201 lines
67 KiB
Plaintext
201 lines
67 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import sys\n",
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"import matplotlib\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"%matplotlib inline\n",
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"plt.style.use('ggplot')\n",
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"experiment_dir = 'path/to/mlpractical_directory' #Replace this with your path to the mlpractical directory"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"def collect_experiment_dicts(target_dir, test_flag=False):\n",
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" experiment_dicts = dict()\n",
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" for subdir, dir, files in os.walk(target_dir):\n",
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" for file in files:\n",
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" filepath = None\n",
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" if not test_flag:\n",
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" if file == 'summary.csv':\n",
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" filepath = os.path.join(subdir, file)\n",
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" \n",
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" elif test_flag:\n",
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" if file == 'test_summary.csv':\n",
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" filepath = os.path.join(subdir, file)\n",
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" \n",
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" if filepath is not None:\n",
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" \n",
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" with open(filepath, 'r') as read_file:\n",
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" lines = read_file.readlines()\n",
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" \n",
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" current_experiment_dict = {key: [] for key in lines[0].replace('\\n', '').split(',')}\n",
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" idx_to_key = {idx: key for idx, key in enumerate(lines[0].replace('\\n', '').split(','))}\n",
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" \n",
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" for line in lines[1:]:\n",
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" for idx, value in enumerate(line.replace('\\n', '').split(',')):\n",
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" current_experiment_dict[idx_to_key[idx]].append(float(value))\n",
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" \n",
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" experiment_dicts[subdir.split('/')[-2]] = current_experiment_dict\n",
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" \n",
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" return experiment_dicts\n",
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" \n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"VGG_08 ['train_acc', 'train_loss', 'val_acc', 'val_loss']\n",
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"VGG_38 ['train_acc', 'train_loss', 'val_acc', 'val_loss']\n"
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]
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}
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],
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"source": [
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"result_dict = collect_experiment_dicts(target_dir=experiment_dir)\n",
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"for key, value in result_dict.items():\n",
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" print(key, list(value.keys()))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"%matplotlib inline\n",
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"plt.style.use('ggplot')\n",
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"\n",
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"def plot_result_graphs(plot_name, stats, keys_to_plot, notebook=True):\n",
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" \n",
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" fig_1 = plt.figure(figsize=(8, 4))\n",
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" ax_1 = fig_1.add_subplot(111)\n",
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" for name in keys_to_plot:\n",
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" for k in ['train_loss', 'val_loss']:\n",
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" item = stats[name][k]\n",
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" ax_1.plot(np.arange(0, len(item)), \n",
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" item, label='{}_{}'.format(name, k))\n",
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" \n",
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" ax_1.legend(loc=0)\n",
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" ax_1.set_ylabel('Loss')\n",
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" ax_1.set_xlabel('Epoch number')\n",
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"\n",
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" # Plot the change in the validation and training set accuracy over training.\n",
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" fig_2 = plt.figure(figsize=(8, 4))\n",
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" ax_2 = fig_2.add_subplot(111)\n",
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" for name in keys_to_plot:\n",
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" for k in ['train_acc', 'val_acc']:\n",
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" item = stats[name][k]\n",
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" ax_2.plot(np.arange(0, len(item)), \n",
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" item, label='{}_{}'.format(name, k))\n",
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" \n",
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" ax_2.legend(loc=0)\n",
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" ax_2.set_ylabel('Accuracy')\n",
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" ax_2.set_xlabel('Epoch number')\n",
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" \n",
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" fig_1.savefig('../data/{}_loss_performance.pdf'.format(plot_name), dpi=None, facecolor='w', edgecolor='w',\n",
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" orientation='portrait', papertype=None, format='pdf',\n",
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" transparent=False, bbox_inches=None, pad_inches=0.1,\n",
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" frameon=None, metadata=None)\n",
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" \n",
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" fig_2.savefig('../data/{}_accuracy_performance.pdf'.format(plot_name), dpi=None, facecolor='w', edgecolor='w',\n",
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" orientation='portrait', papertype=None, format='pdf',\n",
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" transparent=False, bbox_inches=None, pad_inches=0.1,\n",
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" frameon=None, metadata=None)\n",
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" \n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"<ipython-input-23-0810a62e0421>:32: MatplotlibDeprecationWarning: \n",
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"The frameon kwarg was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use facecolor instead.\n",
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" fig_1.savefig('../data/{}_loss_performance.pdf'.format(plot_name), dpi=None, facecolor='w', edgecolor='w',\n",
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"<ipython-input-23-0810a62e0421>:37: MatplotlibDeprecationWarning: \n",
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"The frameon kwarg was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use facecolor instead.\n",
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" fig_2.savefig('../data/{}_accuracy_performance.pdf'.format(plot_name), dpi=None, facecolor='w', edgecolor='w',\n"
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]
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},
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 576x288 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfQAAAEJCAYAAABi2tVNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXSU5dn48e8ze/ZlspFAWBLCDrIrsggEUECWvljaX62nVVtrN6q1r2Cx1h7bUmvVYrWv9aXU81ZPLValKFJAQZbIvhsCWSAkgSRkT2afeZ7fHyMjMQlMyB6uzzk5JDPPcs+dIdfc66VomqYhhBBCiB5N19UFEEIIIUTbSUAXQgghegEJ6EIIIUQvIAFdCCGE6AUkoAshhBC9gAR0IYQQohcwdHUB2urixYvtdq24uDgqKira7Xo3K6nHtpM6bDupw7aTOmy7jqjD5OTkZh+XFroQQgjRC0hAF0IIIXoBCehCCCFELyABXQghhOgFJKALIYQQvYAEdCGEEKIXkIAuhBBC9AI9fh26EEII0RE8bg1V0zCbr9/21TQNl1Ojod6Hx63hdml4PBqF+kr6pWnodEqHl1cCuhBCiB7B69XQ60FR2h4cnQ6V6kovbpeGTq+g14NOpwQer6n00VCvAhAWriMmTk9snIHQcB0+L3g9Gl6vhsOuUlfjo7bah8upNbmPonPSJzUSk1kCuhBCiJuQy6VSU+kPlP4vLw67ht4A4RF6IqJ0hEfo0TRwOVXcLg2XS8Pr0fD5NFQf+HwaBoOCJVSHJUQhJESHw6FSXeHDblNbvLfJrBBj1ZPS34ROB9WVPsoveSk+72lyrKJARKSOhCQjUTF6wqN0mEw6jCYFk0khMSmOysrKjqyqAAnoQgghOoTToVJT5aOmyktNlQ9bvYolVCEsXE9YhI7QMB2KApoGmupvgddW+aiq8AZaxwBhETpirAZSB+lxu1Tq61Qqyr4IsEajgsmiYDIrmC0KOr0OvR70egWPR8NpV6m67MPp1DCZFGLiDAxINxETZyAkVIfq01BV/wcAk1khJFTXpBdA0zTsDSoOh4rBoGAwKhgMCkaTgl7fcuu7PXoTgiUBXQghRNC8Xu2qLmaViCg9kdF6wsL9wbmmykdpiYeyix7qa/1BWVEgIkpPtFWP065SfsmD61zT7mkAo8nfOu47wERsnIGoGD0GY/NB0evR0OlAd42AejVN0z4vT+uDrKIohEXoCYvQt/rcziIBXQghehGXS6XkvJui8x48Hg2LRcES4u9yNlt0mC3+f01mBUXxT/xyfz6JS1U19Hrl8y+oraynvNyJ067isKvY6tVGLeer6XSgNyh43BqKArHxBoaP8beCI6P1GAyNg6jHo+H4vNtb0fmDvk6nEBKqBB1wWwr0LenM1nJXkIAuhBA9mKpq2G3+iVkXL3govehBUyE6Vk9snB6XQ6O+1sflMhVv0yHg67AD/lZzSIhCWKSO5FQTUTF6omL0mMwKDXUqdbU+6mt8uF0acUkGEvoYMJmuPTPcaFQwRnff1m5PJAFdCCG6KbdbpbrSR3WFfwza59PQKQqKzt8idtj8LWb180azyawwMN1Mv4EmIpsJlj6vf+KYy6kGZmRfmbxlMivodOD7fDKZ6oOY2BicztprtoSvBHfR9SSgCyFEF3PYVaoqvNgaVBw2f/e2vUHF1tB4DNpoUvD5NDQvqCpYQnXE9zESEemf9R0Zpb/meLLeoBBqUAgNa7n1bLzq++gYExUVvbubujeRgC6EEB3A69Ww1avYGvyzux12Fb1B8Xc1m5TABLLKcm+jJVRXZllHRuvpO9BErFVPdKyh1ePF4uYjAV0IIW6QpvkndtXXqTTU+TcisTWo2Op9OB2NZ3EbTQqqT8Pna/yYNd7AwAwz1ng94RF69AYJ3OLGSEAXQoireNwa1VVe6mt9OO0aToeK06HidmvoFFB0/ta1qoKt3tcoQJvMCmHhOuISDYRF6AkP1xEWoSMs/IulV6rPvyWoz0erZnQLcT0S0IUQvZ6m+YOopn6+iYnmX8Pscqo4nRouh39yWXWlN7B2GkCnh5DPl3yFR+rRtC+uoShgTTATEakjIlJPeKQOUxB7fuv0CuYg100L0RoS0IUQvZbXq1F0zk3+GVdgzXNLjEaFaKue5H4mYqz+mdv+sW4JvqJnkIAuhOiRAjuWVfl3LdPQCA3TExqmIyRMR/G5Kj47XofbpRFj1TMw3YJOr3y+gYl/xrfZomCx6DBbdBiMvX/jEdG7SUAXQvQIXq9G1WUvl8u8VJR5qKtV4fN5Z1fWUDsdjXdOSUw2kDbUQmycXoK16PUkoAshui27TaWsxL/7WdVlL6rqb13HxBnIGG4kKsa/17clxN817vP501nabSrJKVa8vrqufglCdJpOC+jHjh1j/fr1qKrK7NmzWbJkSaPnP/vsM5599lkSEhIAmDx5MsuWLeus4gkhuoGrd0Yru+ihrsY/7h0eqWNAupn4JAOx8YYm+4JfodcrhEf4l3/5N0XpzNIL0bU6JaCrqsq6detYvXo1VquVVatWMWHCBPr27dvouGHDhrFy5crOKJIQohtwu9TPu9C9/pSZdZ9PXFMg1qpn+BgLiSlGwrtxhishuotOCeh5eXkkJSWRmJgIwJQpUzh48GCTgC6E6HlU1Z/84+q11lfYGnwUn3dTdtGLTucf6zaZ/bmqqyv9k9nAP8M8Jk5PSn/ZGU2IG9UpAb2qqgqr1Rr42Wq1kpub2+S4s2fP8rOf/YyYmBi++c1v0q9fvybHbN++ne3btwOwZs0a4uLi2q2cBoOhXa93s5J6bLueUIder0peTj2njlZTX+cFIMZqIj7RQkSUkaJzNspLnQAk9rGg1ys4HT7qa324XSqxcWbGTooiuV8ocQlmdLr2DeA9oQ67O6nDtuvMOuyUgH4lqfzVvjzjdODAgbzyyitYLBaOHDnC73//e9auXdvkvMzMTDIzMwM/V7TjIFlcXFy7Xu9mJfXYdt25Dh12lZILbgrOuHA5NaJj9YyZGILDrlFd6eVcbj0ej0Z4pI5hoy2k9DcREtrShisq0EBVVUO7l7M712FPIXXYdh1Rh8nJyc0+3ikB3Wq1UllZGfi5srKSmJiYRseEhoYGvh83bhzr1q2jrq6OyMjIziiiEOIqXq+Gz6t9/q8/iF8u83K51BMY545LNDDuVjPWBEOjD+iapuF2aZjMsimL6N18Ph9ut5uQkJCuLgrQSQE9LS2NS5cuUV5eTmxsLFlZWfz4xz9udExNTQ1RUVEoikJeXh6qqhIREdEZxRPipuX1alRe9lJT6f08M5j/y+Nu2qum04M13kDqIBMJSUYiopqfqKYo/g1bhOjNLl26xLZt26ipqWHAgAHccsst9OvXr0s/xHZKQNfr9dx///38+te/RlVVZs6cSb9+/di6dSsAc+fOZd++fWzduhW9Xo/JZOInP/mJfLoXop1caTX7E434d1i7XOalusK/thsgJExHWLiO5H5GQsN0GAwKeoOC3gAmk0KM1SCZwES7KS0tJT8/n6SkJFJTUzEajdc/qRW8Xi/l5eVERETcUOPQbrdz9OhRbDYbgwYNon///hiNRrxeL/v37+fIkSOEh4czduxYcnJyeO+994iJiWHYsGFERUURHh5OeHh4k97ojqRozQ1w9yAXL15st2vJeFH7kHpsu/aoQ9WncanEQ2Gei6pKH9qXtjKPjNYRn2gkPslATFzLa7t7KnkfNuZ2u3G5XJjNZoxGY1ANppbqUNM06urqcDqdJCQkBN340jSNwsJCDh8+TElJSeBxg8FA//79SUtLIyUlhfDw8FY36DRNo7S0lMLCQkpKSigtLcX3eSq8/v37M2LECAYOHIher8fhcFBaWkpZWRk6nY7ExEQSExOxWCzY7XYOHz7MyZMn8fl8mM1mnE4ner2e/v37U1tbS2VlJcOHD2fatGmYzWa8Xi+5ubmcOHGCsrKyRuVSFIUHH3ywXbvlu3QMXQjReewNPgoL3FwocON2aYSG6Rg02ExIqA5LqH/v8tBw//7lovdzOp0cO3aMY8e
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 576x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plot_result_graphs('problem_model', result_dict, keys_to_plot=['VGG_38', 'VGG_08'])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "mlp",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.12.5"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|