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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2024-11-18 21:40:20 +01:00
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"execution_count": 6,
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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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]
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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": 7,
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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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"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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2024-11-18 21:40:20 +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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"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', 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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" \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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2024-11-18 21:40:20 +01:00
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"execution_count": 14,
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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": "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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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]
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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
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"text/plain": [
|
2024-11-18 21:40:20 +01:00
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"<Figure size 800x400 with 1 Axes>"
|
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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"source": [
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"plot_result_graphs('problem_model', result_dict, keys_to_plot=['VGG_38', 'VGG_08'])"
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]
|
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},
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{
|
|
|
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"cell_type": "code",
|
2024-11-18 21:40:20 +01:00
|
|
|
"execution_count": 15,
|
2024-11-11 10:57:57 +01:00
|
|
|
"metadata": {},
|
2024-11-18 21:40:20 +01:00
|
|
|
"outputs": [
|
|
|
|
|
{
|
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"data": {
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"text/plain": [
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" 'train_loss': [4.827323,\n",
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|
|
" 1.7184331,\n",
|
|
|
|
|
" 1.7116771,\n",
|
|
|
|
|
" 1.7009526,\n",
|
|
|
|
|
" 1.6991171,\n",
|
|
|
|
|
" 1.6958193,\n",
|
|
|
|
|
" 1.6907407,\n",
|
|
|
|
|
" 1.6808176,\n",
|
|
|
|
|
" 1.676356,\n",
|
|
|
|
|
" 1.6731659,\n",
|
|
|
|
|
" 1.662152,\n",
|
|
|
|
|
" 1.6638054,\n",
|
|
|
|
|
" 1.6575475,\n",
|
|
|
|
|
" 1.6595734,\n",
|
|
|
|
|
" 1.6536722,\n",
|
|
|
|
|
" 1.6495628,\n",
|
|
|
|
|
" 1.6488388,\n",
|
|
|
|
|
" 1.6408547,\n",
|
|
|
|
|
" 1.632917,\n",
|
|
|
|
|
" 1.6340653,\n",
|
|
|
|
|
" 1.6340532,\n",
|
|
|
|
|
" 1.6246406,\n",
|
|
|
|
|
" 1.6288266,\n",
|
|
|
|
|
" 1.6240481,\n",
|
|
|
|
|
" 1.6208181,\n",
|
|
|
|
|
" 1.6164333,\n",
|
|
|
|
|
" 1.6169226,\n",
|
|
|
|
|
" 1.6159856,\n",
|
|
|
|
|
" 1.6175526,\n",
|
|
|
|
|
" 1.6149833,\n",
|
|
|
|
|
" 1.6063902,\n",
|
|
|
|
|
" 1.6096952,\n",
|
|
|
|
|
" 1.6084315,\n",
|
|
|
|
|
" 1.6069487,\n",
|
|
|
|
|
" 1.6030664,\n",
|
|
|
|
|
" 1.6043342,\n",
|
|
|
|
|
" 1.6039867],\n",
|
|
|
|
|
" 'val_acc': [0.024800000000000003,\n",
|
|
|
|
|
" 0.0604,\n",
|
|
|
|
|
" 0.09480000000000001,\n",
|
|
|
|
|
" 0.12159999999999999,\n",
|
|
|
|
|
" 0.15439999999999998,\n",
|
|
|
|
|
" 0.1864,\n",
|
|
|
|
|
" 0.20720000000000002,\n",
|
|
|
|
|
" 0.22880000000000003,\n",
|
|
|
|
|
" 0.24760000000000001,\n",
|
|
|
|
|
" 0.2552,\n",
|
|
|
|
|
" 0.2764,\n",
|
|
|
|
|
" 0.2968,\n",
|
|
|
|
|
" 0.3016,\n",
|
|
|
|
|
" 0.322,\n",
|
|
|
|
|
" 0.3176,\n",
|
|
|
|
|
" 0.33159999999999995,\n",
|
|
|
|
|
" 0.342,\n",
|
|
|
|
|
" 0.34119999999999995,\n",
|
|
|
|
|
" 0.3332,\n",
|
|
|
|
|
" 0.36160000000000003,\n",
|
|
|
|
|
" 0.3608,\n",
|
|
|
|
|
" 0.3732,\n",
|
|
|
|
|
" 0.3716,\n",
|
|
|
|
|
" 0.37439999999999996,\n",
|
|
|
|
|
" 0.3772,\n",
|
|
|
|
|
" 0.3876,\n",
|
|
|
|
|
" 0.37800000000000006,\n",
|
|
|
|
|
" 0.38160000000000005,\n",
|
|
|
|
|
" 0.39840000000000003,\n",
|
|
|
|
|
" 0.4044,\n",
|
|
|
|
|
" 0.398,\n",
|
|
|
|
|
" 0.41200000000000003,\n",
|
|
|
|
|
" 0.4096,\n",
|
|
|
|
|
" 0.4104,\n",
|
|
|
|
|
" 0.4244,\n",
|
|
|
|
|
" 0.42719999999999997,\n",
|
|
|
|
|
" 0.4204,\n",
|
|
|
|
|
" 0.4244,\n",
|
|
|
|
|
" 0.4128,\n",
|
|
|
|
|
" 0.4204,\n",
|
|
|
|
|
" 0.4244,\n",
|
|
|
|
|
" 0.424,\n",
|
|
|
|
|
" 0.43,\n",
|
|
|
|
|
" 0.4463999999999999,\n",
|
|
|
|
|
" 0.44439999999999996,\n",
|
|
|
|
|
" 0.43079999999999996,\n",
|
|
|
|
|
" 0.44920000000000004,\n",
|
|
|
|
|
" 0.44799999999999995,\n",
|
|
|
|
|
" 0.4428,\n",
|
|
|
|
|
" 0.4436,\n",
|
|
|
|
|
" 0.4548,\n",
|
|
|
|
|
" 0.4548,\n",
|
|
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|
|
" 0.44240000000000007,\n",
|
|
|
|
|
" 0.4548,\n",
|
|
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|
|
" 0.458,\n",
|
|
|
|
|
" 0.4596,\n",
|
|
|
|
|
" 0.45679999999999993,\n",
|
|
|
|
|
" 0.4444000000000001,\n",
|
|
|
|
|
" 0.4616,\n",
|
|
|
|
|
" 0.4464,\n",
|
|
|
|
|
" 0.4656,\n",
|
|
|
|
|
" 0.46,\n",
|
|
|
|
|
" 0.45960000000000006,\n",
|
|
|
|
|
" 0.46279999999999993,\n",
|
|
|
|
|
" 0.46399999999999997,\n",
|
|
|
|
|
" 0.46679999999999994,\n",
|
|
|
|
|
" 0.4604,\n",
|
|
|
|
|
" 0.4692,\n",
|
|
|
|
|
" 0.4700000000000001,\n",
|
|
|
|
|
" 0.4708,\n",
|
|
|
|
|
" 0.4736,\n",
|
|
|
|
|
" 0.4715999999999999,\n",
|
|
|
|
|
" 0.47239999999999993,\n",
|
|
|
|
|
" 0.47839999999999994,\n",
|
|
|
|
|
" 0.4672,\n",
|
|
|
|
|
" 0.4692,\n",
|
|
|
|
|
" 0.4768,\n",
|
|
|
|
|
" 0.4824,\n",
|
|
|
|
|
" 0.4816,\n",
|
|
|
|
|
" 0.47600000000000003,\n",
|
|
|
|
|
" 0.478,\n",
|
|
|
|
|
" 0.48,\n",
|
|
|
|
|
" 0.4828,\n",
|
|
|
|
|
" 0.4776,\n",
|
|
|
|
|
" 0.47759999999999997,\n",
|
|
|
|
|
" 0.48119999999999996,\n",
|
|
|
|
|
" 0.4864,\n",
|
|
|
|
|
" 0.48279999999999995,\n",
|
|
|
|
|
" 0.4804,\n",
|
|
|
|
|
" 0.47839999999999994,\n",
|
|
|
|
|
" 0.47800000000000004,\n",
|
|
|
|
|
" 0.4828,\n",
|
|
|
|
|
" 0.48560000000000003,\n",
|
|
|
|
|
" 0.48119999999999996,\n",
|
|
|
|
|
" 0.4835999999999999,\n",
|
|
|
|
|
" 0.48120000000000007,\n",
|
|
|
|
|
" 0.4867999999999999,\n",
|
|
|
|
|
" 0.4831999999999999,\n",
|
|
|
|
|
" 0.49079999999999996,\n",
|
|
|
|
|
" 0.486,\n",
|
|
|
|
|
" 0.48480000000000006],\n",
|
|
|
|
|
" 'val_loss': [4.5659676,\n",
|
|
|
|
|
" 4.136276,\n",
|
|
|
|
|
" 3.8678854,\n",
|
|
|
|
|
" 3.6687074,\n",
|
|
|
|
|
" 3.4829779,\n",
|
|
|
|
|
" 3.3093607,\n",
|
|
|
|
|
" 3.2223148,\n",
|
|
|
|
|
" 3.1171055,\n",
|
|
|
|
|
" 3.0554724,\n",
|
|
|
|
|
" 2.9390912,\n",
|
|
|
|
|
" 2.9205213,\n",
|
|
|
|
|
" 2.7410471,\n",
|
|
|
|
|
" 2.7083752,\n",
|
|
|
|
|
" 2.665904,\n",
|
|
|
|
|
" 2.688214,\n",
|
|
|
|
|
" 2.648656,\n",
|
|
|
|
|
" 2.5658453,\n",
|
|
|
|
|
" 2.5403407,\n",
|
|
|
|
|
" 2.6936982,\n",
|
|
|
|
|
" 2.4663532,\n",
|
|
|
|
|
" 2.4559999,\n",
|
|
|
|
|
" 2.3644555,\n",
|
|
|
|
|
" 2.4516551,\n",
|
|
|
|
|
" 2.4189563,\n",
|
|
|
|
|
" 2.3899698,\n",
|
|
|
|
|
" 2.3215945,\n",
|
|
|
|
|
" 2.3831298,\n",
|
|
|
|
|
" 2.3436418,\n",
|
|
|
|
|
" 2.3471045,\n",
|
|
|
|
|
" 2.2744477,\n",
|
|
|
|
|
" 2.245617,\n",
|
|
|
|
|
" 2.216309,\n",
|
|
|
|
|
" 2.2329648,\n",
|
|
|
|
|
" 2.1841388,\n",
|
|
|
|
|
" 2.1780539,\n",
|
|
|
|
|
" 2.1625984,\n",
|
|
|
|
|
" 2.2195568,\n",
|
|
|
|
|
" 2.1803434,\n",
|
|
|
|
|
" 2.1858895,\n",
|
|
|
|
|
" 2.1908271,\n",
|
|
|
|
|
" 2.1841395,\n",
|
|
|
|
|
" 2.1843896,\n",
|
|
|
|
|
" 2.154806,\n",
|
|
|
|
|
" 2.1130056,\n",
|
|
|
|
|
" 2.113019,\n",
|
|
|
|
|
" 2.1191697,\n",
|
|
|
|
|
" 2.1213412,\n",
|
|
|
|
|
" 2.1077166,\n",
|
|
|
|
|
" 2.0737479,\n",
|
|
|
|
|
" 2.07655,\n",
|
|
|
|
|
" 2.0769904,\n",
|
|
|
|
|
" 2.061769,\n",
|
|
|
|
|
" 2.0676718,\n",
|
|
|
|
|
" 2.0859065,\n",
|
|
|
|
|
" 2.0704215,\n",
|
|
|
|
|
" 2.1113508,\n",
|
|
|
|
|
" 2.0382714,\n",
|
|
|
|
|
" 2.0911386,\n",
|
|
|
|
|
" 2.0458508,\n",
|
|
|
|
|
" 2.0786576,\n",
|
|
|
|
|
" 2.026332,\n",
|
|
|
|
|
" 2.0141299,\n",
|
|
|
|
|
" 2.0226884,\n",
|
|
|
|
|
" 2.0182638,\n",
|
|
|
|
|
" 2.0110855,\n",
|
|
|
|
|
" 2.0191038,\n",
|
|
|
|
|
" 2.0334535,\n",
|
|
|
|
|
" 2.0072439,\n",
|
|
|
|
|
" 2.0296187,\n",
|
|
|
|
|
" 1.9912667,\n",
|
|
|
|
|
" 2.006095,\n",
|
|
|
|
|
" 2.012164,\n",
|
|
|
|
|
" 1.9955354,\n",
|
|
|
|
|
" 2.005768,\n",
|
|
|
|
|
" 2.015392,\n",
|
|
|
|
|
" 1.9890119,\n",
|
|
|
|
|
" 2.0090258,\n",
|
|
|
|
|
" 1.9728817,\n",
|
|
|
|
|
" 1.9769167,\n",
|
|
|
|
|
" 1.9980135,\n",
|
|
|
|
|
" 1.9884782,\n",
|
|
|
|
|
" 1.9772192,\n",
|
|
|
|
|
" 1.9732709,\n",
|
|
|
|
|
" 1.9623082,\n",
|
|
|
|
|
" 1.9812362,\n",
|
|
|
|
|
" 1.9846246,\n",
|
|
|
|
|
" 1.9822198,\n",
|
|
|
|
|
" 1.9768158,\n",
|
|
|
|
|
" 1.9625885,\n",
|
|
|
|
|
" 1.9738724,\n",
|
|
|
|
|
" 1.9842362,\n",
|
|
|
|
|
" 1.9709526,\n",
|
|
|
|
|
" 1.967775,\n",
|
|
|
|
|
" 1.9626708,\n",
|
|
|
|
|
" 1.96621,\n",
|
|
|
|
|
" 1.9742922,\n",
|
|
|
|
|
" 1.9604725,\n",
|
|
|
|
|
" 1.9733659,\n",
|
|
|
|
|
" 1.9693874,\n",
|
|
|
|
|
" 1.9647765,\n",
|
|
|
|
|
" 1.9649359]},\n",
|
|
|
|
|
" 'VGG_38': {'train_acc': [0.009263157894736843,\n",
|
|
|
|
|
" 0.009810526315789474,\n",
|
|
|
|
|
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|
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|
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|
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|
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|
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|
|
|
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" 0.009810526315789474,\n",
|
|
|
|
|
" 0.009094736842105263,\n",
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|
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|
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|
|
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|
|
" 0.009894736842105263,\n",
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|
|
|
|
" 0.00934736842105263,\n",
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|
|
|
|
" 0.009473684210526316,\n",
|
|
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|
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|
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|
|
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|
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|
|
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|
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|
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|
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|
|
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|
|
|
|
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|
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|
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|
" 0.009326315789473686,\n",
|
|
|
|
|
" 0.01,\n",
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|
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|
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|
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|
|
" 0.008842105263157896,\n",
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|
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|
|
" 0.008799999999999999,\n",
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|
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|
" 0.009326315789473686,\n",
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|
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|
|
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|
|
" 0.00934736842105263,\n",
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
" 0.009536842105263158,\n",
|
|
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|
|
" 0.009663157894736842,\n",
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|
|
" 0.008821052631578948,\n",
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|
" 0.009768421052631579,\n",
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|
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|
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|
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|
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|
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|
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|
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|
" 0.008821052631578948,\n",
|
|
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|
|
" 0.009326315789473684,\n",
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|
|
|
" 0.009557894736842105,\n",
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|
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|
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|
|
" 0.00856842105263158,\n",
|
|
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|
|
" 0.009894736842105263,\n",
|
|
|
|
|
" 0.009494736842105262,\n",
|
|
|
|
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" 0.008673684210526314,\n",
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" 0.009410526315789475,\n",
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" 0.010042105263157896,\n",
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" 0.009642105263157895,\n",
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" 0.009305263157894737],\n",
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" 'train_loss': [4.8649125,\n",
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" 4.6264124,\n",
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" 4.621914,\n",
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" 4.6168556,\n",
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" 4.6156826,\n",
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" 4.614809,\n",
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" 4.6117926,\n",
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" 4.611283,\n",
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" 4.6105323,\n",
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" 4.6103206,\n",
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" 4.6095214,\n",
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" 4.609189,\n",
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" 4.605153,\n",
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" 4.6051517],\n",
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" 'val_acc': [0.0104,\n",
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" 0.0064],\n",
|
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" 'val_loss': [4.630689,\n",
|
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" 4.618983,\n",
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" 4.6184525,\n",
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|
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" 4.6068635,\n",
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" 4.6068873,\n",
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" 4.6068926,\n",
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" 4.6068554,\n",
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" 4.6068707,\n",
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" 4.6068635,\n",
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" 4.6068773,\n",
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" 4.606883,\n",
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" 4.6069,\n",
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|
|
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" 4.6068873,\n",
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|
|
|
" 4.6068654,\n",
|
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|
|
" 4.6068883,\n",
|
|
|
|
|
" 4.606894,\n",
|
|
|
|
|
" 4.6068826,\n",
|
|
|
|
|
" 4.6068697,\n",
|
|
|
|
|
" 4.6068807,\n",
|
|
|
|
|
" 4.606872,\n",
|
|
|
|
|
" 4.6068883,\n",
|
|
|
|
|
" 4.606871,\n",
|
|
|
|
|
" 4.606871,\n",
|
|
|
|
|
" 4.6068654,\n",
|
|
|
|
|
" 4.6068764,\n",
|
|
|
|
|
" 4.6068697,\n",
|
|
|
|
|
" 4.6068673,\n",
|
|
|
|
|
" 4.606873,\n",
|
|
|
|
|
" 4.6068773,\n",
|
|
|
|
|
" 4.606874,\n",
|
|
|
|
|
" 4.606877,\n",
|
|
|
|
|
" 4.606874,\n",
|
|
|
|
|
" 4.606874,\n",
|
|
|
|
|
" 4.6068716,\n",
|
|
|
|
|
" 4.6068726,\n",
|
|
|
|
|
" 4.606872,\n",
|
|
|
|
|
" 4.6068726]}}"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"execution_count": 15,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"result_dict"
|
|
|
|
|
]
|
2024-11-11 10:57:57 +01:00
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"metadata": {
|
|
|
|
|
"kernelspec": {
|
2024-11-18 21:40:20 +01:00
|
|
|
"display_name": "Python 3 (ipykernel)",
|
2024-11-11 10:57:57 +01:00
|
|
|
"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",
|
2024-11-18 21:40:20 +01:00
|
|
|
"version": "3.11.10"
|
2024-11-11 10:57:57 +01:00
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"nbformat": 4,
|
2024-11-18 21:40:20 +01:00
|
|
|
"nbformat_minor": 4
|
2024-11-11 10:57:57 +01:00
|
|
|
}
|
