169 lines
202 KiB
Plaintext
169 lines
202 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": 18,
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"id": "3c568ab9",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib as mpl\n",
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"from matplotlib import pyplot as plt\n",
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"\n",
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"fig_dpi = 200\n",
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"lw = 3"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7ecc547f",
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"metadata": {},
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"source": [
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"# Epochs\n",
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"80/10/10 Split, Sigmoid, 0.01, G: 0.1\n",
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"\n",
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"## Index\n",
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"0. Epochs\n",
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"1. top-1 accuracy\n",
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"2. top-5 accuracy\n",
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"3. last val loss\n",
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"4. last val accuracy"
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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": 26,
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"id": "1b2471d2",
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"metadata": {},
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"outputs": [],
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"source": [
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"epoch_results = np.array([\n",
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" [10, 3.46, 11.74, 4.85, 5.51],\n",
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" [20, 11.98, 31.32, 3.87, 15.50],\n",
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" [30, 31.5, 59.17, 2.86, 31.92],\n",
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" [40, 39.22, 68.13, 2.68, 43.63],\n",
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" [60, 43.85, 70.11, 2.72, 47.79],\n",
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" [70, 47.62, 72.88, 2.57, 52.08],\n",
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" [80, 45.21, 73.38, 2.68, 50.43],\n",
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" [100, 45.89, 74.06, 2.74, 50.18],\n",
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" [200, 42.74, 69.24, 2.98, 47.86],\n",
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" [300, 38.91, 64.18, 3.24, 44.67],\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": 27,
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"id": "9b32b8fe",
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"metadata": {},
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"text/plain": "<Figure size 1000x600 with 1 Axes>",
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},
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"metadata": {
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"needs_background": "light"
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}
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}
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],
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"source": [
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"fig = plt.figure(figsize=(5, 3))\n",
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"fig.set_dpi(fig_dpi)\n",
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"\n",
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"plt.plot(epoch_results[:, 0], epoch_results[:, 1], 'x-', label=\"Top-1 Accuracy\", lw=lw)\n",
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"plt.plot(epoch_results[:, 0], epoch_results[:, 2], 'x-', label=\"Top-5 Accuracy\", lw=lw)\n",
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"plt.plot(epoch_results[:, 0], epoch_results[:, 4], 'x-', label=\"Final Val. Accuracy\", lw=lw)\n",
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"\n",
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"plt.ylim(0, 80)\n",
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"# plt.xlim(0, 1)\n",
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"\n",
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"plt.title('Accuracy for Varied Training Epochs')\n",
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"plt.ylabel('% Accuracy')\n",
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"plt.xlabel('Epochs')\n",
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"\n",
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"plt.legend()\n",
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"plt.grid()\n",
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"\n",
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"plt.tight_layout()\n",
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"plt.savefig('epochs-accuracy.png')\n",
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"\n",
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"plt.show()"
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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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|
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"execution_count": 28,
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"metadata": {},
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"outputs": [
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{
|
||
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"output_type": "display_data",
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"data": {
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|
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"text/plain": "<Figure size 1000x600 with 1 Axes>",
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|
||
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|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig = plt.figure(figsize=(5, 3))\n",
|
||
|
|
"fig.set_dpi(fig_dpi)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.plot(epoch_results[:, 0], epoch_results[:, 3], 'x-', label=\"Final Validation Loss\", lw=lw)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.ylim(None, 5)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.title('Final Validation Loss for Varied Training Epochs')\n",
|
||
|
|
"plt.ylabel('Loss')\n",
|
||
|
|
"plt.xlabel('Epochs')\n",
|
||
|
|
"\n",
|
||
|
|
"# plt.legend()\n",
|
||
|
|
"# plt.xscale('log')\n",
|
||
|
|
"plt.grid()\n",
|
||
|
|
"\n",
|
||
|
|
"plt.tight_layout()\n",
|
||
|
|
"plt.savefig('epochs-loss.png')\n",
|
||
|
|
"\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"name": "pythonjvsc74a57bd0333605e348ea7c6bf4ca805dbc845da062650cb5bf1d8f33f5f4a9d3bca7d68b",
|
||
|
|
"display_name": "Python 3.9.3 ('.venv': venv)"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.9.3"
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"interpreter": {
|
||
|
|
"hash": "333605e348ea7c6bf4ca805dbc845da062650cb5bf1d8f33f5f4a9d3bca7d68b"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|