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{
"cells": [
{
"cell_type": "code",
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"execution_count": 133,
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"id": "3c568ab9",
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"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib as mpl\n",
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"from matplotlib import pyplot as plt\n",
"\n",
"fig_dpi = 200\n",
"lw = 3"
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]
},
{
"cell_type": "markdown",
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"id": "7ecc547f",
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"metadata": {},
"source": [
"# Fixed Learning Rate\n",
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"80/10/10 Split, 100/200 epochs\n",
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"\n",
"## Index\n",
"0. learning rate\n",
"1. top-1 accuracy\n",
"2. top-5 accuracy\n",
"3. last val loss\n",
"4. last val accuracy"
]
},
{
"cell_type": "code",
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"execution_count": 134,
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"id": "1b2471d2",
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"metadata": {},
"outputs": [],
"source": [
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"fixed_results_200e = np.array([\n",
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" [1e-6, 0.31, 2.84, 5.28, 0.67],\n",
" [1e-5, 0.8, 2.59, 5.28, 0.55],\n",
" [1e-4, 6.98, 17.23, 4.6, 7.41],\n",
" [1e-3, 21.56, 44.72, 4.97, 26.9],\n",
" [5e-3, 39.35, 66.83, 3.34, 43.5],\n",
" [1e-2, 13.65, 30.02, 4.15, 17.46],\n",
" [5e-2, 1.79, 6.73, 5.13, 1.78],\n",
" [1e-1, 0.8, 2.78, 5.29, 0.55]\n",
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"])\n",
"\n",
"fixed_results_100e = np.array([\n",
" [1e-6, 0.31, 2.9, 5.28, 0.67],\n",
" [1e-5, 0.8, 2.1, 5.28, 0.55],\n",
" [1e-4, 2.35, 8.28, 5.00, 2.63],\n",
" [1e-3, 18.47, 40.09, 4.55, 23.41],\n",
" [5e-3, 35.52, 63.19, 3.33, 40.93],\n",
" [1e-2, 22.42, 47.19, 3.59, 27.02],\n",
" [5e-2, 2.47, 9.02, 5.07, 2.14],\n",
" [1e-1, 0.8, 2.53, 5.28, 0.55]\n",
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"])"
]
},
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{
"cell_type": "markdown",
"id": "ca34155e",
"metadata": {},
"source": [
"## 100 Epochs"
]
},
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{
"cell_type": "code",
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"execution_count": 135,
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"id": "c664a31c",
"metadata": {},
"outputs": [
{
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"output_type": "display_data",
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"data": {
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},
"metadata": {
"needs_background": "light"
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}
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}
],
"source": [
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"fig = plt.figure(figsize=(5, 4))\n",
"fig.set_dpi(fig_dpi)\n",
"\n",
"plt.plot(fixed_results_100e[:, 0], fixed_results_100e[:, 1], 'x-', label=\"Top-1 Accuracy\", lw=lw)\n",
"plt.plot(fixed_results_100e[:, 0], fixed_results_100e[:, 2], 'x-', label=\"Top-5 Accuracy\", lw=lw)\n",
"plt.plot(fixed_results_100e[:, 0], fixed_results_100e[:, 4], 'x-', label=\"Final Val. Accuracy\", lw=lw)\n",
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"\n",
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"plt.ylim(0, 70)\n",
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"\n",
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"plt.title('Accuracy for Fixed Learning Rates')\n",
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"plt.ylabel('% Accuracy')\n",
"plt.xlabel('Learning Rate')\n",
"\n",
"plt.legend()\n",
"plt.xscale('log')\n",
"plt.grid()\n",
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"\n",
"plt.tight_layout()\n",
"plt.savefig('fixed-accuracy.png')\n",
"\n",
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"plt.show()"
]
},
{
"cell_type": "code",
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"execution_count": 136,
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"id": "bf5fa35b",
"metadata": {},
"outputs": [
{
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"output_type": "display_data",
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"data": {
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},
"metadata": {
"needs_background": "light"
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}
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}
],
"source": [
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"fig = plt.figure(figsize=(5, 4))\n",
"fig.set_dpi(fig_dpi)\n",
"\n",
"plt.plot(fixed_results_100e[:, 0], fixed_results_100e[:, 3], 'x-', label=\"Final Validation Loss\", lw=lw)\n",
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"\n",
"# plt.ylim(0)\n",
"\n",
"plt.title('Final Validation Loss for Fixed Learning Rates')\n",
"plt.ylabel('Loss')\n",
"plt.xlabel('Learning Rate')\n",
"\n",
"# plt.legend()\n",
"plt.xscale('log')\n",
"plt.grid()\n",
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"\n",
"plt.tight_layout()\n",
"plt.savefig('fixed-loss.png')\n",
"\n",
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"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "ff7746a4",
"metadata": {},
"source": [
"## 200 Epochs"
]
},
{
"cell_type": "code",
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"execution_count": 137,
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"id": "9f4a799f",
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"metadata": {},
"outputs": [
{
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"output_type": "display_data",
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"data": {
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"text/plain": "<Figure size 432x288 with 1 Axes>",
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},
"metadata": {
"needs_background": "light"
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}
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}
],
"source": [
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"plt.plot(fixed_results_200e[:, 0], fixed_results_200e[:, 1], 'x-', label=\"Top-1 Accuracy\")\n",
"plt.plot(fixed_results_200e[:, 0], fixed_results_200e[:, 2], 'x-', label=\"Top-5 Accuracy\")\n",
"plt.plot(fixed_results_200e[:, 0], fixed_results_200e[:, 4], 'x-', label=\"Final Val. Accuracy\")\n",
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"\n",
"plt.ylim(0)\n",
"\n",
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"plt.title('Accuracy for Fixed Learning Rates')\n",
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"plt.ylabel('% Accuracy')\n",
"plt.xlabel('Learning Rate')\n",
"\n",
"plt.legend()\n",
"plt.xscale('log')\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
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"execution_count": 138,
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"id": "cdf18f4a",
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"metadata": {},
"outputs": [
{
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"output_type": "display_data",
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"data": {
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"text/plain": "<Figure size 432x288 with 1 Axes>",
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},
"metadata": {
"needs_background": "light"
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}
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}
],
"source": [
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"plt.plot(fixed_results_200e[:, 0], fixed_results_200e[:, 3], 'x-', label=\"Final Validation Loss\")\n",
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"\n",
"# plt.ylim(0)\n",
"\n",
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"plt.title('Final Validation Loss for Fixed Learning Rates')\n",
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"plt.ylabel('Loss')\n",
"plt.xlabel('Learning Rate')\n",
"\n",
"# plt.legend()\n",
"plt.xscale('log')\n",
"plt.grid()\n",
"plt.show()"
]
},
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{
"cell_type": "markdown",
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"id": "01a797d7",
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"metadata": {},
"source": [
"# Step-Down\n",
"80/10/10 Split, 100 epochs\n",
"\n",
"## Index\n",
"0. learning rate\n",
"1. step size\n",
"2. gamma\n",
"3. top-1 accuracy\n",
"4. top-5 accuracy\n",
"5. last val loss\n",
"6. last val accuracy"
]
},
{
"cell_type": "code",
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"execution_count": 139,
2021-04-10 12:20:26 +01:00
"id": "a9023eeb",
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"metadata": {},
"outputs": [],
"source": [
"step_down_results = np.array([\n",
" [1e-2, 0.33, 0.1, 43.79, 70.85, 2.79, 47.24],\n",
" [1e-2, 0.33, 0.25, 45.52, 73.07, 2.90, 49.88],\n",
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" [1e-2, 0.33, 0.33, 45.52, 72.64, 3.01, 49.20],\n",
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" [1e-2, 0.33, 0.5, 45.71, 71.71, 2.89, 49.39],\n",
" [1e-2, 0.33, 0.75, 40.09, 68.19, 3.00, 46.38]\n",
"])"
]
},
{
"cell_type": "code",
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"execution_count": 140,
2021-04-10 12:20:26 +01:00
"id": "9b32b8fe",
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"metadata": {},
"outputs": [
{
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"output_type": "display_data",
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"data": {
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},
"metadata": {
"needs_background": "light"
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}
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}
],
"source": [
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"fig = plt.figure(figsize=(5, 4))\n",
"fig.set_dpi(fig_dpi)\n",
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"\n",
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"plt.plot(step_down_results[:, 2], step_down_results[:, 3], 'x-', label=\"Top-1 Accuracy\", lw=lw)\n",
"plt.plot(step_down_results[:, 2], step_down_results[:, 4], 'x-', label=\"Top-5 Accuracy\", lw=lw)\n",
"plt.plot(step_down_results[:, 2], step_down_results[:, 6], 'x-', label=\"Final Val. Accuracy\", lw=lw)\n",
"\n",
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"plt.ylim(20, 80)\n",
"plt.xlim(0, 1)\n",
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"\n",
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"plt.title('Accuracy for Step-Down Learning Rates')\n",
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"plt.ylabel('% Accuracy')\n",
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"plt.xlabel('Step-down Scale Factor, γ
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"\n",
"plt.legend()\n",
"# plt.xscale('log')\n",
"plt.grid()\n",
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"\n",
"plt.tight_layout()\n",
"plt.savefig('step-down-accuracy.png')\n",
"\n",
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"plt.show()"
]
},
{
"cell_type": "code",
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"execution_count": 141,
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"id": "69c98182",
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"metadata": {},
"outputs": [
{
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"output_type": "display_data",
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"data": {
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"text/plain": "<Figure size 432x288 with 1 Axes>",
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},
"metadata": {
"needs_background": "light"
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}
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}
],
"source": [
"plt.plot(step_down_results[:, 2], step_down_results[:, 5], 'x-', label=\"Final Validation Loss\")\n",
"\n",
"# plt.ylim(0)\n",
"\n",
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"plt.title('Final Validation Loss for Step-Down Learning Rates')\n",
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"plt.ylabel('Loss')\n",
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"plt.xlabel('Gamma, γ
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"\n",
"# plt.legend()\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
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"id": "f9c67f09",
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"metadata": {},
"source": [
"# Exponential Decay\n",
"80/10/10 Split, 100 epochs\n",
"\n",
"## Index\n",
"0. learning rate\n",
"1. decay rate\n",
"2. top-1 accuracy\n",
"3. top-5 accuracy\n",
"4. last val loss\n",
"5. last val accuracy"
]
},
{
"cell_type": "code",
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"execution_count": 142,
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"id": "5322e4d4",
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"metadata": {},
"outputs": [],
"source": [
"exp_results = np.array([\n",
" [1e-2, 0.70, 2.35, 8.09, 4.97, 2.75],\n",
" [1e-2, 0.80, 5.0, 15.57, 4.61, 7.23],\n",
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" [1e-2, 0.85, 9.94, 26.31, 4.11, 12.81],\n",
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" [1e-2, 0.90, 25.88, 52.5, 3.28, 29.17],\n",
" [1e-2, 0.925, 37.43, 63.37, 3.13, 40.81],\n",
" [1e-2, 0.95, 44.1, 71.22, 2.99, 48.84],\n",
" [1e-2, 0.98, 44.41, 71.83, 3.04, 47.61],\n",
" [1e-2, 0.99, 42.43, 69.55, 3.25, 45.47],\n",
" \n",
" [1e-1, 0.85, 12.91, 34.96, 3.85, 14.89],\n",
" [1e-1, 0.9, 0.8, 2.29, 5.29, 0.55]\n",
"])\n",
"two_results = 7"
]
},
{
"cell_type": "code",
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"execution_count": 143,
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"id": "959af09b",
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"metadata": {},
"outputs": [
{
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"output_type": "display_data",
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"data": {
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"text/plain": "<Figure size 1000x800 with 1 Axes>",
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},
"metadata": {
"needs_background": "light"
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}
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}
],
"source": [
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"fig = plt.figure(figsize=(5, 4))\n",
"fig.set_dpi(fig_dpi)\n",
"\n",
"plt.plot(exp_results[:two_results, 1], exp_results[:two_results, 2], 'x-', label=\"Top-1 Accuracy\", lw=lw)\n",
"plt.plot(exp_results[:two_results, 1], exp_results[:two_results, 3], 'x-', label=\"Top-5 Accuracy\", lw=lw)\n",
"plt.plot(exp_results[:two_results, 1], exp_results[:two_results, 5], 'x-', label=\"Final Val. Accuracy\", lw=lw)\n",
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"\n",
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"plt.ylim(0, 100)\n",
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"\n",
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"plt.title('Accuracy for Exponential Learning Rates')\n",
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"plt.ylabel('% Accuracy')\n",
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"plt.xlabel('Decay Rate, λ')\n",
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"\n",
"plt.legend()\n",
"# plt.xscale('log')\n",
"plt.grid()\n",
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"\n",
"plt.tight_layout()\n",
"plt.savefig('exp-accuracy.png')\n",
"\n",
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"plt.show()"
]
},
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{
"cell_type": "code",
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"execution_count": 144,
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"id": "fe8641ec",
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"metadata": {},
"outputs": [
{
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"output_type": "display_data",
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"data": {
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"text/plain": "<Figure size 1000x800 with 1 Axes>",
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},
"metadata": {
"needs_background": "light"
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}
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}
],
"source": [
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"fig = plt.figure(figsize=(5, 4))\n",
"fig.set_dpi(fig_dpi)\n",
"\n",
"plt.plot(exp_results[:two_results, 1], exp_results[:two_results, 4], 'x-', label=\"Final Validation Loss\", lw=lw)\n",
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"\n",
"# plt.ylim(0)\n",
"\n",
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"plt.title('Final Validation Loss for Exponential Learning Rates')\n",
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"plt.ylabel('Loss')\n",
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"plt.xlabel('Decay Rate, λ')\n",
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"\n",
"# plt.legend()\n",
"plt.grid()\n",
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"\n",
"plt.tight_layout()\n",
"plt.savefig('exp-loss.png')\n",
"\n",
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"plt.show()"
]
},
{
"cell_type": "markdown",
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"id": "3dfbd4bb",
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"metadata": {},
"source": [
"# Sigmoid Decay\n",
"80/10/10 Split, 100 epochs\n",
"\n",
"## Index\n",
"0. learning rate\n",
"1. step size\n",
"2. gamma\n",
"3. top-1 accuracy\n",
"4. top-5 accuracy\n",
"5. last val loss\n",
"6. last val accuracy"
]
},
{
"cell_type": "code",
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"execution_count": 145,
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"id": "dc7ccd8d",
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"metadata": {},
"outputs": [],
"source": [
"sig_results = np.array([\n",
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" [1e-2, 50, 0.025, 43.30, 70.60, 2.88, 50.61],\n",
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" [1e-2, 50, 0.05, 46.94, 72.88, 2.79, 52.94],\n",
" [1e-2, 50, 0.1, 45.95, 73.63, 2.65, 51.29],\n",
" [1e-2, 50, 0.15, 41.94, 68.56, 2.94, 47.49],\n",
" [1e-2, 50, 0.2, 41.82, 68.13, 2.82, 45.16]\n",
"])"
]
},
{
"cell_type": "code",
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"execution_count": 146,
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"id": "5ab4be17",
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"metadata": {},
"outputs": [
{
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"output_type": "display_data",
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"data": {
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"text/plain": "<Figure size 1000x800 with 1 Axes>",
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},
"metadata": {
"needs_background": "light"
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}
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}
],
"source": [
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"fig = plt.figure(figsize=(5, 4))\n",
"fig.set_dpi(fig_dpi)\n",
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"\n",
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"plt.plot(sig_results[:, 2], sig_results[:, 3], 'x-', label=\"Top-1 Accuracy\", lw=lw)\n",
"plt.plot(sig_results[:, 2], sig_results[:, 4], 'x-', label=\"Top-5 Accuracy\", lw=lw)\n",
"plt.plot(sig_results[:, 2], sig_results[:, 6], 'x-', label=\"Final Val. Accuracy\", lw=lw)\n",
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"\n",
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"plt.ylim(20, 80)\n",
"plt.xlim(0, 0.25)\n",
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"\n",
"plt.title('Accuracy for Sigmoid Learning Rates')\n",
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"plt.ylabel('% Accuracy')\n",
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"plt.xlabel('Gamma, γ
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"\n",
"plt.legend()\n",
"# plt.xscale('log')\n",
"plt.grid()\n",
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"\n",
"plt.tight_layout()\n",
"plt.savefig('sig-accuracy.png')\n",
"\n",
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"plt.show()"
]
},
{
"cell_type": "code",
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"execution_count": 147,
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"id": "49f5ca1a",
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"metadata": {},
"outputs": [
{
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"output_type": "display_data",
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"data": {
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"text/plain": "<Figure size 432x288 with 1 Axes>",
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},
"metadata": {
"needs_background": "light"
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}
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}
],
"source": [
"plt.plot(sig_results[:, 2], sig_results[:, 5], 'x-', label=\"Final Validation Loss\")\n",
"\n",
"# plt.ylim(0)\n",
"\n",
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"plt.title('Final Validation Loss for Sigmoid Learning Rates')\n",
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"plt.ylabel('Loss')\n",
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"plt.xlabel('Gamma, γ
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"\n",
"# plt.legend()\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
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"id": "be993da6",
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"metadata": {},
"source": [
"# Best\n",
"\n",
"100 Epochs\n",
"\n",
"top-1 accuracy indexes: 1, 3, 2, 3"
]
},
{
"cell_type": "code",
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"execution_count": 148,
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"id": "6ab2b999",
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"metadata": {},
"outputs": [
{
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"output_type": "display_data",
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"data": {
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"text/plain": "<Figure size 1200x800 with 1 Axes>",
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},
"metadata": {
"needs_background": "light"
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}
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}
],
"source": [
"\n",
"best_top_1_results = list()\n",
"best_labels = list()\n",
"\n",
"# Fixed\n",
"b_fixed = fixed_results_100e[np.argmax(fixed_results_100e[:, 1])]\n",
"best_top_1_results.append(b_fixed[1:3])\n",
"best_labels.append(f'Fixed\\n{b_fixed[0]}')\n",
"\n",
"# Step Down\n",
"b_sd = step_down_results[np.argmax(step_down_results[:, 3])]\n",
"best_top_1_results.append(b_sd[3:5])\n",
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"best_labels.append(f'Step Down\\n{b_sd[0]}, S: {b_sd[1]},\\nγ
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"\n",
"# Exp\n",
"b_exp = exp_results[np.argmax(exp_results[:, 2])]\n",
"best_top_1_results.append(b_exp[2:4])\n",
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"best_labels.append(f'Exponential\\n{b_exp[0]}, λ: {b_exp[1]}')\n",
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"\n",
"# Sig\n",
"b_sig = sig_results[np.argmax(sig_results[:, 3])]\n",
"best_top_1_results.append(b_sig[3:5])\n",
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"best_labels.append(f'Sigmoid\\n{b_sig[0]}, S: {b_sig[1]},\\nγ
"\n",
"fig = plt.figure(figsize=(6, 4))\n",
"fig.set_dpi(fig_dpi)\n",
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"\n",
"# print(best_top_1_results)\n",
"# print(best_labels)\n",
"# print(best_top_1_results)\n",
"plt.barh(range(len(best_labels)), [i[0] for i in best_top_1_results], tick_label=best_labels, label='Top-1')\n",
"plt.barh(range(len(best_labels)), [i[1] - i[0] for i in best_top_1_results], tick_label=best_labels, label='Top-5', left=[i[0] for i in best_top_1_results])\n",
"\n",
"plt.legend()\n",
"plt.grid(axis='x')\n",
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"plt.title('Best Test Accuracy for Learning Schedule Policies')\n",
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"plt.xlabel('% Test Accuracy')\n",
"plt.ylabel('Learning Schedule Policies')\n",
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"\n",
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"plt.xticks(np.linspace(0, 100, 11))\n",
"plt.xlim(0, 100)\n",
"\n",
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"plt.tight_layout()\n",
"plt.savefig('best-barh.png')\n",
"\n",
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"plt.show()"
]
},
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{
"cell_type": "code",
"execution_count": null,
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"id": "8f36766a",
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"metadata": {},
"outputs": [],
"source": []
}
],
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