training R^2: 0.5970. Test R^2: 0.5848. Loss Train: 0.0210. Loss Val: 0.0286. Time: 2.4123\n", " EPOCH 330. Progress: 66.0%. \n", " Training R^2: 0.5824. Test R^2: 0.5670. Loss Train: 0.0225. Loss Val: 0.0268. Time: 2.2330\n", " EPOCH 340. Progress: 68.0%. \n", " Training R^2: 0.5783. Test R^2: 0.5184. Loss Train: 0.0204. Loss Val: 0.0268. Time: 2.1709\n", " EPOCH 350. Progress: 70.0%. \n", " Training R^2: 0.5371. Test R^2: 0.4856. Loss Train: 0.0262. Loss Val: 0.0311. Time: 2.0219\n", " EPOCH 360. Progress: 72.0%. \n", " Training R^2: 0.5605. Test R^2: 0.4832. Loss Train: 0.0221. Loss Val: 0.0295. Time: 2.0305\n", " EPOCH 370. Progress: 74.0%. \n", " Training R^2: 0.5855. Test R^2: 0.5614. Loss Train: 0.0216. Loss Val: 0.0264. Time: 2.3363\n", " EPOCH 380. Progress: 76.0%. \n", " Training R^2: 0.6143. Test R^2: 0.5463. Loss Train: 0.0218. Loss Val: 0.0280. Time: 3.0703\n", " EPOCH 390. Progress: 78.0%. \n", " Training R^2: 0.5974. Test R^2: 0.5755. Loss Train: 0.0276. Loss Val: 0.0264. Time: 2.8149\n", " EPOCH 400. Progress: 80.0%. \n", " Training R^2: 0.6077. Test R^2: 0.6129. Loss Train: 0.0216. Loss Val: 0.0263. Time: 1.9911\n", " EPOCH 410. Progress: 82.0%. \n", " Training R^2: 0.6044. Test R^2: 0.6259. Loss Train: 0.0217. Loss Val: 0.0260. Time: 2.0055\n", " EPOCH 420. Progress: 84.0%. \n", " Training R^2: 0.6007. Test R^2: 0.5875. Loss Train: 0.0213. Loss Val: 0.0248. Time: 1.9980\n", " EPOCH 430. Progress: 86.0%. \n", " Training R^2: 0.5965. Test R^2: 0.5867. Loss Train: 0.0229. Loss Val: 0.0257. Time: 1.8969\n", " EPOCH 440. Progress: 88.0%. \n", " Training R^2: 0.6066. Test R^2: 0.5855. Loss Train: 0.0229. Loss Val: 0.0264. Time: 2.3634\n", " EPOCH 450. Progress: 90.0%. \n", " Training R^2: 0.5972. Test R^2: 0.6188. Loss Train: 0.0219. Loss Val: 0.0253. Time: 1.9153\n", " EPOCH 460. Progress: 92.0%. \n", " Training R^2: 0.6156. Test R^2: 0.6333. Loss Train: 0.0207. Loss Val: 0.0257. Time: 2.0352\n", " EPOCH 470. Progress: 94.0%. \n", " Training R^2: 0.6052. Test R^2: 0.5840. Loss Train: 0.0243. Loss Val: 0.0284. Time: 2.0433\n", " EPOCH 480. Progress: 96.0%. \n", " Training R^2: 0.5111. Test R^2: 0.4789. Loss Train: 0.0250. Loss Val: 0.0313. Time: 1.9572\n", " EPOCH 490. Progress: 98.0%. \n", " Training R^2: 0.5837. Test R^2: 0.5839. Loss Train: 0.0233. Loss Val: 0.0282. Time: 1.9536\n", " EPOCH 500. Progress: 100.0%. \n", " Training R^2: 0.5336. Test R^2: 0.5838. Loss Train: 0.0228. Loss Val: 0.0285. Time: 2.0231\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "saving ./plots/lr0.01_3FFNet_2ReLU_Drop_momentum0.9_wdecay0.001_dampening0_nesterovFalse_HidDim128.png\n" ] } ], "source": [ "# Testing a regular FFnet\n", "class FeedforwardNeuralNetModel(nn.Module):\n", " def __init__(self, input_dim, hidden_dim, output_dim):\n", " super(FeedforwardNeuralNetModel, self).__init__()\n", " # Linear function\n", " self.fc1 = nn.Linear(input_dim, hidden_dim) \n", " self.drop = nn.Dropout(0.2)\n", " # Non-linearity\n", " self.relu = nn.ReLU()\n", " # Linear function (readout)\n", " self.fc2 = nn.Linear(hidden_dim, hidden_dim)\n", " self.fc3 = nn.Linear(hidden_dim, output_dim)\n", "\n", " def forward(self, x):\n", " # Linear function\n", " out = self.relu(self.fc1(x))\n", " out = self.drop(out)\n", " out = self.relu(self.fc2(out))\n", " out = self.drop(out)\n", " out = self.fc3(out)\n", " return out\n", "\n", "# Best configuration for longer\n", "lr_range = [0.001,0.01]\n", "hid_dim_range = [64,128]\n", "weight_decay_range = [0.001]\n", "momentum_range = [0.9]\n", "dampening_range = [0]\n", "nesterov_range = [False]\n", "for lr in lr_range:\n", " for momentum in momentum_range:\n", " for weight_decay in weight_decay_range:\n", " for nesterov in nesterov_range:\n", " for dampening in dampening_range:\n", " for hid_dim in hid_dim_range:\n", " print('\\nlr: {}, momentum: {}, weight_decay: {}, dampening: {}, nesterov: {} '.format(lr, momentum, weight_decay, dampening, nesterov))\n", " model = FeedforwardNeuralNetModel(input_dim=47,hidden_dim=hid_dim, output_dim=1).to(device)\n", " print(model)\n", " SGD = torch.optim.Adam(model.parameters(), lr = lr) # This is absurdly high.\n", " loss_fn = nn.MSELoss().to(device)\n", " config_str = 'lr' + str(lr) + '_3FFNet_2ReLU_Drop_momentum' + str(momentum) + '_wdecay' + str(weight_decay) + '_dampening' + str(dampening) +'_nesterov' + str(nesterov) + '_HidDim' + str(hid_dim)\n", " train(model,loss_fn, SGD, dataloaders['train'], dataloaders['val'], config_str,num_epochs=500, verbose=False)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "lr: 0.001, momentum: 0.9, weight_decay: 0.001, dampening: 0, nesterov: False \n", "FeedforwardNeuralNetModel(\n", " (fc1): Linear(in_features=47, out_features=128, bias=True)\n", " (drop): Dropout(p=0.2, inplace=False)\n", " (relu): ReLU()\n", " (fc2): Linear(in_features=128, out_features=128, bias=True)\n", " (fc3): Linear(in_features=128, out_features=1, bias=True)\n", ")\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: 1000\n", " EPOCH 0. Progress: 0.0%. \n", " Training R^2: 0.0927. Test R^2: 0.0356. Loss Train: 0.1167. Loss Val: 0.0563. Time: 3.3496\n", " EPOCH 10. Progress: 1.0%. \n", " Training R^2: 0.4223. Test R^2: 0.4131. Loss Train: 0.0295. Loss Val: 0.0375. Time: 2.4228\n", " EPOCH 20. Progress: 2.0%. \n", " Training R^2: 0.5436. Test R^2: 0.5038. Loss Train: 0.0266. Loss Val: 0.0323. Time: 2.4585\n", " EPOCH 30. Progress: 3.0%. \n", " Training R^2: 0.5576. Test R^2: 0.5039. Loss Train: 0.0248. Loss Val: 0.0303. Time: 2.6364\n", " EPOCH 40. Progress: 4.0%. \n", " Training R^2: 0.5357. Test R^2: 0.5614. Loss Train: 0.0220. Loss Val: 0.0256. Time: 4.1378\n", " EPOCH 50. Progress: 5.0%. \n", " Training R^2: 0.6205. Test R^2: 0.6498. Loss Train: 0.0191. Loss Val: 0.0246. Time: 2.1606\n", " EPOCH 60. Progress: 6.0%. \n", " Training R^2: 0.6510. Test R^2: 0.6262. Loss Train: 0.0191. Loss Val: 0.0223. Time: 2.5680\n", " EPOCH 70. Progress: 7.000000000000001%. \n", " Training R^2: 0.6858. Test R^2: 0.6749. Loss Train: 0.0182. Loss Val: 0.0210. Time: 2.3999\n", " EPOCH 80. Progress: 8.0%. \n", " Training R^2: 0.6682. Test R^2: 0.6727. Loss Train: 0.0174. Loss Val: 0.0197. Time: 2.4131\n", " EPOCH 90. Progress: 9.0%. \n", " Training R^2: 0.7282. Test R^2: 0.6968. Loss Train: 0.0162. Loss Val: 0.0181. Time: 2.8423\n", " EPOCH 100. Progress: 10.0%. \n", " Training R^2: 0.7063. Test R^2: 0.6932. Loss Train: 0.0162. Loss Val: 0.0208. Time: 2.4898\n", " EPOCH 110. Progress: 11.0%. \n", " Training R^2: 0.7479. Test R^2: 0.7652. Loss Train: 0.0148. Loss Val: 0.0158. Time: 3.4190\n", " EPOCH 120. Progress: 12.0%. \n", " Training R^2: 0.7348. Test R^2: 0.7248. Loss Train: 0.0150. Loss Val: 0.0175. Time: 2.4495\n", " EPOCH 130. Progress: 13.0%. \n", " Training R^2: 0.7539. Test R^2: 0.6989. Loss Train: 0.0133. Loss Val: 0.0163. Time: 2.4874\n", " EPOCH 140. Progress: 14.000000000000002%. \n", " Training R^2: 0.7633. Test R^2: 0.7787. Loss Train: 0.0133. Loss Val: 0.0138. Time: 2.5096\n", " EPOCH 150. Progress: 15.0%. \n", " Training R^2: 0.7354. Test R^2: 0.7661. Loss Train: 0.0132. Loss Val: 0.0136. Time: 2.4669\n", " EPOCH 160. Progress: 16.0%. \n", " Training R^2: 0.7896. Test R^2: 0.7857. Loss Train: 0.0121. Loss Val: 0.0133. Time: 2.5675\n", " EPOCH 170. Progress: 17.0%. \n", " Training R^2: 0.7779. Test R^2: 0.7814. Loss Train: 0.0121. Loss Val: 0.0131. Time: 2.3809\n", " EPOCH 180. Progress: 18.0%. \n", " Training R^2: 0.7951. Test R^2: 0.7855. Loss Train: 0.0112. Loss Val: 0.0140. Time: 3.3293\n", " EPOCH 190. Progress: 19.0%. \n", " Training R^2: 0.7885. Test R^2: 0.8003. Loss Train: 0.0110. Loss Val: 0.0118. Time: 2.8167\n", " EPOCH 200. Progress: 20.0%. \n", " Training R^2: 0.7988. Test R^2: 0.8261. Loss Train: 0.0110. Loss Val: 0.0111. Time: 2.5940\n", " EPOCH 210. Progress: 21.0%. \n", " Training R^2: 0.7719. Test R^2: 0.7948. Loss Train: 0.0102. Loss Val: 0.0126. Time: 2.6867\n", " EPOCH 220. Progress: 22.0%. \n", " Training R^2: 0.7516. Test R^2: 0.7911. Loss Train: 0.0108. Loss Val: 0.0125. Time: 2.1718\n", " EPOCH 230. Progress: 23.0%. \n", " Training R^2: 0.8133. Test R^2: 0.8168. Loss Train: 0.0102. Loss Val: 0.0098. Time: 1.7636\n", " EPOCH 240. Progress: 24.0%. \n", " Training R^2: 0.8212. Test R^2: 0.8234. Loss Train: 0.0090. Loss Val: 0.0092. Time: 2.1938\n", " EPOCH 250. Progress: 25.0%. \n", " Training R^2: 0.8284. Test R^2: 0.8352. Loss Train: 0.0103. Loss Val: 0.0113. Time: 2.8295\n", " EPOCH 260. Progress: 26.0%. \n", " Training R^2: 0.8318. Test R^2: 0.8382. Loss Train: 0.0094. Loss Val: 0.0104. Time: 2.3420\n", " EPOCH 270. Progress: 27.0%. \n", " Training R^2: 0.8218. Test R^2: 0.8272. Loss Train: 0.0093. Loss Val: 0.0099. Time: 3.1340\n", " EPOCH 280. Progress: 28.000000000000004%. \n", " Training R^2: 0.7975. Test R^2: 0.8056. Loss Train: 0.0099. Loss Val: 0.0130. Time: 2.1983\n", " EPOCH 290. Progress: 28.999999999999996%. \n", " Training R^2: 0.8144. Test R^2: 0.8154. Loss Train: 0.0086. Loss Val: 0.0107. Time: 2.5251\n", " EPOCH 300. Progress: 30.0%. \n", " Training R^2: 0.8338. Test R^2: 0.8196. Loss Train: 0.0088. Loss Val: 0.0118. Time: 2.5312\n", " EPOCH 310. Progress: 31.0%. \n", " Training R^2: 0.8166. Test R^2: 0.8148. Loss Train: 0.0094. Loss Val: 0.0107. Time: 3.2249\n", " EPOCH 320. Progress: 32.0%. \n", " Training R^2: 0.8454. Test R^2: 0.8754. Loss Train: 0.0084. Loss Val: 0.0098. Time: 2.2585\n", " EPOCH 330. Progress: 33.0%. \n", " Training R^2: 0.8487. Test R^2: 0.8329. Loss Train: 0.0082. Loss Val: 0.0095. Time: 2.5670\n", " EPOCH 340. Progress: 34.0%. \n", " Training R^2: 0.8199. Test R^2: 0.8629. Loss Train: 0.0090. Loss Val: 0.0098. Time: 2.3122\n", " EPOCH 350. Progress: 35.0%. \n", " Training R^2: 0.8398. Test R^2: 0.8288. Loss Train: 0.0086. Loss Val: 0.0089. Time: 3.6857\n", " EPOCH 360. Progress: 36.0%. \n", " Training R^2: 0.8452. Test R^2: 0.8503. Loss Train: 0.0078. Loss Val: 0.0103. Time: 2.3320\n", " EPOCH 370. Progress: 37.0%. \n", " Training R^2: 0.8209. Test R^2: 0.8351. Loss Train: 0.0086. Loss Val: 0.0087. Time: 2.4615\n", " EPOCH 380. Progress: 38.0%. \n", " Training R^2: 0.8335. Test R^2: 0.8488. Loss Train: 0.0080. Loss Val: 0.0092. Time: 2.4492\n", " EPOCH 390. Progress: 39.0%. \n", " Training R^2: 0.8596. Test R^2: 0.8726. Loss Train: 0.0095. Loss Val: 0.0088. Time: 2.3357\n", " EPOCH 400. Progress: 40.0%. \n", " Training R^2: 0.8378. Test R^2: 0.8771. Loss Train: 0.0084. Loss Val: 0.0087. Time: 2.3776\n", " EPOCH 410. Progress: 41.0%. \n", " Training R^2: 0.8691. Test R^2: 0.8665. Loss Train: 0.0078. Loss Val: 0.0078. Time: 2.3687\n", " EPOCH 420. Progress: 42.0%. \n", " Training R^2: 0.8564. Test R^2: 0.8439. Loss Train: 0.0076. Loss Val: 0.0108. Time: 2.3925\n", " EPOCH 430. Progress: 43.0%. \n", " Training R^2: 0.8437. Test R^2: 0.8196. Loss Train: 0.0090. Loss Val: 0.0090. Time: 2.5062\n", " EPOCH 440. Progress: 44.0%. \n", " Training R^2: 0.8572. Test R^2: 0.8826. Loss Train: 0.0082. Loss Val: 0.0086. Time: 3.2034\n", " EPOCH 450. Progress: 45.0%. \n", " Training R^2: 0.8639. Test R^2: 0.8509. Loss Train: 0.0071. Loss Val: 0.0086. Time: 2.7788\n", " EPOCH 460. Progress: 46.0%. \n", " Training R^2: 0.8728. Test R^2: 0.8566. Loss Train: 0.0067. Loss Val: 0.0101. Time: 2.2754\n", " EPOCH 470. Progress: 47.0%. \n", " Training R^2: 0.8457. Test R^2: 0.8577. Loss Train: 0.0076. Loss Val: 0.0090. Time: 2.5371\n", " EPOCH 480. Progress: 48.0%. \n", " Training R^2: 0.8591. Test R^2: 0.8624. Loss Train: 0.0083. Loss Val: 0.0085. Time: 2.4443\n", " EPOCH 490. Progress: 49.0%. \n", " Training R^2: 0.8629. Test R^2: 0.8648. Loss Train: 0.0079. Loss Val: 0.0089. Time: 3.1879\n", " EPOCH 500. Progress: 50.0%. \n", " Training R^2: 0.8636. Test R^2: 0.8729. Loss Train: 0.0077. Loss Val: 0.0084. Time: 2.5132\n", " EPOCH 510. Progress: 51.0%. \n", " Training R^2: 0.8715. Test R^2: 0.8626. Loss Train: 0.0073. Loss Val: 0.0096. Time: 2.3002\n", " EPOCH 520. Progress: 52.0%. \n", " Training R^2: 0.8685. Test R^2: 0.8791. Loss Train: 0.0071. Loss Val: 0.0076. Time: 2.3063\n", " EPOCH 530. Progress: 53.0%. \n", " Training R^2: 0.8638. Test R^2: 0.8644. Loss Train: 0.0076. Loss Val: 0.0088. Time: 2.3837\n", " EPOCH 540. Progress: 54.0%. \n", " Training R^2: 0.8898. Test R^2: 0.8858. Loss Train: 0.0070. Loss Val: 0.0081. Time: 2.3558\n", " EPOCH 550. Progress: 55.00000000000001%. \n", " Training R^2: 0.8539. Test R^2: 0.8689. Loss Train: 0.0085. Loss Val: 0.0081. Time: 2.4504\n", " EPOCH 560. Progress: 56.00000000000001%. \n", " Training R^2: 0.8736. Test R^2: 0.9091. Loss Train: 0.0068. Loss Val: 0.0072. Time: 2.4290\n", " EPOCH 570. Progress: 56.99999999999999%. \n", " Training R^2: 0.8794. Test R^2: 0.8838. Loss Train: 0.0066. Loss Val: 0.0069. Time: 2.3208\n", " EPOCH 580. Progress: 57.99999999999999%. \n", " Training R^2: 0.8754. Test R^2: 0.8821. Loss Train: 0.0069. Loss Val: 0.0076. Time: 2.3361\n", " EPOCH 590. Progress: 59.0%. \n", " Training R^2: 0.8846. Test R^2: 0.8744. Loss Train: 0.0065. Loss Val: 0.0078. Time: 2.4653\n", " EPOCH 600. Progress: 60.0%. \n", " Training R^2: 0.8788. Test R^2: 0.8516. Loss Train: 0.0069. Loss Val: 0.0089. Time: 2.2863\n", " EPOCH 610. Progress: 61.0%. \n", " Training R^2: 0.8901. Test R^2: 0.8855. Loss Train: 0.0069. Loss Val: 0.0079. Time: 2.4429\n", " EPOCH 620. Progress: 62.0%. \n", " Training R^2: 0.8856. Test R^2: 0.8852. Loss Train: 0.0060. Loss Val: 0.0079. Time: 2.4149\n", " EPOCH 630. Progress: 63.0%. \n", " Training R^2: 0.8649. Test R^2: 0.8844. Loss Train: 0.0066. Loss Val: 0.0083. Time: 2.9441\n", " EPOCH 640. Progress: 64.0%. \n", " Training R^2: 0.8742. Test R^2: 0.8713. Loss Train: 0.0072. Loss Val: 0.0074. Time: 2.3610\n", " EPOCH 650. Progress: 65.0%. \n", " Training R^2: 0.8897. Test R^2: 0.8725. Loss Train: 0.0067. Loss Val: 0.0074. Time: 2.3834\n", " EPOCH 660. Progress: 66.0%. \n", " Training R^2: 0.8690. Test R^2: 0.8578. Loss Train: 0.0066. Loss Val: 0.0100. Time: 2.4020\n", " EPOCH 670. Progress: 67.0%. \n", " Training R^2: 0.8719. Test R^2: 0.8608. Loss Train: 0.0061. Loss Val: 0.0078. Time: 2.3500\n", " EPOCH 680. Progress: 68.0%. \n", " Training R^2: 0.8727. Test R^2: 0.8649. Loss Train: 0.0078. Loss Val: 0.0076. Time: 2.4209\n", " EPOCH 690. Progress: 69.0%. \n", " Training R^2: 0.8731. Test R^2: 0.8843. Loss Train: 0.0061. Loss Val: 0.0067. Time: 2.4152\n", " EPOCH 700. Progress: 70.0%. \n", " Training R^2: 0.8366. Test R^2: 0.8930. Loss Train: 0.0067. Loss Val: 0.0072. Time: 2.4015\n", " EPOCH 710. Progress: 71.0%. \n", " Training R^2: 0.8710. Test R^2: 0.9092. Loss Train: 0.0059. Loss Val: 0.0078. Time: 2.3803\n", " EPOCH 720. Progress: 72.0%. \n", " Training R^2: 0.8553. Test R^2: 0.8707. Loss Train: 0.0067. Loss Val: 0.0078. Time: 2.3663\n", " EPOCH 730. Progress: 73.0%. \n", " Training R^2: 0.8767. Test R^2: 0.8790. Loss Train: 0.0073. Loss Val: 0.0076. Time: 2.4198\n", " EPOCH 740. Progress: 74.0%. \n", " Training R^2: 0.8737. Test R^2: 0.8797. Loss Train: 0.0057. Loss Val: 0.0063. Time: 2.2871\n", " EPOCH 750. Progress: 75.0%. \n", " Training R^2: 0.8882. Test R^2: 0.8922. Loss Train: 0.0070. Loss Val: 0.0065. Time: 2.3605\n", " EPOCH 760. Progress: 76.0%. \n", " Training R^2: 0.8805. Test R^2: 0.9162. Loss Train: 0.0067. Loss Val: 0.0058. Time: 2.4398\n", " EPOCH 770. Progress: 77.0%. \n", " Training R^2: 0.8902. Test R^2: 0.8882. Loss Train: 0.0054. Loss Val: 0.0076. Time: 2.3947\n", " EPOCH 780. Progress: 78.0%. \n", " Training R^2: 0.8868. Test R^2: 0.9099. Loss Train: 0.0066. Loss Val: 0.0065. Time: 2.3312\n", " EPOCH 790. Progress: 79.0%. \n", " Training R^2: 0.8956. Test R^2: 0.8954. Loss Train: 0.0060. Loss Val: 0.0067. Time: 2.2812\n", " EPOCH 800. Progress: 80.0%. \n", " Training R^2: 0.8889. Test R^2: 0.8974. Loss Train: 0.0058. Loss Val: 0.0067. Time: 2.3829\n", " EPOCH 810. Progress: 81.0%. \n", " Training R^2: 0.9014. Test R^2: 0.8878. Loss Train: 0.0063. Loss Val: 0.0067. Time: 2.3537\n", " EPOCH 820. Progress: 82.0%. \n", " Training R^2: 0.9012. Test R^2: 0.9011. Loss Train: 0.0067. Loss Val: 0.0058. Time: 2.5143\n", " EPOCH 830. Progress: 83.0%. \n", " Training R^2: 0.8852. Test R^2: 0.8669. Loss Train: 0.0064. Loss Val: 0.0063. Time: 2.3291\n", " EPOCH 840. Progress: 84.0%. \n", " Training R^2: 0.8828. Test R^2: 0.8866. Loss Train: 0.0067. Loss Val: 0.0072. Time: 2.4063\n", " EPOCH 850. Progress: 85.0%. \n", " Training R^2: 0.8931. Test R^2: 0.9013. Loss Train: 0.0060. Loss Val: 0.0068. Time: 2.4110\n", " EPOCH 860. Progress: 86.0%. \n", " Training R^2: 0.8884. Test R^2: 0.8992. Loss Train: 0.0059. Loss Val: 0.0064. Time: 2.4111\n", " EPOCH 870. Progress: 87.0%. \n", " Training R^2: 0.8783. Test R^2: 0.8800. Loss Train: 0.0062. Loss Val: 0.0078. Time: 2.5044\n", " EPOCH 880. Progress: 88.0%. \n", " Training R^2: 0.8779. Test R^2: 0.8987. Loss Train: 0.0062. Loss Val: 0.0066. Time: 2.4080\n", " EPOCH 890. Progress: 89.0%. \n", " Training R^2: 0.8950. Test R^2: 0.8487. Loss Train: 0.0063. Loss Val: 0.0056. Time: 4.4715\n", " EPOCH 900. Progress: 90.0%. \n", " Training R^2: 0.8870. Test R^2: 0.8729. Loss Train: 0.0060. Loss Val: 0.0068. Time: 2.4720\n", " EPOCH 910. Progress: 91.0%. \n", " Training R^2: 0.9006. Test R^2: 0.8966. Loss Train: 0.0055. Loss Val: 0.0063. Time: 2.3388\n", " EPOCH 920. Progress: 92.0%. \n", " Training R^2: 0.8962. Test R^2: 0.9175. Loss Train: 0.0059. Loss Val: 0.0058. Time: 5.9139\n", " EPOCH 930. Progress: 93.0%. \n", " Training R^2: 0.8890. Test R^2: 0.9077. Loss Train: 0.0055. Loss Val: 0.0065. Time: 2.3512\n", " EPOCH 940. Progress: 94.0%. \n", " Training R^2: 0.8723. Test R^2: 0.8937. Loss Train: 0.0068. Loss Val: 0.0062. Time: 2.3694\n", " EPOCH 950. Progress: 95.0%. \n", " Training R^2: 0.8747. Test R^2: 0.8997. Loss Train: 0.0067. Loss Val: 0.0066. Time: 2.4371\n", " EPOCH 960. Progress: 96.0%. \n", " Training R^2: 0.8907. Test R^2: 0.9183. Loss Train: 0.0055. Loss Val: 0.0054. Time: 2.4315\n", " EPOCH 970. Progress: 97.0%. \n", " Training R^2: 0.8956. Test R^2: 0.9058. Loss Train: 0.0054. Loss Val: 0.0053. Time: 2.3393\n", " EPOCH 980. Progress: 98.0%. \n", " Training R^2: 0.8887. Test R^2: 0.9052. Loss Train: 0.0063. Loss Val: 0.0067. Time: 2.3698\n", " EPOCH 990. Progress: 99.0%. \n", " Training R^2: 0.8926. Test R^2: 0.9005. Loss Train: 0.0064. Loss Val: 0.0056. Time: 2.3520\n", " EPOCH 1000. Progress: 100.0%. \n", " Training R^2: 0.9004. Test R^2: 0.9033. Loss Train: 0.0061. Loss Val: 0.0066. Time: 2.7625\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "saving ./plots/lr0.001_3FFNet_2ReLU_Drop_momentum0.9_wdecay0.001_dampening0_nesterovFalse_HidDim128.png\n" ] } ], "source": [ "# ./plots/lr0.001_3FFNet_2ReLU_Drop_momentum0.9_wdecay0.001_dampening0_nesterovFalse_HidDim128.png\n", "# Testing a regular FFnet\n", "class FeedforwardNeuralNetModel(nn.Module):\n", " def __init__(self, input_dim, hidden_dim, output_dim):\n", " super(FeedforwardNeuralNetModel, self).__init__()\n", " # Linear function\n", " self.fc1 = nn.Linear(input_dim, hidden_dim) \n", " self.drop = nn.Dropout(0.2)\n", " # Non-linearity\n", " self.relu = nn.ReLU()\n", " # Linear function (readout)\n", " self.fc2 = nn.Linear(hidden_dim, hidden_dim)\n", " self.fc3 = nn.Linear(hidden_dim, output_dim)\n", "\n", " def forward(self, x):\n", " # Linear function\n", " out = self.relu(self.fc1(x))\n", " out = self.drop(out)\n", " out = self.relu(self.fc2(out))\n", " out = self.drop(out)\n", " out = self.fc3(out)\n", " return out\n", "\n", "# Best configuration for longer\n", "lr_range = [0.001]\n", "hid_dim_range = [128]\n", "weight_decay_range = [0.001]\n", "momentum_range = [0.9]\n", "dampening_range = [0]\n", "nesterov_range = [False]\n", "for lr in lr_range:\n", " for momentum in momentum_range:\n", " for weight_decay in weight_decay_range:\n", " for nesterov in nesterov_range:\n", " for dampening in dampening_range:\n", " for hid_dim in hid_dim_range:\n", " print('\\nlr: {}, momentum: {}, weight_decay: {}, dampening: {}, nesterov: {} '.format(lr, momentum, weight_decay, dampening, nesterov))\n", " model = FeedforwardNeuralNetModel(input_dim=47,hidden_dim=hid_dim, output_dim=1).to(device)\n", " print(model)\n", " SGD = torch.optim.Adam(model.parameters(), lr = lr) # This is absurdly high.\n", " loss_fn = nn.MSELoss().to(device)\n", " config_str = 'lr' + str(lr) + '_3FFNet_2ReLU_Drop_momentum' + str(momentum) + '_wdecay' + str(weight_decay) + '_dampening' + str(dampening) +'_nesterov' + str(nesterov) + '_HidDim' + str(hid_dim)\n", " train(model,loss_fn, SGD, dataloaders['train'], dataloaders['val'], config_str,num_epochs=1000, verbose=False)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "predicted.shape: torch.Size([335])\n", "predicted[:20]: \ttensor([ 0.8374, 0.4542, 0.3902, 0.4156, 0.7975, 0.6249, 0.4712, 0.2219,\n", " 0.6796, 0.5515, 0.0676, 0.5882, 0.6912, 0.6129, -0.0232, 0.1744,\n", " 0.7455, 0.0933, 0.4177, 0.4790])\n", "targets[:20]: \t\ttensor([0.9596, 0.4605, 0.8906, 0.3956, 0.8300, 0.6755, 0.6471, 0.1907, 0.6822,\n", " 0.5598, 0.0016, 0.5665, 0.6302, 0.6531, 0.0855, 0.1301, 0.6856, 0.2712,\n", " 0.6779, 0.5099])\n", "predicted.shape: torch.Size([1118])\n", "predicted[:20]: \ttensor([0.0534, 0.2025, 0.3275, 0.2179, 0.1938, 0.2668, 0.3248, 0.3472, 0.5163,\n", " 0.5817, 0.5051, 0.5089, 0.4742, 0.5025, 0.5184, 0.5432, 0.5099, 0.5161,\n", " 0.5317, 0.4993])\n", "targets[:20]: \t\ttensor([0.0280, 0.1301, 0.2970, 0.2207, 0.2648, 0.3342, 0.2873, 0.3362, 0.6023,\n", " 0.5031, 0.5335, 0.6247, 0.5454, 0.4926, 0.4442, 0.5030, 0.5472, 0.5128,\n", " 0.5702, 0.5017])\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Test\n", "with torch.no_grad():\n", " data, targets_val = next(iter(dataloaders['all_val']))\n", " model_input = data.to(device)# TODO: Turn the 28 by 28 image tensors into a 784 dimensional tensor.\n", " predicted_val = model(model_input).squeeze()\n", "# _, predicted = torch.max(out, 1)\n", " print('predicted.shape: {}'.format(predicted_val.shape))\n", " print('predicted[:20]: \\t{}'.format(predicted_val[:20]))\n", " print('targets[:20]: \\t\\t{}'.format(targets_val[:20]))\n", " \n", "# Test\n", "with torch.no_grad():\n", " data, targets = next(iter(dataloaders['test']))\n", " model_input = data.to(device)# TODO: Turn the 28 by 28 image tensors into a 784 dimensional tensor.\n", " predicted = model(model_input).squeeze()\n", " print('predicted.shape: {}'.format(predicted.shape))\n", " print('predicted[:20]: \\t{}'.format(predicted[:20]))\n", " print('targets[:20]: \\t\\t{}'.format(targets[:20]))\n", " \n", "#Time for a real test\n", "path_to_save = './plots'\n", "if not os.path.exists(path_to_save):\n", " os.makedirs(path_to_save)\n", " \n", "fig, (ax1,ax2) = plt.subplots(1,2, sharey=True)\n", "# test_predictions = model(normed_test_data).flatten()\n", "r = r2_score(targets_val, predicted_val.cpu())\n", "ax1.scatter(targets_val, predicted_val.cpu(),alpha=0.5, label='$R^2$ = %.3f' % (r))\n", "ax1.legend(loc=\"upper left\")\n", "ax1.set_xlabel('True Values [Mean Conc.]')\n", "ax1.set_ylabel('Predictions [Mean Conc.]')\n", "ax1.axis('equal')\n", "ax1.axis('square')\n", "ax1.set_xlim([0,1])\n", "ax1.set_ylim([0,1])\n", "_ = ax1.plot([-100, 100], [-100, 100], 'r:')\n", "ax1.set_title('Test dataset')\n", "fig.set_figheight(30)\n", "fig.set_figwidth(10)\n", "#plt.show()\n", "#plt.close('all')\n", "\n", "#Whole dataset\n", "# dataset_predictions = model.predict(normed_dataset).flatten()\n", "r = r2_score(targets, predicted.cpu())\n", "ax2.scatter(targets, predicted.cpu(), alpha=0.5, label='$R^2$ = %.3f' % (r))\n", "ax2.legend(loc=\"upper left\")\n", "ax2.set_xlabel('True Values [Mean Conc.]')\n", "ax2.set_ylabel('Predictions [Mean Conc.]')\n", "ax2.axis('equal')\n", "ax2.axis('square')\n", "ax2.set_xlim([0,1])\n", "ax2.set_ylim([0,1])\n", "_ = ax2.plot([-100, 100], [-100, 100], 'r:')\n", "ax2.set_title('Whole dataset')\n", "# plt.show()\n", "fig.savefig(os.path.join(path_to_save, '3FFNet_2ReLU_Drop_ADAM_LONGER_R2Score_' + config_str + '.png'), bbox_inches='tight')\n", "# #plt.close('all')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 4 }