151 lines
59 KiB
Plaintext
151 lines
59 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": 1,
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"id": "a6961e73",
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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"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5d93832a",
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"metadata": {},
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"source": [
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"# Fixed Learning Rate\n",
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"80/10/10 Split, 200 epochs\n",
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"\n",
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"## Index\n",
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"0. learning rate\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": 2,
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"id": "95b71471",
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"metadata": {},
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"outputs": [],
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"source": [
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"fixed_results = np.array([\n",
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" [1e-6, 0.31, 2.84, 5.28, 0.67],\n",
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" [1e-5, 0.8, 2.59, 5.28, 0.55],\n",
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" [1e-4, 6.98, 17.23, 4.6, 7.41],\n",
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" [1e-3, 21.56, 44.72, 4.97, 26.9],\n",
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" [5e-3, 39.35, 66.83, 3.34, 43.5],\n",
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" [1e-2, 13.65, 30.02, 4.15, 17.46],\n",
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" [5e-2, 1.79, 6.73, 5.13, 1.78],\n",
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" [1e-1, 0.8, 2.78, 5.29, 0.55]\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": 3,
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"id": "4d1d5559",
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"metadata": {},
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"outputs": [
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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 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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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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"plt.plot(fixed_results[:, 0], fixed_results[:, 1], 'x-', label=\"Top-1 Accuracy\")\n",
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"plt.plot(fixed_results[:, 0], fixed_results[:, 2], 'x-', label=\"Top-5 Accuracy\")\n",
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"plt.plot(fixed_results[:, 0], fixed_results[:, 4], 'x-', label=\"Final Val. Accuracy\")\n",
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"\n",
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"plt.ylim(0)\n",
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"\n",
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"plt.title('Model Accuracy for Varied Learning Rates')\n",
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"plt.ylabel('% Accuracy')\n",
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"plt.xlabel('Learning Rate')\n",
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"\n",
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"plt.legend()\n",
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"plt.xscale('log')\n",
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"plt.grid()\n",
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"plt.show()"
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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": 4,
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"id": "21a098c5",
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"metadata": {},
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"outputs": [
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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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|
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"<Figure size 432x288 with 1 Axes>"
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]
|
||
|
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},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.plot(fixed_results[:, 0], fixed_results[:, 3], 'x-', label=\"Final Validation Loss\")\n",
|
||
|
|
"\n",
|
||
|
|
"# plt.ylim(0)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.title('Final Validation Loss for Varied Learning Rates')\n",
|
||
|
|
"plt.ylabel('Loss')\n",
|
||
|
|
"plt.xlabel('Learning Rate')\n",
|
||
|
|
"\n",
|
||
|
|
"# plt.legend()\n",
|
||
|
|
"plt.xscale('log')\n",
|
||
|
|
"plt.grid()\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "32fb71f0",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"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": "3.9.2"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|