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August 22, 2022 11:36
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/XinyueZ/fc9cb565f581f9122e392abaa802e548/cnn_transfer_learning_paper_scissors_rock-ipynb-completed.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "_BtzG3-TryHY" | |
| }, | |
| "source": [ | |
| "Case study CNNs plus SVM to classify \"Paper, Scissors and Rock\"\n", | |
| "====\n", | |
| "\n", | |
| "In general, a classical convolutional network is activated by a fully connected network and then by a classification, such as softmax or sigmoid, and in this experiment the output of the convolution could be \"flattened\" and fed into an SVM.\n", | |
| "\n", | |
| "[In fact, R-CNN uses SVM.](https://https://www.mihaileric.com/posts/object-detection-with-rcnn/)\n", | |
| "\n", | |
| "Compare different metrics for those models:\n", | |
| "\n", | |
| "1. Simple and naive CNN, `SimpleCNN`\n", | |
| "2. Transfer-learning, `VGG-16`, `RESNET-50`\n", | |
| "3. `SimpleCNN`, `VGG-16`, `RESNET-50` feeds `SVM`: \n", | |
| " - All models as feature extractor respectively to figures out image features.\n", | |
| " - Feed feature units into a `SVM`. \n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Goal\n", | |
| "\n", | |
| "CNN -> FeatureModel -> Extract features -> SVM -> SVM Wrapper -> Predict\n", | |
| "\n", | |
| "🚨 **Quickly** go through the NOTEBOOK and adjust EPOCHS=2." | |
| ], | |
| "metadata": { | |
| "id": "qAReW-zBaF9M" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Cases with different hyperparameters\n", | |
| "\n", | |
| "Because of the limited hardware conditions, we temporarily tune hyperparamers a few times. In addition, SVM could have been operated by sklearn's Grid Search CV, but it was not used because of environmental constraints.\n", | |
| "\n", | |
| "[Tuning 1](https://colab.research.google.com/drive/16lx0Ye5Xqd3J1-JOcfGAqyhfY2ztYNSG?usp=sharing)\n", | |
| "\n", | |
| "[Tuning 2](https://colab.research.google.com/drive/1BpirmvtWnhW1-dH2HJpkwzyxD0bqZfeF?usp=sharing)\n", | |
| "\n", | |
| "[Tuning 3](https://colab.research.google.com/drive/12Ny1Ekfz50hxU1Fc96Wp-W3x7npZAQYZ?usp=sharing)" | |
| ], | |
| "metadata": { | |
| "id": "cheq-J1FJhBV" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "" | |
| ], | |
| "metadata": { | |
| "id": "A1dbGAYK9Gkw" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## 👨🏻💻 Imprint \n", | |
| "\n", | |
| "Contact: chris.at.de@gmail.com\n", | |
| "\n", | |
| "Notebook share: https://colab.research.google.com/drive/1n7yd6HFFsdR7hKKFPXbzKtv_oX9oA1lc?usp=sharing\n", | |
| "\n", | |
| "#### About me \n", | |
| "\n", | |
| "I am an ML advocate and enthusiastic developer. Please share this notebook for growing skill purpose.\n", | |
| "\n", | |
| "When you are so interesting in ML and AI like me, please conntact me, let's exchange knowledge." | |
| ], | |
| "metadata": { | |
| "id": "IBJEUVMIQHW1" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "25f0kw6oqK1H" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def warn(*args, **kwargs):\n", | |
| " pass\n", | |
| "import warnings\n", | |
| "warnings.warn = warn" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Imports" | |
| ], | |
| "metadata": { | |
| "id": "40UHWAXutiwF" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "vWxtPvunIROO", | |
| "outputId": "3fcabc88-10c1-48d7-a8e2-0c002c39d935" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", | |
| "Requirement already satisfied: torchsummary in /usr/local/lib/python3.7/dist-packages (1.5.1)\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "<torch._C.Generator at 0x7f626035bcd0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 2 | |
| } | |
| ], | |
| "source": [ | |
| "import os\n", | |
| "import sys\n", | |
| "import math\n", | |
| "import time\n", | |
| "from IPython.display import clear_output\n", | |
| "\n", | |
| "import numpy as np\n", | |
| "np.random.seed(4)\n", | |
| "\n", | |
| "import pandas as pd\n", | |
| "import seaborn as sns\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "\n", | |
| "import cv2\n", | |
| "from PIL import Image, ImageOps\n", | |
| "\n", | |
| "from tensorflow.keras.utils import *\n", | |
| "\n", | |
| "from sklearn.svm import SVC\n", | |
| "from sklearn.model_selection import GridSearchCV\n", | |
| "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report, confusion_matrix\n", | |
| "\n", | |
| "import torch \n", | |
| "import torch.cuda\n", | |
| "import torch.nn as nn\n", | |
| "import torch.optim as opt\n", | |
| "!pip install torchsummary\n", | |
| "from torchsummary import summary\n", | |
| "import torchvision.models as models\n", | |
| "import torchvision.transforms as transforms\n", | |
| "from torch.utils.data import Dataset, DataLoader\n", | |
| "torch.manual_seed(0)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "-9fN6ECPvG1g", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "f0b1c364-cb71-4da1-d9a7-22306709588b" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "cuda\n", | |
| "GPU 0: Tesla P100-PCIE-16GB (UUID: GPU-639c6bff-226f-8fe8-e895-311d5481317c)\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", | |
| "print(device)\n", | |
| "!nvidia-smi -L" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "FTvKHdZnI0KA" | |
| }, | |
| "source": [ | |
| "## Download dataset" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "XSKN2zBQI4C1" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def download_dataset(src, name, default_root=os.getcwd()):#\"/users/xinyue.zhao/Desktop\"):\n", | |
| " download_dir = default_root\n", | |
| " dataset_root = default_root\n", | |
| " dataset_subdir = \"my_datasets\"\n", | |
| "\n", | |
| " # Location of downloaded file will be \"$dataset_root/$name.zip\".\n", | |
| " # Location of all extracted data will be in \"$dataset_root/$dataset_subdir/$name\".\n", | |
| "\n", | |
| " download_file = os.path.join(download_dir, f\"{name}.zip\")\n", | |
| " dataset_dir = os.path.join(dataset_root, dataset_subdir, name)\n", | |
| "\n", | |
| " get_file(\n", | |
| " fname=download_file,\n", | |
| " cache_dir=dataset_root,\n", | |
| " cache_subdir=dataset_subdir,\n", | |
| " origin=src,\n", | |
| " extract=True)\n", | |
| "\n", | |
| " assert os.path.exists(download_file)\n", | |
| " assert os.path.exists(dataset_dir)\n", | |
| "\n", | |
| " return download_file, dataset_dir" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "XYbQWDAMI6ud", | |
| "outputId": "7388c0b1-20f7-4a82-e7b3-86c6ea3f9d61" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Downloading data from https://dl.dropbox.com/s/w19k944w35af101/train.zip\n", | |
| "201834496/201833815 [==============================] - 2s 0us/step\n", | |
| "201842688/201833815 [==============================] - 2s 0us/step\n", | |
| "/content/train.zip\n", | |
| "['rock', '.DS_Store', 'paper', 'scissors']\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "train_download_file, train_dir = download_dataset(\"https://dl.dropbox.com/s/w19k944w35af101/train.zip\", \"train\")\n", | |
| "print(train_download_file)\n", | |
| "print(os.listdir(train_dir))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "13XfR5nDI8oa", | |
| "outputId": "a130cc95-b3f8-44ca-a648-98e4eec67fa7" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Downloading data from https://dl.dropbox.com/s/tyha48tjw4gkiy8/cv.zip\n", | |
| "29687808/29683601 [==============================] - 0s 0us/step\n", | |
| "29696000/29683601 [==============================] - 0s 0us/step\n", | |
| "/content/valid.zip\n", | |
| "['rock', '.DS_Store', 'paper', 'scissors']\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "cv_download_file, cv_dir = download_dataset(\"https://dl.dropbox.com/s/tyha48tjw4gkiy8/cv.zip\", \"valid\")\n", | |
| "print(cv_download_file)\n", | |
| "print(os.listdir(cv_dir))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "uoJUcTifzhF4" | |
| }, | |
| "source": [ | |
| "## Auxiliary, plot, metric and evalutation tools" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "uK6Mpt2zEdKy" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def label_idx_2_name(idx): return ['scissors', 'paper', 'rock'][idx]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "def measure_error(y_true, y_pred, duration, column_name):\n", | |
| " return pd.Series({'durdation(sec.)' : duration,\n", | |
| " 'accuracy':accuracy_score(y_true, y_pred),\n", | |
| " 'precision': precision_score(y_true, y_pred, average=\"weighted\"),\n", | |
| " 'recall': recall_score(y_true, y_pred, average=\"weighted\"),\n", | |
| " 'F1-Score': f1_score(y_true, y_pred, average=\"weighted\")\n", | |
| " },\n", | |
| " name=column_name)\n", | |
| " \n", | |
| "def measure_cnn_models(test_batch, feature_models, device):\n", | |
| " y_test = torch.argmax(test_batch[1], 1)\n", | |
| "\n", | |
| " y_test_preds = []\n", | |
| " model_measurements = []\n", | |
| " for feature_model in feature_models:\n", | |
| " model = feature_model.model\n", | |
| " name = feature_model.name\n", | |
| " start = time.process_time()\n", | |
| " preds = model(test_batch[0].to(device))\n", | |
| " end = time.process_time()\n", | |
| " y_test_pred = np.argmax(preds.cpu().detach().numpy(), 1)\n", | |
| " model_measurements.append(measure_error(y_test, y_test_pred, \n", | |
| " end - start,\n", | |
| " f'{name} ~test'))\n", | |
| " y_test_preds.append(y_test_pred)\n", | |
| " return pd.concat(model_measurements, axis=1), y_test, y_test_preds\n", | |
| "\n", | |
| "def measure_svm_models(test_batch, svm_models, device):\n", | |
| " y_test = torch.argmax(test_batch[1], 1).numpy()\n", | |
| "\n", | |
| " # Extracting features to feed SVM\n", | |
| " y_test_preds = []\n", | |
| " model_measurements = []\n", | |
| " for svm_model in svm_models: \n", | |
| " start = time.process_time()\n", | |
| " y_test_pred = svm_model.predict(test_batch[0].to(device))\n", | |
| " end = time.process_time()\n", | |
| " model_measurements.append(measure_error(y_test, y_test_pred, \n", | |
| " end - start,\n", | |
| " f'{svm_model.name} ~test'))\n", | |
| " y_test_preds.append(y_test_pred)\n", | |
| "\n", | |
| " return pd.concat(model_measurements, axis=1), y_test, y_test_preds" | |
| ], | |
| "metadata": { | |
| "id": "Q7UDyeSZMYAt" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### Plot `classification_report` of sklearn" | |
| ], | |
| "metadata": { | |
| "id": "1zfdbjbHMYt-" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "mk6Njd2UvG1j" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def show_values(pc, fmt=\"%.2f\", **kw):\n", | |
| " '''\n", | |
| " Heatmap with text in each cell with matplotlib's pyplot\n", | |
| " Source: https://stackoverflow.com/a/25074150/395857 \n", | |
| " By HYRY\n", | |
| " '''\n", | |
| " pc.update_scalarmappable()\n", | |
| " ax = pc.axes\n", | |
| " #ax = pc.axes# FOR LATEST MATPLOTLIB\n", | |
| " #Use zip BELOW IN PYTHON 3\n", | |
| " for p, color, value in zip(pc.get_paths(), pc.get_facecolors(), pc.get_array()):\n", | |
| " x, y = p.vertices[:-2, :].mean(0)\n", | |
| " if np.all(color[:3] > 0.5):\n", | |
| " color = (0.0, 0.0, 0.0)\n", | |
| " else:\n", | |
| " color = (1.0, 1.0, 1.0)\n", | |
| " ax.text(x, y, fmt % value, ha=\"center\", va=\"center\", color=color, **kw)\n", | |
| "\n", | |
| "\n", | |
| "def cm2inch(*tupl):\n", | |
| " '''\n", | |
| " Specify figure size in centimeter in matplotlib\n", | |
| " Source: https://stackoverflow.com/a/22787457/395857\n", | |
| " By gns-ank\n", | |
| " '''\n", | |
| " inch = 2.54\n", | |
| " if type(tupl[0]) == tuple:\n", | |
| " return tuple(i/inch for i in tupl[0])\n", | |
| " else:\n", | |
| " return tuple(i/inch for i in tupl)\n", | |
| "\n", | |
| "def heatmap(AUC, title, xlabel, ylabel, xticklabels, yticklabels, figure_width=40, figure_height=20, correct_orientation=False, cmap='RdBu', fig=None, ax=None):\n", | |
| " '''\n", | |
| " Inspired by:\n", | |
| " - https://stackoverflow.com/a/16124677/395857 \n", | |
| " - https://stackoverflow.com/a/25074150/395857\n", | |
| " '''\n", | |
| "\n", | |
| " # Plot it out\n", | |
| " fig, ax = plt.subplots() if fig == None and ax == None else fig, ax\n", | |
| " #c = ax.pcolor(AUC, edgecolors='k', linestyle= 'dashed', linewidths=0.2, cmap='RdBu', vmin=0.0, vmax=1.0)\n", | |
| " c = ax.pcolor(AUC, edgecolors='k', linestyle= 'dashed', linewidths=0.2, cmap=cmap)\n", | |
| "\n", | |
| " # put the major ticks at the middle of each cell\n", | |
| " ax.set_yticks(np.arange(AUC.shape[0]) + 0.5, minor=False)\n", | |
| " ax.set_xticks(np.arange(AUC.shape[1]) + 0.5, minor=False)\n", | |
| "\n", | |
| " # set tick labels\n", | |
| " #ax.set_xticklabels(np.arange(1,AUC.shape[1]+1), minor=False)\n", | |
| " ax.set_xticklabels(xticklabels, minor=False)\n", | |
| " ax.set_yticklabels(yticklabels, minor=False)\n", | |
| "\n", | |
| " # set title and x/y labels\n", | |
| " ax.set_title(title)\n", | |
| " ax.set_xlabel(xlabel)\n", | |
| " ax.set_ylabel(ylabel) \n", | |
| "\n", | |
| " # Remove last blank column\n", | |
| " plt.xlim( (0, AUC.shape[1]) )\n", | |
| "\n", | |
| " # Turn off all the ticks\n", | |
| " ax = plt.gca() if ax == None else ax \n", | |
| " for t in ax.xaxis.get_major_ticks():\n", | |
| " t.tick1On = False\n", | |
| " t.tick2On = False\n", | |
| " for t in ax.yaxis.get_major_ticks():\n", | |
| " t.tick1On = False\n", | |
| " t.tick2On = False\n", | |
| "\n", | |
| " # Add color bar if necessary\n", | |
| " # plt.colorbar(c)\n", | |
| "\n", | |
| " # Add text in each cell \n", | |
| " show_values(c)\n", | |
| "\n", | |
| " # Proper orientation (origin at the top left instead of bottom left)\n", | |
| " if correct_orientation:\n", | |
| " ax.invert_yaxis()\n", | |
| " ax.xaxis.tick_top() \n", | |
| "\n", | |
| " # resize \n", | |
| " fig = plt.gcf() if fig == None else fig\n", | |
| " #fig.set_size_inches(cm2inch(40, 20))\n", | |
| " #fig.set_size_inches(cm2inch(40*4, 20*4))\n", | |
| " fig.set_size_inches(cm2inch(figure_width, figure_height))\n", | |
| "\n", | |
| "def plot_classification_report(classification_report, number_of_classes=2, title='Classification report ', cmap='RdYlGn', fig=None, ax=None):\n", | |
| " '''\n", | |
| " Plot scikit-learn classification report.\n", | |
| " Extension based on https://stackoverflow.com/a/31689645/395857 \n", | |
| " '''\n", | |
| " lines = classification_report.split('\\n')\n", | |
| " \n", | |
| " #drop initial lines\n", | |
| " lines = lines[2:]\n", | |
| "\n", | |
| " classes = []\n", | |
| " plotMat = []\n", | |
| " support = []\n", | |
| " class_names = []\n", | |
| " for line in lines[: number_of_classes]:\n", | |
| " t = list(filter(None, line.strip().split(' ')))\n", | |
| " if len(t) < 4: continue\n", | |
| " classes.append(t[0])\n", | |
| " v = [float(x) for x in t[1: len(t) - 1]]\n", | |
| " support.append(int(t[-1]))\n", | |
| " class_names.append(t[0])\n", | |
| " plotMat.append(v)\n", | |
| "\n", | |
| "\n", | |
| " xlabel = ''\n", | |
| " ylabel = 'Classes'\n", | |
| " xticklabels = ['Precision', 'Recall', 'F1-score']\n", | |
| " yticklabels = ['{0} ({1})'.format(class_names[idx], sup) for idx, sup in enumerate(support)]\n", | |
| " figure_width = len(class_names) + 50\n", | |
| " figure_height = len(class_names) + 200\n", | |
| " correct_orientation = True\n", | |
| " heatmap(np.array(plotMat), \n", | |
| " title, \n", | |
| " xlabel, \n", | |
| " ylabel, \n", | |
| " xticklabels, \n", | |
| " yticklabels, \n", | |
| " figure_width, \n", | |
| " figure_height, \n", | |
| " correct_orientation, \n", | |
| " cmap=cmap, fig=fig, ax=ax)\n", | |
| " \n", | |
| "def report_confusion_matrix(y_test, y_test_preds, names, n_classes=3):\n", | |
| " n_models = len(names)\n", | |
| " fig, ax = plt.subplots(nrows=n_models, ncols=2)\n", | |
| " for idx, (y_test_pred, name) in enumerate(zip(y_test_preds, names)):\n", | |
| " rep = classification_report(y_test, y_test_pred)\n", | |
| " sns.heatmap(confusion_matrix(y_test, y_test_pred),\n", | |
| " annot=True, fmt='d', cbar=False,\n", | |
| " annot_kws={\"size\": 20}, ax=ax[idx, 0]) \n", | |
| " ax[idx, 0].set_title(f\"{name} ~confusion matrix\")\n", | |
| " plot_classification_report(rep, n_classes, f\"{name} classification report\", fig=fig, ax=ax[idx, 1])\n", | |
| "\n", | |
| " plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "-AI0S_lCzgMo" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from IPython.core.pylabtools import figsize\n", | |
| "def plot_model_cost_accuracy(cost_list, accuracy_list, title):\n", | |
| " epochs=len(cost_list)\n", | |
| " fig, ax1 = plt.subplots(figsize=(15,5) )\n", | |
| "\n", | |
| " color = 'tab:red'\n", | |
| " ax1.plot(cost_list, color=color)\n", | |
| " ax1.set_xlabel('Epochs', color=color)\n", | |
| " ax1.set_ylabel('Cost', color=color)\n", | |
| "\n", | |
| " ax1.set_xticks(range(1, epochs, 2));\n", | |
| " ax1.tick_params(axis='y', color=color)\n", | |
| "\n", | |
| " ax2 = ax1.twinx()\n", | |
| "\n", | |
| " color = 'tab:green'\n", | |
| " ax2.set_ylabel('Accuracy', color=color)\n", | |
| " ax2.plot(accuracy_list, color=color)\n", | |
| " ax2.set_xlabel('Epochs', color=color)\n", | |
| " ax2.tick_params(axis='y', color=color)\n", | |
| "\n", | |
| " fig.tight_layout()\n", | |
| " plt.title(title)\n", | |
| " plt.show()\n", | |
| "\n", | |
| "def plot_model_metrics(metrics, labels, xlabel, ylabel, title):\n", | |
| " epochs=len(metrics[0])\n", | |
| " fig, ax1 = plt.subplots( figsize=(15,5) )\n", | |
| " for idx, metric in enumerate(metrics):\n", | |
| " ax1.plot(metric, label=labels[idx])\n", | |
| " ax1.set_xlabel(xlabel)\n", | |
| " ax1.set_ylabel(ylabel)\n", | |
| "\n", | |
| " fig.tight_layout()\n", | |
| " plt.title(title)\n", | |
| " plt.legend()\n", | |
| " plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "_C0ZC4RnERXP" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def plot_image_batch(one_batch, title, sparse=False):\n", | |
| " image_tensors=one_batch[0]\n", | |
| " label_tensors=one_batch[1] if sparse else torch.argmax(one_batch[1], 1)\n", | |
| " \n", | |
| " image_count = len(image_tensors)\n", | |
| " fig, axs = plt.subplots(ncols=image_count, constrained_layout=True)\n", | |
| " fig.suptitle(title, fontsize=16)\n", | |
| " for idx, ax in enumerate(axs):\n", | |
| " ax.set_axis_off()\n", | |
| " ax.set_title(label_idx_2_name(label_tensors[idx]))\n", | |
| " # Show norm images will bring warnings: https://discuss.pytorch.org/t/how-does-torchvision-transforms-normalize-work/57670\n", | |
| " # We've to restore image tensor from norm state to origin.\n", | |
| " image_tensor_restored = image_tensors[idx] * torch.tensor(norm.std).view(3, 1, 1) + torch.tensor(norm.mean).view(3, 1, 1)\n", | |
| " ax.imshow(\n", | |
| " image_tensor_restored.permute(1, 2, 0)\n", | |
| " )\n", | |
| " plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Hyperparameters" | |
| ], | |
| "metadata": { | |
| "id": "lfekDoC-pRIB" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "LR=1e-4 #learning_rate\n", | |
| "EPOCHS=100\n", | |
| "BATCH_SIZE=32\n", | |
| "SVM_Cs = [10**i for i in range(-2, 2)]\n", | |
| "SVM_Cs" | |
| ], | |
| "metadata": { | |
| "id": "h9K7rlfbpTu6", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "36303113-59a2-4cd7-fb84-98155e7ad426" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "[0.01, 0.1, 1, 10]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 12 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "PECuaN-aj7_1" | |
| }, | |
| "source": [ | |
| "## Build dataset and loader\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "2ApSf7QngUvA" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "class MyDataset(Dataset):\n", | |
| " def __init__(self, dir, transform=None, sparse=False):\n", | |
| " self.sparse = sparse\n", | |
| " self.transform=transform\n", | |
| " self.X = []\n", | |
| " self.y = [] \n", | |
| " for label_idx, label in enumerate(['scissors', 'paper', 'rock']):\n", | |
| " path_label_dir = os.path.join(dir, label)\n", | |
| " for image_path in os.listdir(path_label_dir):\n", | |
| " image_path = os.path.join(path_label_dir, image_path)\n", | |
| " self.X.append(image_path)\n", | |
| " self.y.append(label_idx) \n", | |
| "\n", | |
| " self.len=len(self.X)\n", | |
| "\n", | |
| " def __len__(self): return self.len\n", | |
| "\n", | |
| " def __getitem__(self, idx): \n", | |
| " image = Image.open(self.X[idx]).convert('RGB')\n", | |
| " image = transforms.ToTensor()(image) if self.transform is None else self.transform(image) \n", | |
| " label = self.y[idx] if self.sparse else torch.as_tensor(torch.nn.functional.one_hot(torch.tensor(self.y[idx]), 3), dtype=torch.float)\n", | |
| " return image, label" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "i6a4NiS1EHk2" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "class MyTestset(Dataset):\n", | |
| " def __init__(self, dir, transform=None, sparse=False):\n", | |
| " self.transform=transform\n", | |
| " self.sparse = sparse\n", | |
| " self.X = []\n", | |
| " self.y = [] \n", | |
| "\n", | |
| " for image_path in os.listdir(dir):\n", | |
| " image_path = os.path.join(dir, image_path)\n", | |
| " self.X.append(image_path)\n", | |
| " if 'scissors' in image_path: self.y.append(0) \n", | |
| " if 'paper' in image_path: self.y.append(1) \n", | |
| " if 'rock' in image_path: self.y.append(2) \n", | |
| "\n", | |
| " self.len=len(self.X)\n", | |
| "\n", | |
| " def __len__(self): return self.len\n", | |
| "\n", | |
| " def __getitem__(self, idx): \n", | |
| " image = Image.open(self.X[idx]).convert('RGB').resize((300,300))\n", | |
| " image = transforms.ToTensor()(image) if self.transform is None else self.transform(image) \n", | |
| " label = self.y[idx] if self.sparse else torch.as_tensor(torch.nn.functional.one_hot(torch.tensor(self.y[idx]), 3), dtype=torch.float)\n", | |
| " return image, label" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### Dataset, loader and image augmentation" | |
| ], | |
| "metadata": { | |
| "id": "mzji3x_epC8Y" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "norm = transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))\n", | |
| "# The mean and std of ImageNet are: mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\n", | |
| "\n", | |
| "transform_train = transforms.Compose([\n", | |
| " transforms.RandomAdjustSharpness(.5),\n", | |
| " transforms.RandomVerticalFlip(),\n", | |
| " transforms.RandomHorizontalFlip(),\n", | |
| " transforms.RandomRotation(45),\n", | |
| " transforms.ToTensor(),\n", | |
| " norm,\n", | |
| "])\n", | |
| "transform_cv = transforms.Compose([\n", | |
| " transforms.ToTensor(),\n", | |
| " norm,\n", | |
| "])" | |
| ], | |
| "metadata": { | |
| "id": "PrTwHwkTpIAK" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "train_dataset = MyDataset(dir=train_dir, transform=transform_train)\n", | |
| "cv_dataset = MyDataset(dir=cv_dir, transform=transform_cv)\n", | |
| "\n", | |
| "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n", | |
| "cv_loader = DataLoader(cv_dataset, batch_size=BATCH_SIZE, shuffle=False)\n", | |
| "\n", | |
| "# Just plot some train and cv data randomly.\n", | |
| "\n", | |
| "plot_image_batch(next(iter(DataLoader(MyDataset(train_dir, transform_train),\n", | |
| " batch_size=5, shuffle=True))),title=\"Training\",)\n", | |
| "\n", | |
| "plot_image_batch(next(iter(DataLoader(MyDataset(cv_dir, transform_cv),\n", | |
| " batch_size=5, shuffle=True))),title=\"Validation\",)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 433 | |
| }, | |
| "id": "tqPdVY15o4cJ", | |
| "outputId": "f594af22-cdce-4aaf-adfd-f54c8e67a60f" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 5 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 5 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "xmerWjEXsbam" | |
| }, | |
| "source": [ | |
| "## Model Architecture" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### Feature extractor model\n", | |
| "\n", | |
| "Wrap the torch model and extract the last output from CNN." | |
| ], | |
| "metadata": { | |
| "id": "zlSajMIeiRl7" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "class FeatureModel:\n", | |
| " def __init__(self, model, name, features_extractor_layer):\n", | |
| " self.model = model\n", | |
| " self.name = name\n", | |
| " self.extracting_features = False\n", | |
| " self.extracted_features = []\n", | |
| " self.feature_labels = [] # Sparse label (not ONE-HOT)\n", | |
| " self.features_extractor_layer = features_extractor_layer\n", | |
| " self.handle = None\n", | |
| "\n", | |
| " def get_features_activation(self, model, input, output):\n", | |
| " if self.extracting_features:\n", | |
| " x = input[0].cpu().detach().numpy()\n", | |
| " self.extracted_features.append(x)\n", | |
| " \n", | |
| " def add_feature_labels(self, label): \n", | |
| " if self.extracting_features:\n", | |
| " y = np.argmax(label.cpu().detach().numpy(), 1)\n", | |
| " self.feature_labels.append(np.expand_dims(y, 1))\n", | |
| "\n", | |
| " def start_extracting_features(self): \n", | |
| " if self.extracting_features == False:\n", | |
| " self.extracting_features = True\n", | |
| " self.extracted_features = []\n", | |
| " self.feature_labels = []\n", | |
| " self.handle = self.features_extractor_layer.register_forward_hook(\n", | |
| " self.get_features_activation)\n", | |
| "\n", | |
| " def stop_extracting_features(self):\n", | |
| " if self.extracting_features == True:\n", | |
| " self.extracting_features = False\n", | |
| "\n", | |
| " if self.handle != None: \n", | |
| " self.handle.remove() \n", | |
| "\n", | |
| " if self.extracted_features != None and len(self.extracted_features) > 0:\n", | |
| " self.extracted_features = np.vstack(self.extracted_features)\n", | |
| "\n", | |
| " if self.feature_labels != None and len(self.feature_labels) > 0:\n", | |
| " self.feature_labels = np.vstack(self.feature_labels)\n", | |
| "\n", | |
| " def flush(self, clean_only=False):\n", | |
| " if clean_only:\n", | |
| " if self.extracted_features != None:\n", | |
| " self.extracted_features.clear()\n", | |
| " \n", | |
| " if self.feature_labels != None:\n", | |
| " self.feature_labels.clear()\n", | |
| " else:\n", | |
| " del self.extracted_features\n", | |
| " del self.feature_labels" | |
| ], | |
| "metadata": { | |
| "id": "Oz5z-XFjiVEl" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### SVM Wrapper\n", | |
| "\n", | |
| "SVM as classifier which consumes the features extracted by the CNNs." | |
| ], | |
| "metadata": { | |
| "id": "sH5JxxFdzab8" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "class FeatureSVM:\n", | |
| " def __init__(self, C, feature_model, name, kernel=\"rbf\"):\n", | |
| " self.feature_model = feature_model\n", | |
| " self.svm = SVC(C=C, kernel=kernel)\n", | |
| " self.name=name\n", | |
| " def fit(self, flush_feature_model=False):\n", | |
| " self.svm.fit(self.feature_model.extracted_features, self.feature_model.feature_labels)\n", | |
| " if flush_feature_model:\n", | |
| " self.feature_model.flush()\n", | |
| " def predict(self, X):\n", | |
| " self.feature_model.start_extracting_features()\n", | |
| " self.feature_model.model(X)\n", | |
| " self.feature_model.stop_extracting_features()\n", | |
| " return self.svm.predict(self.feature_model.extracted_features)" | |
| ], | |
| "metadata": { | |
| "id": "Po3U7uGWzZjL" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "DzrBMBectxtt" | |
| }, | |
| "source": [ | |
| "### Simple CNN" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "DkOWQ1NDtzQZ" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "class SimpleCNN(nn.Module):\n", | |
| " def __init__(self, n_classes, in_channels=3, \n", | |
| " out_1=2**2, out_2=2**3, out_3=2**4, out_4=2**5, out_5=2**6, \n", | |
| " kernel_size=2):\n", | |
| " super(SimpleCNN, self).__init__()\n", | |
| "\n", | |
| " # features\n", | |
| " self.cnn1 = nn.Conv2d(in_channels=in_channels, out_channels=out_1, kernel_size=kernel_size, stride=1)\n", | |
| " nn.init.kaiming_normal_(self.cnn1.weight)\n", | |
| " self.conv1_bn = nn.BatchNorm2d(out_1)\n", | |
| " self.maxpool1 = nn.MaxPool2d(kernel_size=kernel_size)\n", | |
| " self.dropout1 = nn.Dropout(.01)\n", | |
| " \n", | |
| " self.cnn2 = nn.Conv2d(in_channels=out_1, out_channels=out_2, kernel_size=kernel_size, stride=1)\n", | |
| " nn.init.kaiming_normal_(self.cnn2.weight)\n", | |
| " self.conv2_bn = nn.BatchNorm2d(out_2)\n", | |
| " self.maxpool2 = nn.MaxPool2d(kernel_size=kernel_size)\n", | |
| " self.dropout2 = nn.Dropout(.01)\n", | |
| "\n", | |
| " self.cnn3 = nn.Conv2d(in_channels=out_2, out_channels=out_3, kernel_size=kernel_size, stride=1)\n", | |
| " nn.init.kaiming_normal_(self.cnn3.weight)\n", | |
| " self.conv3_bn = nn.BatchNorm2d(out_3)\n", | |
| " self.maxpool3 = nn.MaxPool2d(kernel_size=kernel_size)\n", | |
| " self.dropout3 = nn.Dropout(.01)\n", | |
| " \n", | |
| " self.cnn4 = nn.Conv2d(in_channels=out_3, out_channels=out_4, kernel_size=kernel_size, stride=1)\n", | |
| " nn.init.kaiming_normal_(self.cnn4.weight)\n", | |
| " self.conv4_bn = nn.BatchNorm2d(out_4)\n", | |
| " self.maxpool4 = nn.MaxPool2d(kernel_size=kernel_size)\n", | |
| " self.dropout4 = nn.Dropout(.02)\n", | |
| "\n", | |
| " self.cnn5 = nn.Conv2d(in_channels=out_4, out_channels=out_5, kernel_size=kernel_size, stride=1)\n", | |
| " nn.init.kaiming_normal_(self.cnn5.weight)\n", | |
| " self.conv5_bn = nn.BatchNorm2d(out_5)\n", | |
| " self.maxpool5 = nn.MaxPool2d(kernel_size=kernel_size)\n", | |
| " self.dropout5 = nn.Dropout(.02)\n", | |
| "\n", | |
| " # classifier\n", | |
| " classifier_in = out_5 * 8 * 8\n", | |
| " classifier_out = math.ceil(classifier_in/2)\n", | |
| "\n", | |
| " self.fc1 = nn.Linear(classifier_in, classifier_out)\n", | |
| " nn.init.kaiming_normal_(self.fc1.weight)\n", | |
| " self.bn_fc1 = nn.BatchNorm1d(classifier_out)\n", | |
| " self.dropout6 = nn.Dropout(.02)\n", | |
| "\n", | |
| " self.fc2 = nn.Linear(classifier_out, classifier_out)\n", | |
| " nn.init.kaiming_normal_(self.fc2.weight)\n", | |
| " self.bn_fc2 = nn.BatchNorm1d(classifier_out)\n", | |
| " self.dropout7 = nn.Dropout(.02)\n", | |
| "\n", | |
| " self.fc3 = nn.Linear(classifier_out, n_classes)\n", | |
| " nn.init.kaiming_normal_(self.fc3.weight)\n", | |
| " self.bn_fc3 = nn.BatchNorm1d(n_classes)\n", | |
| "\n", | |
| " # Prediction\n", | |
| " def forward(self, x):\n", | |
| " x = self.cnn1(x)\n", | |
| " x = self.conv1_bn(x)\n", | |
| " x = torch.relu(x)\n", | |
| " x = self.maxpool1(x)\n", | |
| " x = self.dropout1(x)\n", | |
| "\n", | |
| " x = self.cnn2(x)\n", | |
| " x = self.conv2_bn(x)\n", | |
| " x = torch.relu(x)\n", | |
| " x = self.maxpool2(x)\n", | |
| " x = self.dropout2(x)\n", | |
| "\n", | |
| " x = self.cnn3(x)\n", | |
| " x = self.conv3_bn(x)\n", | |
| " x = torch.relu(x)\n", | |
| " x = self.maxpool3(x)\n", | |
| " x = self.dropout3(x)\n", | |
| " \n", | |
| " x = self.cnn4(x)\n", | |
| " x = self.conv4_bn(x)\n", | |
| " x = torch.relu(x)\n", | |
| " x = self.maxpool4(x)\n", | |
| " x = self.dropout4(x)\n", | |
| "\n", | |
| " x = self.cnn5(x)\n", | |
| " x = self.conv5_bn(x)\n", | |
| " x = torch.relu(x)\n", | |
| " x = self.maxpool5(x)\n", | |
| " x = self.dropout5(x)\n", | |
| " \n", | |
| " #print(x.shape)\n", | |
| " x = x.view(x.size(0), -1)\n", | |
| " \n", | |
| " x = self.fc1(x)\n", | |
| " x = torch.relu(x)\n", | |
| " x = self.bn_fc1(x)\n", | |
| " x = self.dropout6(x)\n", | |
| "\n", | |
| " x = self.fc2(x)\n", | |
| " x = torch.relu(x)\n", | |
| " x = self.bn_fc2(x)\n", | |
| " x = self.dropout7(x)\n", | |
| "\n", | |
| " x = self.fc3(x)\n", | |
| " x = self.bn_fc3(x)\n", | |
| "\n", | |
| " return x\n", | |
| "\n", | |
| "simple_cnn=SimpleCNN(n_classes=3)\n", | |
| "simple_cnn=FeatureModel(simple_cnn, \"Simple CNN\", simple_cnn.fc1)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### VGG-16" | |
| ], | |
| "metadata": { | |
| "id": "gRLOD3RnS_iW" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "def get_vgg16_model(n_classes):\n", | |
| " model=models.vgg16_bn(pretrained=True)\n", | |
| " \n", | |
| " def set_param(param): param.requires_grad=False\n", | |
| " [set_param(param) for param in model.parameters()]\n", | |
| " \n", | |
| " num_features = model.classifier[6].in_features\n", | |
| " features = list(model.classifier.children())[:-1] # Remove last layer\n", | |
| " features.extend([nn.Linear(4096, n_classes)]) # Add our output layer\n", | |
| " model.classifier = nn.Sequential(*features) # Replace the model classifier\n", | |
| "\n", | |
| " return model\n", | |
| "\n", | |
| "vgg = get_vgg16_model(n_classes=3);\n", | |
| "vgg = FeatureModel(vgg, \"VGG-16\", list(vgg.classifier.children())[0])" | |
| ], | |
| "metadata": { | |
| "id": "cxaLoqIvS6yH", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 66, | |
| "referenced_widgets": [ | |
| "56e28627a57041c4af960abeb1fd9792", | |
| "e0a4703d5adc4afd95adc10e73943da4", | |
| "70f0319786f5417283c3995051ffb34e", | |
| "298a8c7e3a084f94a182ccd204734812", | |
| "a63497dec4bb430ba9674e91da58a9c1", | |
| "2b774b8cc34d4e06b836c34b11766dca", | |
| "0e3657e89de24666a0b6be212453e896", | |
| "a5df8151859f47e78a488ea8a0a62b19", | |
| "3c837839aa9641f78d8fb146a99c75d2", | |
| "bfbaa458e16a4b6387fc2750d79a9ab9", | |
| "fd79fb5f7fca4c108f8d87d49aafa38f" | |
| ] | |
| }, | |
| "outputId": "de23bacc-3190-4f46-bf0b-98525f194675" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Downloading: \"https://download.pytorch.org/models/vgg16_bn-6c64b313.pth\" to /root/.cache/torch/hub/checkpoints/vgg16_bn-6c64b313.pth\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| " 0%| | 0.00/528M [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "56e28627a57041c4af960abeb1fd9792" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### Resnet50" | |
| ], | |
| "metadata": { | |
| "id": "dfmi4bUvwNNN" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "def get_resnet50_model(n_classes):\n", | |
| " model=models.resnet50(pretrained=True)\n", | |
| " def set_param(param): param.requires_grad=False\n", | |
| " [set_param(param) for param in model.parameters()]\n", | |
| " model.fc=nn.Linear(2048, n_classes)\n", | |
| " return model\n", | |
| " \n", | |
| "resnet = get_resnet50_model(n_classes=3)\n", | |
| "resnet = FeatureModel(resnet, \"RESNET-50\", resnet.fc)" | |
| ], | |
| "metadata": { | |
| "id": "Zs_KoMddwtmc", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 66, | |
| "referenced_widgets": [ | |
| "de89b4eb4abc4fb1bc570975c590eb9a", | |
| "f621610792ed407792b94fa2ab1c8f9c", | |
| "f82d06ec991844bf8051c6769fc94862", | |
| "691049c32bfd4222ab743a76eb71a95d", | |
| "8645d34114a944cb946bd43301567335", | |
| "c01caa8d475841bdba9a1d94cbd3a789", | |
| "76c3763b506a4e3d840fd9f516f6273f", | |
| "0d1741a6379c43e1af3eecda9e440239", | |
| "5f16d170700047549b69ed98ed33300c", | |
| "db9074b296eb4e12b723b4d6639f7b0e", | |
| "db25860eb971436e9ff27b400c3bd5f0" | |
| ] | |
| }, | |
| "outputId": "ca897177-0ece-4d5e-8e40-6cd429e320bb" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| " 0%| | 0.00/97.8M [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "de89b4eb4abc4fb1bc570975c590eb9a" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### Model trainer" | |
| ], | |
| "metadata": { | |
| "id": "UWmOhspDoJSb" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "class ModelTrainer: \n", | |
| " def __init__(self, feature_model, name, device, print_summary=False, print_summary_input_shape=None): \n", | |
| " self.feature_model = feature_model\n", | |
| " self.device = device\n", | |
| " self.feature_model.model.to(device)\n", | |
| " self.name = name\n", | |
| " self.train_cost_list, self.train_accuracy_list, self.cv_cost_list, self.cv_accuracy_list = [],[],[],[]\n", | |
| " if print_summary: \n", | |
| " print(f\"\\033[92m{name}\\033[00m\")\n", | |
| " print(summary(self.feature_model.model, print_summary_input_shape))\n", | |
| " \n", | |
| " def plot_model_cost_accuracy(self):\n", | |
| " plot_model_cost_accuracy(self.train_cost_list, self.train_accuracy_list, f\"{self.name} ~Train\")\n", | |
| " plot_model_cost_accuracy(self.train_cost_list, self.train_accuracy_list, f\"{self.name} ~Validation\")\n", | |
| "\n", | |
| " def plot_model_cost_compare(self):\n", | |
| " plot_model_metrics(metrics=[self.train_cost_list, self.cv_cost_list],\n", | |
| " labels=[\"Train\", \"Validation\"],\n", | |
| " xlabel=\"Epochs\", \n", | |
| " ylabel=\"Cost\",\n", | |
| " title=f\"{self.name} ~Cost train and validation\")\n", | |
| "\n", | |
| " def plot_model_accuracy_compare(self):\n", | |
| " plot_model_metrics(metrics=[self.train_accuracy_list, self.cv_accuracy_list],\n", | |
| " labels=[\"Train\", \"Validation\"],\n", | |
| " xlabel=\"Epochs\", \n", | |
| " ylabel=\"Accuracy\",\n", | |
| " title=f\"{self.name} ~Accuracy train and validation\")\n", | |
| "\n", | |
| " def fit(self, loss_object, optimizer, epochs, \n", | |
| " train_loader, cv_loader, \n", | |
| " m_train, m_cv, \n", | |
| " train_steps, cv_steps, clear_output_factor=10): \n", | |
| "\n", | |
| " for epoch in range(epochs):\n", | |
| " print(f\"Epoch: {epoch+1}/{epochs}:\")\n", | |
| "\n", | |
| " self.feature_model.model.train()\n", | |
| " train_cost, train_accuracy = 0, 0\n", | |
| " for x, y in train_loader:\n", | |
| " x, y=x.to(self.device), y.to(self.device)\n", | |
| " optimizer.zero_grad()\n", | |
| "\n", | |
| " if epoch == epochs - 1: \n", | |
| " self.feature_model.start_extracting_features()\n", | |
| " self.feature_model.add_feature_labels(y)\n", | |
| "\n", | |
| " o = self.feature_model.model(x) \n", | |
| "\n", | |
| " loss = loss_object(o, y)\n", | |
| " loss.backward()\n", | |
| " optimizer.step()\n", | |
| " \n", | |
| " #--------------------------------\n", | |
| " # Evaluation\n", | |
| " #--------------------------------\n", | |
| " with torch.no_grad():\n", | |
| " self.feature_model.model.eval()\n", | |
| " self.feature_model.stop_extracting_features()\n", | |
| " # Training-set\n", | |
| " for x_train, y_train in train_loader:\n", | |
| " x_train, y_train = x_train.to(self.device), y_train.to(self.device)\n", | |
| " o = self.feature_model.model(x_train)\n", | |
| " \n", | |
| " train_loss = loss_object(o, y_train)\n", | |
| " train_cost += train_loss.cpu().detach()\n", | |
| " \n", | |
| " score=accuracy_score(torch.argmax(y_train.cpu().detach(), 1), torch.argmax(o.cpu().detach(), 1), normalize=False)\n", | |
| " train_accuracy += score\n", | |
| "\n", | |
| " train_cost /= train_steps\n", | |
| " self.train_cost_list.append(train_cost)\n", | |
| "\n", | |
| " train_accuracy /= m_train\n", | |
| " self.train_accuracy_list.append(train_accuracy)\n", | |
| "\n", | |
| " # CV-Set\n", | |
| " cv_cost, cv_accuracy = 0, 0\n", | |
| " for x_cv, y_cv in cv_loader: \n", | |
| " x_cv, y_cv = x_cv.to(self.device), y_cv.to(self.device)\n", | |
| " o = self.feature_model.model(x_cv)\n", | |
| "\n", | |
| " cv_loss = loss_object(o, y_cv)\n", | |
| " cv_cost += cv_loss.cpu().detach()\n", | |
| " \n", | |
| " score = accuracy_score(torch.argmax(y_cv.cpu().detach(),1),torch.argmax(o.cpu().detach(),1), normalize=False)\n", | |
| " cv_accuracy += score\n", | |
| "\n", | |
| " cv_cost /= cv_steps\n", | |
| " self.cv_cost_list.append(cv_cost)\n", | |
| "\n", | |
| " cv_accuracy /= m_cv\n", | |
| " self.cv_accuracy_list.append(cv_accuracy)\n", | |
| " \n", | |
| " if epoch % 10 == 0: clear_output()\n", | |
| " print(f\"cost: {train_cost:.3f}, accuracy: {train_accuracy:.3f}, validation: cost: {cv_cost:.3f}, accuracy: {cv_accuracy:.3f}\")\n", | |
| " \n", | |
| " del train_cost, train_accuracy, cv_cost, cv_accuracy\n", | |
| "\n", | |
| "\n", | |
| "simple_cnn_trainer = ModelTrainer(simple_cnn, \"Trained Simple CNN\", device, True, next(iter(train_loader))[0][0].shape)\n", | |
| "resnet_trainer = ModelTrainer(resnet, \"Trained RESNET-50\", device, True, next(iter(train_loader))[0][0].shape)\n", | |
| "vgg_trainer = ModelTrainer(vgg, \"Trained VGG-16\", device, True, next(iter(train_loader))[0][0].shape)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "D9HldDpooNhK", | |
| "outputId": "c7beb5dc-65a0-464d-bdda-a1ef879b697f" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\u001b[92mTrained Simple CNN\u001b[00m\n", | |
| "----------------------------------------------------------------\n", | |
| " Layer (type) Output Shape Param #\n", | |
| "================================================================\n", | |
| " Conv2d-1 [-1, 4, 299, 299] 52\n", | |
| " BatchNorm2d-2 [-1, 4, 299, 299] 8\n", | |
| " MaxPool2d-3 [-1, 4, 149, 149] 0\n", | |
| " Dropout-4 [-1, 4, 149, 149] 0\n", | |
| " Conv2d-5 [-1, 8, 148, 148] 136\n", | |
| " BatchNorm2d-6 [-1, 8, 148, 148] 16\n", | |
| " MaxPool2d-7 [-1, 8, 74, 74] 0\n", | |
| " Dropout-8 [-1, 8, 74, 74] 0\n", | |
| " Conv2d-9 [-1, 16, 73, 73] 528\n", | |
| " BatchNorm2d-10 [-1, 16, 73, 73] 32\n", | |
| " MaxPool2d-11 [-1, 16, 36, 36] 0\n", | |
| " Dropout-12 [-1, 16, 36, 36] 0\n", | |
| " Conv2d-13 [-1, 32, 35, 35] 2,080\n", | |
| " BatchNorm2d-14 [-1, 32, 35, 35] 64\n", | |
| " MaxPool2d-15 [-1, 32, 17, 17] 0\n", | |
| " Dropout-16 [-1, 32, 17, 17] 0\n", | |
| " Conv2d-17 [-1, 64, 16, 16] 8,256\n", | |
| " BatchNorm2d-18 [-1, 64, 16, 16] 128\n", | |
| " MaxPool2d-19 [-1, 64, 8, 8] 0\n", | |
| " Dropout-20 [-1, 64, 8, 8] 0\n", | |
| " Linear-21 [-1, 2048] 8,390,656\n", | |
| " BatchNorm1d-22 [-1, 2048] 4,096\n", | |
| " Dropout-23 [-1, 2048] 0\n", | |
| " Linear-24 [-1, 2048] 4,196,352\n", | |
| " BatchNorm1d-25 [-1, 2048] 4,096\n", | |
| " Dropout-26 [-1, 2048] 0\n", | |
| " Linear-27 [-1, 3] 6,147\n", | |
| " BatchNorm1d-28 [-1, 3] 6\n", | |
| "================================================================\n", | |
| "Total params: 12,612,653\n", | |
| "Trainable params: 12,612,653\n", | |
| "Non-trainable params: 0\n", | |
| "----------------------------------------------------------------\n", | |
| "Input size (MB): 1.03\n", | |
| "Forward/backward pass size (MB): 12.92\n", | |
| "Params size (MB): 48.11\n", | |
| "Estimated Total Size (MB): 62.06\n", | |
| "----------------------------------------------------------------\n", | |
| "None\n", | |
| "\u001b[92mTrained RESNET-50\u001b[00m\n", | |
| "----------------------------------------------------------------\n", | |
| " Layer (type) Output Shape Param #\n", | |
| "================================================================\n", | |
| " Conv2d-1 [-1, 64, 150, 150] 9,408\n", | |
| " BatchNorm2d-2 [-1, 64, 150, 150] 128\n", | |
| " ReLU-3 [-1, 64, 150, 150] 0\n", | |
| " MaxPool2d-4 [-1, 64, 75, 75] 0\n", | |
| " Conv2d-5 [-1, 64, 75, 75] 4,096\n", | |
| " BatchNorm2d-6 [-1, 64, 75, 75] 128\n", | |
| " ReLU-7 [-1, 64, 75, 75] 0\n", | |
| " Conv2d-8 [-1, 64, 75, 75] 36,864\n", | |
| " BatchNorm2d-9 [-1, 64, 75, 75] 128\n", | |
| " ReLU-10 [-1, 64, 75, 75] 0\n", | |
| " Conv2d-11 [-1, 256, 75, 75] 16,384\n", | |
| " BatchNorm2d-12 [-1, 256, 75, 75] 512\n", | |
| " Conv2d-13 [-1, 256, 75, 75] 16,384\n", | |
| " BatchNorm2d-14 [-1, 256, 75, 75] 512\n", | |
| " ReLU-15 [-1, 256, 75, 75] 0\n", | |
| " Bottleneck-16 [-1, 256, 75, 75] 0\n", | |
| " Conv2d-17 [-1, 64, 75, 75] 16,384\n", | |
| " BatchNorm2d-18 [-1, 64, 75, 75] 128\n", | |
| " ReLU-19 [-1, 64, 75, 75] 0\n", | |
| " Conv2d-20 [-1, 64, 75, 75] 36,864\n", | |
| " BatchNorm2d-21 [-1, 64, 75, 75] 128\n", | |
| " ReLU-22 [-1, 64, 75, 75] 0\n", | |
| " Conv2d-23 [-1, 256, 75, 75] 16,384\n", | |
| " BatchNorm2d-24 [-1, 256, 75, 75] 512\n", | |
| " ReLU-25 [-1, 256, 75, 75] 0\n", | |
| " Bottleneck-26 [-1, 256, 75, 75] 0\n", | |
| " Conv2d-27 [-1, 64, 75, 75] 16,384\n", | |
| " BatchNorm2d-28 [-1, 64, 75, 75] 128\n", | |
| " ReLU-29 [-1, 64, 75, 75] 0\n", | |
| " Conv2d-30 [-1, 64, 75, 75] 36,864\n", | |
| " BatchNorm2d-31 [-1, 64, 75, 75] 128\n", | |
| " ReLU-32 [-1, 64, 75, 75] 0\n", | |
| " Conv2d-33 [-1, 256, 75, 75] 16,384\n", | |
| " BatchNorm2d-34 [-1, 256, 75, 75] 512\n", | |
| " ReLU-35 [-1, 256, 75, 75] 0\n", | |
| " Bottleneck-36 [-1, 256, 75, 75] 0\n", | |
| " Conv2d-37 [-1, 128, 75, 75] 32,768\n", | |
| " BatchNorm2d-38 [-1, 128, 75, 75] 256\n", | |
| " ReLU-39 [-1, 128, 75, 75] 0\n", | |
| " Conv2d-40 [-1, 128, 38, 38] 147,456\n", | |
| " BatchNorm2d-41 [-1, 128, 38, 38] 256\n", | |
| " ReLU-42 [-1, 128, 38, 38] 0\n", | |
| " Conv2d-43 [-1, 512, 38, 38] 65,536\n", | |
| " BatchNorm2d-44 [-1, 512, 38, 38] 1,024\n", | |
| " Conv2d-45 [-1, 512, 38, 38] 131,072\n", | |
| " BatchNorm2d-46 [-1, 512, 38, 38] 1,024\n", | |
| " ReLU-47 [-1, 512, 38, 38] 0\n", | |
| " Bottleneck-48 [-1, 512, 38, 38] 0\n", | |
| " Conv2d-49 [-1, 128, 38, 38] 65,536\n", | |
| " BatchNorm2d-50 [-1, 128, 38, 38] 256\n", | |
| " ReLU-51 [-1, 128, 38, 38] 0\n", | |
| " Conv2d-52 [-1, 128, 38, 38] 147,456\n", | |
| " BatchNorm2d-53 [-1, 128, 38, 38] 256\n", | |
| " ReLU-54 [-1, 128, 38, 38] 0\n", | |
| " Conv2d-55 [-1, 512, 38, 38] 65,536\n", | |
| " BatchNorm2d-56 [-1, 512, 38, 38] 1,024\n", | |
| " ReLU-57 [-1, 512, 38, 38] 0\n", | |
| " Bottleneck-58 [-1, 512, 38, 38] 0\n", | |
| " Conv2d-59 [-1, 128, 38, 38] 65,536\n", | |
| " BatchNorm2d-60 [-1, 128, 38, 38] 256\n", | |
| " ReLU-61 [-1, 128, 38, 38] 0\n", | |
| " Conv2d-62 [-1, 128, 38, 38] 147,456\n", | |
| " BatchNorm2d-63 [-1, 128, 38, 38] 256\n", | |
| " ReLU-64 [-1, 128, 38, 38] 0\n", | |
| " Conv2d-65 [-1, 512, 38, 38] 65,536\n", | |
| " BatchNorm2d-66 [-1, 512, 38, 38] 1,024\n", | |
| " ReLU-67 [-1, 512, 38, 38] 0\n", | |
| " Bottleneck-68 [-1, 512, 38, 38] 0\n", | |
| " Conv2d-69 [-1, 128, 38, 38] 65,536\n", | |
| " BatchNorm2d-70 [-1, 128, 38, 38] 256\n", | |
| " ReLU-71 [-1, 128, 38, 38] 0\n", | |
| " Conv2d-72 [-1, 128, 38, 38] 147,456\n", | |
| " BatchNorm2d-73 [-1, 128, 38, 38] 256\n", | |
| " ReLU-74 [-1, 128, 38, 38] 0\n", | |
| " Conv2d-75 [-1, 512, 38, 38] 65,536\n", | |
| " BatchNorm2d-76 [-1, 512, 38, 38] 1,024\n", | |
| " ReLU-77 [-1, 512, 38, 38] 0\n", | |
| " Bottleneck-78 [-1, 512, 38, 38] 0\n", | |
| " Conv2d-79 [-1, 256, 38, 38] 131,072\n", | |
| " BatchNorm2d-80 [-1, 256, 38, 38] 512\n", | |
| " ReLU-81 [-1, 256, 38, 38] 0\n", | |
| " Conv2d-82 [-1, 256, 19, 19] 589,824\n", | |
| " BatchNorm2d-83 [-1, 256, 19, 19] 512\n", | |
| " ReLU-84 [-1, 256, 19, 19] 0\n", | |
| " Conv2d-85 [-1, 1024, 19, 19] 262,144\n", | |
| " BatchNorm2d-86 [-1, 1024, 19, 19] 2,048\n", | |
| " Conv2d-87 [-1, 1024, 19, 19] 524,288\n", | |
| " BatchNorm2d-88 [-1, 1024, 19, 19] 2,048\n", | |
| " ReLU-89 [-1, 1024, 19, 19] 0\n", | |
| " Bottleneck-90 [-1, 1024, 19, 19] 0\n", | |
| " Conv2d-91 [-1, 256, 19, 19] 262,144\n", | |
| " BatchNorm2d-92 [-1, 256, 19, 19] 512\n", | |
| " ReLU-93 [-1, 256, 19, 19] 0\n", | |
| " Conv2d-94 [-1, 256, 19, 19] 589,824\n", | |
| " BatchNorm2d-95 [-1, 256, 19, 19] 512\n", | |
| " ReLU-96 [-1, 256, 19, 19] 0\n", | |
| " Conv2d-97 [-1, 1024, 19, 19] 262,144\n", | |
| " BatchNorm2d-98 [-1, 1024, 19, 19] 2,048\n", | |
| " ReLU-99 [-1, 1024, 19, 19] 0\n", | |
| " Bottleneck-100 [-1, 1024, 19, 19] 0\n", | |
| " Conv2d-101 [-1, 256, 19, 19] 262,144\n", | |
| " BatchNorm2d-102 [-1, 256, 19, 19] 512\n", | |
| " ReLU-103 [-1, 256, 19, 19] 0\n", | |
| " Conv2d-104 [-1, 256, 19, 19] 589,824\n", | |
| " BatchNorm2d-105 [-1, 256, 19, 19] 512\n", | |
| " ReLU-106 [-1, 256, 19, 19] 0\n", | |
| " Conv2d-107 [-1, 1024, 19, 19] 262,144\n", | |
| " BatchNorm2d-108 [-1, 1024, 19, 19] 2,048\n", | |
| " ReLU-109 [-1, 1024, 19, 19] 0\n", | |
| " Bottleneck-110 [-1, 1024, 19, 19] 0\n", | |
| " Conv2d-111 [-1, 256, 19, 19] 262,144\n", | |
| " BatchNorm2d-112 [-1, 256, 19, 19] 512\n", | |
| " ReLU-113 [-1, 256, 19, 19] 0\n", | |
| " Conv2d-114 [-1, 256, 19, 19] 589,824\n", | |
| " BatchNorm2d-115 [-1, 256, 19, 19] 512\n", | |
| " ReLU-116 [-1, 256, 19, 19] 0\n", | |
| " Conv2d-117 [-1, 1024, 19, 19] 262,144\n", | |
| " BatchNorm2d-118 [-1, 1024, 19, 19] 2,048\n", | |
| " ReLU-119 [-1, 1024, 19, 19] 0\n", | |
| " Bottleneck-120 [-1, 1024, 19, 19] 0\n", | |
| " Conv2d-121 [-1, 256, 19, 19] 262,144\n", | |
| " BatchNorm2d-122 [-1, 256, 19, 19] 512\n", | |
| " ReLU-123 [-1, 256, 19, 19] 0\n", | |
| " Conv2d-124 [-1, 256, 19, 19] 589,824\n", | |
| " BatchNorm2d-125 [-1, 256, 19, 19] 512\n", | |
| " ReLU-126 [-1, 256, 19, 19] 0\n", | |
| " Conv2d-127 [-1, 1024, 19, 19] 262,144\n", | |
| " BatchNorm2d-128 [-1, 1024, 19, 19] 2,048\n", | |
| " ReLU-129 [-1, 1024, 19, 19] 0\n", | |
| " Bottleneck-130 [-1, 1024, 19, 19] 0\n", | |
| " Conv2d-131 [-1, 256, 19, 19] 262,144\n", | |
| " BatchNorm2d-132 [-1, 256, 19, 19] 512\n", | |
| " ReLU-133 [-1, 256, 19, 19] 0\n", | |
| " Conv2d-134 [-1, 256, 19, 19] 589,824\n", | |
| " BatchNorm2d-135 [-1, 256, 19, 19] 512\n", | |
| " ReLU-136 [-1, 256, 19, 19] 0\n", | |
| " Conv2d-137 [-1, 1024, 19, 19] 262,144\n", | |
| " BatchNorm2d-138 [-1, 1024, 19, 19] 2,048\n", | |
| " ReLU-139 [-1, 1024, 19, 19] 0\n", | |
| " Bottleneck-140 [-1, 1024, 19, 19] 0\n", | |
| " Conv2d-141 [-1, 512, 19, 19] 524,288\n", | |
| " BatchNorm2d-142 [-1, 512, 19, 19] 1,024\n", | |
| " ReLU-143 [-1, 512, 19, 19] 0\n", | |
| " Conv2d-144 [-1, 512, 10, 10] 2,359,296\n", | |
| " BatchNorm2d-145 [-1, 512, 10, 10] 1,024\n", | |
| " ReLU-146 [-1, 512, 10, 10] 0\n", | |
| " Conv2d-147 [-1, 2048, 10, 10] 1,048,576\n", | |
| " BatchNorm2d-148 [-1, 2048, 10, 10] 4,096\n", | |
| " Conv2d-149 [-1, 2048, 10, 10] 2,097,152\n", | |
| " BatchNorm2d-150 [-1, 2048, 10, 10] 4,096\n", | |
| " ReLU-151 [-1, 2048, 10, 10] 0\n", | |
| " Bottleneck-152 [-1, 2048, 10, 10] 0\n", | |
| " Conv2d-153 [-1, 512, 10, 10] 1,048,576\n", | |
| " BatchNorm2d-154 [-1, 512, 10, 10] 1,024\n", | |
| " ReLU-155 [-1, 512, 10, 10] 0\n", | |
| " Conv2d-156 [-1, 512, 10, 10] 2,359,296\n", | |
| " BatchNorm2d-157 [-1, 512, 10, 10] 1,024\n", | |
| " ReLU-158 [-1, 512, 10, 10] 0\n", | |
| " Conv2d-159 [-1, 2048, 10, 10] 1,048,576\n", | |
| " BatchNorm2d-160 [-1, 2048, 10, 10] 4,096\n", | |
| " ReLU-161 [-1, 2048, 10, 10] 0\n", | |
| " Bottleneck-162 [-1, 2048, 10, 10] 0\n", | |
| " Conv2d-163 [-1, 512, 10, 10] 1,048,576\n", | |
| " BatchNorm2d-164 [-1, 512, 10, 10] 1,024\n", | |
| " ReLU-165 [-1, 512, 10, 10] 0\n", | |
| " Conv2d-166 [-1, 512, 10, 10] 2,359,296\n", | |
| " BatchNorm2d-167 [-1, 512, 10, 10] 1,024\n", | |
| " ReLU-168 [-1, 512, 10, 10] 0\n", | |
| " Conv2d-169 [-1, 2048, 10, 10] 1,048,576\n", | |
| " BatchNorm2d-170 [-1, 2048, 10, 10] 4,096\n", | |
| " ReLU-171 [-1, 2048, 10, 10] 0\n", | |
| " Bottleneck-172 [-1, 2048, 10, 10] 0\n", | |
| "AdaptiveAvgPool2d-173 [-1, 2048, 1, 1] 0\n", | |
| " Linear-174 [-1, 3] 6,147\n", | |
| "================================================================\n", | |
| "Total params: 23,514,179\n", | |
| "Trainable params: 6,147\n", | |
| "Non-trainable params: 23,508,032\n", | |
| "----------------------------------------------------------------\n", | |
| "Input size (MB): 1.03\n", | |
| "Forward/backward pass size (MB): 523.61\n", | |
| "Params size (MB): 89.70\n", | |
| "Estimated Total Size (MB): 614.34\n", | |
| "----------------------------------------------------------------\n", | |
| "None\n", | |
| "\u001b[92mTrained VGG-16\u001b[00m\n", | |
| "----------------------------------------------------------------\n", | |
| " Layer (type) Output Shape Param #\n", | |
| "================================================================\n", | |
| " Conv2d-1 [-1, 64, 300, 300] 1,792\n", | |
| " BatchNorm2d-2 [-1, 64, 300, 300] 128\n", | |
| " ReLU-3 [-1, 64, 300, 300] 0\n", | |
| " Conv2d-4 [-1, 64, 300, 300] 36,928\n", | |
| " BatchNorm2d-5 [-1, 64, 300, 300] 128\n", | |
| " ReLU-6 [-1, 64, 300, 300] 0\n", | |
| " MaxPool2d-7 [-1, 64, 150, 150] 0\n", | |
| " Conv2d-8 [-1, 128, 150, 150] 73,856\n", | |
| " BatchNorm2d-9 [-1, 128, 150, 150] 256\n", | |
| " ReLU-10 [-1, 128, 150, 150] 0\n", | |
| " Conv2d-11 [-1, 128, 150, 150] 147,584\n", | |
| " BatchNorm2d-12 [-1, 128, 150, 150] 256\n", | |
| " ReLU-13 [-1, 128, 150, 150] 0\n", | |
| " MaxPool2d-14 [-1, 128, 75, 75] 0\n", | |
| " Conv2d-15 [-1, 256, 75, 75] 295,168\n", | |
| " BatchNorm2d-16 [-1, 256, 75, 75] 512\n", | |
| " ReLU-17 [-1, 256, 75, 75] 0\n", | |
| " Conv2d-18 [-1, 256, 75, 75] 590,080\n", | |
| " BatchNorm2d-19 [-1, 256, 75, 75] 512\n", | |
| " ReLU-20 [-1, 256, 75, 75] 0\n", | |
| " Conv2d-21 [-1, 256, 75, 75] 590,080\n", | |
| " BatchNorm2d-22 [-1, 256, 75, 75] 512\n", | |
| " ReLU-23 [-1, 256, 75, 75] 0\n", | |
| " MaxPool2d-24 [-1, 256, 37, 37] 0\n", | |
| " Conv2d-25 [-1, 512, 37, 37] 1,180,160\n", | |
| " BatchNorm2d-26 [-1, 512, 37, 37] 1,024\n", | |
| " ReLU-27 [-1, 512, 37, 37] 0\n", | |
| " Conv2d-28 [-1, 512, 37, 37] 2,359,808\n", | |
| " BatchNorm2d-29 [-1, 512, 37, 37] 1,024\n", | |
| " ReLU-30 [-1, 512, 37, 37] 0\n", | |
| " Conv2d-31 [-1, 512, 37, 37] 2,359,808\n", | |
| " BatchNorm2d-32 [-1, 512, 37, 37] 1,024\n", | |
| " ReLU-33 [-1, 512, 37, 37] 0\n", | |
| " MaxPool2d-34 [-1, 512, 18, 18] 0\n", | |
| " Conv2d-35 [-1, 512, 18, 18] 2,359,808\n", | |
| " BatchNorm2d-36 [-1, 512, 18, 18] 1,024\n", | |
| " ReLU-37 [-1, 512, 18, 18] 0\n", | |
| " Conv2d-38 [-1, 512, 18, 18] 2,359,808\n", | |
| " BatchNorm2d-39 [-1, 512, 18, 18] 1,024\n", | |
| " ReLU-40 [-1, 512, 18, 18] 0\n", | |
| " Conv2d-41 [-1, 512, 18, 18] 2,359,808\n", | |
| " BatchNorm2d-42 [-1, 512, 18, 18] 1,024\n", | |
| " ReLU-43 [-1, 512, 18, 18] 0\n", | |
| " MaxPool2d-44 [-1, 512, 9, 9] 0\n", | |
| "AdaptiveAvgPool2d-45 [-1, 512, 7, 7] 0\n", | |
| " Linear-46 [-1, 4096] 102,764,544\n", | |
| " ReLU-47 [-1, 4096] 0\n", | |
| " Dropout-48 [-1, 4096] 0\n", | |
| " Linear-49 [-1, 4096] 16,781,312\n", | |
| " ReLU-50 [-1, 4096] 0\n", | |
| " Dropout-51 [-1, 4096] 0\n", | |
| " Linear-52 [-1, 3] 12,291\n", | |
| "================================================================\n", | |
| "Total params: 134,281,283\n", | |
| "Trainable params: 12,291\n", | |
| "Non-trainable params: 134,268,992\n", | |
| "----------------------------------------------------------------\n", | |
| "Input size (MB): 1.03\n", | |
| "Forward/backward pass size (MB): 575.02\n", | |
| "Params size (MB): 512.24\n", | |
| "Estimated Total Size (MB): 1088.29\n", | |
| "----------------------------------------------------------------\n", | |
| "None\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "t3HWVVYMX5LY" | |
| }, | |
| "source": [ | |
| "# Prepare training\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "ND9prMmrxBi1" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "m_train, m_cv = len(train_loader.dataset), len(cv_loader.dataset)\n", | |
| "train_steps, cv_steps = len(train_loader.dataset) // BATCH_SIZE, len(cv_loader.dataset) // BATCH_SIZE\n", | |
| "loss_object = nn.CrossEntropyLoss()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "PhwwZ060KSan" | |
| }, | |
| "source": [ | |
| "# Train Simple CNN" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "simple_cnn_trainer.fit(loss_object, \n", | |
| " opt.Adam(simple_cnn.model.parameters(), lr=LR), \n", | |
| " EPOCHS, \n", | |
| " train_loader, cv_loader, \n", | |
| " m_train, m_cv, \n", | |
| " train_steps, cv_steps)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "u19VjWY6slAl", | |
| "outputId": "97903daa-f198-46b7-fcab-704c64302201" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "cost: 0.194, accuracy: 0.975, validation: cost: 1.100, accuracy: 0.664\n", | |
| "Epoch: 92/100:\n", | |
| "cost: 0.190, accuracy: 0.976, validation: cost: 1.018, accuracy: 0.669\n", | |
| "Epoch: 93/100:\n", | |
| "cost: 0.212, accuracy: 0.965, validation: cost: 1.062, accuracy: 0.653\n", | |
| "Epoch: 94/100:\n", | |
| "cost: 0.184, accuracy: 0.979, validation: cost: 1.063, accuracy: 0.651\n", | |
| "Epoch: 95/100:\n", | |
| "cost: 0.242, accuracy: 0.961, validation: cost: 1.034, accuracy: 0.704\n", | |
| "Epoch: 96/100:\n", | |
| "cost: 0.177, accuracy: 0.977, validation: cost: 1.132, accuracy: 0.651\n", | |
| "Epoch: 97/100:\n", | |
| "cost: 0.170, accuracy: 0.983, validation: cost: 0.995, accuracy: 0.651\n", | |
| "Epoch: 98/100:\n", | |
| "cost: 0.139, accuracy: 0.992, validation: cost: 0.985, accuracy: 0.669\n", | |
| "Epoch: 99/100:\n", | |
| "cost: 0.173, accuracy: 0.982, validation: cost: 0.984, accuracy: 0.694\n", | |
| "Epoch: 100/100:\n", | |
| "cost: 0.175, accuracy: 0.977, validation: cost: 1.044, accuracy: 0.661\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Transfer-learning via RESNET-50" | |
| ], | |
| "metadata": { | |
| "id": "G63iN79uxtOE" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "resnet_trainer.fit(loss_object, \n", | |
| " opt.Adam(resnet.model.parameters(), lr=LR), \n", | |
| " EPOCHS, \n", | |
| " train_loader, cv_loader, \n", | |
| " m_train, m_cv, \n", | |
| " train_steps, cv_steps)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "LwX2DfuiyFyH", | |
| "outputId": "e2b6d54f-abeb-4f3d-8a2f-5a8f438fcd0a" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "cost: 0.153, accuracy: 0.970, validation: cost: 0.515, accuracy: 0.772\n", | |
| "Epoch: 92/100:\n", | |
| "cost: 0.152, accuracy: 0.970, validation: cost: 0.541, accuracy: 0.755\n", | |
| "Epoch: 93/100:\n", | |
| "cost: 0.148, accuracy: 0.973, validation: cost: 0.514, accuracy: 0.772\n", | |
| "Epoch: 94/100:\n", | |
| "cost: 0.153, accuracy: 0.974, validation: cost: 0.505, accuracy: 0.782\n", | |
| "Epoch: 95/100:\n", | |
| "cost: 0.149, accuracy: 0.975, validation: cost: 0.551, accuracy: 0.747\n", | |
| "Epoch: 96/100:\n", | |
| "cost: 0.149, accuracy: 0.965, validation: cost: 0.447, accuracy: 0.817\n", | |
| "Epoch: 97/100:\n", | |
| "cost: 0.147, accuracy: 0.974, validation: cost: 0.487, accuracy: 0.793\n", | |
| "Epoch: 98/100:\n", | |
| "cost: 0.148, accuracy: 0.973, validation: cost: 0.528, accuracy: 0.766\n", | |
| "Epoch: 99/100:\n", | |
| "cost: 0.145, accuracy: 0.970, validation: cost: 0.541, accuracy: 0.750\n", | |
| "Epoch: 100/100:\n", | |
| "cost: 0.141, accuracy: 0.974, validation: cost: 0.467, accuracy: 0.798\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Transfer learning via VGG-16" | |
| ], | |
| "metadata": { | |
| "id": "EGyLiF3bTPWc" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "vgg_trainer.fit(loss_object, \n", | |
| " opt.Adam(vgg.model.parameters(), lr=LR), \n", | |
| " EPOCHS, \n", | |
| " train_loader, cv_loader, \n", | |
| " m_train, m_cv, \n", | |
| " train_steps, cv_steps)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "bhjx6FUjw0xg", | |
| "outputId": "235e7ee5-a64e-41be-be10-3988c991b136" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "cost: 0.111, accuracy: 0.972, validation: cost: 0.502, accuracy: 0.812\n", | |
| "Epoch: 92/100:\n", | |
| "cost: 0.120, accuracy: 0.970, validation: cost: 0.470, accuracy: 0.815\n", | |
| "Epoch: 93/100:\n", | |
| "cost: 0.114, accuracy: 0.970, validation: cost: 0.544, accuracy: 0.812\n", | |
| "Epoch: 94/100:\n", | |
| "cost: 0.118, accuracy: 0.972, validation: cost: 0.496, accuracy: 0.809\n", | |
| "Epoch: 95/100:\n", | |
| "cost: 0.118, accuracy: 0.971, validation: cost: 0.549, accuracy: 0.804\n", | |
| "Epoch: 96/100:\n", | |
| "cost: 0.109, accuracy: 0.975, validation: cost: 0.494, accuracy: 0.812\n", | |
| "Epoch: 97/100:\n", | |
| "cost: 0.110, accuracy: 0.975, validation: cost: 0.497, accuracy: 0.809\n", | |
| "Epoch: 98/100:\n", | |
| "cost: 0.115, accuracy: 0.973, validation: cost: 0.497, accuracy: 0.804\n", | |
| "Epoch: 99/100:\n", | |
| "cost: 0.118, accuracy: 0.971, validation: cost: 0.503, accuracy: 0.809\n", | |
| "Epoch: 100/100:\n", | |
| "cost: 0.108, accuracy: 0.972, validation: cost: 0.484, accuracy: 0.815\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# SVM models\n", | |
| "\n", | |
| "Fit by feeding the features extracted from the CNN models." | |
| ], | |
| "metadata": { | |
| "id": "LTCbdoDbESPR" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "simple_cnn_svms=[FeatureSVM(c, simple_cnn, f\"Simple CNN SVM C={c}\") for c in SVM_Cs]\n", | |
| "vgg_svms = [FeatureSVM(c, vgg, f\"VGG-16 SVM C={c}\") for c in SVM_Cs]\n", | |
| "resnet_svms = [FeatureSVM(c, resnet, f\"RESNET-50 SVM C={c}\") for c in SVM_Cs]\n", | |
| "\n", | |
| "[svm.fit(idx == len(simple_cnn_svms)-1) for idx, svm in enumerate(simple_cnn_svms)]\n", | |
| "[svm.fit(idx == len(vgg_svms)-1) for idx, svm in enumerate(resnet_svms)]\n", | |
| "[svm.fit(idx == len(resnet_svms)-1) for idx, svm in enumerate(vgg_svms)] " | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "cpdLYnwcEVnG", | |
| "outputId": "f7adebc1-0c2a-4bdd-f761-5f682aca42dc" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "[None, None, None, None]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 27 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Report" | |
| ], | |
| "metadata": { | |
| "id": "0lkAwejN8qXo" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "CNNs = [simple_cnn, resnet, vgg]\n", | |
| "SVMs = simple_cnn_svms + resnet_svms + vgg_svms\n", | |
| "\n", | |
| "CNN_Trainers = [simple_cnn_trainer, resnet_trainer, vgg_trainer]" | |
| ], | |
| "metadata": { | |
| "id": "hQ54sguv8tx4" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Training performance" | |
| ], | |
| "metadata": { | |
| "id": "rEZ6LfHb8OC7" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### Accuracy compare" | |
| ], | |
| "metadata": { | |
| "id": "eji2lp7J8QKx" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "plot_model_metrics(metrics=[trainer.train_accuracy_list for trainer in CNN_Trainers ],\n", | |
| " labels=[trainer.name for trainer in CNN_Trainers ],\n", | |
| " xlabel=\"Epochs\", \n", | |
| " ylabel=\"Accuracy\",\n", | |
| " title=f\"Accuracy on train\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 385 | |
| }, | |
| "id": "5Z7-3vos8VAp", | |
| "outputId": "4a4c0e29-df8e-4e2e-97ae-54d18a6bb2f4" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 1080x360 with 1 Axes>" | |
| ], | |
| "image/png": 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| |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "plot_model_metrics(metrics=[trainer.cv_accuracy_list for trainer in CNN_Trainers ],\n", | |
| " labels=[trainer.name for trainer in CNN_Trainers ],\n", | |
| " xlabel=\"Epochs\", \n", | |
| " ylabel=\"Accuracy\",\n", | |
| " title=f\"Accuracy on validation\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 385 | |
| }, | |
| "id": "cHcDC6CL95dI", | |
| "outputId": "1b921046-55da-41cd-df6d-3d0136a2a802" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 1080x360 with 1 Axes>" | |
| ], | |
| "image/png": 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| |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### Cost compare" | |
| ], | |
| "metadata": { | |
| "id": "ohqgHals8SaP" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "plot_model_metrics(metrics=[trainer.train_cost_list for trainer in CNN_Trainers ],\n", | |
| " labels=[trainer.name for trainer in CNN_Trainers ],\n", | |
| " xlabel=\"Epochs\", \n", | |
| " ylabel=\"Cost\",\n", | |
| " title=f\"Cost on train\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 385 | |
| }, | |
| "id": "ZEcERbN5-LbM", | |
| "outputId": "a0294784-d12a-4002-a3fc-7a30881c2b82" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 1080x360 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "plot_model_metrics(metrics=[trainer.cv_cost_list for trainer in CNN_Trainers ],\n", | |
| " labels=[trainer.name for trainer in CNN_Trainers ],\n", | |
| " xlabel=\"Epochs\", \n", | |
| " ylabel=\"Cost\",\n", | |
| " title=f\"Cost on validation\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 385 | |
| }, | |
| "id": "iFBl4bnk-RM9", | |
| "outputId": "3776198a-d5f0-4374-b88c-6e8beb6fac54" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 1080x360 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "hd6QPJ3bY805" | |
| }, | |
| "source": [ | |
| "## Measurements with test-set" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### Load data" | |
| ], | |
| "metadata": { | |
| "id": "59S-bRVd6JQu" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "CFw8ZU-gu5OV", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "50cc23a4-4796-478d-ca8f-fc0da33f0b63" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Downloading data from https://dl.dropbox.com/s/1x0tt0j53tymn1b/test.zip\n", | |
| "6643712/6638511 [==============================] - 0s 0us/step\n", | |
| "6651904/6638511 [==============================] - 0s 0us/step\n", | |
| "/content/test.zip\n", | |
| "['paper-hires1.png', 'paper2.png', 'scissors5.png', 'scissors4.png', 'scissors1.png', 'rock8.png', 'scissors9.png', 'rock5.png', 'scissors6.png', 'rock2.png', 'scissors2.png', 'paper1.png', 'rock-hires1.png', 'scissors8.png', 'paper-hires2.png', 'scissors3.png', 'paper5.png', 'paper3.png', 'rock7.png', 'paper8.png', 'scissors-hires2.png', 'scissors-hires1.png', 'rock-hires2.png', 'rock9.png', 'rock6.png', 'paper9.png', 'paper4.png', 'paper7.png', 'rock3.png', 'rock1.png', 'rock4.png', 'paper6.png', 'scissors7.png']\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "test_download_file, test_dir = download_dataset(\"https://dl.dropbox.com/s/1x0tt0j53tymn1b/test.zip\", \"test\")\n", | |
| "print(test_download_file)\n", | |
| "print(os.listdir(test_dir))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "iw8rnZpPu0hC" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "transform_test = transforms.Compose([\n", | |
| " transforms.ToTensor(),\n", | |
| " norm,\n", | |
| "])\n", | |
| "\n", | |
| "test_set = MyTestset(dir=test_dir, transform=transform_test)\n", | |
| "test_loader = DataLoader(test_set, batch_size=len(test_set))\n", | |
| "test_batch = next(iter(test_loader))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "E7C4ZHswwHON", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "2e81cf7e-4a58-4a49-fc33-6e38c290a230" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 5 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ], | |
| "source": [ | |
| "# Just plot some test data randomly.\n", | |
| "plot_image_batch(next(iter(DataLoader(test_set, batch_size=5, shuffle=True))), \"Test set\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Result " | |
| ], | |
| "metadata": { | |
| "id": "_qwLnDWv6Me9" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "\n", | |
| "df_svm, y_test, y_test_svm_preds = measure_svm_models(test_batch, \n", | |
| " SVMs,\n", | |
| " device)\n", | |
| "df_svm" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 206 | |
| }, | |
| "id": "aGdefH33ybq7", | |
| "outputId": "e61139c9-5c4f-4486-de6c-600b91a0e5c4" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " Simple CNN SVM C=0.01 ~test Simple CNN SVM C=0.1 ~test \\\n", | |
| "durdation(sec.) 0.378731 0.345762 \n", | |
| "accuracy 0.818182 0.818182 \n", | |
| "precision 0.862500 0.859829 \n", | |
| "recall 0.818182 0.818182 \n", | |
| "F1-Score 0.816578 0.795940 \n", | |
| "\n", | |
| " Simple CNN SVM C=1 ~test Simple CNN SVM C=10 ~test \\\n", | |
| "durdation(sec.) 0.233737 0.232974 \n", | |
| "accuracy 0.757576 0.757576 \n", | |
| "precision 0.812500 0.812500 \n", | |
| "recall 0.757576 0.757576 \n", | |
| "F1-Score 0.740808 0.740808 \n", | |
| "\n", | |
| " RESNET-50 SVM C=0.01 ~test RESNET-50 SVM C=0.1 ~test \\\n", | |
| "durdation(sec.) 0.243704 0.224628 \n", | |
| "accuracy 0.727273 0.757576 \n", | |
| "precision 0.803571 0.815873 \n", | |
| "recall 0.727273 0.757576 \n", | |
| "F1-Score 0.683104 0.726496 \n", | |
| "\n", | |
| " RESNET-50 SVM C=1 ~test RESNET-50 SVM C=10 ~test \\\n", | |
| "durdation(sec.) 0.176158 0.174298 \n", | |
| "accuracy 0.848485 0.818182 \n", | |
| "precision 0.851075 0.815385 \n", | |
| "recall 0.848485 0.818182 \n", | |
| "F1-Score 0.844949 0.813492 \n", | |
| "\n", | |
| " VGG-16 SVM C=0.01 ~test VGG-16 SVM C=0.1 ~test \\\n", | |
| "durdation(sec.) 11.695399 11.715489 \n", | |
| "accuracy 0.696970 0.727273 \n", | |
| "precision 0.708333 0.725649 \n", | |
| "recall 0.696970 0.727273 \n", | |
| "F1-Score 0.691922 0.718469 \n", | |
| "\n", | |
| " VGG-16 SVM C=1 ~test VGG-16 SVM C=10 ~test \n", | |
| "durdation(sec.) 9.483871 9.488991 \n", | |
| "accuracy 0.848485 0.848485 \n", | |
| "precision 0.895833 0.895833 \n", | |
| "recall 0.848485 0.848485 \n", | |
| "F1-Score 0.840232 0.840232 " | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <div id=\"df-532bf8d8-7baa-407f-a432-68fabce2f29f\">\n", | |
| " <div class=\"colab-df-container\">\n", | |
| " <div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Simple CNN SVM C=0.01 ~test</th>\n", | |
| " <th>Simple CNN SVM C=0.1 ~test</th>\n", | |
| " <th>Simple CNN SVM C=1 ~test</th>\n", | |
| " <th>Simple CNN SVM C=10 ~test</th>\n", | |
| " <th>RESNET-50 SVM C=0.01 ~test</th>\n", | |
| " <th>RESNET-50 SVM C=0.1 ~test</th>\n", | |
| " <th>RESNET-50 SVM C=1 ~test</th>\n", | |
| " <th>RESNET-50 SVM C=10 ~test</th>\n", | |
| " <th>VGG-16 SVM C=0.01 ~test</th>\n", | |
| " <th>VGG-16 SVM C=0.1 ~test</th>\n", | |
| " <th>VGG-16 SVM C=1 ~test</th>\n", | |
| " <th>VGG-16 SVM C=10 ~test</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>durdation(sec.)</th>\n", | |
| " <td>0.378731</td>\n", | |
| " <td>0.345762</td>\n", | |
| " <td>0.233737</td>\n", | |
| " <td>0.232974</td>\n", | |
| " <td>0.243704</td>\n", | |
| " <td>0.224628</td>\n", | |
| " <td>0.176158</td>\n", | |
| " <td>0.174298</td>\n", | |
| " <td>11.695399</td>\n", | |
| " <td>11.715489</td>\n", | |
| " <td>9.483871</td>\n", | |
| " <td>9.488991</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>accuracy</th>\n", | |
| " <td>0.818182</td>\n", | |
| " <td>0.818182</td>\n", | |
| " <td>0.757576</td>\n", | |
| " <td>0.757576</td>\n", | |
| " <td>0.727273</td>\n", | |
| " <td>0.757576</td>\n", | |
| " <td>0.848485</td>\n", | |
| " <td>0.818182</td>\n", | |
| " <td>0.696970</td>\n", | |
| " <td>0.727273</td>\n", | |
| " <td>0.848485</td>\n", | |
| " <td>0.848485</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>precision</th>\n", | |
| " <td>0.862500</td>\n", | |
| " <td>0.859829</td>\n", | |
| " <td>0.812500</td>\n", | |
| " <td>0.812500</td>\n", | |
| " <td>0.803571</td>\n", | |
| " <td>0.815873</td>\n", | |
| " <td>0.851075</td>\n", | |
| " <td>0.815385</td>\n", | |
| " <td>0.708333</td>\n", | |
| " <td>0.725649</td>\n", | |
| " <td>0.895833</td>\n", | |
| " <td>0.895833</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>recall</th>\n", | |
| " <td>0.818182</td>\n", | |
| " <td>0.818182</td>\n", | |
| " <td>0.757576</td>\n", | |
| " <td>0.757576</td>\n", | |
| " <td>0.727273</td>\n", | |
| " <td>0.757576</td>\n", | |
| " <td>0.848485</td>\n", | |
| " <td>0.818182</td>\n", | |
| " <td>0.696970</td>\n", | |
| " <td>0.727273</td>\n", | |
| " <td>0.848485</td>\n", | |
| " <td>0.848485</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>F1-Score</th>\n", | |
| " <td>0.816578</td>\n", | |
| " <td>0.795940</td>\n", | |
| " <td>0.740808</td>\n", | |
| " <td>0.740808</td>\n", | |
| " <td>0.683104</td>\n", | |
| " <td>0.726496</td>\n", | |
| " <td>0.844949</td>\n", | |
| " <td>0.813492</td>\n", | |
| " <td>0.691922</td>\n", | |
| " <td>0.718469</td>\n", | |
| " <td>0.840232</td>\n", | |
| " <td>0.840232</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>\n", | |
| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-532bf8d8-7baa-407f-a432-68fabce2f29f')\"\n", | |
| " title=\"Convert this dataframe to an interactive table.\"\n", | |
| " style=\"display:none;\">\n", | |
| " \n", | |
| " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", | |
| " width=\"24px\">\n", | |
| " <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n", | |
| " <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n", | |
| " </svg>\n", | |
| " </button>\n", | |
| " \n", | |
| " <style>\n", | |
| " .colab-df-container {\n", | |
| " display:flex;\n", | |
| " flex-wrap:wrap;\n", | |
| " gap: 12px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: #1967D2;\n", | |
| " height: 32px;\n", | |
| " padding: 0 0 0 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
| " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: #174EA6;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert:hover {\n", | |
| " background-color: #434B5C;\n", | |
| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
| " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", | |
| " fill: #FFFFFF;\n", | |
| " }\n", | |
| " </style>\n", | |
| "\n", | |
| " <script>\n", | |
| " const buttonEl =\n", | |
| " document.querySelector('#df-532bf8d8-7baa-407f-a432-68fabce2f29f button.colab-df-convert');\n", | |
| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-532bf8d8-7baa-407f-a432-68fabce2f29f');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
| " [key], {});\n", | |
| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
| " element.innerHTML = '';\n", | |
| " dataTable['output_type'] = 'display_data';\n", | |
| " await google.colab.output.renderOutput(dataTable, element);\n", | |
| " const docLink = document.createElement('div');\n", | |
| " docLink.innerHTML = docLinkHtml;\n", | |
| " element.appendChild(docLink);\n", | |
| " }\n", | |
| " </script>\n", | |
| " </div>\n", | |
| " </div>\n", | |
| " " | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 36 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df_svm[1:].plot.bar(figsize=(40,10))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 644 | |
| }, | |
| "id": "wzFkDT-xMAa1", | |
| "outputId": "183d2944-2941-448c-a1f3-f8ac5aa058d1" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x7f625000e750>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 37 | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 2880x720 with 1 Axes>" | |
| ], | |
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| |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df_svm.iloc[0].plot.bar(figsize=(10,2.5))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 346 | |
| }, | |
| "id": "vrjoCNFhQqB4", | |
| "outputId": "f582d558-9df5-4411-d19e-57f86d0fae87" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x7f606d70b090>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 38 | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 720x180 with 1 Axes>" | |
| ], | |
| "image/png": 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kvbl0XKOQ9FbgDSxtErgS8JlyEY3sE8DtNGecANcC7ygXzqykjD3xtpJ2H816TIS8nzlJ90/Iu73UcmzpfJIFHAZsA/wJICIuADYsGdCIPkqzcdwBEBEXAXsXjWh0uwP/BPwFICJ+S9PArnYPi4j/ZOlnfhtLG9nVLmvsWbcVyLuPHkbOYyLk/cyz7p+Qd3up4tiyEJKsO4ZUctxVJJLZuXdEnDOwrObOxv1uj2YcOgAkrVo4nlHd3l4W78X9MOBvZUMaWdbYs24rkHcfzXpMhLyfedb9E/JuL1UcWzp7W50+l0p6HrBCOw7+z8APC8c0ihvaHbG3gTwH+F3ZkEb2+baqYw1JLwVeDHyscEyjOIzmkvhDJH0WeALNpM8MDiNn7Fm3Fci7j2Y9JkLez/wwcu6fkHd7qeLY0vmJ7+2Ew38DdmgXfRN4e0RUfRbRVs0cRzOG/0fg18Dz26671ZO0Pc1nLuCbEXFG4ZBGIun+wLY0cZ8dETcUDmlkWWNPvK2k3EezHhMh72cOqffPzNtL+WNLRHT6Aew5yrLaHsAG7c9VgdX7l9X+AI4cZVltD+DMUZbV+Mgae9ZtpY0z5T6a9ZiY/DNPuX9OtW1k2F5qObYshDlZbxxxWW1OBoiIv0TEre2yLxaMZza2H7Jsp4lHMSJJq7S36VhL0v0krdk+1gfWLRvd9DLH3kq1rQzIuo9mPSZCss+8A/sn5N1eqji2dHZOlqSdgGcC60o6pm/Vfah4oqSkhwOPBO4raY++VfcBVikT1WgkvRx4BbChpIv6Vq0O/KBMVCM5CDgYeBBwHkurfm4Bji0V1IhSxp54W0m7j2Y9JkLez5yk+yfk3V5qO7Z0dk6WpM2BLWg6BL+lb9WtwLej0ju3S9oV2I2m9LS/F8mtwEkRUe2EQ0n3Be4HvAs4tG/VrRFxU5moRifp1RHx/tJxzEW22DNvK1n30azHRMj7mfdk2z8h7/ZS27Gls0lWj6SVormNQSqSHhcRPyodx0Ij6bER8ePSccxF5tgzyrqPZj0mQt7PHPLun5m3lxp0fk5WRNwh6Z2SHlU6ltmIiB9JeomkNUrHMheSPtaWV2fzREmHlQ5ijlLGnnVbybqPZj0mQt7PvJVy/8y8vdRwbOl8ktU6FXhv6SDm4CbgmBnfVaf3kWNy5KCv0txYNKOssWfdViDvPpr1mAh5P/Os+yfk3V6KH1sWSpJ1O7BW6SDm4FJg69JBzNFDaPrYZPMkYHHpIOYoa+xZtxXIu49mPSZC3s886/4JebeX4seWzlYXDtgf+EDpIObg2bQlywm9BPiP0kHMwf40sWe0Pzljz7qtQN59dH9yHhMh92eecf+EvNtL8WPLQkmynkhzK4Bsng1sVzqIOdogIs4qHcQcrAz8snQQc5Q19qzbCuTdR7MeEyHvZ551/4S820vxY8tCGS78FbBp6SDm4Gbae3Ql9ENJu5cOYg5OAt5ROog5yhp71m0F8u6jWY+JkPczz7p/Qt7tpfixpfMtHGDJHc+fFBGfLB3LbLT3XdoqIt5VOpbZau94/qiIOLt0LFa3zNtK1n006zER8n7mmWXdXmo4tiyIJMvMzMxs0hbKcKGZmZnZRDnJMjMzMxuDhVJdaDYjSWsD67Yvr42IP5SMx8zMcluQV7Ikvah0DHPR3ok+JUnHlY5hKpK2kHQ28B3gP9vHdyWdLenRRYObB0kXl45hLmreVmZS+z4q6RmSPiTplPbxIUk7lo5rPmr/zKeSdf+Eur9DJd1X0hGSfibpJkk3Svppu2zit2NakBPfJf0mItYrHcds1R63pDWnWgVcGBEPnmQ8o5J0AXDQ4M1bJW0LfCQiNi8T2cwk7THVKuDDEbFokvGMKuu2MpOa91FJ7wM2AT4FXNMufjCwL3B5RLymVGzzUflnnnL/nEnln/k3gbOAEyLi9+2yBwL7AdtFxA4TjaerSZaki6ZaBWwSEStPMp5RSZrqnlwC9ouI+0wyntmQ9HfgKppYe6J9vW5E3LNIYDOQdHlEbDzFuisiYqNJxzQqSXcAn2V436DnRMTqEw5pJFm3Fci7j0r6RURsMmS5gF9MtQ/UIPFnnnL/hNTfoT+PiKE9vaZbNy5dnpO1NvAM7n7fIgE/nHw4I3sR8Frgb0PW7TPhWGbrVzRnCr8ZXCHp6gLxjOobkk6lOcPvxfkQmjP804tFNZqLgKMi4pLBFZKeXiCeUWXdViDvPvpXSVtHxLkDy7cG/loioFnI+pln3T8h73foVZJeT3Ml6w+wZL7t/iw9vk9Ml5OsrwOrRcQFgyskfWfy4YzsXOCSiLjbRizpsMmHMyvvA+4H3O2Lk2aeU5Ui4p8l7QTsSt/Ed+ADEXFauchGcjBwyxTrau6innJbaWXdR/cHPiRpdZYOFz6EpoP6/oViGlXWzzzr/gl5v0P3Ag6lmVf7gHbZH4BTgOdOOpjODhdm1c5V+WtE3FY6FjO7u+z7aDs/pb+K9vcl4xlF9s/cFq5OVxeq8VhJe7SPx7bzD6oVETd14UDSq/jJWvnTU3ulm6QVJR0k6XRJF7WPb0h6maSVSsc3F20SUK3s+2hE/D4izmsfvYnBVe+n/Z+5pDWnKZxIQ9KzSsew0JSoiuxskiVpB+By4DDgme3jcODydl06lV8WH/S5gZ/V6h20hzzuT7Pd1OzTwBbcfTvfHPhMubDm5eOlAxhVW7G35Gdi3yodwHQkrSfpJEnXAz8GzpF0Xbts/bLRzdnWpQMYlaTv9/9M7PBJ/8Euz8k6Gnh6RFzZv1DSBsBpwCNKBDVP55UOYA6qvnLYup6pK90eMPRf1OMxQyrGrgHOlvSLEgHNV0TsXDqGWXhy+/Mfi0Yxghkq9CbeP2iW/ptmHt/zI+LvAJJWAPYETgK2LRjbnETEW0vHMAv3bn+uWjSKEcxQFbn2JGOBbidZK7J0cme/a4GUwygR8bXSMXRU5kq3myTtCZwcEXcBSLoHzZfPYFVQVdqh+21YttjgnPBE0XHJWqEHsFZE/Hf/gjbZOknS2wvFNJJ2KHawqOaUiPhpuag6raqqyC4nWccD50o6iWXL8vem4uEISSsCB9BUnjyoXXwt8FXg4xFxR6nYOixzpdvewJHAByX1DiprAN9u11WpHbL/IM2Q/rXt4gcDG0l6RURUPXyVVNYKPYDzJH0QOIFlj+f7AT8pFtUMJL2BJoE9CTinXfxg4ERJJ0XEEcWC666qqiI7XV0o6REMP4O4rFxU05N0IvAnmoNJf1fm/YA1I2KvUrHNhqTzI+LRvZ+l41kI2jlkRMSNpWOZiaSfAjtNNZwfESmG8/u2859ExJal45lO5go9SfekOfnsP55fA3yN5uRz2NW54toh+0cOnhy3/59La24A2y/Tdl6bLl/Jor0cm+2SbFfm2GjgZwqSHh4RP+v9LB3PbAwmV5IeWHF5fueG82sXETeVjmGuIuJ24EPtI5O7aEYkrhpYvk67LotUx/GadLa6cDqVXxq/SdKe7bwaoJljI2kvKp9jM+BJAz+zSFMVOYJqh8VZOpz/BknPax9voKkcqznuQb3t5LNFo5ilDlVF1t4K4WDgzLatynHt43TgTCDTvSL/ZeBnCjVURXZ6uHAqknapdRJ5W458JPA0liZVvTk2h0bEr8tENjNJGwFrR8QPBpY/Afh9RPyyTGSj82Xxyck4nN8VXdrOJR1ec6Vee8I8WOBxbq9KslaSVgFWj4jrB5YvAm6NiNpvxVTFdt7p4cKp1JpgAbRzVPaCXHNsWu8D3jhk+S3tul0mG87CkLVKL+lwPpIOAW6OiI8PLD+A5ksp/dWhTGpOsADaqt+zS8cxB8fQ3Lv1SwPLnwjsALx84hEl1Nnhwo50wl4UETfW3o25z9oRcfHgwnbZ+pMPp/u60HQ34dDV82luJj7o08CLJxzLgiHp4e3w8jHt4w3t1dAUahi6mqXHRMRggkVEfJml/eFsBl2+kvVpmiq9w7h7ld5naK8WVe5zwKP7ftZuuoaG95pYFMtH1VeB+nSh6W6ahp6tFYe1UomI29uriracdaQVQpqGnq17T7OusxdolrcuJ1ldqdKDPJUdiyW9NCI+2r9Q0kvI060+W1Wkq/Qm7x6S1o6IP/QvlDTxbtILyAEMb4XwXuBSIEOSlc11kraJiHP6F0ramuYuGRkUP453OclK2wk7sYOBL0t6PkuTqq2Ae9I0V80gW1Vkyqa7yb0bOFXSa4Hz22WPaZcfVSyq2clWFdmVVgiZvA74vKRPsuzxfF8qbnQ8oHhVZGerCzNX6fXUUBkxF5KeCvxD+/LSiDirZDwzyV4Vmb1KL+N2Lmkn4FCa7Txor6ZExDeKBtZRknYEjqWZf9g7mVgP2Ah4VUScXiq2USXdzh8AvJK+4zlwbERcVy6q6dVWFdnZJKtfwio9wF3TJ0XS14E3Dk7al/Qo4D8iwlWRY5Txyyej7FWRWVsh9PS2b2/n4yXpOOD0wUn7knYHdoiIiVZFLpTJa9mq9HqyzQ/KqhNVkQmr9HqyDV1llboqMiLuioizI+Lk9nF2lgSrVXzoaoGoqipyoSRZWbt4Z5sflFVXqiKzVekBEBFH9f+0sZmyKpJEJ3KZWiFIWqUdpiIivtP7KWlRO6xly19VVZELJcnqqf5AImmjdi4QEfHn3k9JT5D0sLLRddZiSS8dXJisKjIVSYe0w1SDyw+QdHCJmBaAewyrgExYFZmpFcIxDD9JfiLwXxOOZaG4TtI2gwtLVUV2ubowq7Rd0yXdyrL9pdS+FhARcZ8igc2sC1WR2Twf2HbI8k8Di2m29Sq1c5umFBHvnVQss9SFqshsHhMRBw4ujIgvS3pHiYBGJemU6dZHxD9NKpZZqqoq0klWfaacH9RWTNbsTOCBNLdhOCkiflM4npG0/Y4eP1AVeWrtVZHJZW7oeRRwAfAN4G8kuEIOEBGfknQ98DaWrYp8i6six6aqoatZehxNJeeJNDduz7Kdn9NeyXolsH+7+FLgsSWqIhdakpWhlDLt/KCI2E3SfYE9gI+2cw7+mybhuqlsdDOLiG/TtPiw8cvc0HNLmu7jO9OcKZ8InFn7vSIB2mTKCdXkZG7o+UBge5pt/XnAqcCJEXFp0ahG0CZTVdzTsvZMennJVKWXen5QRNwcEZ8AdgI+QnPWvH/RoBaOTFV6vaGrf5S0evt4CvB1Kh+6iogLI+LQiNiCpuHrrsBlkmodPumaDMfxnt7Q1WGSdmkfhwOfb9dVKyL+HhGnR8R+NEP7VwDfkfSqwqGlslD6ZK3WTh5frTeZvFbtmfyXgdsZMj8oIn5fKrZRSHo8zZnPk4DvA/8dEd8rG5XVKHtDz7Zq7Lk0d5G4A/j3iDi7bFTdJ+kpbYXeU3oVezXL2NCzR9LKNFds96FpZ3MKcHxEXFsyrkw6m2R1oIt3qq7pAJKupLkp90nAWcCd/esj4vwh/8zmIXuDyYwkvZgmuVoF+CLw+QxfmJnV1sV7IZD0KZrvoNNopnxcUjiklLqcZLmL94RJ+g5L5731qgp7IiKeNvGgRpC4KhJJ5wHbDrlx7j2BxRGxWZnIukvSXcAlLL2P3jIH0VqrrhJXRVbXxXshaLfzv7Qv73Z8rPW4WFtVZJcnvmeu0stq+2EVYwmkrIpsZa7Sy+qppQOYo5RVka20rRCyioisc7arqorscpKVtkovsWvbs4jPAd/OUG0F6asiM1fpZbUNzbZx9YzvrEvaqkhyt0JISdKpNMfyr0TEX2Z6f0Wqqors8nDhicBZEfHRgeUvobnisleZyLqrvRH3c2gavm0MnEyzcaeZDNzehHZvmk7N/1HzEAqApH2BfwaGNZg8NiJOKBXbdJIPXf0XzXZ+JU2i8oXBuUK16ytQeTrwhoiYdoilNEnfBV43RSuE90TExO9JN4rahq5mQ9KuNMfCp9O0tjmRpn/g7UUDm4V24v4+NMfDwyPi2InH0OEkK2WVXub5Qf0kPYim6mpv4AE0Z/7/VjaqqWWuisxYpdfO95hy6CoiDi8R16jaodgn02zfuwEX0nwJfSkibi0Z20wyVkW2zSU/D3ySIV28I+LHhUKbVtv8dcqhq4j4bom4ZkPSvWnuNLI3zVDcN4DPRcQZRQObRk1VkZ1NsnqyVelJ+gp55zp5p18AABorSURBVActQ9JqNENwhwDrRESVQ1iuipw8SZvTHAB3JN/Q1TIkrUBztn8EsGlETDe0VUz2qsiMrRDabaM3dLUZiRp6DiNpM+AEYLOIWKF0PMPUVhXZ+SQro775QXvTHBCzzA/qlVrvQnNQeTxwOk3yckZE/L1kbFPJWhXZFdmGrvq11cp7A3sBN9B8gR5dNqrhslZFdkUNQ1dz0Y4KPZdmO1+H5oriiRFxYdHAplBbVWSXJ76nFRE3A5+QdAJL5wetAlQ7TwVA0udovii/S9N1/HlJ+tdkrYpMrx262hJ4FHANUO1ViR5JG9Psl3sDf6c5idghIn5VNLCZZa2KTG3I0NUxNFNZqtbeeWQfYFOa+bWvi4gflo1qZrVVRTrJqtCQ+UG7J5kfdDpwUO1zUoZIWRWZ2ZChq+fWPOwz4HSa4c29Sg9FzFLWqsi0BoauDk+2vTwOeBfNMP5dpYMZVW1VkR4urEzm+UGSdgEuioir2tdvAZ5NMzzxmoj4dcn4ppK5KjJrlV6Xhq7a7efJwG8iotr7i3ahKjKb2oauZkPSQ4E/tSMrvfnNu9Hss8fWWmVYW1VkZ5OsrFV6mecHSbqIpvv4bZKeRTO8uQ/NcNCeEfGMogGOIGFVZMoqPUn/ON36mquu2rtJHBoRl0hah6Z1xmLgYcBxUfGtjLJWRWZuhZCVpB/TjKL8VtIWwP/QXNnaDLgjIl5SNMAZ1FIV2eUkK2WVnqSVss4PknRhRGzePj8e+HlEHNm+Pj8iHl00wBFlqYqEvFV6kl5H0qErSZdGxCPb528CHh4R+0paHfhBJLmVUZaqSMjbCqG2oavZkHRRb1uWdBRwV0S8vu0leEGW7RzKVkVWNUFseYqI3YBnANfTdPH+rqRXSFqzcGgzuVbSxyQ9LeFtUSRptXYn3I7mdjU9qxSKaSSSVpG0p6QvAVcAT6PpPfWgspFNLyIujIhDI2IL4OPArsBlkmo/s38Q8ENJ32v3y0WlA5qF/pOg7Wjm29BeCUoxd6Wtinwb8AGaK6BvLBvRjB4IvIlmftPRNG0RboiI79aaYLWOA54FXCnp85J2V3Nf0Qz6v3+eRns8zzI/S9Lakl4t6QfAV4BvAhM/0e/slax+mbp4J58f9GKaA+EtwHURsWO7fEvgqIjYrmR8UxmoijyJZvw+Q1XkEkkbTGYduvoa8C2aasjjgQ0i4k+S7kVzU+5HFg1wClNURZ6UoCpyGRlbIdQydDUbko6madnwO+CfgE0i4o52iPxrEbFV0QCnMKQq8qSSVZGdTrIyd/GGfPODACStSxPrhb0znnanXKnWIVs1t6b5cs1f7FPJ3mCyJ9nQ1QNorgKtA3wgIr7VLn8qzY2MjyoZ31Qk/ZImiS3eoHEuauriPR8ZGnrCkpOgvWh7Y/U+5/ak+QER8c2S8U2lnarSmzZR/KpbZ5OszFV6/TLND8oqa1UkdKNKL1NDzy7JUhUJ9XXxnq1sDT0zq60qsstJ1nfIW6WXrmt6ZpmrIrNW6XVl6CqT5FWRKVsh1DZ0tRDUVhXZ5WakKbt4K2/X9MwiIm5rn+8BfLw9sz9P0isKxjWKrA0mszb0zGyDvs/6RTQnbUuqIoFqk6yorIv3LKRs6JncvSLit+3zF9AMKb+nVxU56WCybrijyFqldzrwsIjYMyJOdoI1EWmrIklapRcRD4uIN/e+9CXdv628ekzp2DosbVWkpFMlPV/SqqVjmaXDgXP65qc+VdLRkg5JVGW4RDvEXLuqqiK7nGQ9AjgX+Hfg6nbD3rZwTKP4I7CkzYSkt0i6UNIpkjYoGNeMJN0q6Zb2cWvf69sk3TnzbyjmfTRnOIuBn0bEYlgywfN3JQObSUT8C7Ae8Gaa+/9dJOl0Sfu1VyiqJOnrkv6hfb4OzbyyFwOflnRw0eBmIGlFSQe1n/NF7eMbkl4maaXS8U3j6rakfXeaUvbTAdqqyJrjhrytED4PrArQDl19AfgNsDnwwYJxzUjSEZLWap9vJelXwI8lXTXTNIXCzmq3kaOB+9HMye4dZybe9b2zc7L6ZarSyzw/aFA7af+VwEE01XuvLRzSlDJWRQ6TpUpPiRt6SjqRpqjmBJo2DgAPBvYD1oyIvUrFNp2sVZH9lKwVghI39JR0cUQ8qn3+beD1EXGupE1oPvNaWzhUVRW5IJIsyFOlpw50TZe0BnAwsC9Nt+P/iogby0bVfZmq9CRd0DZQRdKZwEcj4qTBdTWS9IuI2GS262z5UoJWCAOJyvnAG3tf8v0JWI0k/RR4VETcKensiNi2b92S/5dNr8sT36eq0jsUqPKsp6U2IbyNZt5E/yXlqucHtZeWX0vzJX88sGWvjNbGY4oqvR0SVOldLenVNFeCsg1d3SRpT+Dkvque96C5Wv7HopF1nIa3Qti/ZEwzOEvS52mmHRQfupqlDwKnSToCOL0dfvsSzTyniU8gz6qzV7KUtIu3knZNB5D0F5rbGH0CuFtjz6i4035WStpgMvPQlaT1gSNpvmz+SDPR9r7At2laJFTbVy0rJW2FUNvQ1WxJegrwcmATmosyV9PcouYTGav3S+hykpW5i3fK+UGSDmOgGWa/iDh8ctEsTErUYLILetVWHg4fL1XWxdtykXT/Uvtol5OstF28bbIk3crS5LBX/hs0Z273jIhqh9WVuMFkV7RVv1sCl0XEz0rHMxVJKwIHALuz9Mbn1wJfpekNV+2VCVXWxXuhk/SiiPhE6TiGaYc3j4qIGyRtRTOkfBfNNIR9J92guctJVmeq9LJoE9mpRES8fWLBzEOyqsi0VXpZSfpKROzWPt+VpgXId4An0NyA/pPlopta1qpIqK+L90In6TcRsV7pOIaprSqy2jP05SBzF++s/jJk2ao0Z8/3B6pOsoZURW6dYBhosMHkR6FpMKnmViS2/D207/kbgKdFxK/bwo8zgU8WiWpmjxlS+XgNcLakX5QIaBaq6uI9HyWHrmajvVAxdBVQZXV+a0VJK0bEnTTbzbkAEfELNTcZn2wwk/6DE5S2Si+riHhP73l7JeU1NLfvOAl4z1T/rrTkVZEpq/QyD12x7LzDFXtTD9rhiZoT28xVkYNdvN8ITRdvVXxDj6mGrtQ0rZ340NUsrQ08g7tvGwJqLjqoqiqyy0lWr4v3LSTq4p15fhCApDVpepE9n2ZY4tERUfsB/CqWVkXeBhzQf+CuvCryAJoqvafT3AfwT+3ybWn+P7X6NM3Q1WHcfejqMzQJb602l3QLzf65sqR1IuJ3ajqQV9mvqbU3TVXkByUNVkXuXTKwEWRthbBzRBzaPn83zT66ZOgKqLKhZ+vrwGoRcbfERNJ3Jh/OaCLi/ZIuZtmqyI1pqiLfMel4OjsnC/JW6fVLNj/o3TRDs8fRlOX/uXBII3FV5OR1saFnO9z8iIj4UelYZpKtKjJrKwQ39LROJ1mZDZkfVH3X9Hao5G/AnSybtIhmjtx9igRm1ZF0Ns0Q8rChq0Mi4rEl45stSc+KiK+XjmM2slRFZtYO5e9Cc5urJ9NchesNXW0YES8sGN6sSTowIo4rHcdclaiKdJJVmSHzg96faH5QSl2pisykaw09leCWV1mrIrPrUkPPDNv5dEpURTrJqkzmrumStgbWiohvDCzfiaZ7fZXNMSUNG4JdUhUZEatNOKQFJeHQ1XqD0w0k/SQitiwV0yj6Y5T0Q+D5/VWR0d4z1WwqSbbz6aoiN4mIiVYYVj2JeoF6N0uH2lYvGcgcHElTTTjoMpqk8WmTDWc0WasiIX2VHtAkV5I2kPSP5Bi6+gpNJWe/g0oEMktZqyKHytIKYSo1N/Scxi6lAxhBVVWRnU2yslbpRcRhpWOYh9V7Hfb7RcRV7dlytZJWRULSKr1phq6OkFT70NXdegZExDklApmlrFWR2VshTOVwKq4AlvRgYP2I+H77+hBgtbby+nMRcUXJ+KZRVVXkghkuzFKll3l+kKQrImKj2a4rLWtVJOSt0ss8dCXpOpqrnENFxD9PMJx5y1AVWVsX71HVNnQ1G2ruEPDZXkGHpJ/THCPvTXNnieeXjC+LKq/mLE8Ju3hn7pr+P5LeCbw52uy9Lb0+nLavTaVeS1MV+Wbg3/p6ZGWoiszaYDLz0NX/AVXOL5ytvqrIahOsVlVdvGehqqGrWdp0oGL2tt7UCknfKxTTnJSsiuxskpW1i3fm+UE0n/fHgCsk9S7Vbk5zw+Jq7y0WEfcoHcM8ZG0wmXboCrgxIk4oHcRy8jaa4ZXaVdXFexaqGrqapcE7o2zX97zq6R9DvIzmKtzEdXa4MHmV3uD8oKOTzA8CQNKGwCPbl5dGxK9KxjOTrFWRg7JV6Q2TZOhqmaaSWWStiuzpUiuEDNTclPuFEfGLgeUPBz4VEduUiWz2Sm7nXU6yDiNhF+/M84OyknQW8KLBSfuSHkpzAK+yKnKYzA0mszT0lPSCiPhM+/wJEfGDvnWviohjy0U3tWE9jiRtk2TSfmdkaegpaUfgGOCdwPnt4scAbwJeM3hSWjNJD46Ia2Z+5xj+dleTrKzcNX3yJJ0bEVtPse6iiNhs0jGNqksNJrM0OuyPczDmmv8Pma5azUa2Vgg1byODJP0D8Hr6RiaA/4yIS8pFNb2pqiLb1ROviuzynKyUVXrJ5wdldb9p1t17YlHMzUP7nr8BeFp/lR7wySJRzWDY0BVDWiNUSlM8H/a6JutKOmaqldmqIvtU3QphiJq3kWW0ydS+peOYpXcDn+17fRBLqyIPp5mGMzGdTbJIWqXXoflBTwQ2johPSFpEM/mz1lulZK2KhLxVelkbesKyn/ngUEDNQwNpqyJnaIWw9iRjWQ4yNPTsHcM3jIhPta+/CKzZrn5HRNR6bKyqKnJBDBf2VekdQNPE7j0RcV3ZqIbrwvwgSW8FtqLZ2DeR9CDgCxHxhMKhDSVpVZqqyG1YWqm0pCqy5nlxkv5Oc0IhYGXgoX1VeotrHerMPHQl6TbgCprP/GHtc9rXG0bEqqVim06mYapBkv7ANK0QIuJBd/9X5dU2dDUbks4EXh0Rl7WvLwb2p7lY8aaI2LFgeFOSdFlE/L++12tGxE3t859GxCMmGU+Xr2Rl7eKdtmt6n91pJl+fDxARv20T3SpFxF+AfbJVRQJExFTtDu5N3VeGMg9dTfQgvRzdXjqAecjaCqGqoatZuk8vwWpd3htJkfSuQjGN4lZJm/SqIvsSrIczpNPAuHU2yRqo0ntUzVcjBmSeH9Rze0SEpN7QW5Vn9oPapKr6xGomSRpMph26GnYSlMSSqsdMVZEAEXHANOueN8lYZqmqoatZWqP/RUTs0fey5iHatwJfb6eA3K0qctLBdHa4MGuVnqQPAzcyfH7QAyPiwJLxjULSvwIbA9sD7wJeTHNp/P1FA1sgMgwLZYhxKpJ+zZBjSvs8IuJhk49qZlmrIqeSoRVCbUNXsyHpa8CHI+LUgeXPAl4eETuXiWxmNVVFdvZKVuIqvZRd0/tFxFGStgduATYF3hIRZxQOq5MSV+llHroavE/ePYDnAv8K/GTy4Ywsa1XkVIp18Z6FqoauZukQmitCz2HZK0KPB55VLKoR1FQV2dkkK2uVXub5Qf3apCpdYpWsKhLyVullHrq6EejdI/KFwOtoCiZ2HpjDUpusVZFTyZAYVjV0NRsRcbmkzWjmjfW+i/4XeFlE/LVcZNOrrSqyy8OF6av0spF0K8MP1lUP0fZkq4qEvFV6mYeuJK1EMwT+L8D3gSNqrhLryVoVOZWSXbxno6ahq9mQ9AGaaR4/mPHNFamtKrKzV7LoRpVeKhFRbQXhiFJVRbayVullHrr6Nc1cz/cBvwE2a8/4AYiIL5UKbAbVzv+ZyVStEJrpqnW3Qqhp6GqWfgEcJWkdmtZHJ0ZEzcPhPVVVRXY5yepClV5akh4NPJHmytb3k+ycGasis1bpZR66+h+aGDdvH/0CqDLJSlwVCUlbIdQ2dDUbEXE0cHQ7+rM3cLykewEn0iRcv5j2F5RTVVVkl4cLu1Cll21+ELDklkZ7svTLZjeaYbd3lItqZhmrImsfWptK14auMshaFQlDh5SXDJNL+l5EPKlcdFOrbehqviRtCRwPbDZNj76iaquK7HKSlbaLN+ScH9Qj6efA5r3Jke3ZzwURsWnZyGbWVkXuQPMF9M3aqyIlnR0R25aOY7bas+Mp1XzVRdIuwEW9GNuTimcDVwGvqfVESNL9Bxb1V0WeHxHPnnxUo8naCkEDN5+X9KXelRVJP0hyPF8R2InmatZ2NDegPzEivloyrqlI2pimee0PGVIVOekrcJ0dLuxAlV7G+UE9vwVWAXoVKCsD15YLZ3QJqyJTVunVnESN4J3AtrDk7PgFwD40++uHaW7/Up3EVZGQtxVCVUNXs9GecO4DPBM4BzgJOLD9bq1WbVWRnU2yeiJvF++M84N6bgYulXQGzXDE9sA5vQnatU3GTl4VeQjwmfb5+1m2ncOL6UvCapJ56Iomvtva53sAH28n1p4n6RUF45rWkKrI3WqeMD4gayuEn0naeYqhq58XimlUbwQ+B7w26r8d3RJ9VZHHl44FFkCSldjnJX0EWEPSS2kOjh8tHNOovtw+er5TKI6RJK+KzFqll7WhJzTTO1cDbqMZPvlg37pVyoQ0kqxVkUTE6ZL2oGmF0DtJuxTYo/JWCJkbemZtc1RVVWRn52R1Qbb5QV2QrSoyc78pGDp09R+1D11JejHNFZRbaBob79gu3xI4KiK2KxnfVCR9kqkrNyMiXjzBcBYMSSuz7NDVpTRXWqpt6NkFfVWRewPFqiIXRJKVtUovq/ZS+NuBh9JcLc0w7JayKjJrlV7Whp49ktYFHgBcGBF3tcvWAVaKu9/myOYpayuErA09u6ZkVWTnk6xsVXrJ5wcBIOkKmrkqF0eiDSxjVWTWKj1J17Ds0NUyah66kvSCiPhM+zxNsUHWqkjI2wpB0mtorqRUMXS1kNRSFbkQkqwLaKv0+vqqXBQRm03/L22uJH0b2K53hp9FG/fuEfGn9vUawJcSz02oVuahq6xDtJIuAraNiNvaq83vZWlV5J4RUWVVJORvhVDL0NVCMEVV5FdLVUUuhInvaav0ss0P6vN64DRJ3wX+1lsYEe8tF9JIUlVFQt4qvYjYv3QM85C12CBlVWQrbSsEWHJF+UjgyL6hq7cAVTb0TK6qqsiFkGSlrNIbMj/ok5Kqnh/U553An2kqre5ZOJbZSFUV2UpZpZd56Iq8twTKWhUJuVshTDV0dVjBkDqrtpGHzg8XQs4qvYzzg3okXRIR/1A6joUkW5Ve8qGrrMUGKasiob4u3qOqbejKJm9BJFkZZZ4fJOk/gf+JiG+VjmU2MlZFZq3Sk3RhRGzePj8e+HlEHNm+rnZeE+QtNoDcVZEZWyFIOotm6OrkGoaubPI6m2Rlr9KT9BVga5pbvCyZHwRcA3XOD+ppP/tVaeZj3UGezzxdVWTWKr32StbjaYaufg08OyIWt+uWuU9dFu3VxH0i4rOlYxkma1UkuBWC5dXZJCs7SftNtz4iTphULAtFxqrIrFV6yYeu7gO8ElgXOIXmROhVwGtprhDtWjC8KWWtigS3QrC8FkSSlbhKLxVJD4+In7Wf991ExPnDltdC0tY0w4XZqiJTyjp0JemrwB+BH9FMYn4AzdXa10TEBSVjm46kn/S1sVnyfNjrWrkVgmXT+SQrYxdvSDs/6LiIOLC9IjQoap9PJulbNFWRFwNLrmZFxOHFgppB1iq95ENXF0fEo9rnKwC/A9areW4Q5L6SNUzJLt5mo1oISVbKKr2M84Oyy1gVmbVKL/MXfrZ4e7JWRfarpYu32agWQp+s39L0gOmdZa4MXFsunJFdDVySKcFqh9uujojft6/3ZelVlcMi4qaS8Y3gNEk7JKuKzNpgMmtDT4DNJd3SPhdwr/Z17VebH1E6gLmaohXCgW6FYLVbCElWui7erYxd0z8CPB1A0pOBI4BXA1sAxwHPKRfaSF4O/KukTFWRWRtMZm3oSdahqalaS/SqImlOhmpVVRdvs1EthCQrYxdvyNk1fYW+q1V7AcdFxMnAyWruIVm1iFi9dAxz8D6a5qO3AD/ta4OwJc1coVo9vB3qFPCw9jnt6w3LhdVdM1VFAlW2noD6unibjarzc7KySjo/6BJgi4i4U9LPaC7n/29vXa3/nw5URaar0svc0DOrrFWRZpl1/kpWxiq9Vsb5QScC35V0A/B/wPcAJG1EM2xbq0OAA4H3DFkXQLVn0X1VetdKegLwA4CI+J2kVwFVVuklH7rKasO+qsiPkaQq0iyzzl/Jylqll7hr+rY0DQO/1ZuUKmkTYLXarwhllLVKL2tDz8wybR9mXdH5K1kkrNKDtPODiIizhyyrulFg8qrIrFV6n2bp0NVLaLq/C9jNQ1djk7Uq0iythZBkparSyz4/KKnMVZFZq/Q8dDVhWasizTJbCElWtiq9tPODEstcFZm1Su+O3pOI+Luka5xgmVnXLIQk60G1VrUNExEHtj+fWjqWBWQFSStGxJ00VVcH9q2rfR/J2mDSQ1dm1nm1f4EsD6mq9JLPD8oqa1Vk2io9D12Z2UKwEKoLU1XpSTofeHpE3NTODzqJpfODHhERNc8PSitrVaSr9MzM6tX5JCsbSRdGxObt8w8A10fEYe3rCyJii5LxWV3cYNLMrF6dHS5MXKWXeX6QTZ6r9MzMKtXlL+2sVXpp5wdZEa7SMzOrlIcLK5R1fpBNnqS/A3/pvQTuBdxG5XMPzcwWgs4mWa7SMzMzs5LuUTqAMfoIcDss08X7UzRDbscVjMvMzMwWgC7PycrcxdvMzMyS6/KVrBUk9ZLI7YCz+tZ1Obk0MzOzCnQ52XCVnpmZmRXT2Ynv4Co9MzMzK6fTSZa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| |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
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| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "ZAtlH54cZTZu", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 206 | |
| }, | |
| "outputId": "c105a96b-016b-4452-8da2-6c98bd1f3762" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " Simple CNN ~test RESNET-50 ~test VGG-16 ~test\n", | |
| "durdation(sec.) 0.011119 0.015231 0.010450\n", | |
| "accuracy 0.818182 0.757576 0.727273\n", | |
| "precision 0.855556 0.769444 0.811463\n", | |
| "recall 0.818182 0.757576 0.727273\n", | |
| "F1-Score 0.807200 0.749888 0.686508" | |
| ], | |
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| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Simple CNN ~test</th>\n", | |
| " <th>RESNET-50 ~test</th>\n", | |
| " <th>VGG-16 ~test</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>durdation(sec.)</th>\n", | |
| " <td>0.011119</td>\n", | |
| " <td>0.015231</td>\n", | |
| " <td>0.010450</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>accuracy</th>\n", | |
| " <td>0.818182</td>\n", | |
| " <td>0.757576</td>\n", | |
| " <td>0.727273</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>precision</th>\n", | |
| " <td>0.855556</td>\n", | |
| " <td>0.769444</td>\n", | |
| " <td>0.811463</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>recall</th>\n", | |
| " <td>0.818182</td>\n", | |
| " <td>0.757576</td>\n", | |
| " <td>0.727273</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>F1-Score</th>\n", | |
| " <td>0.807200</td>\n", | |
| " <td>0.749888</td>\n", | |
| " <td>0.686508</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>\n", | |
| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ff81bce3-c844-4545-89fa-d7f29a0abc95')\"\n", | |
| " title=\"Convert this dataframe to an interactive table.\"\n", | |
| " style=\"display:none;\">\n", | |
| " \n", | |
| " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", | |
| " width=\"24px\">\n", | |
| " <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n", | |
| " <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n", | |
| " </svg>\n", | |
| " </button>\n", | |
| " \n", | |
| " <style>\n", | |
| " .colab-df-container {\n", | |
| " display:flex;\n", | |
| " flex-wrap:wrap;\n", | |
| " gap: 12px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: #1967D2;\n", | |
| " height: 32px;\n", | |
| " padding: 0 0 0 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
| " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: #174EA6;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert:hover {\n", | |
| " background-color: #434B5C;\n", | |
| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
| " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", | |
| " fill: #FFFFFF;\n", | |
| " }\n", | |
| " </style>\n", | |
| "\n", | |
| " <script>\n", | |
| " const buttonEl =\n", | |
| " document.querySelector('#df-ff81bce3-c844-4545-89fa-d7f29a0abc95 button.colab-df-convert');\n", | |
| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-ff81bce3-c844-4545-89fa-d7f29a0abc95');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
| " [key], {});\n", | |
| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
| " element.innerHTML = '';\n", | |
| " dataTable['output_type'] = 'display_data';\n", | |
| " await google.colab.output.renderOutput(dataTable, element);\n", | |
| " const docLink = document.createElement('div');\n", | |
| " docLink.innerHTML = docLinkHtml;\n", | |
| " element.appendChild(docLink);\n", | |
| " }\n", | |
| " </script>\n", | |
| " </div>\n", | |
| " </div>\n", | |
| " " | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 39 | |
| } | |
| ], | |
| "source": [ | |
| "\n", | |
| "cnn_df, y_test, y_test_cnn_preds = measure_cnn_models(test_batch, \n", | |
| " CNNs,\n", | |
| " device)\n", | |
| "cnn_df" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "cnn_df[1:].plot.bar(figsize=(30,5))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 372 | |
| }, | |
| "id": "o5kU2Jn-L99Y", | |
| "outputId": "3e67447e-b1dd-4ad3-8324-fff3bca79252" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x7f625025f6d0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 40 | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 2160x360 with 1 Axes>" | |
| ], | |
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| |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "cnn_df.iloc[0].plot.bar(figsize=(5,2.5))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 280 | |
| }, | |
| "id": "OuX3asmgRPE4", | |
| "outputId": "3a593677-a040-4d5a-aaa2-787dd03c46b1" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x7f606d586510>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 41 | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 360x180 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "total=[cnn_df, df_svm]\n", | |
| "total_df=pd.concat(total, axis=1)\n", | |
| "total_df[1:].plot.bar(figsize=(40,10))\n" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 644 | |
| }, | |
| "id": "vPAjl6u2DM9H", | |
| "outputId": "09c97e70-3b66-439f-8270-7f327991f2ec" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x7f606d565090>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 42 | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 2880x720 with 1 Axes>" | |
| ], | |
| "image/png": 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IiIiIiIiIiIiINJCabkREREREREREREREREREREREGkhNNyIiIiIiIiIiIiIiIiIiIiIiDaSmGxERERERERERERERERERERGRBlLTjYiIiIiIiIg0Oc2aNcNsNmM0Ghk6dCjnz58HoKysDE9PT8xms+Pz4YcfAvDBBx8QGBiIyWTCaDSybt06AJKTk/H29ubq1asAnDt3ji5dutS5XlhYGGazmU6dOtG+fXtHrKyszGWdZrOZuLg4x/jRo0cJCwvDz8+PhIQEqqqqnJ7xzJkzxMbGEhQURM+ePYmJiQHA19eXQ4cO1cidMmUK8+bN48svv8RgMLB06VJHzGazYTAYyMzMdPkuP/zwQ4xGI4GBgQQHB9ea92sVFRVYLBZat25NSkpKvea4kp2dzf79+xs112azsWHDhkbvLSIiIiIiIiJSF/c/ugARERERERERadruy7f9put9bzHfNsfT0xOb7ca+Y8aMYfHixUyfPh2Abt26OWI3nTx5ktmzZ1NUVETbtm2prKzkn//8pyPerFkzPvjgAyZOnOi0l6v1Ro8eDcDy5cvZvXs3ixYtum2dt3r55Zd54YUXSExM5Nlnn2XZsmVOe8+YMYOBAwfy/PPPA1BSUgJAYmIiVquV1157DYDr16+zZs0adu7cydGjRzEajaxatYoJEyYAsHLlSoKCglzWt3HjRubPn8/mzZvp2LEjV69edTQp3U6LFi3429/+ht1ux26312uOK9nZ2cTGxtKzZ88Gz7XZbOzevdvRkCQiIiIiIiIi8lvSSTciIiIiIiIi0qSFh4dz6tSpOnPOnj1LmzZtaN26NQCtW7ema9eujviUKVN4++23uXbt2u9aK0B1dTXbtm1j5MiRwI2moezsbKe806dP4+Pj4/htMpkASEpKIisryzG+fft2OnfuTOfOnQHo3LkzV65c4cyZM1RXV5Obm0t0dLTLWubMmUNmZiYdO3YEwMPDg6eeeqpez9GqVSseeeQRWrRoUa98VwoKCsjJySEtLQ2z2UxpaSmlpaUMHjyY3r17069fPw4ePAjA6tWrMRqNBAUFERkZSVVVFTNmzCArKwuz2VzjnYiIiIiIiIiI/BZ00o2IiIiIiIiINFm//PILeXl5jB8/3jFWWlqK2fz/TstZuHAhERERdOjQga5duzJgwABGjBjB0KFDHTmdOnXikUce4aOPPqoxXtt6/fr1q1d9V65coU+fPri7u5Oens7w4cOpqKjgrrvuwt39xr9tfHx8XDYNPffccyQkJLBo0SIeffRRxo4dS8eOHQkMDMTNzY3i4mKCgoKwWq0kJSXVmDty5EhWr15NcHAwvXr1wsPDw2V9drud3r17u4xlZGTwySefOI1HRkayYMGCej0/wObNm5kzZw7nz5/n8ccfZ+TIkezbt4+Kigqefvpp4uLiiI2NdTQhDRgwgCVLlvDggw/y9ddfM2nSJLZt28bMmTPZtGkT3t7enD9/nubNmzNz5sw6TxoSEREREREREflXqOlGRERERERERJqcy5cvYzabOXXqFP7+/gwcONARc3UdFEBubi6FhYXk5eXxwgsvsGfPHl5//XVHfNq0aQwbNowhQ4bUmFfbevVx7NgxvL29OXLkCP379ycwMJC2bdvWa+6gQYM4cuQIubm5bNy4keDgYOx2O+3btycpKQmr1UpAQADZ2dn89a9/rTE3Pj6ehIQEDh48SFJSEgUFBQ2uPS0tjbS0tAbP+7XCwkLWrl1LdXU1b775JsOGDSMkJMRl405lZSUFBQU88cQTjrGrV68C0LdvX5KTk4mPj2fEiBH/cl0iIiIiIiIiIrej66VEREREREREpMnx9PTEZrNx7NgxqqurWbx48W3nGAwGQkNDmTZtGlarlc8++6xG/MEHH8RsNrNq1apG1fT1119jNpsxm83k5OQA4O3tDYCvry9RUVHs3buXdu3acf78ecdVVidPnnTk/ZqXlxejRo3io48+IiQkhO3btwOQmJjIqlWr2Lp1KyaTiQ4dOtSYd99993HHHXewZcsWBgwYUGvNAQEB7Nmzx2UsIyPD8Ty3flJTUxv0XqZPn87dd9+Nl5cXs2fP5tChQ3z88cd4eXk55V6/fp277roLm83m+Bw4cACAJUuWMGvWLE6cOEHv3r2pqKhoUB0iIiIiIiIiIg2lphsRERERERERabJatmzJggULePPNNx1NLK6Ul5dTVFTk+G2z2ejcubNT3vTp08nMzGxULWFhYY5Gkbi4OH788UfHKS3nzp1j586d9OzZE4PBgMViYc2aNQCsWLGCYcOGOa23bds2Ll26BMCFCxcoLS2lU6dOwI3Td+655x7S09Odrpa6aebMmcybN49mzZrVWvO0adNIS0vj+++/B6CqqoqlS5cCN066ubX55eanIVdL1UebNm24cOECAHfeeSddu3Zl9erVAFRXV1NcXAzcuOYrLCyMmTNn0r59e06cOFFjroiIiIiIiIjIb01NNyIiIiIiIiLSpAUHB2MymVi5ciVwoznj1pNZFixYwM8//8zUqVPp0aMHZrOZrKws3nnnHae1AgIC6NWrV40xV+vVx4EDB+jTpw9BQUFYLBbS09Pp2bMnAPPmzeOtt97Cz8+PiooKxo8f7zR/z5499OnTB5PJRHh4OBMmTCAkJMQRT0pK4uDBg7VetRQREcHw4cPrrDEmJoaUlBQeffRRx7P/9NNP9Xo+gC5duvDnP/+Z5cuX4+Pjw/79++s996bExEQyMjIIDg6mtLSUTz75hGXLlhEUFERAQADr1q0DbjQBBQYGYjQaiYiIcLzX/fv3O/6mIiIiIiIiIiK/Jfc/ugARERERERERadq+t5j/7XtWVlbW+L1+/XrH98uXL7ucs23bNpfjy5cvr/F77dq1ju9dunSpdT2A5ORkkpOTXcYiIiL45ptvXMZ8fX3ZtWtXrevCjSaTtLS0WuNTpkxhypQpNcaioqKIiopyyn399ddrXWfs2LGMHTu2zlpqU1ZW1qh5t+rbt69Ts05ubq5T3q1/l5u8vLwoLCz8l2sQEREREREREXFFJ92IiIiIiIiIiIiIiIiIiIiIiDSQmm5ERERERERERERERERERERERBpITTciIiIiIiIiIiIiIiIiIiIiIg2kphsRERERERERERERERERERERkQZS042IiIiIiIiIiIiIiIiIiIiISAOp6UZEREREREREREREREREREREpIHUdCMiIiIiIiIiTU6zZs0wm80YjUaGDh3K+fPnASgrK8PT0xOz2ez4fPjhhwB88MEHBAYGYjKZMBqNrFu3DoDk5GS8vb25evUqAOfOnaNLly51rhcWFobZbKZTp060b9/eESsrK3NZp9lsJi4uzjF+9OhRwsLC8PPzIyEhgaqqKqdnPHPmDLGxsQQFBdGzZ09iYmIA8PX15dChQzVyp0yZwrx58/jyyy8xGAwsXbrUEbPZbBgMBjIzM12+yw8//BCj0UhgYCDBwcG15v1aRUUFFouF1q1bk5KSUiO2Z88eAgMD8fPzIzU1lerq6nqteSubzcaGDRsaPA/g/PnzvPvuu42aKyIiIiIiIiJyk/sfXYCIiIiIiIiING1d0r/4Tdcrmzvktjmenp7YbDYAxowZw+LFi5k+fToA3bp1c8RuOnnyJLNnz6aoqIi2bdtSWVnJP//5T0e8WbNmfPDBB0ycONFpL1frjR49GoDly5eze/duFi1adNs6b/Xyyy/zwgsvkJiYyLPPPsuyZcuc9p4xYwYDBw7k+eefB6CkpASAxMRErFYrr732GgDXr19nzZo17Ny5k6NHj2I0Glm1ahUTJkwAYOXKlQQFBbmsb+PGjcyfP5/NmzfTsWNHrl696mhSup0WLVrwt7/9Dbvdjt1urxGbOHEif//73wkLCyMmJobc3Fyio6Prte5NNpuN3bt3O5qNGuJm082kSZMaPFdERERERERE5CaddCMiIiIiIiIiTVp4eDinTp2qM+fs2bO0adOG1q1bA9C6dWu6du3qiE+ZMoW3336ba9eu/a61AlRXV7Nt2zZGjhwJ3Ggays7Odso7ffo0Pj4+jt8mkwmApKQksrKyHOPbt2+nc+fOdO7cGYDOnTtz5coVzpw5Q3V1dZ0NL3PmzCEzM5OOHTsC4OHhwVNPPVWv52jVqhWPPPIILVq0cKr7p59+4uGHH8ZgMDB69GiXz1eXqqoqZsyYQVZWFmazmaysLC5evMi4ceMIDQ0lODjYcVLRvn37CA0NxWw2YzKZOHz4MOnp6ZSWlmI2m0lLS2vQ3iIiIiIiIiIiN6npRkRERERERESarF9++YW8vLwaVzfdbLa4+dmxYwdBQUF06NCBrl27MnbsWNavX19jnU6dOvHII4/w0UcfOe3har36unLlCn369OHhhx92NJ5UVFRw11134e5+44BiHx8fl01Dzz33HOPHj8disTB79mzKy8sBCAwMxM3NjeLiYgCsVitJSUk15o4cOZLVq1dTUFBAr1698PDwcFmf3W6nd+/eLmMZGRk1nvvmJzU1tc5nPnXqVI1modqe77vvvuOxxx7DZDIxadIkCgsL2blzJ9OmTaN58+bMnDmThIQEbDYbCQkJzJ49m/79+7Nr1y7y8/NJS0vj4sWLLFmyhOeff95xMo6Pjw9z5851nFCUkZFRZ70iIiIiIiIiIrXR9VIiIiIiIiIi0uRcvnwZs9nMqVOn8Pf3Z+DAgY6Yq+ugAHJzcyksLCQvL48XXniBPXv28Prrrzvi06ZNY9iwYQwZUvN6q9rWq49jx47h7e3NkSNH6N+/P4GBgbRt27ZecwcNGsSRI0fIzc1l48aNBAcHY7fbad++PUlJSVitVgICAsjOzuavf/1rjbnx8fEkJCRw8OBBkpKSKCgoaHDtaWlpv+spMTt27GDWrFl0796d//mf/+Gpp56iXbt2vPXWWy7zN2/eTE5ODpmZmcCNhqbjx48THh7O7NmzOXnyJCNGjODBBx/83WoWERERERERkf8sOulGRERERERERJocT09PbDYbx44do7q6msWLF992jsFgIDQ0lGnTpmG1Wvnss89qxB988EHMZjOrVq1qVE1ff/214zSYnJwcALy9vQHw9fUlKiqKvXv30q5dO86fP++4yurkyZOOvF/z8vJi1KhRfPTRR4SEhLB9+3YAEhMTWbVqFVu3bsVkMtGhQ4ca8+677z7uuOMOtmzZwoABA2qtOSAggD179riMNfakG29vb06ePOn4XdvzjR07loCAANzd3Xnqqaew2Wzk5eURFBTkct3q6mo+++wzbDYbNpuN48eP4+/vz6hRo8jJycHT05OYmBi2bdtWZ30iIiIiIiIiIvWlphsRERERERERabJatmzJggULePPNNx1NLK6Ul5dTVFTk+G2z2ejcubNT3vTp0x0nqTRUWFiYoyEkLi6OH3/8katXrwJw7tw5du7cSc+ePTEYDFgsFtasWQPAihUrGDZsmNN627Zt49KlSwBcuHCB0tJSOnXqBNw4feeee+4hPT3d6Wqpm2bOnMm8efNo1qxZrTVPmzaNtLQ0vv/+ewCqqqpYunQpcOOkm5vPc+tnwYIFdb6H+++/nzvvvJOvvvqK6upqPvzwQ5fPdztt2rThwoULjt+DBg1i4cKFVFdXA7B3714Ajhw5gq+vL6mpqQwbNoySkhKnuSIiIiIiIiIijaGmGxERERERERFp0oKDgzGZTKxcuRKA0tLSGiezLFiwgJ9//pmpU6fSo0cPzGYzWVlZvPPOO05rBQQE0KtXrxpjrtarjwMHDtCnTx+CgoKwWCykp6fTs2dPAObNm8dbb72Fn58fFRUVjB8/3mn+nj176NOnDyaTifDwcCZMmEBISIgjnpSUxMGDBxkxYoTL/SMiIhg+fHidNcbExJCSksKjjz7qePaffvqpXs8H0KVLF/785z+zfPlyfHx82L9/PwDvvvsuEyZMwM/Pj27duhEdHV3vNW+yWCzs37/f8ff6y1/+ws8//4zJZCIgIIC//OUvAKxatQqj0YjZbMZutzN69GjatWtH3759MRqNv+sVWSIiIiIiIiLStLn/0QWIiIiIiIiISNNWNnfIv33PysrKGr/Xr1/v+H758mWXc2q7dmj58uU1fq9du9bxvUuXLrWuB5CcnExycrLLWEREBN98843LmK+vL7t27ap1Xbhx0kxdDSNTpkxhypQpNcaioqKIiopyyn399ddrXWfs2LGMHTu2zlpqU1ZW5nK8T58+2O32Rq15k5eXF4WFhTXG3n//fae89PR00tPTncY//fTTf2l/ERERERERERGddCMiIiIiIiIiIiIiIiIiIiIi0kBquhERERERERERERERERERERERaSA13YiIiIiIiIiIiIiIiIiIiIiINJCabkREREREREREREREREREREREGkhNNyIiIiIiIiIiIiIiIiIiIiIiDaSmGxERERERERERERERERERERGRBlLTjYiIiIiIiIg0KRaLhU2bNtUYmz9/PhMnTgTg8OHDxMbG0q1bN3r37o3FYmH79u2O3NzcXEJDQ+nRowdms5mEhASOHz/ucq9x48Zx7733YjQanWILFy6kR48eBAQE8NJLLznFr1+/TmpqKkajkcDAQEJCQjh69Chjx47l/fffr5GbnZ1NdHQ0AAaDgSeffNIRu3btGu3btyc2NtZljbt27SIyMpLu3bsTHBzMhAkTuHTpksvcX8vNzaV79+74+fkxd+5clzlXr14lISEBPz8/wsLCKCsrA6CiogKLxULr1q1JSUmp136uZGdns3///kbNtdlsbNiwodF7i4iIiIiIiIjUxf2PLkBEREREREREmrjX2/7G6/1fneGkpCSsViuDBg1yjFmtVt544w2uXLnCkCFDyMzMJC4uDgC73c7u3buJjIzEbrczefJkcnJy8Pf3ByAnJ4eysjI6derktFdycjIpKSmMHj26xnh+fj7r1q2juLgYDw8Pzp496zQ3KyuL8vJySkpKcHNz4+TJk7Rq1YqkpCTmzJnDM888U6P+pKQkAFq1aoXdbufy5ct4enqyZcsWvL29Xb6LM2fO8MQTT2C1WgkPDwdgzZo1XLhwgZYtW9b5Hn/55Reee+45tmzZgo+PDyEhIcTFxdGzZ88aecuWLePuu+/mu+++w2q18vLLL5OVlUWLFi3429/+ht1ux26317lXXbKzs4mNjXXatz5sNhu7d+8mJiam0fuLiIiIiIiIiNRGJ92IiIiIiIiISJMycuRIvvjiC6qqqgAoKyujvLycfv368cknnxAeHu5ouAEwGo0kJycDMG/ePF555RVHww1AXFwckZGRLveKjIzEy8vLafy9994jPT0dDw8PAO69916nnNOnT3P//ffj5nbj3zM+Pj7cfffdDBgwgIMHD3L69GkALl68yNatWxk+fLhjbkxMDF988QUAK1eudDTk/NrixYsZM2aMo+Hm5vvp0KGDy/xb7dq1Cz8/P3x9fWnevDmJiYmsW7fOKW/dunWMGTPGsXZeXh7V1dW0atWKRx55hBYtWtx2r9oUFBSQk5NDWloaZrOZ0tJSSktLGTx4ML1796Zfv34cPHgQgNWrV2M0GgkKCiIyMpKqqipmzJhBVlYWZrOZrKysRtchIiIiIiIiIuKKmm5EREREREREpEnx8vIiNDSUjRs3AjdOiYmPj8dgMLBv3z569epV69zbxevr22+/ZceOHYSFhfGnP/2JwsJCp5z4+HjWr1+P2WzmxRdfZO/evQA0a9aMxx9/nFWrVgGwfv16oqKiuPPOOx1zExMTsVqtXLlyhZKSEsLCwlzWYbfb6d27t8tYfn4+ZrPZ6RMREQHAqVOneOCBBxz5Pj4+nDp1ymmdW/Pc3d1p27YtFRUV9XlNAGzevBmLxUJwcDCzZs3i4MGDfPbZZ/z3f/83ERERxMXFkZGRgc1mo1u3bjz99NMsXLiQPXv2kJmZyaRJkwCYOXMmmzZtori4mJycHJo3b87MmTNJSEjAZrORkJBQ75pEREREREREROpDTTciIiIiIiIi0uTcvGIKal7N9GuPPfYYRqORESNGOMUqKiowm8089NBDZGZmNmj/a9eu8cMPP/DVV1+RkZFBfHw81dXVNXJ8fHw4dOgQc+bMwc3NjQEDBpCXl1ev+k0mE2VlZaxcubLRVydZLBZsNpvTp6CgoFHrNVZhYSFr164lLy+Py5cvM2zYMD7//HNGjhzplFtZWUlBQQFPPPEEZrOZZ555xnEiUN++fUlOTubvf/87v/zyy7/1GURERERERETkP5OabkRERERERESkyRk2bBh5eXkUFRVx6dIlx2kvAQEBFBUVOfI+//xzli9fzg8//OAUb9euHTabjaeffprKykpOnDjhOA1myZIlde7v4+PDiBEjMBgMhIaG4ubmxrlz55zyPDw8iI6OJiMjg1deeYXs7GwAIiIiOH36NMXFxRQUFDBkyBCnuXFxcUydOrXWhqKbz7Nnzx6XsduddOPt7c2JEycc+SdPnsTb29tpnVvzrl27xv/93//Rrl27Ot5OTdOnT+fuu+/Gy8uL2bNnc+jQIT7++GOX13Zdv36du+66q0aT0IEDBwBYsmQJs2bN4sSJE/Tu3btBp+2IiIiIiIiIiDSGmm5EREREREREpMlp3bo1FouFcePG1WhKGTVqFDt37iQnJ8cxdunSJcf3l156idmzZzsaOW6NP/DAA45Gj2effbbO/YcPH05+fj5w46qpqqoq7rnnnho5RUVFlJeXAzeaSUpKSujcuTMABoOBhIQExowZQ3R0NC1atHDaY9y4cbz22msEBgbWWkdKSgorVqzg66+/doytXbuWM2fO3Pakm5CQEA4fPszRo0epqqrCarUSFxfntEdcXBwrVqwAYM2aNfTv3x+DwVDn+2mINm3acOHCBQDuvPNOunbtyurVqwGorq6muLgYgNLSUsLCwpg5cybt209lB/IAACAASURBVLfnxIkTNeaKiIiIiIiIiPzW1HQjIiIiIiIiIk1SUlISxcXFNZpuPD09+cc//sGSJUvw9fUlPDycWbNm8eqrrwIQGBjIO++8w+jRo+nevTt9+/blwIEDjBo1qtY9wsPDOXToED4+Pixbtgy40RBz5MgRjEYjiYmJrFixwqkR5ezZswwdOhSj0YjJZMLd3Z2UlJQ667+Vj48Pqampdb6DDh06YLVamTp1Kt27d8ff359NmzbRpk2b274/d3d3Fi1axKBBg/D39yc+Pp6AgAAAZsyY4WhcGj9+PBUVFfj5+fHWW28xd+5cxxpdunThz3/+M8uXL8fHx4f9+/ffdt9fS0xMJCMjg+DgYEpLS/nkk09YtmwZQUFBBAQEsG7dOgDS0tIIDAzEaDQSERFBUFAQFouF/fv3YzabycrKavDeIiIiIiIiIiJ1cf+jCxARERERERGRJu71//tDth0+fDjV1dVO4z169GDDhg21zhsyZIjL65xcWblypcvx5s2b8/HHH9c5d/DgwQwePLjWuNlsdll/ZWWl01hUVBRRUVEu1wkPD2fHjh111lKbmJgYYmJinMZnzpzp+N6iRQvHyTO/VlZW1qh9b9W3b1+nZp3c3FynvLVr1zqNeXl5UVhY+C/XICIiIiIiIiLiik66ERERERERERERERERERERERFpIDXdiIiIiIiIiIiIiIiIiIiIiIg0kJpuREREREREREREREREREREREQaSE03IiIiIiIiIiIiIiIiIiIiIiINpKYbEREREREREREREREREREREZEGUtONiIiIiIiIiIiIiIiIiIiIiEgDqelGRERERERERJoUi8XCpk2baozNnz+fiRMnAnD48GFiY2Pp1q0bvXv3xmKxsH37dkdubm4uoaGh9OjRA7PZTEJCAsePH3e517hx47j33nsxGo1OsYULF9KjRw8CAgJ46aWXnOLXr18nNTUVo9FIYGAgISEhHD16lLFjx/L+++/XyM3OziY6OhoAg8HAk08+6Yhdu3aN9u3bExsb67LGXbt2ERkZSffu3QkODmbChAlcunTJZe6v5ebm0r17d/z8/Jg7d67LnO3bt9OrVy/c3d1Zs2ZNvdZ1Zf78+fWu69eys7PZv39/o/cWEREREREREWkM9z+6ABERERERERFp2gJXBP6m630z5ps640lJSVitVgYNGuQYs1qtvPHGG1y5coUhQ4aQmZlJXFwcAHa7nd27dxMZGYndbmfy5Mnk5OTg7+8PQE5ODmVlZXTq1Mlpr+TkZFJSUhg9enSN8fz8fNatW0dxcTEeHh6cPXvWaW5WVhbl5eWUlJTg5ubGyZMnadWqFUlJScyZM4dnnnmmRv1JSUkAtGrVCrvdzuXLl/H09GTLli14e3u7fBdnzpzhiSeewGq1Eh4eDsCaNWu4cOECLVu2rPM9/vLLLzz33HNs2bIFHx8fQkJCiIuLo2fPnjXyOnXqxPLly8nMzKxzvduZP38+Tz755G3rciU7O5vY2Fin2kREREREREREfk866UZEREREREREmpSRI0fyxRdfUFVVBUBZWRnl5eX069ePTz75hPDwcEfDDYDRaCQ5ORmAefPm8corrzgabgDi4uKIjIx0uVdkZCReXl5O4++99x7p6el4eHgAcO+99zrlnD59mvvvvx83txv/nvHx8eHuu+9mwIABHDx4kNOnTwNw8eJFtm7dyvDhwx1zY2Ji+OKLLwBYuXKloyHn1xYvXsyYMWMcDTc330+HDh1c5t9q165d+Pn54evrS/PmzUlMTGTdunVOeV26dMFkMjmeozEWLFhAeXk5FosFi8UCwObNmwkPD6dXr1488cQTVFZWApCenk7Pnj0xmUxMnTqVgoICcnJySEtLw2w2U1pa2ug6REREREREREQaQk03IiIiIiIiItKkeHl5ERoaysaNG4Ebp8TEx8djMBjYt28fvXr1qnXu7eL19e2337Jjxw7CwsL405/+RGFhoVNOfHw869evx2w28+KLL7J3714AmjVrxuOPP86qVasAWL9+PVFRUdx5552OuYmJiVitVq5cuUJJSQlhYWEu67Db7fTu3dtlLD8/H7PZ7PSJiIgA4NSpUzzwwAOOfB8fH06dOtW4FwK8/fbbhIWF0a9fPz744AMOHz5MZmYm//u//0tqaiodO3YkPz+f/Px8zp07x6xZs9i6dStFRUX06dOHt956i4qKCj7//HP27dtHSUkJr776KhEREcTFxZGRkYHNZqNbt26NrlFEREREREREpCHUdCMiIiIiIiIiTc7NK6ag5tVMv/bYY49hNBoZMWKEU6yiogKz2cxDDz3U4KuTrl27xg8//MBXX31FRkYG8fHxVFdX18jx8fHh0KFDzJkzBzc3NwYMGEBeXl696jeZTJSVlbFy5UpiYmIaVNtNFosFm83m9CkoKGjUerdz5swZdu7cydKlS8nPz2fo0KH89NNPLhuGvvrqK/bv30/fvn0xm82sWLGCY8eO0bZtW1q0aMH48eNZu3Zto66iEhERERERERH5rbj/0QWIiIiIiIiIiPzWhg0bxgsvvEBRURGXLl1ynPYSEBDA9u3bHXmff/45u3fvZurUqY54UVERQUFBtGvXDpvNRmZmJpWVlZw4cYKhQ4cC8Oyzz/Lss8/Wur+Pjw8jRozAYDAQGhqKm5sb586do3379jXyPDw8iI6OJjo6mg4dOpCdnc2AAQOIiIjg9OnTFBcXU1BQ4GjAuVVcXBxTp07lyy+/pKKiwmUdAQEB7Nmzh2HDhjnF8vPzeeGFF5zGW7ZsSUFBAd7e3pw4ccIxfvLkSby9vWt95tuZO3cuAN27d+ejjz6qM7e6upqBAweycuVKp9iuXbvIy8tjzZo1LFq0iG3btjW6JhERERERERGRf4WabkRERERERESkyWndujUWi4Vx48bVOCVm1KhRzJkzh5ycHOLi4gC4dOmSI/7SSy/x2GOP8fDDD+Pv718j/sADD2Cz2eq1//Dhw8nPz8disfDtt99SVVXFPffcUyOnqKiI++67j44dO3L9+nVKSkowmUwAGAwGEhISGDNmDNHR0bRo0cJpj3HjxnHXXXcRGBjIl19+6bKOlJQUQkNDGTJkiONEmbVr19K3b1/HSTe1CQkJ4fDhwxw9ehRvb2+sViuffvppvZ6/Mdq0acOFCxe45557ePjhh3nuuef47rvv8PPz4+LFi5w6dYqOHTty6dIlYmJi6Nu3L76+vjXmioiIiIiIiIj8O+l6KRERERERERFpkpKSkiguLq7RdOPp6ck//vEPlixZgq+vL+Hh4cyaNYtXX30VgMDAQN555x1Gjx5N9+7d6du3LwcOHGDUqFG17hEeHs6hQ4fw8fFh2bJlwI2GmCNHjmA0GklMTGTFihUYDIYac8+ePcvQoUMxGo2YTCbc3d1JSUmps/5b+fj4kJqaWuc76NChA1arlalTp9K9e3f8/f3ZtGkTbdq0ue37c3d3Z9GiRQwaNAh/f3/i4+MJCAgAYMaMGeTk5ABQWFiIj48Pq1ev5plnnnHkNNTTTz/N4MGDsVgstG/fnuXLl5OUlITJZCI8PJyDBw9y4cIFYmNjMZlMPPLII7z11lsAJCYmkpGRQXBwMKWlpY3aX0RERERERESkoXTSjYiIiIiIiIj8rr4Z880fsu/w4cOprq52Gu/RowcbNmyodd6QIUMYMmRIvfZwdf0RQPPmzfn444/rnDt48GAGDx5ca9xsNrusv7Ky0mksKiqKqKgol+uEh4ezY8eOOmupTUxMDDExMU7jM2fOdHwPCQnh5MmTjVr/VpMnT2by5MmO3/3796ewsNApb9euXU5jffv2Zf/+/f9yDSIiIiIiIiIiDaGTbkREREREREREREREREREREREGkhNNyIiIiIiIiIiIiIiIiIiIiIiDaSmGxERERERERERERERERERERGRBlLTjYiIiIiIiIiIiIiIiIiIiIhIA6npRkRERERERERERERERERERESkgdR0IyIiIiIiIiIiIiIiIiIiIiLSQGq6EREREREREZEmxWKxsGnTphpj8+fPZ+LEiQAcPnyY2NhYunXrRu/evbFYLGzfvt2Rm5ubS2hoKD169MBsNpOQkMDx48dd7jVu3DjuvfdejEajU2zhwoX06NGDgIAAXnrpJaf49evXSU1NxWg0EhgYSEhICEePHmXs2LG8//77NXKzs7OJjo4GwGAw8OSTTzpi165do3379sTGxrqscdeuXURGRtK9e3eCg4OZMGECly5dcpnbkOerL5vNxoYNGxo19/z587z77ruN3ltERERERERE5Pfk/kcXICIiIiIiIiJN24Ee/r/pev4HD9QZT0pKwmq1MmjQIMeY1WrljTfe4MqVKwwZMoTMzEzi4uIAsNvt7N69m8jISOx2O5MnTyYnJwd//xt15+TkUFZWRqdOnZz2Sk5OJiUlhdGjR9cYz8/PZ926dRQXF+Ph4cHZs2ed5mZlZVFeXk5JSQlubm6cPHmSVq1akZSUxJw5c3jmmWdq1J+UlARAq1atsNvtXL58GU9PT7Zs2YK3t7fLd3HmzBmeeOIJrFYr4eHhAKxZs4YLFy7QsmXLOt9jXc/XEDabjd27dxMTE9PguTebbiZNmtTo/UVEREREREREfi866UZEREREREREmpSRI0fyxRdfUFVVBUBZWRnl5eX069ePTz75hPDwcEfDDYDRaCQ5ORmAefPm8corrzgabgDi4uKIjIx0uVdkZCReXl5O4++99x7p6el4eHgAcO+99zrlnD59mvvvvx83txv/nvHx8eHuu+9mwIABHDx4kNOnTwNw8eJFtm7dyvDhwx1zY2Ji+OKLLwBYuXKloyHn1xYvXsyYMWMcDTc330+HDh1c5tf3+eqrqqqKGTNmkJWVhdlsJisri4sXLzJu3DhCQ0MJDg5m3bp1AOzbt4/Q0FDMZjMmk4nDhw+Tnp5OaWkpZrOZtLS0RtchIiIiIiIiIvJ7UNONiIiIiIiIiDQpXl5ehIaGsnHjRuDGKTHx8fEYDAb27dtHr169ap17u3h9ffvtt+zYsYOwsDD+9Kc/UVhY6JQTHx/P+vXrMZvNvPjii+zduxeAZs2a8fjjj7Nq1SoA1q9fT1RUFHfeeadjbmJiIlarlStXrlBSUkJYWJjLOux2O71793YZy8/Px2w2O30iIiIa9Kzfffcdjz32GCaTiUmTJlFYWMjOnTuZNm0azZs3Z+bMmSQkJGCz2UhISGD27Nn079+fXbt2kZ+fT1paGhcvXmTJkiU8//zzjpNxfHx8mDt3Lt26dcNms5GRkdGgukREREREREREfm9quhERERERERGRJufmFVNQ82qmX3vssccwGo2MGDHCKVZRUYHZbOahhx4iMzOzQftfu3aNH374ga+++oqMjAzi4+Oprq6ukePj48OhQ4eYM2cObm5uDBgwgLy8vHrVbzKZKCsrY+XKlY26tgnAYrFgs9mcPgUFBQ1aZ8eOHcyaNYuioiKCg4N56qmnmDFjBomJiS7zN2/ezNy5czGbzURFRXHlyhWOHz9OeHg4//Vf/8W8efM4duwYnp6ejXouEREREREREZF/FzXdiIiIiIiIiEiTM2zYMPLy8igqKuLSpUuO014CAgIoKipy5H3++ecsX76cH374wSnerl07bDYbTz/9NJWVlZw4ccJxGsySJUvq3N/Hx4cRI0ZgMBgIDQ3Fzc2Nc+fOOeV5eHgQHR1NRkYGr7zyCtnZ2QBERERw+vRpiouLKSgoYMiQIU5z4+LimDp1aq0NRTefZ8+ePS5jv9VJN2PHjiUgIAB3d3eeeuopbDYbeXl5BAUFucyvrq7ms88+czT5HD9+HH9/f0aNGkVOTg6enp7ExMSwbdu2BtUhIiIiIiIiIvLvpqYbEREREREREWlyWrdujcViYdy4cTWaUkaNGsXOnTvJyclxjF26dMnx/aWXXmL27NkcOHDAKf7AAw84GkWeffbZOvcfPnw4+fn5wI2rpqqqqrjnnntq5BQVFVFeXg7A9evXKSkpoXPnzgAYDAYSEhIYM2YM0dHRtGjRwmmPcePG8dprrxEYGFhrHSkpKaxYsYKvv/7aMbZ27VrOnDnzm510cztt2rThwoULjt+DBg1i4cKFjpN/bl6rdeTIEXx9fUlNTWXYsGGUlJQ4zRURERERERER+f8TNd2IiIiIiIiISJOUlJREcXFxjaYbT09P/vGPf7BkyRJ8fX0JDw9n1qxZvPrqqwAEBgbyzjvvMHr0aLp3707fvn05cOAAo0aNqnWP8PBwDh06hI+PD8uWLQNuNMQcOXIEo9FIYmIiK1aswGAw1Jh79uxZhg4ditFoxGQy4e7uTkpKSp3138rHx4fU1NQ630GHDh2wWq1MnTqV7t274+/vz6ZNm2jTps3tX2Adz9cQFouF/fv3YzabycrK4i9/+Qs///wzJpOJgIAA/vKXvwCwatUqjEYjZrMZu93O6NGjadeuHX379sVoNJKWltbgvUVEREREREREfk/uf3QBIiIiIiIiItK0+R88cPuk38Hw4cMdp6ncqkePHmzYsKHWeUOGDHF5nZMrK1eudDnevHlzPv744zrnDh48mMGDB9caN5vNLuuvrKx0GouKiiIqKsrlOuHh4ezYsaPOWmpT2/M1hJeXF4WFhTXG3n//fae89PR00tPTncY//fTTf7kGEREREREREZHfg066ERERERERERERERERERERERFpIDXdiIiIiIiIiIiIiIiIiIiIiIg0kJpuREREREREREREREREREREREQaSE03IiIiIiIiIiIiIiIiIiIiIiINpKYbEREREREREREREREREREREZEGUtONiIiIiIiIiIiIiIiIiIiIiEgDqelGRERERERERJoUi8XCpk2baozNnz+fiRMnAnD48GFiY2Pp1q0bvXv3xmKxsH37dkdubm4uoaGh9OjRA7PZTEJCAsePH3e517hx47j33nsxGo1OsYULF9KjRw8CAgJ46aWXnOLXr18nNTUVo9FIYGAgISEhHD16lLFjx/L+++/XyM3OziY6OhoAg8HAk08+6Yhdu3aN9u3bExsb67LGXbt2ERkZSffu3QkODmbChAlcunTJZW59n++HH35g4MCBPPjggwwcOJAff/yxXuvd6vz587z77rsNnnfT/Pnz6/0cIiIiIiIiIiK/B/c/ugARERERERERadoWP7vtN13vuSX964wnJSVhtVoZNGiQY8xqtfLGG29w5coVhgwZQmZmJnFxcQDY7XZ2795NZGQkdrudyZMnk5OTg7+/PwA5OTmUlZXRqVMnp72Sk5NJSUlh9OjRNcbz8/NZt24dxcXFeHh4cPbsWae5WVlZlJeXU1JSgpubGydPnqRVq1YkJSUxZ84cnnnmmRr1JyUlAdCqVSvsdjuXL1/G09OTLVu24O3t7fJdnDlzhieeeAKr1Up4eDgAa9as4cKFC7Rs2bLO91jX882dO5cBAwaQnp7O3LlzmTt3LvPmzbvtere62XQzadKkBs27af78+Tz5/7V371FWlWeex79PgYJ2vIFKtMsYJYrBAgtoMXgFsRWkxRtBRQ0JdoxrQGk7EEnGFZY9OhkHSU/s8ZJ0axYdFUym1aCigoQoE2IUChC8IB1jIsJgJLFbJAYvz/xRp8qCOlCgZW1O1fez1lns97L3/p3Cxdqees77XnLJDr0PSZIkSZKkT4Ir3UiSJEmSpHZl1KhRPPzww2zevBmAV155hbVr13LSSSdx9913M2jQoMaCG4Camhq+/OUvA3DjjTfyrW99q7HgBmDkyJGcfPLJZe918skn061bt2b9t912G1OmTKFLly4AHHjggc3mrFu3joMOOoiqqvqPZ6qrq9lvv/0YOnQoL774IuvWrQPg7bff5vHHH+ecc85pPPfMM8/k4YcfBmDmzJmNBTlbu+WWWxg7dmxjwU3Dz6dHjx5l5+/o+/vpT3/K2LFjARg7diwPPPDADl2vqSlTpvDrX/+a2tpaJk+eDMC0adM49thj6du3L1OnTgXq3/+IESM45phjqKmp4d577+Xmm29m7dq1DBkyhCFDhuz0vSVJkiRJklqDRTeSJEmSJKld6datGwMHDuSRRx4B6leJGT16NBHBc889R//+/bd5bkvjO+qll15i4cKFHHfccZxyyik888wzzeaMHj2aBx98kNraWr7+9a+zdOlSADp16sT555/Pj3/8YwAefPBBBg8ezN5779147oUXXsisWbN45513ePbZZznuuOPK5li5ciUDBgwoO7ZgwQJqa2ubvY4//vgW39/69es56KCDAPj0pz/N+vXrm83ZuHEj48ePp1+/fpx77rnMmTOHlStXctlllwH1q+X07NmTZcuWMW3aNObOncvq1at5+umnWbZsGUuWLOHJJ5/k0Ucf5eCDD2b58uWsXLmSYcOGcdVVV3HwwQezYMECFixY0GJeSZIkSZKkT4JFN5IkSZIkqd1p2GIKttyaaWvnnnsuNTU1nHfeec3GNmzYQG1tLUceeSQ33XTTTt3/vffe4w9/+ANPPfUU06ZNY/To0WTmFnOqq6tZtWoV3/nOd6iqqmLo0KHMnz9/h/L37duXV155hZkzZ3LmmWfuVLYGQ4YMYdmyZc1eixYt2qnrRAQR0ax/xYoVnHrqqSxdupTx48czffp0Lr300i22/Wpq7ty5zJ07l379+tG/f39efPFFVq9eTZ8+fZg3bx7XXHMNCxcuZJ999vlI71eSJEmSJKm1dS46gCRJkiRJUms7++yzufrqq6mrq2PTpk2Nq70cffTRPPnkk43z7r//fhYvXsykSZMax+vq6jjmmGPo3r07y5Yt46abbmLjxo28+uqrnHXWWQBcccUVXHHFFdu8f3V1Needdx4RwcCBA6mqquKNN97ggAMO2GJely5dGD58OMOHD6dHjx488MADDB06lOOPP55169axfPlyFi1a1FiA09TIkSOZNGkSP//5z9mwYUPZHEcffTRLlizh7LPPbja2YMECrr766mb9e+65Z4uFNz169GjcHmvdunVlt89quqXVaaedxmmnnbbda2Ym3/zmN/na177WbKyuro45c+Zw7bXXMnToUL797W9v91qSJEmSJEltwZVuJEmSJElSu/OpT32KIUOGMG7cuC1WiRkzZgy/+MUvmD17dmPfpk2bGo+/8Y1vcMMNN/DCCy80Gz/kkEMaV4PZXsENwDnnnNO47dFLL73E5s2b2X///beYU1dXx9q1awH44IMPePbZZzn00EOB+tVjLrjgAsaOHcvw4cPp2rVrs3uMGzeOqVOn0qdPn23mmDBhAjNmzOBXv/pVY999993H+vXrP9ZKNyNHjmTGjBkAzJgxo2xRT0v22msv3nrrrcb2GWecwZ133snGjRsBeO2113j99ddZu3Yte+65J5dccgmTJ0+mrq6u7PmSJEmSJEltzaIbSZIkSZLULl100UUsX758i6KbPfbYg4ceeojbb7+dww8/nEGDBnH99ddz7bXXAtCnTx++973v8aUvfYlevXpxwgkn8MILLzBmzJht3mPQoEGsWrWK6upq7rjjDqC+IObll1+mpqaGCy+8kBkzZjTbgun111/nrLPOoqamhr59+9K5c2cmTJiw3fxNVVdXc9VVV233Z9CjRw9mzZrFpEmT6NWrF5///Od57LHH2GuvvVr+AW7n/U2ZMoV58+ZxxBFH8PjjjzNlypQdul5T3bt354QTTqCmpobJkydz+umnM2bMGAYNGkSfPn0YNWoUb731FitWrGDgwIHU1tZy3XXXNf5dXX755QwbNowhQ4bs9L0lSZIkSZJag9tLSZIkSZKkT9T4208t5L7nnHMOmdms/6ijjmLOnDnbPG/EiBGMGDFih+4xc+bMsv277747d91113bPHTZsGMOGDdvmeG1tbdn8DSvBNDV48GAGDx5c9jqDBg1i4cKF282yLdt6f927d2f+/Pkf6ZpN3XPPPVu0J06cyMSJE7fo69mzJ2eccUazc6+88kquvPLKj51BkiRJkiTpo3KlG0mSJEmSJEmSJEmSJGknWXQjSZIkSZIkSZIkSZIk7SSLbiRJkiRJkiRJkiRJkqSdZNGNJEmSJElqdZlZdASpTfnfvCRJkiRJHY9FN5IkSZIkqVV17dqVDRs2WISgDiMz2bBhA127di06iiRJkiRJakOdiw4gSZIkSZLal+rqatasWcPvf//7oqNIbaZr165UV1cXHUOSJEmSJLWhHSq6iYhhwPeATsC/ZOb/2Gq8C/CvwABgA3BBZr7SulElSZIkSVIl2G233TjssMOKjiFJkiRJkiR9olrcXioiOgG3AMOB3sBFEdF7q2mXAX/MzM8B/wjc2NpBJUmSJEmSJEmSJEmSpF1Fi0U3wEDg3zPz5czcDMwCzt5qztnAjNLx/wGGRkS0XkxJkiRJkiRJkiRJkiRp17EjRTd/CbzapL2m1Fd2Tma+B/wH0L01AkqSJEmSJEmSJEmSJEm7msjM7U+IGAUMy8y/LbUvBY7LzAlN5qwszVlTav+6NOeNra51OXB5qdkLWNVab0SSSvYH3mhxliRJUvF8bpEkSZXC5xZJklQpfG6R9Ek4NDMPKDfQeQdOfg04pEm7utRXbs6aiOgM7ANs2PpCmfkD4Ac7kliSPoqIWJyZf1V0DkmSpJb43CJJkiqFzy2SJKlS+Nwiqa3tyPZSzwBHRMRhEbE7cCEwe6s5s4GxpeNRwM+ypSV0JEmSJEmSJEmSJEmSpArV4ko3mfleREwAHgM6AXdm5nMR8Q/A4sycDdwB/Cgi/h34A/WFOZIkSZIkSZIkSZIkSVK7tCPbS5GZc4A5W/V9u8nxO8AXWzeaJH0kbmEnSZIqhc8tkiSpUvjcIkmSKoXPLZLaVLgLlCRJkiRJkiRJkiRJkrRzqooOIEmSJEmSJEmSJEmSJFUai24kSZIkLjfk0AAAC/VJREFUSZIkSZIkSZKknWTRjSRJkiRJkiRJkiRJkrSTOhcdQJI+roi4D7gDeCQzPyg6jyRJ0vZExF8Ch9Lk/8cy88niEkmSJH0oIv5+e+OZ+d22yiJJkrQjIuJI4DagR2bWRERfYGRmXl9wNEkdgEU3ktqDW4GvADdHxE+AH2bmqoIzSZIkNRMRNwIXAM8D75e6E7DoRpIk7Sr2KjqAJEnSTvpnYDLwfYDMfDYi7gEsupH0iYvMLDqDJLWKiNgHuAj4r8Cr1D9k3ZWZ7xYaTJIkqSQiVgF9M/PPRWeRJEmSJElqDyLimcw8NiKWZma/Ut+yzKwtOpuk9s+VbiS1CxHRHbgEuBRYCtwNnAiMBQYXl0ySJGkLLwO7ARbdSJKkXVJE3Ly98cy8qq2ySJIk7aA3IqIn9asJExGjgHXFRpLUUVh0I6niRcT9QC/gR8BZmdnwIHVvRCwuLpkkSVIzm4BlETGfJoU3/vJKkiTtQpYUHUCSJGknjQd+ABwVEa8BvwEuLjaSpI7C7aUkVbyIGJKZC4rOIUmS1JKIGFuuPzNntHUWSZIkSZKkShcRnYAbM3NSRPwFUJWZbxWdS1LH4Uo3ktqD3qV9Ot8EiIj9gIsy89aCc0mSJG0hM2dExO7AkaWuVZn5bpGZJEmSyomIA4BrgN5A14b+zDy1sFCSJElbycz3I+LE0vHbReeR1PFUFR1AklrBVxsKbgAy84/AVwvMI0mSVFZEDAZWA7cAtwIvRcTJhYaSJEkq727gBeAw4DrgFeCZIgNJkiRtw9KImB0Rl0bEeQ2vokNJ6hhc6UZSe9ApIiJL++WVlhLcveBMkiRJ5UwHTs/MVQARcSQwExhQaCpJkqTmumfmHRExMTOfAJ6ICItuJEnSrqgrsAFouiJfAvcVE0dSR2LRjaT24FHg3oj4fqn9tVKfJEnSrma3hoIbgMx8KSJ2KzKQJEnSNjRsgbkuIkYAa4FuBeaRJEkqKzO/UnQGSR1XlBaGkKSKFRFV1BfaDC11zQP+JTPfLy6VJElScxFxJ/ABcFep62KgU2aOKy6VJElScxHxN8BC4BDgn4C9gesyc3ahwSRJkrYSEdXUP6+cUOpaCEzMzDXFpZLUUVh0I0mSJEltJCK6AOOBE0tdC4FbM/PPxaWSJEmSJEmqXBExD7gH+FGp6xLg4sz86+JSSeooLLqRVPEi4gjgO0Bv6vftBCAzDy8slCRJkiRJUgWLiBnUf0P8zVJ7P2C6K/RJkqRdTUQsy8zalvok6ZNQVXQASWoFPwRuA94DhgD/yodbNkiSJBUuIn5c+nNFRDy79avofJIkSWX0bSi4AcjMPwL9CswjSZK0LRsi4pKI6FR6XQJsKDqUpI7BlW4kVbyIWJKZAyJiRWb2adpXdDZJkiSAiDgoM9dFxKHlxjPzt22dSZIkaXsiYjkwuFRsQ0R0A55o+OxFkiRpV1H6vOWfgEFAAouAqzLzd4UGk9QhdC46gCS1gj9HRBWwOiImAK8Bnyo4kyRJUqPMXFc6fAP4U2Z+EBFHAkcBjxSXTJIkaZumA7+MiJ+U2l8EbigwjyRJUlmlLzONLDqHpI7JlW4kVbyIOBZ4AdgX+G/A3sC0zHyq0GCSJElbiYglwEnAfsAvgGeAzZl5caHBJEmSyoiI3sCppebPMvP5IvNIkiSVExEzgIkNW2NGxH7A9MwcV2wySR1BVdEBJOnjiIhOwAWZuTEz12TmVzLzfAtuJEnSLioycxNwHnBrZn4ROLrgTJIkSdvSDXg7M/838PuIOKzoQJIkSWX0bSi4AShtj9mvwDySOhCLbiRVtMx8Hzix6BySJEk7KCJiEHAx8HCpr1OBeSRJksqKiKnANcA3S127AXcVl0iSJGmbqkqr2wAQEd2AzgXmkdSB+I+NpPZgaUTMBn4CvN3QmZn3FRdJkiSprL+j/hdX92fmcxFxOLCg4EySJEnlnEv9N8TrADJzbUTsVWwkSZKksqYDv4yInwABjAJuKDaSpI4iMrPoDJL0sUTED8t0p3t1SpIkSZIkfTQR8XRmDoyIuszsHxF/AfwyM/sWnU2SJGlrEdEbOBVIYEFmPl9wJEkdhCvdSKp4mfmVojNIkiRtT0T8r8z8u4h4kPoPf7aQmSMLiCVJklRWRATwUER8H9g3Ir4KjAP+udhkkiRJH4qIPYF3M/PdzHw+It4HzgSOAiy6kdQmXOlGUsUrrXRT7pdXrnQjSZJ2CRExIDOXRMQp5cYz84m2ziRJkrQ9EbEC+HvgdOq3aXgsM+cVm0qSJOlDEfEkcFlmro6IzwFPA3cDvYFnMnNKoQEldQiudCOpPXioyXFX6vccX1tQFkmSpGYyc0npcDHwp8z8ACAiOgFdCgsmSZK0bXXAm5k5ueggkiRJ27BfZq4uHY8FZmbmlRGxO7AEsOhG0ifOohtJFS8z/61pOyJmAv+3oDiSJEnbMx84DdhYau8BzAWOLyyRJElSeccBF0fEb4G3Gzozs29xkSRJkrbQdBeEU4FpAJm5OSI+KCaSpI7GohtJ7dERwIFFh5AkSSqja2Y2FNyQmRtL+49LkiTtas4oOoAkSVILno2Im4DXgM9R/8UmImLfQlNJ6lAsupFU8SLiLbasZv5/wDUFxZEkSdqetyOif2bWAUTEAOBPBWeSJElqJjN/W3QGSZKkFnwVmAh8Fjg9MzeV+nsDNxUVSlLHEpnZ8ixJkiRJ0scWEccCs4C1QACfBi7IzCWFBpMkSZIkSWoHmn7ZSZLagkU3kipeRJwL/Cwz/6PU3hcYnJkPFJtMkiSpuYjYDehVaq7KzHeLzCNJkiRJktReRERdZvYvOoekjqOq6ACS1AqmNhTcAGTmm8DUAvNIkiSVFRF7Ur8N5sTMXAl8NiL+puBYkiRJkiRJ7UUUHUBSx2LRjaT2oNy/ZZ3bPIUkSVLLfghsBgaV2q8B1xcXR5IkSZIkqV25rugAkjoWi24ktQeLI+K7EdGz9PousKToUJIkSWX0zMz/CbwLkJmb8BtYkiRJkiRJrSIzHwCIiKOKziKpY7DoRlJ7cCX13xi/F5gFvAOMLzSRJElSeZsjYg8gASKiJ/DnYiNJkiRJkiS1O3OLDiCpY3D7FUkVLzPfBqYUnUOSJGkHTAUeBQ6JiLuBE4AvF5pIkiRJkiSpAkXEzdsaAvZtyyySOq7IzKIzSNLHEhHzgC9m5pul9n7ArMw8o9hkkiRJH4qIKmAUMB/4AvUfAD2VmW8UGkySJEmSJKkCRcRbwNcpv4rw9Mzcv40jSeqALLqRVPEiYmlm9mupT5IkqWgRsTgz/6roHJIkSZIkSZUuIn4GXJuZi8qM/SYzDysglqQOpqroAJLUCj6IiM80NCLis4AVhZIkaVf0eERMiohDIqJbw6voUJIkSZIkSRVoFLCs3IAFN5LaiivdSKp4ETEM+AHwBPXbNJwEXJ6ZjxUaTJIkaSsR8RvKFAdn5uEFxJEkSZIkSapYEfGZzPxd0TkkdWwW3UhqFyLiQOByYCmwB/B6Zj5ZbCpJkqQtRcQewH8BTqS++GYhcHtm/qnQYJIkSZIkSRUmIuoys3/p+N8y8/yiM0nqeDoXHUCSPq6I+FtgIlBN/TKCXwB+CZxaZC5JkqQyZgD/Cdxcao8p9Y0uLJEkSZIkSVJliibHriIsqRAW3UhqDyYCxwJPZeaQiDgK+O8FZ5IkSSqnJjN7N2kviIjnC0sjSZIkSZJUuXIbx5LUZiy6kdQevJOZ70QEEdElM1+MiF5Fh5IkSSqjLiK+kJlPAUTEccDigjNJkiRJkiRVomMi4j+pX/Fmj9IxpXZm5t7FRZPUUVh0I6k9WBMR+wIPAPMi4o/AbwvOJEmSVM4AYFFE/K7U/gywKiJWUP9hUN/iokmSJEmSJFWOzOxUdAZJikxX2pLUfkTEKcA+wKOZubnoPJIkSU1FxKHbG89MC4clSZIkSZIkqUJYdCNJkiRJkiRJkiRJkiTtpKqiA0iSJEmSJEmSJEmSJEmVxqIbSZIkSZIkSZIkSZIkaSdZdCNJkiRJkiRJkiRJkiTtJItuJEmSJEmSJEmSJEmSpJ1k0Y0kSZIkSZIkSZIkSZK0k/4/MFqBezpFyzMAAAAASUVORK5CYII=\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "total_df.iloc[0].plot.bar(figsize=(10,2.5))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 346 | |
| }, | |
| "id": "wChbeZi0RyCu", | |
| "outputId": "bfc72f80-2a71-4ae6-e782-c5f1f6f4932f" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x7f606d3c9450>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 43 | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 720x180 with 1 Axes>" | |
| ], | |
| "image/png": 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ZwD8cU18Zj1/G89zXg8nJep5nPH7Df++1nrddyPOtbwduo9xPUcBqwI8ax7QScADw3Pp8VWDPxjEtAmxMnR+nTBlmWFvwL8AxwO71+RrAQY1j2gW4Cvhsfb4mcFrjmJakfDpcqz5fmRxr6pYDFq2PlwJWckzT4/glPc99PZh8XBnP84zHb+jvvZGoLhyPpBlRhixbxrAa5aB+X9KSlDfD7IbxCNgDWDMiPiBpVcqb8hetYuqQ9Fhg1Yj4TetYMqtrCtaKiBPqJ9elI+L3DeNZkvJhYtWI2FfSWsA6EdHs037GmDqyHb+sfD2YWPLzPN3xG/Z7r/UQ9UIlaUVJX5J0Tn3+L8BejWN6A6X77ufrplWAM9pFBMBngGcDu9fns4FPtwunkPRS4Arg3Pp8Q0lnNo5pbUkXSLqmPl9f0vsax/R+4CDmNkFcDPhqu4gAOIHSA+459fltwIfahQPkjCnl8Ut6nvt6MDlZz/OMx2/o772RSrKAE4HzKA3HAK6nNCBr6S2UNg6zAKL07Hpi04jgWRHxFuq8fZQeL60X3gIcCmwO/A0gIq6gDMe39AXKG/IBgIi4CtitaUTwMuDfgXsBIuKPtC+meGpEfJS5v6f7aF9MkTEmyHn8Mp7nh+LrwWRkPc8PJd/xG/p7b9SSrBUi4uvURdx1mvCh8f/JwP0zIu7vPJE0g/aL8R9Qub1PANQh0xQL32P+ypjWcS3ZZxq16fQzcH+Uef7O8VuqcTwA99epgU5MTwX+2TaklDFBzuOX8Tz39WBysp7nGY/f0N97o5Zk3Stpeeb+ArcAWpez/lBS5/Y62wLfAL7TOKZjgNOBJ0r6MPBjSnfe1q6V9CpgUUlrSfoU8NPGMd1RL1qdc+oVwJ/ahsTXa4XM4+t09PcpTWVbOpQyLfAUSV+jNEU8qGlEOWOCnMcv43nu68HkHErO8zzj8Rv6e2+kFr5L2phyQ+hnUpqRzgR2iYgrG8a0CLAPpfGngPMi4gut4umQtC6lv4uACyLiV41D6izg/E/K7wrK1O8HI6LZpzJJa1Ia6T0HuBv4PbBHRNzcKqYa17bMe06d3zIegPoBZwtKTBdHxB2NQ0oZE+Q7fhnPc18PHlFc6c7zjMcPGrz3WpZTLuwvYHHKTa+fQUm0FqO29W8Y0/6T2TbkmL4ymW0N4tplMtuGHNMa9ftSzL0dwxqNYzpyMtuGHNMFk9n2aI8p8fHLeJ77ejC5mLKe5xmP39Dfe6M2XfiziHgwIq6NiGuiNCNtfePMftWNew87iB7P6H5S12dt0iiWbv1upN365tqnAUTEvTG37cY3G8YDsG2fbdsPPQpA0hIqtxhZQdITJC1Xv1anVNI6pvmlOX5dMp7nvh6MYxqc5xmP39DfezMG+Z8Pi6SVKCfVYyVtxNzKimUpzcdaxLQ78CpgjZ6y1WWAVneSfw/QWR82q7OZUv57XIuYalzbU7rPryLpmK5dy9JoUWmdTn0G8DhJO/fEtESjmN4EvBlYU9JVXbuWAX7SIiZgP0oF75OAy5j73psFHOuY5sp4/JKe574eTE7W8zzj8Wv23huJNVmS9qKMDm0KXNq1axZwUkR8q0FMq1E63B4OHNy1azZwVTRskCrp8Iho/YliDpWbZ28IfAA4pGvXbODCKC0mhh3TjpTbM/070J0kzwZOjYihL+CU9DjgCfQ5pyKiSeLeIeltEfGpljH0yhZTxuOX9Dz39eARSHieZzx+zd57I5FkdUh6eUSc1jqO6UDSKhFxW+s4uklarE7xpiHp2RHResp5WpD0rIj4ees4umWMKaOM57mvB5OX8TzPePxaGKk1WRFxmqQdJWVorDmHpGMk/WvrOHq8VdJbWgfRLSIekPRhSeu1jqUjIn4m6fWSHt86lm6SvljLxzPZStKhrYPokTGmdMcv43nu68Ejku48z3j8YPjvvZFKsqrlgKNaB9HjBOAjrYPocSJlTj+bs4BPtA6ix12U3mKZfJL2i0h7fRt4ZesgemSMCXIev4znua8Hk5P1PM94/Ib63hvFJOtHzO3LkcUy5Ptdrwf8tnUQfdwPrNA6iB7XApu1DqLHUyh9ejJ5LvOuicwgY0yQ8/hlPM99PZicrOd5xuM31PfeSFQX9ngx0LwxY499gI+3DqLH6ymN4rLZmwQ3q+7xcmrpdiKvJ9/o6N6UuDLZm3wxQc7jl/E83xtfDyZjb3Ke53uT7/gN9b03iknWqyg3gcxk/Yjo1y+rpeUj4rLWQfSxFfD21kH0eDmlO34ma0TED1oH0WNx8o2OZowJch6/jOe5rweTk/U8z3j8hvreyzaFtTA8QKM+VOO4RtKWrYPocV62he/V74B1WgfR4x7a39S7108lZfswcSrwodZB9MgYE+Q8fhnPc18PJifreZ7x+A31vTdSLRwA6g0p14mI97eOpUPSTODZEXHmhD88JLXL+5Mi4pbWsXSrN199bkSc2DqWjnqvq00jIsNNtIE5d49fLyIubh2LPXIZj1/S89zXg2ks6fEb6ntv5JIsMzMzswxGcbrQzMzMrDknWWZmZmYDMIrVhfYISFo+Iu5sHYdNb5JWpNykHeC2iPhLy3jMzDLwSNaASJohaT9J50q6qn6dI+mNkhZrFNMRklaojzeV9Dvg55JuTnjbHwAkvbZ1DL0krds6hl6Sjmv0uhtKuhi4CPho/fqhpIslbdwipvFIurp1DP20On7jaXmeS3qhpM9KOrN+fVbSi1rFM56k14Os53mT67mkx9W/f7+WdJekOyX9qm4b6C2SRmLhu6TZzC2pVf0elJG6x0TE0EfsJJ0C/A04Cbi1bn4ysBewXETs2iCmqyNivfr4QuDdEXGJpLWBkyNi02HHNBFJf4iIVVvH0a1VTJKWG2sXcGVEPHmY8QBIugLYr/fmtJK2AD4fERs0iGnnsXYBn4uImcOMZ86LJzx+42l4nn8SWBv4MvNeO/cEboiI/Ycd03ga/p5Snufjafi7Og/4AXBSRPy5bluJ8vd4m4gY2F1iRmK6MCKW6X4uaWngLZR7853eJCjYJCLW7tl2K3CxpOtbBATMkDQjIh4EHhsRlwBExPWSFm8UE5KuGmsXsOIwY5nzwtJY9yYT0OrmsLcDNzP3gwSUDxMCntgkIliqN8ECiIiLa6l0C/8DfI3+vYyWGHIs3dIdv6Tn+Yv7XDuR9D/A9cDQk6ykv6eU53nG6zmwekQc2b2hJltHSnrdIF94JJKsjjrs9w7KJ56Tgc0arje6S9IuwGkR8XCNbxFgF9rds+wzwNmSjgDOlXQ08C1ga+CKRjFBeeO9kPl/LwJ+OvxwAHgtcCDwzz77dh9yLB2/o3zq+kPvDkmt+p2dI+ksyqhDJ4anUN6D5zaK6SrgqIi4pneHpBc0iKcj4/HLeJ7/Q9JmnQ+BXTYD/tEiIHL+nrKe5xmv5zdLejdlJOsvMGcd6d7MvW4NxEgkWXWd0YHArsDxwEYRcU/bqNgNOBL4jKS7KSfY44AL676hi4hP1bn6N1GG42cAawFn0LZb8HeBpSNivkRP0kXDDweAS4BrImK+i4KkQ4cfDlDuHv8EYL4/0pS1UEMXEW+XtD2wI10L34FPR8TZLWKifNCaNca+ll3W0x0/cp7newOflbQMc6cLn0LptL53o5gy/p6ynucZr+e7AgdT1ot2Ro3/ApwJvHKQLzwqa7LupQzFnwDM7t0fEZ8YelBdJC1f43AV3zRR18/8IyLuax2L2aBkPs/rmpnuitU/N4wl7e/JchuV6sKPURIsgGX6fDVVk6tlJe2csRIF2lfxqXhW/R3tXB9r4n85GBFxV+YLauc8yno+QdOKx3SVvROpCcXQZT7PI+LPEXFZ/eosVm5yvnf/niQtN04RQwqSdmgdw3Qx6L99IzGSlZGkMyJip/p4R8pUwUXAlsBHMt3LCdpW8UnajrJe7AbKVBOUaqKnAW+OiO+1iGsskg6NiEMbx3B5RGzc+d4wjnQVcxkreyci6ayIeEnjGD4ZEe/ofG8Zy1gaVqetSpnS3YZybglYllKxdnBE3DTsmMYj6bAM9++V9OOI2KrzvXU8/Qz6nBqVNVmHjLM7IuKDQwtmrtW6Hh8EbB0Rv6/rxy4AThx2QEmrPgCOBl7Qe6GStAZwNvD0FkGN47LWAXRpNtpXpauYI2dl77haJ1jV8+r3pj3zElfyfRLYIyIeApC0KKWQ6VRgi0Zx9ZUhwaqWrN9bVRoDbf/2jUSSBdzbZ9tSwD7A8kCLJKt7iHBGRPweICLukPRwg3ggZ9UHlPPw1j7bbwPSTe9ExHdax5BIxoq5jJW91DgEbM68RQK/CE8pdMtYybdCRPxP94aabJ0qqcXfF2DO9Glv0cmZEfGrVjEl1exv30gkWRHx8c7jWpGyP+WNeirw8bH+3YBtIGkW5SAuLmnliPiTpMcAizaKKWPVB5SK0Eskncq8bQB2A77UIiBJMyhJ+suAJ9XNtwHfBr4UEQ+0iCuhjBVzvZW9UEZAmlX2wvjT4pLSTYs3lLGS7zJJn6FMQXdfo/YCftkiIEkHUZLOU4Ff1M1PBk6RdGpEHNEirqSa/e0bmTVZdW3IAcAelDfC0RHR9FNrP7WX19Mj4metY8lE0tPp/4nsukbxpF7Xk2VNVnaZKnsl/QrYfqxp8YhoOi3edU79MiI2ahhHukq++uF4H+a9Rt0KfIfyoavfqNugY7oeeEbvB74a67URsdawY+qV5ZxqaSRGsiR9DNgZOA5YLyL+3jikviTtEBHfBdIkWJL2jYjm902rw9uZhrizr+tRz/fmJK0bEb/ufG8dD8yfXElaqWErgGk1Ld5KRNzVOoZeEXE/8Nn6lcXDlFH2m3u2r1z3ZZDm+tTKqLRwOJBysr0P+KOkWfVrdp2yy+IDrQPo442tAxhPw+mBuyTtUtfydGJZRNKuNF7XUz2353sGJ/d8z6jJ9HPVmRY/SNKr6tdBwM8bx9XROW5faxpFF5X7GM75nlHDdgnvAC6o7UmOq1/nUgqrstzf8T96vjcn6cfd3wdtJEayIiJdsihp1T6LgTNm9Rlj6taqki/duh5JTwNWjIifdEZrI+LvkrYE/hwRv20RVx9pz6mWVXwRcbikMyhTTs+um2+jVKw1mRbvFhFHdX9PIkXF4wQ2o6z5GaqIOFfS2sxfSHFJpwKyBUlLAMtExO0RcRFARFwkaSYwOyJa3RqpY6gVjyORZEnajFL9cU7P9u2Bv0ZEiz/UZwC9a2X2axDHRF7aOoDxtKrkq+tmdoVU63o+Cbynz/ZZdV/qYzlMWav4sk2LSzoAuCcivtSzfR/KH8q0I0hZtGyXUKtnL271+mM4hnLf0m/1bN8K2I5yW7dHjXQjQAvoSKDfJ8HrKN3gW5jv03xE/KLfDw6LpCdL2qrr+QHA6yQdUkdJWsWVvUP3zIi4s1W36S4rRsTVvRvrttWHH05OtYrvBuBQ4MX16zDghrqvuUTTYHtQbu7d6yvA64YcS2qS1q1TvcfUr4NqwU5zw54Cm8AmEdGbYBERpzN3ZPJRYyRGsiifuHoX/xERN9fmny2sMk5TPSLi7cMMpvoY86632I9SLLAk5Y/QHg1ignJB/xvlj2JvJd9XqSNKDZ1MGZXsfG9lvEaMjx1aFBNrXbI8HZrbZpkGm9GvHUlE3F9HA41p0S4hRdPPaslx9o3KwM6kjUqS9YRx9o13wAfp/8jVGRxgnVrd2HFfp8eYpB81ignyV/J1tP6jc6mkN0TEF7o3Sno9Oc61LBWPruKbvEUkrRgRf+neKKnlHSAy2of+7RI+AVwLtE6yMvmrpM17Z27qsp7bG8XUbajXp1FJsr4v6cPA+zprLuqnsMMo95Zq4c6IOKnRa49liZ7n23Q9bjXiB4k7dCfzDuB0SXswN6naFHgMpWlqa1kqHtM1t03sY8BZkg4ELq/bNqnbMyyAz1LxOB3aJWTxLuDrkk5k3uvUnjRsBtxlqBWPo5JkHQh8EbhRUqej6wbApcDrG8V0f6PXHc9sSWtHxPUwtx9NXWs0u2Fc6Sr5MqqjDc+R9G/AM+vmsyKi1QeJlBWP2av4MomIL0u6ndJe5pmUqd5rgUN6C4laSFTx2GmXcANzE/dVKTexf2uzqBKKiF9I2hx4C7B33Xwt8KyI+Cb/H9MAACAASURBVGuLmFpWPI5Mx3cASWsCz6hPr42I3zWM5dUR8dX6eMuI+EnXvrdGxLENYnoRpfLjw8z7qfW9wP4ZLqqJKvnmcHf1sUn6LvCe3gX5ktYDPhIRrnjsw52wx5a14rGOrqdql9DROY98PvUn6Tjg3N4F+ZJeBmwXEQOreBypJCuT7j/IvX+cW/6xlvRM4N10JaPARyPimhbx9MrYNdwXsLFJuiQiNhtj39URsd6wY+qJ4ZMR8Y7O95axdJP0zog4qvO9dTyZSLoM2GKM28VcGhHrt4ksL0nPryMzz++M1Nhcki6LiE3G2HdtRDyj376F4VG30n+INMbjfs+HJiKuiYg9I2KT+rVnlgSrytg1PMtao4yyVzxmqeKbR6JpsIzGrHikfVFFmnYJkpao0130ToHV6TGbq1nFo5OswYkxHvd7PhSStpK0Z9fzb0r6Qf3aukVM42h6MZX0tLquiN61RpKe2jK2ZC6V9IbejYkqHtOQdECd8urdvo+kNKNsCSzSr7oxUcVjlnYJx9D/g99WwH8POZbs/lrXic1jGBWPo7LwfQ6VZptrRcQJNctfOiJ+3yCUdSVdRUkWnlofU5+v2SAeKNWWb+t6vg5lYeJSlHVZzRZQJ5Suu7qk2cyboKs+FxARseywYyJ/xWMmewBb9Nn+FUqRztDXGtX1T2OKiE8MK5Yu2Sses9gkIvbt3RgRp0v6UIuAACSdOd7+iPj3YcXSpVnF40glWZLeT/nFrQOcQOmJ81VgywbhZGh42GvZnuqqGzq3HJJ0eKOYshqzu7qk1YcfDlBu/LoS5XYVp8b898YcuowVj4llbPx5FHAFcA7wTxJMx2WveEwka9PPZ1MqME+h3Pw8wznVrOJxpJIsyifnjaiffiLij5KWaRFIvw70CcyzfiYidu56mmUovqN1RUa6tUYRsZOkxwE7A1+o6y7+h5Jw3dUipq7YLqS03LCxZWz8uRGlk/lLKJ/wTwEu6PQbbKUmU06oxpe16edKwLaU8+pVwFnAKRFxbcOYqMnU0O8zOWprsu6vF4dOQ9Jmc+aSfi/pd11f3c+H3juo+rWkl/RulLQD8JsG8fSTpWt4yrVGEXFPRJwAbA98nvJpf+9W8UwDWZpZwtxpsH+VtEz9ej7wXRpNg0XElRFxcERsSGnUuiNwnaQWUzrTRetrU0dnCuxQSS+tX4cBX6/7moiIhyLi3IjYizI9fiNwkaRHZT+xkWrhIOmdwFqULPpwyg1OT46ITzWIZfmeTYsArwTeCVweES9vENNalAv6T5l3rcNzgB06TUpbkrR0XWC+dGfBeaM4VgROpzSVnW+tUUT8uVFcz6F8Qnwu8GPgfyKi5S2R7BGQtD1wMPNOgx3Rehqsrl99JeUuCw8A/xURF7eMKatM7RIkPZEyBdaZqr8WOLZV088OSYtTRkd3p9y8/kzg+Ii4rWVcLYxUkgUgaVtgO8qnjfMi4vzG8SwCvIbyyeIKSoPGZl2n68m/B/P2yTp5kB1vJxHTnK7hPdubdQ3viqF7rdG1LdcaSbqJciPtUylFCg9274+Iy/v8s0edrM0sM5L0OkpytQTwTeDrrf9AZ6SujuE92wfeMXy6kfRlyjXzbMpShkwtgoZu5JKsLCQtRhlJ+w/KiMMREXFj45g+TUmofjLhDw+R3DV8UiRdxNy1ap2qwo6IiKG34chY8ehmlpMn6WHgGubek2+ePwgtKsEyVjyqYcfw6aaeU/fWp/NdGxpdE5pVPI7Ewvc+F/o5u2hX2v57ykjDJ4E/AOtLmnNx732zDsn1wFGSVqbM258SEb9sEEevjJV8GW3brzqtsXQVj+Ss4svq31oH0Ee6ikeStkvIKCIyrvVuVvE4EklWRDSpIJzA9ymJ3wb1q1tQ/igNVUQcDRwtaTVKb5DjJT2WcuKd0nBNVrpKvqRuq5/ITgYubF0BBmkrHjNW8WW1OeVY3TLhTw5PxorHrO0S0pF0FuUadUZE3DvRzw9Js4rHkZsulLQxpeNtAD9OMlKTlqSNgOOB9SNi0UYxnAL8ICK+0LP99ZTRm11bxJVNLaZ4BSVBXgs4jXKhSLFAua4/3I3SifojjRpZonJXg7cD/ZpZHhsRJzWKK+M02H9TzqmbKMnMN3rXHbXUVejxAuCgiBh32meAcfwQeNcY7RI+HhHP6/8vBxpTxqafSNqRch14AaWtyymU3nn3t4inV12XvDvlenBYRBw70NcbpSRL0iGU6pjOKNFOlIvG0IdzJb0UuKrTL6vG9nLK2of9o00X+k5sMygtAHYDtgEuovyx/najeNJV8mVca9RN0pMo5/puwBMpoxH/2SiWdBWPGav46lqVMafBIuKwRnGJco/H3SjXzCspfxi/FRGzW8RU40pT8VgbWX4dOJE+HcMj4ucNYrqdcabAIuKHw46pm6QlKXfG2I0yXXcOZU1wk2K0VhWPo5Zk/QbYoFPpUafCroiIdRrEchVl8e19tQ/VJygHdyNgl4h4YYOYOsOlLwZ+QalS+3aWId1klXxnkG+t0TwkLU2ZpjsAWDkihj4d5orHyZO0AeX99yLyTIPNQ9KilBGII4B1ImK8abJBxZCy4jFbu4R6rDrX9PVJ0vSzn7oe+SQazZi0rHgctSTrQsrIx9/q88dTPo21qLq6MiI2qI+PB34TEUfW55dHxMYNYvoBZa78tIi4e9ivP910rTXajXLBb73WqFNK/lLKhfU5wLmUBOf8iHioQTwXkazicTrIMg3WrVbz7gbsCtxB+YN9dIM40lU8ZjfsKbDJqDMUr6ScU93FVlc2iKVZxeNILHzvcg9wraTzKb/IbYFfSDoGICLePsRYVEca7qNMyX2ma98SQ4xjDv/Be2Qi4h7gBEknMXet0RKUUcmhk3Qy5Y/yDykdzF+VoD9PxorH1Oo02EbAesCtQLNRmtqgeLf69RAlYd8uIn7XKiZyVjym1GcK7BjK0ouWMb2hxrMOZd3ouyLipy1jalnxOGpJ1unMe4Jd1CgOKK0brgBmAb+KiEthzkLzPzWMyyapz1qjlzVea3QusF/LdTJ9pKt4zKrPNNgrE0yDnUuZttx1mFMoE8hY8ZhOzxTYYYmO37Mpd1y5ICIebh0MtK14HKnpwmwkrUJZlHxl52SrPaoWy7jGx+bKuNYoYzFFxorHjFV8MD2mwerxfB7wh4hoco/O7BWPWWRs+glQWwT9rc4EdNba7kQ5749tUWXYsuJxpJKsusD8g8BqlFG6lh1mXx0RX62Pt4yuLuuS3pphzjyLjJV8GdcaZSym6IkvRcVj4iq+fx1vf4tqMJW7LRwcEdfUD4CXA5cCTwWOi0a3IMpW8Zi1XUJGkn5OGfX/o6QNKT0jD6cszn8gIl7fMLahVzyOWpJ1I2Wh8tWtpy26F7f3LnRvtfA9q4yVfJIWy7bWKGMxRa8kFY8pq/gkvYtk02CSro2IZ9TH7wXWjYg9JS0D/CQS3IIoScVjunYJLafAxiPpqs55I+ko4OGIeHfto3dFhnMKhlfxOGqdam8Brml9Ma00xuN+zx/VImIn4IXA7ZSu4T+U9GZJyzUM6zZJX5S0df1UnYEkLV0vVttQbmnT0aSYAkrFo6RdJH0LuBHYmtKj6kkt4omIKyPi4IjYEPgSsCNwnaTWow1PAn4q6Uf1/J7ZOB4o/ac6tqGs76GOFjVfT1MrHj8AfJoyKvmeRqGsBLyXsgbqaEpR1R0R8cMWCVZ1HLADcJOkr0t6mcr9OVvrvl5uTb1OZVifJWlFSW+T9BPgDOA8YKAfTkdt4fu7gbNVuvP+s7Ox0RqMGONxv+ePetkq+YCnU9aF/BfwZUnN1xqRsJgiacUjkKuKDyAi/qOuF+tMg/2XpNaNP2+R9DbK72djykL4To/BxRrEk7LisbZHORc4t6tdwkWSmrVLiNI8+ttdU2B7Ap+V1LTpJ/ADSV+nXJOeQFnT2lmP3KTre8uKx1GbLvwe8Hfgaro+hbVYgyHpPsqnelHWN9zY2QWsGRFLDTumzPpU8jXvGt6RZa1RjSVVMYXKLWxOz1Tx2KeKL0Uzy15JpsGeSBkpWhn4dER8r27/N8pNkY9qENNvKYnnUJtGTqRPu4ShdAx/JIY1BTZBDKL0WluZ8t67rW7fCHhiRJzXIKbjmbtsYKgjaqOWZF0TEc+c+CcHr1ZYjKlTIWY5K/l6ZVhrlFHSisfpUMWXovHndJCk4rFZx/CJKFHTz6xaVjyOWpL1UeD7nU9jGdX1NLtHxNdax5JFxko+IF139YwyVjxmrOKDMafBTm05DZZRxopHJWyX0GcK7NRhTYFNNy0rHkctyZoNLEVZj/UAbd8Ay1Luc7UKZVj5fOCtwIGUqZ4dhx1TVkkr+brXGp1K6amSYq1RJhkrHjNW8UHeabBspkPFYwYtp8Cmm5YVjyO18D0ilmkdQ5evAHcDPwNeT6lMEbBTRFzRMrCEMnYNz9hdPSMp2e2jmFvFdxOJmllGxFO7n2eYBkuqt+LxC1AqHuuI0tAlbZdwGGUKrLM2s3nTz7FIWj4i7mwZQtfjralVqhHx8KCLx0eihYOkdev3jft9NQprzYjYOyI+TxnS/RfghU6w+no6cAmlku8WSUdL2qJxTHcDc1pISDpE0pWSzpS0RouAJM2WNKt+ze56fp+kByf+HwaiU/F4KUkqHiPiP4BVgfdRKguvknSupL3qaEgTkr4r6Zn18cqUdWOvA74i6R2NYpohab/6+7mqfp0j6Y2SmlQXUiseJb2MJBWP5GyX8HXKzA11CuwbwB+ADZj3w85QSTpC0gr18aaSfgf8XNLNE03lD9AP6nE7miFXPI7EdKGk4yJiX0kX9tndZE1P71RJq6mT6SZLJV/GtUa96gjSW4D9KBV+BzaKI1XFY68MVXw1jnTTYJJOoRSdnERp4wDwZGAvYLmI2LVBTOkqHrtiG3rH8HFiSdn0U9LVEbFefXwh8O6IuETS2pTf1aYNYmpW8TgSSVZGkh5i7kJJAY+lTKk0va/UdKAElXwZ1xp1xfZ44B2UvjgnA//deCg+LSWq4pN0RZQGqUi6APhCRJzau2/IMV0fEWs/0n3Wvl1CTzJzOfCeTrLQnYA1iOtXwHoR8aCkiyNii659c2J+tBiJNVmSNgNuiYg/1+d7MreM/NCIuGvYMbXqUTJdqX8l38GUgoFGIeVaa1SH4A+kJAzHAxt1SpJtLiVsZlmla/wJ3CVpF+C0rlHIRSijyXc3iikt9W+XsHejcNI1/aw+Q2kKfgSleevRlFumbU1ZWvCoMhIjWTWLf0FE3CXpeZSL6tuADYGnR8QrmgZo41LCSj6VhpbvpXRX/2tEvKhu3wg4KiK2aRDTvZRbD50AzLcgP9rc2SAdJa3iyzgNJml14EjKH8C7KSPtjwMupLRRGHqfs4yUsF1CyymwScT2fOBNwNqUwZxbKLexOSFbJfmgjUqS1T2182ng9og4tD5vMgxvk6eEXcMh31ojSYcyzi2ZosGdDaYDuYpvUurvCU89z09ulzByNKSKx1FJsq4BNqxzwL8G9o2I/+3siyRd4K0/JewabpOj0puucxHp1EIH5dPrYyJi6EsSlLCZ5XShUjm7EXBdRPy6UQwzgH2AlzH3JuO3Ad8GvtRiJEQNO4aPEkmvjYgTGrzuEZQZiDskbUqZ5n2YMk2/ZwywQfGoJFn/CbyYsrB1VWDjiAhJTwNOiogtmwZo45oOlXwZ1ORzLBERHxxaMGPIUPGYsYovK0lnRMRO9fGOlJYcFwFbAh+JiBMbxJSx4rFZx/BRIukPEbFqg9dtVvE4EgvfI+LDtVpnZeB7MTdzXISyNstyi4i4rz7emfJp9TLgMklvbhhXNv2aIC5F+dS/PNAsyepT8bhZw2mndM0sE+u+x+pBwNYR8ftaZHEBcGKDmDbpU9V4K3CxpOsbxAPw2Ij4Y338asqNoT/eaZfQKKb5DGsKbIIYrhprF9Dqnq8zJM2IiAcpx/ISgIi4XuXG34N74UH+58MUERf32dbqDWmPTLpKvowi4uOdx3VUZn/gtZRigY+P9e8GKWnFY8YqvpTTYMy7xm9GZ2q+Tqu0SkgzVjw26xg+ZkBjTIGpNJEd6BTYBFYEXsj8x0pAq2KBZhWPI5Nk2bTW6Ro+iyRdwzOuNapxLUfpH7YHZTpl44hoWWp/M3MrHu8D9un+o9Oo4nEfShXfC4BdI+JvdfsWlDhb+QplGuxQ5p8G+yolUR22DSTNopzji0taOSL+pNLNvFUbmt0oFY+fkdRb8bhbo5gytkt4SUQcXB9/jHKuz5kCA4be9LP6LrB09Lm7iaSLhh8ORMSnJF3NvBWPa1EqHj80yNceiTVZNv1lq+TrlWSt0cco06nHUVoA/H3YMfRyxePkjdfcc7x9LdTp36dHxM8ax5Gi4jFjuwS56ee04CTLbBx91ho1665ep2/+CTzIvImN7yIwDUi6mDKt228a7ICIeFbL+Dok7RAR320dR0eGiseM6pT4Sym3i3oeZYStMwW2ZkS8pmF485C0b0Qc1zqOfgZd8egky6yPPmuNPpVgrVE606HiMYvp0vhT7W8bla7iMavp0vSz9Tk1nkFXPDrJMusjY3d1ldtHrRAR5/Rs357SlX7ojTYl9Zs2nVPxGBFLDzmkaSHRNNiqvdPxkn4ZERs1jGnO60v6KbBHd8Vj1MbTNn0kOKfGq3hcOyIGVmHohe9m/X2MuVNyy7QMpMuRlGrCXtdRksGthxtO2orHjFV884iIOyWtIelfaTsNdgalArPbfi0C6ZKx4nE+GdoljKVV089xvLTx6zereHSSZc1lrOSLelumZJaJ2hW/W0TcXD/lN5Gw4jFjFd9402BHSGo1DTZf/4GI+EWDOLqlq3hM3C5hLIfRqJJW0pOB1SPix/X5AcDSter45Ii4sUFYzSoePV1o6SSp5Eu31kjSjRHxtEe6b8AxZax4TFnFl3EaTNJfKaOOfUXE24cYzrhaVjy27Bg+TkzNpsDGo9Kx/2ud4glJv6FcH5ak3H1hjxZxteKRLEsjWdfwjN3Vvy/pw8D7on46qqXlh1H79jRwIKXi8X3Af3b1yGpZ8ZixmSXknAb7PyD9TbO7Kh5btZRo1jF8HBmbfgKs01Odel9nWYGkHzWKaT7Dqnh0kmXNZewannGtEeV39EXgRkmdYe8NKDc/bnLvtIhYpMXrTiBjM0tIOA0G3BkRJzV67UfiA5Qpn1aadQwfR7qmn1XvXTq26XrcbFlDH2+kjLANlKcLrbmMlXzQd63R0Y3XGgEgaU3gGfXptRHxu4axpKt47IkjRRXfeBpPg83TxDKDjBWPNYbnMw3aJbSmcjPt10TPbe0krQt8OSI2bxPZvIZ1TjnJsuYydg3PuNYoI0k/AF7buyBf0mqUPz5Dr3jslbmZZevGn5JeHRFfrY+3jIifdO17a0Qc2yCm+XoqSdo8wYL89DI0/ZT0IuAY4MPA5XXzJsB7gf17P5C1IunJEXHrxD85xddxkmU2P3dXnxxJl0TEZmPsuyoi1m8Q07RpZtm6SWP36/fG0iq2DKNWj0Smdgmtz6euOJ4JvJuuEXfgoxFxTaN4+lY81t0DrXj0mixrLmMlX9K1Rhk9YZx9Sw4tinmt1vX4IGDr7io+4MQWQfWbBqNPC4Uh0xiP+z0fllUkHTPWzkwVj1Wzdgl9tD6fAKjJ1J6t4+jyMeBrXc/3Y27F42GUJSED4STLMkhXyTcN1hptBawVESdImklZANvitiwZKx4zVvFB/safvdMaraY50lU8TtAuYcVhxjKB1k0/O9emNSPiy/X5N4Hl6u4PRUSL60KzikdPF1oqXZV8+1Aa/n08Iv7aII60a40kvR/YlHLhWFvSk4BvRMSWDWJZilLxuDlzq6zmVDy2WMsm6SFK4i5gcWC1riq+S1tMYda40k2DSboPuJHyu3pqfUx9vmZELNUgphRTXt0k/YVx2iVExJPm/1cDj6nZFNgEcV0AvC0irqvPrwb2pnxwfm9EvKhBTNdFxL90PV8uIu6qj38VEU8f1Gt7JMtS6FPJ17preMru6tXLKAu5LweIiD/W5HToIuJeYPdMFY8RMVY7hCVpO3KUcRpsYH9cpuD+1gH0kbFdQrMpsAks20mwqhs6I/+SDm8U02xJa3cqHrsSrHXpU9G+MDnJsuZ6KvnWS1LJl3GtUcf9ERGSOtNzQx9t6FWTqmaJ1UQSNLOEhNNg/T5IJDCnojFLxWNE7DPOvlcNM5YuWZt+Pr77SUTs3PW01dTq+4Hv1qUN81U8DvKFPV1ozWWs5JP0OeBO+q81Wiki9h12TF2xvRNYC9gWOBx4HWV64FOtYsouwxRUhhh6Sfo9fd5z9XFExFMbxJSu4rGf1u0SWk6BTRDXd4DPRcRZPdt3AN4UES9pFFeTikePZFlzSSv50nVX74iIoyRtC8wC1gEOiYjzW8aUSdIqPsg5DdZ7z71FgFcC7wR+OfxwgJwVj/0MpWP4OJpNgU3gAMqo0SuYd9ToOcAOrYJqVfHoJMuay1jJl3GtUbeaVKVKrBJVPGas4oOc02B31tdfBHgN8C5KAcNLetbVDDWsMR73e95S64Sv2RTYeCLiBknrU9aEda6d/wu8MSL+0SKmlhWPni605jJX8mUiaTb9/8g0b5CarOIxXRUf5JwGk7QYZbr5P4AfA0e0qkrriildxWM/w+oYPkEMqZp+1pg+TVm+8JMJf3hIWlY8eiTLMshcyZdGRDSpIJykNBWP5Kzig5zTYL+nrIX8JPAHYP06CgFARHyrQUzpKh7HapdQlmm2a5fQagpsAtcDR0lamdKG55SIaDX13NGs4tFJlmWQuZIvJUkbA1tRRrZ+nOAilqniMV0VX5VxGuz79bU3qF/dAhh6kpW04jFdu4SkTT+JiKOBo+tMxG7A8ZIeC5xCSbiuH/c/GIxmFY+eLrTmMlfy1ViyrDXqxHMIsAtz/wDuRJma+1DDmNJUPGaqQOs2XabBWste8Vifz5mSlvSjiHhug5jSNf0ci6SNgOOB9cfpYzfI129W8egky5rL2DW8K7Y0a426YvoNsEFnEWn9lHhFRKzTKqYax7bAdpQ/iue1qniUdHFEbNHitcdTP9mPqcUIjqSXAld1Xrsm8C8Hbgb2b/FhQtLyPZu6Kx4vj4iXN4gpXbsE9dycXdK3OiM0kn7S8hpVY5gBbE8ZzdqGcpP2UyLi2w1iWYvSUPan9Kl4HOTomqcLrbnklXyZ1hp1/BFYAuhU6iwO3NYunCJRxWO6Kj5IOw32YWALmPOp/tXA7pRz/nOUW8kMVdKKx4ztEjI2/ex82NodeDHwC+BUYN96nW+iZcWjkyxLI3J2Dc+01qjjHuBaSedTplG2BX7RWew9zIXdSSseDwC+Wh9/innbObyOriRsmDJOg9XXva8+3hn4Ul0QfJmkNzeIp1/F406tKx7J2S7h15JeMsYU2G8axQTwHuBk4MBoe2u0OboqHo8f9ms7yTIb39clfR54vKQ3UC7+X2gc0+n1q+OiRnFkrXjMWMUHSRt/SloauI8ypfOZrn1LtAkpX8VjRJwraWdKu4TOh5hrgZ0btkvI2vQzY8udZhWPXpNlNoEsa42yy1LxmLEfVbc+02AfaTUNJul1lNGYWZTGvy+q2zcCjoqIbRrEdCJjV1tGRLxuiOGkJmlx5p0Cu5YyYtOk6Wd2XRWPuwFDqXh0kmWpZKvky6hOB3wQWI0yGp2hGWmaisesVXwZG38CSFoFeCJwZUQ8XLetDCwW89+e6FEpY7uEjE0/p5NhVTw6ybI0MlXyJV1rVAKQbqSsn7k6kryBM1U8ZqziA5B0K/NOg82jxTSYpFdHxFfr4xRFAkkrHtO1S5C0P2VEJlPTz9RaVDw6ybI0VG7EvBGlTLvTg+aqiFh//H/56CLpQmCbzqhDBjWml0XE3+rzxwPfSro+o4mM02AZp1YlXQVsERH31VHbTzC34nGXiBh6xWPmdgktpsCmmzEqHr89jIpHL3y3TDJW8qVZa9Tl3cDZkn4I/LOzMSI+0S6kVBWPGav4iIi9W7zuBDIWCaSreCRpuwSYMzJ7JHBk1xTYIcDQm34m1qzi0UmWZZKukq/PWqMTJTXtrk7pbfR3SvXXYxrG0S1NxSM5q/hSToOR81Y/GSses7ZLGGsK7NCGIaXTckTd04WWSrZKvkxrjbpiuiYintnq9aeLTFV8NZ6M02DpigSSVjw26xg+TkzNpsBs8pxkmY0j41ojSR8Fvh8R32sVQ69MFY+Jq/iujIgN6uPjgd9ExJH1eav1T1mLBNJVPGZrlyDpB5QpsNOyNP20+TnJsuaSV/KdAWxGuV3MnLVGwK0w3LVGXTHNplQ1/RN4gBy/pzQVjxmr+GDOSNZzKNNgvwdeHhGX1n3z3BuvtToKuHtEfK3Ba2eseHS7BFsgTrLMxiFpr/H2R8RJw4ols0wVjxmr+CDtNNiywFuAVYAzKR8m3gocSBlF2rFBTBkrHt0uwRaIkyxLJWElXxqS1o2IX9ff0Xwi4vJ+24dB0maU6cJMFY/pZJsGk/Rt4G7gZ5RF00+kjIzuHxFXDDueGtMvu1q4zHnc73mD2NwuwR4RJ1mWRqau4V0xZVprdFxE7FtHjXpF43Vi36NUPF4NzBnNiojDGsSSsYov6zTY1RGxXn28KPAnYNVW64xqHOlGsvoZVsdwm96cZFkaSSv50qw1yixTxWPGKr4aV7rkIUscPTGlq3jsim3oHcNtenOfLMvkj5Q+OJ1P0YsDt7ULB4BbgGsyJFh1Su6WiPhzfb4nc0doDo2IuxqGd7ak7ZJUPGZsZgk5G39uIGlWVwyPrc9bFlM8vcFrjmuMdgn7ul2CTcRJlmWSpmt4l0zd1T8PvABA0vOAI4C3ARsCxwGvaBBTx5uAd0rKUPGYsZklJGz8mXGaa6y2EZ2KR8qHimFr1jHcpjcnWZZJpq7hHZm6qy/aNVq1K3BcU5kGbgAAAqNJREFURJwGnKZy38dmImKZlq/f45OU5qOzgF91tUnYiLLmqJV161SmgKfWx9Tna7YLK5eJKh6BobeVaLne0aY3r8kyG0eytUbXABtGxIOSfk2Zrvjfzr4WcWateMxWxVdfP2Xjz2wyVjyaLSiPZFkamSr5umRaa3QK8ENJdwD/B/wIQNLTKFOtLRwA7At8vM++AIY+AtBVxXebpC2BnwBExJ8kvRUYehVfff2M02AZrdlV8fhFElQ8mi0oj2RZGhkr+bJ1V5e0BaUh4vc6i24lrQ0s3bJPViYZq/jqa6dr/JlRpmNmNlUeybJM0lTydSRba0REXNxnW7NGiEkrHjNW8QF8hbnTYK+ndH8XsJOnweaRseLRbIE4ybJM0lTyZV1rlFDGisd0VXyVp8EmIWPFo9mCcpJlmWSq5Eu31iipjBWPWav4Hug8iIiHJN3qBMtstDnJskyelKWSLyL2rd//rXUsyS0qaUZEPEipBNu3a1+r60u6ZpaVp8HMHmWcZFkmaSr5kq41yihdxWPWKj5Pg5k9+ri60NLIVMkn6XLgBRFxV11rdCpz1xo9PSJadldPJVvFo6v4zCwLJ1lmfUi6MiI2qI8/DdweEYfW51dExIYt47OxuZmlmWXh6UJrLmklX8a1RjY5ruIzsxT8x8IyyFjJl26tkU2aq/jMLAVPF5qNIdtaI5scSQ8B93aeAo8F7sNVfGY2ZE6yrDlX8pmZ2ShapHUAZpSu4ffDPF3Dv0yZljuuYVxmZmYLzGuyLIOMXcPNzMymxCNZlsGikjoJ/zbAD7r2+YOAmZlNS/4DZhm4ks/MzEaOF75bCq7kMzOzUeMky8zMzGwAvCbLzMzMbACcZJmZmZkNgJMsMzMzswFwkmVmZmY2AE6yzMzMzAbg/wEHDIe5duzIpAAAAABJRU5ErkJggg==\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "BN9R-IIypzcY", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 1000 | |
| }, | |
| "outputId": "ac1e7252-072f-4a65-c2e4-b4e3012b1e2a" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 1502.36x5754.33 with 30 Axes>" | |
| ], | |
| "image/png": 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