2024-11-11 10:57:57 +01:00
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{
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"cells": [
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{
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"cell_type": "code",
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2024-11-19 18:04:58 +01:00
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"execution_count": 3,
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2024-11-11 10:57:57 +01:00
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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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2024-11-18 21:40:20 +01:00
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"experiment_dir = '/home/anton/uni/MLP/mlpractical' #Replace this with your path to the mlpractical directory"
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2024-11-11 10:57:57 +01:00
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]
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},
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{
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"cell_type": "code",
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2024-11-19 18:04:58 +01:00
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"execution_count": 4,
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2024-11-11 10:57:57 +01:00
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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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2024-11-18 21:40:20 +01:00
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"execution_count": 8,
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2024-11-11 10:57:57 +01:00
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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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2024-11-19 18:04:58 +01:00
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"VGG_38 ['train_acc', 'train_loss', 'val_acc', 'val_loss']\n",
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"VGG38_BN ['train_acc', 'train_loss', 'val_acc', 'val_loss']\n",
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"VGG38_BN_RC ['train_acc', 'train_loss', 'val_acc', 'val_loss']\n"
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2024-11-11 10:57:57 +01:00
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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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"\n",
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2024-11-11 10:57:57 +01:00
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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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2024-11-19 18:04:58 +01:00
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"execution_count": 10,
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2024-11-11 10:57:57 +01:00
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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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2024-11-18 21:40:20 +01:00
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" orientation='portrait', format='pdf',\n",
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2024-11-11 10:57:57 +01:00
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" transparent=False, bbox_inches=None, pad_inches=0.1,\n",
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" metadata=None)\n",
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2024-11-11 10:57:57 +01:00
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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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2024-11-18 21:40:20 +01:00
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" orientation='portrait', format='pdf',\n",
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2024-11-11 10:57:57 +01:00
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" transparent=False, bbox_inches=None, pad_inches=0.1,\n",
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2024-11-18 21:40:20 +01:00
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" metadata=None)\n",
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2024-11-11 10:57:57 +01:00
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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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2024-11-19 18:04:58 +01:00
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"execution_count": 13,
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2024-11-11 10:57:57 +01:00
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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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"data": {
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2024-11-18 21:40:20 +01:00
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAArQAAAF3CAYAAABUqthdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAACURUlEQVR4nOzdd3xUVf74/9edmmTSSSOFkIRQEqqCgEhHXFgF4YcIruvqsrZ1Xb/rrhVFxFXEup+1r3xAxUXBglIUUAFBaR+KIIQSSAIkISQhvU29vz+GDIQkMAlJJgPv54N5JHPvnXvPzMmEd868z/soqqqqCCGEEEII4aU0nm6AEEIIIYQQl0ICWiGEEEII4dUkoBVCCCGEEF5NAlohhBBCCOHVJKAVQgghhBBeTQJaIYQQQgjh1SSgFUIIIYQQXk0CWiGEEEII4dUkoBVCCCGEEF5NAlohhBBCCOHVdJ5uwNKlS/n888/rbAsKCuL9999v9DFpaWl8+OGHZGdnExISwoQJExg7dmxrN1UIIYQQQrRDHg9oAeLi4nj66add9zWaxgeO8/PzmTt3LqNHj+bBBx/k0KFDzJ8/n8DAQAYNGtQWzRVCCCGEEO1IuwhoNRoNwcHBbh27du1awsLCuPPOOwGIjY3l6NGjrFixQgJaIYQQQogrULsIaPPy8rj33nvR6XQkJyczffp0IiMjGzw2PT2d3r1719nWt29f1q9fj81mQ6er/5SsVitWq9V1X1EUfH19KS4uxmazteyTaYSiKISFhVFYWIiqqm1yTdFypP+8n/Sh95M+9G7Sf96vrftQp9MREhLi3rGt3JaLSk5O5oEHHiA6OpqSkhK+/PJLnnrqKV577TUCAgLqHV9SUkJQUFCdbUFBQdjtdsrLyxt84suWLauTp5uQkMC8efPcfpFaUlhYWJtfU7Qc6T/vJ33o/aQPvZv0n/drj33o8YC2X79+ru87depE165defDBB/nxxx+58cYbG3yMoih17tf+lXD+9lqTJk2qc67a4woKCtp0hDYqKoq8vDz5y9QLSf95P+lD7yd96N2k/7xfW/ehTqcjPDzcvWNbuS1N5uPjQ6dOnTh58mSD+4ODgykpKamzraysDK1Wi7+/f4OP0ev16PX6Bve19ZtKVVV5I3sx6T/vJ33o/aQPvZv0n/drj33Y7urQWq1WcnJyGk0HSE5OZu/evXW27dmzh8TExAbzZ4UQQgghxOXN4wHtRx99RFpaGvn5+aSnp/Pqq69SXV3N8OHDAVi8eDFvvvmm6/ixY8dSWFjoqkO7bt061q1bx0033eSppyCEEEIIITzI40OaRUVF/M///A9lZWUEBgaSnJzM888/78qZKC4uprCw0HV8REQETzzxBB9++CFr1qwhJCSEu+66S0p2CSGEEEJcoRS1vSVBtKGCgoI65bxak6IodOzYkZMnT7a7vBNxcdJ/3k/60PtJH3o36T/v19Z9qNfr3Z4U5vGUAyGEEEIIIS6FBLRCCCGEEMKrSUArhBBCCCG8mgS0bUCtKMPxf5swH97v6aYIIYQQQlx2JKBtA+qqpTjee4nK1V95uilCCCGEEJcdCWjbgJLiXN63ZtcWmdkphBBCCNHCJKBtA2pyKma/UCpLq+FUjqebI4QQQghxWZGAtg0cOqzyw7X/4mjnm1D37fJ0c4QQQgghLisS0LYBP5PzZa70i0JN2+3h1gghhBBCXF4koG0D/gFa4ExAe/BX1DZanUwIIYQQ4kogAW0b8A90vszVvmHYbQ44esDDLRJCCCGEuHxIQNsGDEYFnR5QNFT5RkjagRBCCCFEC5KAtg0oinJO2kFH1P0S0AohhBBCtBQJaNuI6Zw8Wo5noJaVeLZBQgghhBCXCQlo24h/wJlKBxHJAKhpv3iwNUIIIYQQlw8JaNuIK+UgqJNzg+TRCiGEEEK0CAlo20htpYNKbTDgHKGVZXCFEEIIIS6dBLRtxOTvHKG12LVY/EKgtBhysjzbKCGEEEKIy4AEtG1Ep1fwM+kAqOw+BECqHQghhBBCtAAJaNtQcKgBgKpOfQAJaIUQQgghWoIEtG0oKNgIQGVIgnNDehqq2ezBFgkhhBBCeD8JaNtQcIhzhLZS9YPQMLBZIX2fh1slhBBCCOHdJKBtQ0G1AW25AyX1KgDU/b94sEVCCCGEEN5PAto2cLrKyr5TlWh9nS93RYUDevQFQN2/y4MtE0IIIYTwfjpPN+BKsPVEBf/ZcQqF49yliwS7wsfWBGIjryKqshDday+h+Pqh8fFF4+uL4uuH1mjAoNejNxowGvToDXp0RiOK0QiGMzejj/OrXg8aLYpG/j4RQgghxJVHAto2EmHSk19ppUy1E6zo2JJlJrfHtPoH2oGKM7fzKKoDg8OM0V6O0WHBaLdidFjROWy1R4ACKAqgoMWBwWHDoNoxqjb0ONCiYtHqqdYaqdEaqNYYqNHoUVEw4MCAHSOOM987sCkaLIoWM1pqFB1mtDhQ8FXs+GDHDzt+ih0fxYHpzPd+GgcmjQM/jQM/jQoo2NFgB+yKBruq4FAUHIoGVdHgOHNTFcV5U0EFHGeelQMFhwp2NDjA+VhVAUVBq4BWAZ0GdApoFQWNcu6LVucVRKM4XyPnF6XOVxTQnHkNldrX0vVQDQGBmZSXV0Dt8RoFFA0anG3Qorrao1HOfPxxpj2KAkrtRWrPW3uf2mufuaec/ejEgeJ6LRxnXjfU85/XOc1U6j8nVOXsa3neWh7KmcfU+dE594VTnNc/dxEQ9bzHA2g0zovVtv/8JUOcfaqc8/3ZE9SeWq33KFDVs+2o/aIo579OZ/tcoyhnXxrlvBdJUaisslNYVHT2orXXUWrbpdZt3zkv9NluO+e85/eDWudLnYOUs9+e/erG2ipqQ8+l3tnPud/ooXV31H0JGvjBcL0/6j5SbeR7dygXeB7uPr7SpqPwdEmDPy/ncz7HS7tmU53/HC/cyobb5m6LL3ju1nraTejD+j+bCpUUU1hY0cjCQue/dk1ffEi51Cfetj8u7Z7JoCMswOjpZrhFUa/g5aoKCgqwWq1tci1FUQgIDeerpUeoLHRQ0sHKflsFheU1qA4HqqricKg4VHCoKnYUrGiwKto2aZ8QQgghxLlG2nP4f3eMdt1XFIWOHTty8uTJNlntVK/XEx4e7taxMkLbhvyNOqLC9BwtNNMv1MTvr7p4JzlUFZtDxWpXsdhVLHYHNTYVs81Bjc2B2WLFZraC6gCHA8XhANWB6rBjtzuw2BxY7A4sNufjbQ4HPjjwwY4PNnxUO75YUex2zHYViwPMDrA4wGIHneLAiAMjdoyqHSN2NA4HNWiodmioUjVUqwpVDi1VqoYqVUuVqqUS59cqtCicGb3EgVZV0XDOTXWgQUVRnTcNKsrZsTznCJyqoj1zvFZ1oFGd50JVsaHBrijYzowC28559PlUzo5oqCrO0eAz37tGIjk7klhv0Epx9sfZXWdGkxXn6LFd0ThHj9FgU848E9d5lTM3XM+tbtvOHKecbb9DUdCoqvOG4+zr08gvkbrXOnsujXr26op69tWpPebs9ZX6T7r2uZ/7qqrnHqk4RzfrPb+zj6t3HvWcK7p2nz1/3Wud+/zqPzf1zPVrr3Tua16nHRf4xasqimu/BtX1/Grbrp7bY+eMTtUdqVRcx5/7OtV98mfPU+cn9GL/KSjKhY9ppE11LnFO++o8tNHjz3ne572eF3otRfvRUH83pLHfl0IA+GptFz+onZCAto35BzhHXCvKHRc50kmjKBi0CgYtmFqzYeKC2vqvUtHy2qoP65y7Ra7ThHM05XKNtu0COS3nX6RFXkb3T6IoCh2jojiZl+deHzbl9W+1HwkP/L5o0s9dK7WvgdMqikJUVCR5eafa7veoV/6+bkdt1uo93QK3SUDbBjIyMjh06BA9e/bEFNARcJbuEkJcfurm2MroV0tSFAXFYETRG7w0ULmyKYqCxs8fxbdc+k+0OJkW3wZKSkpIT0/n6NGjrhHaqioHdru8oYUQQgghLpUEtG0gODgYcE5CM/oo6HSAClUVMkorhBBCCHGp2lXKwbJly/jkk08YP34
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2024-11-11 10:57:57 +01:00
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"text/plain": [
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"<Figure size 800x400 with 1 Axes>"
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2024-11-11 10:57:57 +01:00
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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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2024-11-18 21:40:20 +01:00
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"image/png": "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
|
2024-11-11 10:57:57 +01:00
|
|
|
"text/plain": [
|
2024-11-18 21:40:20 +01:00
|
|
|
"<Figure size 800x400 with 1 Axes>"
|
2024-11-11 10:57:57 +01:00
|
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|
]
|
|
|
|
|
},
|
|
|
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|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
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|
}
|
|
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|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"plot_result_graphs('problem_model', result_dict, keys_to_plot=['VGG_38', 'VGG_08'])"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2024-11-19 18:04:58 +01:00
|
|
|
"execution_count": 16,
|
2024-11-11 10:57:57 +01:00
|
|
|
"metadata": {},
|
2024-11-18 21:40:20 +01:00
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
2024-11-19 18:04:58 +01:00
|
|
|
"image/png": "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
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 800x400 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
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"image/png": "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"text/plain": [
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"<Figure size 800x400 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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"source": [
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"plot_result_graphs('VGG38_BN_RC', result_dict, keys_to_plot=['VGG38_BN_RC'])\n",
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"plt.savefig('../report/figures/VGG38_BN_RC_loss_and_acc.pdf')"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
|
