2021-04-10 12:20:26 +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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2021-04-30 19:47:47 +01:00
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"execution_count": 187,
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2021-04-12 16:06:52 +01:00
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"id": "682fef9a",
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2021-04-10 12:20:26 +01:00
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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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2021-04-27 23:46:23 +01:00
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"from matplotlib import pyplot as plt\n",
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"import seaborn as sns\n",
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"\n",
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"fig_dpi = 200\n",
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"lw = 3"
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2021-04-10 12:20:26 +01:00
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]
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},
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{
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"cell_type": "markdown",
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2021-04-12 16:06:52 +01:00
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"id": "75cc3c1d",
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2021-04-10 12:20:26 +01:00
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"metadata": {},
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"source": [
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"# Dense Layers\n",
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"\n",
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"Exponential LR Decay: 0.98\n",
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"\n",
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"100 Epochs\n",
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"\n",
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"## Index\n",
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"0. fc layers\n",
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"1. nodes per layer\n",
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"2. top-1 accuracy\n",
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"3. top-5 accuracy\n",
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"4. last val loss\n",
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"5. 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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2021-04-30 19:47:47 +01:00
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"execution_count": 188,
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2021-04-12 16:06:52 +01:00
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"id": "85cb4e35",
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2021-04-10 12:20:26 +01:00
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"metadata": {},
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"outputs": [],
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"source": [
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"fc_results = np.array([\n",
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2021-04-10 16:56:01 +01:00
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" [1, 256, 52.44, 79.86, 2.49, 57.66],\n",
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" [1, 512, 49.29, 73.93, 2.95, 53.25],\n",
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" [1, 1024, 40.7, 68.38, 3.66, 45.22],\n",
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" [1, 2048, 32.12, 58.93, 4.66, 35.72],\n",
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" [1, 4096, 24.03, 46.76, 5.61, 27.94],\n",
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" [1, 8192, 19.70, 41.01, 6.42, 23.96],\n",
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" \n",
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" [2, 256, 54.48, 81.22, 1.86, 57.11],\n",
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" [2, 512, 56.64, 82.46, 1.94, 60.23],\n",
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" [2, 1024, 56.39, 81.53, 2.08, 60.91],\n",
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" [2, 2048, 51.39, 79.00, 2.38, 56.74],\n",
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" [2, 4096, 44.41, 71.83, 3.04, 47.61], # DEFAULT ALEXNET\n",
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" [2, 8192, 37.74, 64.36, 3.60, 42.40],\n",
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" \n",
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" [3, 256, 0.80, 2.10, 5.29, 0.55],\n",
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" [3, 512, 30.7, 65.16, 2.57, 30.82],\n",
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" [3, 1024, 48.36, 76.65, 2.30, 49.88],\n",
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" [3, 2048, 54.11, 80.48, 2.38, 58.21],\n",
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" [3, 4096, 54.48, 82.09, 2.39, 57.17],\n",
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" [3, 8192, 50.71, 78.57, 2.55, 55.88],\n",
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" \n",
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" [4, 256, 0.80, 2.29, 5.29, 0.55],\n",
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" [4, 512, 0.80, 2.10, 5.29, 0.55],\n",
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" [4, 1024, 0.80, 2.10, 5.29, 0.55],\n",
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2021-04-10 12:20:26 +01:00
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" [4, 2048, 25.45, 60.9, 2.84, 28.55],\n",
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" [4, 4096, 41.14, 73.81, 2.81, 46.32],\n",
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" [4, 8192, 49.85, 77.58, 2.97, 53.92]\n",
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"])\n",
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"\n",
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"fc_0_results = [0, 196, 28.91, 54.05, 6.52, 33.21]\n",
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"\n",
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"layers = [1, 2, 3, 4]\n",
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"nodes = [256, 512, 1024, 2048, 4096, 8192]\n",
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2021-04-10 12:20:26 +01:00
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"\n",
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2021-04-10 16:56:01 +01:00
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"fc_matrix = np.zeros((len(layers), len(nodes)))\n",
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2021-04-10 12:20:26 +01:00
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"for i in fc_results:\n",
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" fc_matrix[layers.index(i[0]), nodes.index(i[1])] = i[2]"
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]
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},
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{
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"cell_type": "code",
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2021-04-30 19:47:47 +01:00
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"execution_count": 189,
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2021-04-12 16:06:52 +01:00
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"id": "0fd9f416",
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2021-04-10 12:20:26 +01:00
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"metadata": {},
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"outputs": [
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{
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2021-04-27 23:46:23 +01:00
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"output_type": "display_data",
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2021-04-10 12:20:26 +01:00
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"data": {
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2021-04-27 23:46:23 +01:00
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"text/plain": "<Figure size 1200x800 with 2 Axes>",
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2021-04-30 19:47:47 +01:00
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2021-04-27 23:46:23 +01:00
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2021-04-10 12:20:26 +01:00
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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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],
|
|
|
|
|
"source": [
|
|
|
|
|
"X, Y = np.meshgrid(layers, nodes)\n",
|
|
|
|
|
"\n",
|
2021-04-27 23:46:23 +01:00
|
|
|
"fig = plt.figure(figsize=(6, 4))\n",
|
|
|
|
|
"# fig = plt.figure()\n",
|
|
|
|
|
"fig.set_dpi(fig_dpi)\n",
|
|
|
|
|
"\n",
|
2021-04-10 12:20:26 +01:00
|
|
|
"ax = plt.axes(projection='3d')\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"surf = ax.plot_surface(X, Y, fc_matrix.T, cmap='viridis')\n",
|
|
|
|
|
"\n",
|
2021-04-27 23:46:23 +01:00
|
|
|
"ax.set_title('Accuracy For Different MLP Configurations')\n",
|
2021-04-10 12:20:26 +01:00
|
|
|
"ax.set_xlabel('Fully Connected Layers')\n",
|
|
|
|
|
"ax.set_ylabel('Nodes Per Layer')\n",
|
|
|
|
|
"ax.set_zlabel('Top-1 % Test Accuracy')\n",
|
2021-04-27 23:46:23 +01:00
|
|
|
"ax.set_xticks([1, 2, 3, 4])\n",
|
2021-04-10 12:20:26 +01:00
|
|
|
"\n",
|
2021-04-27 23:46:23 +01:00
|
|
|
"ax.view_init(40, -70)\n",
|
|
|
|
|
"fig.colorbar(surf, location=\"left\", shrink=0.7, aspect=15)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"# plt.tight_layout()\n",
|
|
|
|
|
"plt.savefig('fc-accuracy-surf.png')\n",
|
2021-04-10 12:20:26 +01:00
|
|
|
"\n",
|
|
|
|
|
"plt.show()"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2021-04-30 19:47:47 +01:00
|
|
|
"execution_count": 190,
|
2021-04-10 12:20:26 +01:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
2021-04-27 23:46:23 +01:00
|
|
|
"output_type": "display_data",
|
2021-04-10 12:20:26 +01:00
|
|
|
"data": {
|
2021-04-27 23:46:23 +01:00
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"text/plain": "<Figure size 432x288 with 2 Axes>",
|
2021-04-30 19:47:47 +01:00
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2021-04-27 23:46:23 +01:00
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|
2021-04-10 12:20:26 +01:00
|
|
|
},
|
|
|
|
|
"metadata": {
|
|
|
|
|
"needs_background": "light"
|
2021-04-27 23:46:23 +01:00
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"fig = plt.figure()\n",
|
|
|
|
|
"# fig.set_dpi(fig_dpi)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"sns.heatmap(fc_matrix.T, xticklabels=layers, yticklabels=nodes, cmap='inferno')\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"plt.title(\"Accuracy For Different MLP Configurations\")\n",
|
|
|
|
|
"plt.xlabel(\"Fully-Connected Layer\")\n",
|
|
|
|
|
"plt.ylabel(\"Nodes Per Layer\")\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"# plt.tight_layout()\n",
|
|
|
|
|
"# plt.savefig('fc-accuracy-surf.png')\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"plt.show()"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2021-04-30 19:47:47 +01:00
|
|
|
"execution_count": 191,
|
2021-04-27 23:46:23 +01:00
|
|
|
"id": "dc9d04b1",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"output_type": "display_data",
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": "<Figure size 1000x800 with 1 Axes>",
|
2021-04-30 19:47:47 +01:00
|
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2021-04-29 00:53:46 +01:00
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2021-04-10 12:20:26 +01:00
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},
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2021-04-27 23:46:23 +01:00
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"metadata": {
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"needs_background": "light"
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}
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2021-04-10 12:20:26 +01:00
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}
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],
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"source": [
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2021-04-27 23:46:23 +01:00
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"fig = plt.figure(figsize=(5, 4))\n",
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"fig.set_dpi(fig_dpi)\n",
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"\n",
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2021-04-10 12:20:26 +01:00
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"# plt.plot([196], fc_0_results[2], 'x-', label=f'0 Layers')\n",
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"\n",
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"for i in layers:\n",
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2021-04-27 23:46:23 +01:00
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" plt.plot(nodes, fc_matrix[i-1, :], '-', label=f'{i} Layers', ms=8, lw=lw)\n",
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"\n",
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2021-04-29 00:53:46 +01:00
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"plt.plot([4096], [fc_matrix[1, 4]], \"x\", label=\"AlexNet\", ms=\"8\", c=(0, 0, 0))\n",
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2021-04-27 23:46:23 +01:00
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"\n",
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|
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"# plt.annotate('Standard\\nAlexNet', \n",
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"# (4096, fc_matrix[layers.index(2), nodes.index(4096)]),\n",
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"# textcoords=\"offset points\",\n",
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"# xytext=(40, 10),\n",
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"# ha='center',\n",
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"# arrowprops={'arrowstyle': 'simple'}\n",
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"# )\n",
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2021-04-10 12:20:26 +01:00
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" \n",
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2021-04-27 23:46:23 +01:00
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|
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"plt.title('Accuracy for Varied Dense Layer Shapes')\n",
|
2021-04-10 12:20:26 +01:00
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|
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"plt.xlabel('Nodes Per Layer')\n",
|
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|
|
|
"plt.ylabel('Top-1 % Test Accuracy')\n",
|
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"\n",
|
2021-04-29 00:53:46 +01:00
|
|
|
"plt.ylim(0, 60)\n",
|
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"\n",
|
2021-04-10 12:20:26 +01:00
|
|
|
"plt.grid()\n",
|
|
|
|
|
"plt.legend()\n",
|
2021-04-27 23:46:23 +01:00
|
|
|
"\n",
|
|
|
|
|
"plt.tight_layout()\n",
|
|
|
|
|
"plt.savefig('fc-accuracy.png')\n",
|
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"\n",
|
2021-04-10 12:20:26 +01:00
|
|
|
"plt.show()"
|
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]
|
2021-04-12 16:06:52 +01:00
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
"id": "340c4eb8",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"source": [
|
|
|
|
|
"# Convolutional Non-Linearity\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"Exponential LR Decay: 0.98\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"100 Epochs\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"Taking conovlutional layers and distributing the standard number of filters into separate conv layers with ReLu nonlinearity\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"## Index\n",
|
|
|
|
|
"0. convolutional layer\n",
|
|
|
|
|
"1. number of divisions\n",
|
|
|
|
|
"2. top-1 accuracy\n",
|
|
|
|
|
"3. top-5 accuracy\n",
|
|
|
|
|
"4. last val loss\n",
|
|
|
|
|
"5. last val accuracy"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2021-04-30 19:47:47 +01:00
|
|
|
"execution_count": 192,
|
2021-04-12 16:06:52 +01:00
|
|
|
"id": "3426107c",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"conv_nonlin_results = np.array([\n",
|
|
|
|
|
"# [1, 1, 44.41, 71.83, 3.04, 47.61], # STANDARD ALEXNET\n",
|
|
|
|
|
"# [1, 2],\n",
|
|
|
|
|
"# [1, 4],\n",
|
|
|
|
|
" \n",
|
|
|
|
|
" [4, 1, 44.41, 71.83, 3.04, 47.61], # STANDARD ALEXNET\n",
|
|
|
|
|
" [4, 2, 42.31, 71.96, 3.07, 49.75],\n",
|
|
|
|
|
" [4, 4, 0.8, 2.47, 5.29, 0.55],\n",
|
|
|
|
|
" \n",
|
|
|
|
|
" [5, 1, 44.41, 71.83, 3.04, 47.61], # STANDARD ALEXNET\n",
|
|
|
|
|
" [5, 2, 46.08, 73.87, 3.00, 49.20],\n",
|
|
|
|
|
" [5, 4, 0.8, 2.47, 5.29, 0.55]\n",
|
|
|
|
|
"])\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"nonlin_layers = [4, 5]\n",
|
|
|
|
|
"nonlin_div = [1, 2, 4]\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"conv_nonlin_matrix = np.zeros((len(nonlin_layers), len(nonlin_div)))\n",
|
|
|
|
|
"for i in conv_nonlin_results:\n",
|
|
|
|
|
" conv_nonlin_matrix[nonlin_layers.index(i[0]), nonlin_div.index(i[1])] = i[2]"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2021-04-30 19:47:47 +01:00
|
|
|
"execution_count": 193,
|
2021-04-12 16:06:52 +01:00
|
|
|
"id": "d7457e0e",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
2021-04-27 23:46:23 +01:00
|
|
|
"output_type": "display_data",
|
2021-04-12 16:06:52 +01:00
|
|
|
"data": {
|
2021-04-27 23:46:23 +01:00
|
|
|
"text/plain": "<Figure size 432x288 with 1 Axes>",
|
2021-04-30 19:47:47 +01:00
|
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2021-04-27 23:46:23 +01:00
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2021-04-12 16:06:52 +01:00
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},
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"metadata": {
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"needs_background": "light"
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2021-04-27 23:46:23 +01:00
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}
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2021-04-12 16:06:52 +01:00
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}
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],
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"source": [
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"for idx, i in enumerate(nonlin_layers):\n",
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" plt.plot(nonlin_div, conv_nonlin_matrix[idx, :], 'x-', label=f'Layer {i}')\n",
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"\n",
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"plt.title('Accuracy for Varied Convolutional Non-Linearity')\n",
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"plt.xlabel('Layer Divisor')\n",
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"plt.ylabel('Top-1 % Test Accuracy')\n",
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"\n",
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"plt.grid()\n",
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"plt.legend()\n",
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"plt.show()"
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]
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2021-04-25 19:56:49 +01:00
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},
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{
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"source": [
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"# Convolutional Kernel Size\n",
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"\n",
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"Exponential LR Decay: 0.98\n",
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"\n",
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"100 Epochs\n",
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"\n",
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"## Index\n",
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"0. convolutional layer\n",
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"1. kernel size\n",
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"2. top-1 accuracy\n",
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"3. top-5 accuracy\n",
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"4. last val loss\n",
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"5. last val accuracy"
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],
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"cell_type": "markdown",
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"metadata": {}
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},
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{
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"cell_type": "code",
|
2021-04-30 19:47:47 +01:00
|
|
|
"execution_count": 194,
|
2021-04-25 19:56:49 +01:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"kernel_results = np.array([\n",
|
|
|
|
|
" [1, 3, 37.06, 64.73, 3.38, 44.36],\n",
|
2021-04-27 23:46:23 +01:00
|
|
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" [1, 5, 43.73, 70.35, 3.19, 48.22],\n",
|
2021-04-25 19:56:49 +01:00
|
|
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" [1, 7, 44.72, 71.16, 3.00, 48.96],\n",
|
|
|
|
|
" [1, 11, 44.41, 71.83, 3.04, 47.61], # DEFAULT ALEXNET\n",
|
|
|
|
|
" [1, 15, 43.17, 71.59, 3.09, 47.55],\n",
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"\n",
|
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|
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" [2, 3, 41.63, 67.63, 3.24, 45.53],\n",
|
|
|
|
|
" [2, 5, 44.41, 71.83, 3.04, 47.61], # DEFAULT ALEXNET\n",
|
|
|
|
|
" [2, 7, 45.15, 72.21, 2.97, 50.49],\n",
|
|
|
|
|
" [2, 9, 43.61, 71.34, 3.10, 47.37],\n",
|
|
|
|
|
" [2, 11, 39.35, 65.6, 3.36, 44.98],\n",
|
|
|
|
|
"\n",
|
|
|
|
|
" [3, 3, 44.41, 71.83, 3.04, 47.61], # DEFAULT ALEXNET\n",
|
2021-04-27 23:46:23 +01:00
|
|
|
" [3, 5, 50.59, 75.91, 2.74, 53.80],\n",
|
|
|
|
|
" [3, 7, 47.13, 75.48, 3.19, 52.21],\n",
|
|
|
|
|
" [3, 9, 40.83, 69.73, 3.90, 47.00],\n",
|
|
|
|
|
" [3, 11, 33.29, 61.70, 5.27, 38.05],\n",
|
|
|
|
|
"\n",
|
|
|
|
|
" [4, 3, 44.41, 71.83, 3.04, 47.61], # DEFAULT ALEXNET\n",
|
|
|
|
|
" [4, 5, 48.67, 74.74, 2.92, 51.84],\n",
|
|
|
|
|
" [4, 7, 49.54, 76.34, 2.98, 52.63],\n",
|
|
|
|
|
" [4, 9, 47.19, 74.24, 3.40, 50.37],\n",
|
|
|
|
|
" [4, 11, 43.55, 70.29, 3.98, 47.24],\n",
|
|
|
|
|
"\n",
|
|
|
|
|
" [5, 3, 44.41, 71.83, 3.04, 47.61], # DEFAULT ALEXNET\n",
|
|
|
|
|
" [5, 5, 46.94, 74.31, 2.88, 51.53],\n",
|
|
|
|
|
" [5, 7, 48.24, 75.42, 2.87, 51.84],\n",
|
|
|
|
|
" [5, 9, 47.56, 74.68, 2.94, 53.43],\n",
|
|
|
|
|
" [5, 11, 44.1, 72.45, 3.60, 48.28],\n",
|
|
|
|
|
"])\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"default_kernel_sizes = [11, 5, 3, 3, 3]\n",
|
|
|
|
|
"kernel_layers = {i[0] for i in kernel_results}"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2021-04-30 19:47:47 +01:00
|
|
|
"execution_count": 195,
|
2021-04-27 23:46:23 +01:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
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|
|
|
"output_type": "display_data",
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": "<Figure size 1000x700 with 1 Axes>",
|
2021-04-30 19:47:47 +01:00
|
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2021-04-29 00:53:46 +01:00
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2021-04-27 23:46:23 +01:00
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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, 3.5))\n",
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"fig.set_dpi(fig_dpi)\n",
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"\n",
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"kernel_parsed_results = [(list(), list()) for _ in kernel_layers]\n",
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"for row in kernel_results:\n",
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" kernel_parsed_results[int(row[0]) - 1][0].append(row[2]) # top-1 accuracy\n",
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" kernel_parsed_results[int(row[0]) - 1][1].append(row[1]) # kernel size (x value)\n",
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"\n",
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"for idx, l in enumerate(kernel_parsed_results):\n",
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" plt.plot(l[1], l[0], '-', label=f'Layer {idx+1}', lw=lw)\n",
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"\n",
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"for idx, l in enumerate(kernel_parsed_results):\n",
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" # print(l)\n",
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" # print(l[0][l[1].index(default_kernel_sizes[idx])])\n",
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"\n",
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" if idx == len(kernel_parsed_results) - 1:\n",
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" label = 'AlexNet'\n",
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" else:\n",
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" label = None\n",
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2021-04-29 00:53:46 +01:00
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" plt.plot([default_kernel_sizes[idx]], [l[0][l[1].index(default_kernel_sizes[idx])]], \"x\", label=label, ms=6, c=(0, 0, 0))\n",
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2021-04-27 23:46:23 +01:00
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"\n",
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"plt.title('Accuracy for Varied Convolutional Kernel Size')\n",
|
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"plt.xlabel(\"Kernel Size\")\n",
|
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|
|
|
"plt.ylabel('Top-1 % Test Accuracy')\n",
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"\n",
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"plt.grid()\n",
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|
"plt.legend()\n",
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"\n",
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2021-04-29 00:53:46 +01:00
|
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"plt.ylim(20, 60)\n",
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"plt.xlim()\n",
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|
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"plt.xticks([3, 5, 7, 9, 11, 13, 15])\n",
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"\n",
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2021-04-27 23:46:23 +01:00
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"plt.tight_layout()\n",
|
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|
|
"plt.savefig('kernel-accuracy.png')\n",
|
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"\n",
|
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|
"plt.show()"
|
2021-04-25 19:56:49 +01:00
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]
|
2021-04-27 23:46:23 +01:00
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},
|
2021-04-29 00:53:46 +01:00
|
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{
|
|
|
|
|
"source": [
|
|
|
|
|
"# Convolutional New Layers\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"Exponential LR Decay: 0.98\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"100 Epochs\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"L1.5, Stride = 1\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"## Index\n",
|
|
|
|
|
"0. convolutional layer\n",
|
|
|
|
|
"1. kernel size\n",
|
|
|
|
|
"2. filters\n",
|
|
|
|
|
"3. top-1 accuracy\n",
|
|
|
|
|
"4. top-5 accuracy\n",
|
|
|
|
|
"5. last val loss\n",
|
|
|
|
|
"6. last val accuracy"
|
|
|
|
|
],
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
"metadata": {}
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2021-04-30 19:47:47 +01:00
|
|
|
"execution_count": 196,
|
2021-04-29 00:53:46 +01:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"std_results = [44.41, 71.83, 3.04, 47.61]\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"layer1_kernel = np.array([\n",
|
|
|
|
|
" [1.5, 3, 176, 54.23, 80.73, 2.84, 55.76],\n",
|
|
|
|
|
" [1.5, 5, 176, 48.49, 78.38, 3.04, 52.76],\n",
|
2021-04-30 19:47:47 +01:00
|
|
|
" [1.5, 7, 176, 46.14, 74.68, 3.24, 50.25],\n",
|
2021-04-29 00:53:46 +01:00
|
|
|
" [1.5, 9, 176, 40.83, 68.75, 3.59, 43.26],\n",
|
|
|
|
|
" [1.5, 11, 176, 34.28, 66.15, 3.65, 39.22]\n",
|
|
|
|
|
"])\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"layer1_filter = np.array([\n",
|
|
|
|
|
" [1.5, 7, 96, 45.27, 74.80, 3.04, 49.75],\n",
|
|
|
|
|
" [1.5, 7, 176, 45.46, 75.17, 3.18, 49.75],\n",
|
|
|
|
|
" [1.5, 7, 256, 43.17, 72.82, 3.42, 50.06]\n",
|
|
|
|
|
"])\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"layer3_kernel = np.array([\n",
|
|
|
|
|
" [3.5, 3, 384, 45.58, 72.27, 2.92, 48.28],\n",
|
|
|
|
|
" [3.5, 5, 384, 52.56, 78.88, 2.74, 57.05],\n",
|
2021-04-30 19:47:47 +01:00
|
|
|
" [3.5, 7, 384, 50.09, 77.46, 3.29, 52.57],\n",
|
|
|
|
|
" [3.5, 9, 384, 44.78, 71.53, 4.07, 48.35],\n",
|
|
|
|
|
" [3.5, 11, 384, 37.31, 64.42, 4.80, 41.18]\n",
|
2021-04-29 00:53:46 +01:00
|
|
|
"])\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"layer3_filter = np.array([\n",
|
|
|
|
|
" [3.5, 3, 192, 46.20, 74.00, 2.81, 51.41],\n",
|
|
|
|
|
" [3.5, 3, 384, 45.58, 72.27, 2.92, 48.28], # repeated from above\n",
|
|
|
|
|
" [3.5, 3, 576, 44.84, 71.77, 2.98, 50.25]\n",
|
|
|
|
|
"])\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"layer1 = np.append(layer1_kernel, layer1_filter, axis=0)\n",
|
|
|
|
|
"layer3 = np.append(layer3_kernel, layer3_filter, axis=0)"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2021-04-30 19:47:47 +01:00
|
|
|
"execution_count": 197,
|
2021-04-29 00:53:46 +01:00
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"outputs": [
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2021-04-30 19:47:47 +01:00
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2021-04-29 00:53:46 +01:00
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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(layer1_kernel[:, 1], layer1_kernel[:, 3], 'x-', label=\"Layer 1.5\\n176 Filters\", lw=lw)\n",
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"plt.plot(layer3_kernel[:, 1], layer3_kernel[:, 3], 'x-', label=\"Layer 3.5\\n384 Filters\", lw=lw)\n",
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"\n",
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"plt.title(\"Accuracy for Additional Convolutional Layers\")\n",
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"plt.xlabel(\"Kernel Size\")\n",
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"plt.ylabel('Top-1 % Test Accuracy')\n",
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"\n",
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"plt.xticks([3, 5, 7, 9, 11])\n",
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"plt.ylim(30, 60)\n",
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"\n",
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"plt.grid()\n",
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"plt.legend()\n",
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"\n",
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"plt.tight_layout()\n",
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"plt.savefig('new-layer-kernel-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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"cell_type": "code",
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2021-04-30 19:47:47 +01:00
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"execution_count": 198,
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2021-04-29 00:53:46 +01:00
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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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2021-04-30 19:47:47 +01:00
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2021-04-29 00:53:46 +01:00
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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(layer1_filter[:, 2], layer1_filter[:, 3], 'x-', label=\"Layer 1.5\\n7x7 Kernel\", lw=lw)\n",
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|
"plt.plot(layer3_filter[:, 2], layer3_filter[:, 3], 'x-', label=\"Layer 3.5\\n3x3 Kernel\", lw=lw)\n",
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"\n",
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"plt.title(\"Accuracy for Additional Convolutional Layers\")\n",
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"plt.xlabel(\"Layer Filters\")\n",
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"plt.ylabel('Top-1 % Test Accuracy')\n",
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"\n",
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"# plt.xticks([3, 5, 7, 9, 11])\n",
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"plt.xlim(0)\n",
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"plt.ylim(30, 60)\n",
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"\n",
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"plt.grid()\n",
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"plt.legend()\n",
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"\n",
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"plt.tight_layout()\n",
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"plt.savefig('new-layer-filter-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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"cell_type": "code",
|
2021-04-30 19:47:47 +01:00
|
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|
"execution_count": 199,
|
2021-04-29 00:53:46 +01:00
|
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"metadata": {},
|
|
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"outputs": [],
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|
"source": [
|
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|
|
"best_results = [43.11, 71.22, 3.80, 47.06]\n",
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|
"\n",
|
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|
|
|
"standard_res = [44.41, 71.83, 3.04, 47.61]"
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]
|
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},
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{
|
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"cell_type": "code",
|
2021-04-30 19:47:47 +01:00
|
|
|
"execution_count": 200,
|
2021-04-29 00:53:46 +01:00
|
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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 1200x800 with 1 Axes>",
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2021-04-30 19:47:47 +01:00
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"image/png": "iVBORw0KGgoAAAANSUhEUgAABJsAAAMMCAYAAAD0M6ZoAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAB7CAAAewgFu0HU+AADKrUlEQVR4nOzdeZhcVbWw8XcFCFMIhMkwSaKIMgnIoKhIEHEEZVJERUBFEPU6XL1e/VQCXpwnUEFEJQxOCIKgIIoQcGAIgwKCgkBAEJAwBwKBZH1/7NPpSlFVXd1dVd2dvL/nOU+dOmeffVadruqkVu+9TmQmkiRJkiRJUieMG+kAJEmSJEmStOQw2SRJkiRJkqSOMdkkSZIkSZKkjjHZJEmSJEmSpI4x2SRJkiRJkqSOMdkkSZIkSZKkjjHZJEmSJEmSpI4x2SRJkiRJkqSOMdkkSZIkSZKkjjHZJEmSJEmSpI4x2SRJkiRJkqSOMdkkSZIkSZKkjjHZJEmSJEmSpI4x2SRJkiRJkqSOMdkkSZIkSZKkjjHZJEmSJEmSpI4x2SRJkiRJkqSOMdkkSZIkSZKkjjHZJEmSJEmSpI4x2SRJkqSOiIjZEZHVMmWk4+m2iNgkIo6NiBsi4tGa175UvP6xJiIOrPn5zOhQn1Nq+pzdiT4laUlgskmStNSIiJl1Xwbrl4XVF8bbIuKsiDg4IlYZ6bjHmoh4Vd11vXGkY5I6LSLeCFwDvA/YBJgwshF1n59tSVK7TDZJktQvKF8YpwBvAr4H3Fx9qRxzRvAv7gfUPX9BRGzfw/NLXRURE4AZwPLVpruBnwPHAt+plkdGJLju8rM9REvaqL/apONIxyJpdFp2pAOQJGmEzAKuqNs2DlgN2BLYtNr2LOAXEbFHZv6qd+GNTdVIsL0a7DqAZ15vaazaDZhUrf8N2C4z541gPF3nZ1uSNBgmmyRJS6tzM3N6s50R8TLgp8D6wDLAdyNiamY+1aP4xqp9gJWq9XnAitX6WyPiI5k5f2TCUi9k5pSRjqFHXlSz/pMlPdFU8bPdQGbOpoyKlSTVcBqdJEkNZOafgDfXbFoPmDYy0YwptdNsvgTcW62vDuze+3CkrphUs373iEXRW362JUltM9kkSVITmXkZcFvNpk2btVWpEQW8onqawEnAT2qa1Nd7kcaq5WrWF45YFD3iZ1uSNFgmmyRJaq121MLK7RwQERtExGci4g8R8e+IeDIiHoiIayLiqxGxcZv9LBcR74iIX0TErRExNyKeru6Y98+IOD8ijqwv0Nt3e28WT5Rt2OwufO3E0qZ30j+d5I/V9JJTava/LiLWHmynEfHCiPhiRFweEfdExPzqWvwjIn4WEe+OiFXb6GdiRHwwIs6pivXOrX42/46I30fE4RGxWZNja+9kOK2Nc02vaT+93TYRsWL1en4bEXdUrzUjYqu6Y1eNiP0i4vjqusyp2j4SEbdExE8i4i0RMej/6w3neg+2CHL1Ht8/Ik6r3uOPRsRjUe4I+ZOI2DMi2pqiFBHbRcS3I+LqiHiw+qzMi4i7I+KyiDiuuiZtfY4b9L/o58XiyZUTG3yuDmzSx4SI+K/qs3tnRDxRxXp9FfuL24zlGZ/fiNgyIo6u+nqg2n/WUF5rAx37bDf6LEXEOhHxqYi4onrPLYiIh5ocv0z1czy5ek8+GBFPRcT91Xv26IjYpd33TdXnntXvhTuq3wn/qT6D7xion2hxI4bafcCGNbtua/Ceafm7JSLWiIj/jojfRcS/qvfOQxFxQ0R8JyK2bff11vT58up6XVO95qei/A65LiJOivI7ZsWa9tPq33fV9mZ3eJ3S6NiImNlmfAP+OzXcz0KnrmsUe0TEj6v35SPV+/ixKL8XL4yIL0XEzjGE38vSmJSZLi4uLi4uS8UCzKT8VT6B6W0ec0vNMe8aoO044EhKPZNssTwFHAVEi742Bm4YoJ/aZaOaYw8cxHHZwev7z5p+D67Zfn3N9g8Por/VKHWzFrbxOu4ZoK9DgQfavCavHeC9M62N2KcP9F6rbwNsUnetapetao7bC3iizdfyF2Bqr643MLumzZQBzjet7j3TbLkUWK9FP8sCxw/iPf9/Q3x/Tx/EOQ5scPxulOT1QMf+CFhpgFgW+/xWsT3doK+zRttnm7rPEuXOn40+mw81OHZH4B9t/gy+2OD4A2v2zwBWBX45QD/nASu2eD1TatrObrGvnaXh7xbg/cBDAxy7EPgBML6Nn8H6wG/bjOmyus/sYF7PlCbHzmzzvbLY+7zTn4VOXVfKjUT+PIjr8qpOfC5dXEb7YoFwSZKaqP6i+ZyaTX9o0XYZ4GfA3jWb76Lcpek+YALwYuC5lC/HnwLWAt7boK9VgAuADapNC4FrgBuBuZQivetR7pq3ZoNwbqTcen0VyogEgEeBk5vFP1wR8XLKawN4knIb+D6nAF+s1g8AvtlGf+sCFwLPr9n8EPAnyhf25YBnA9sAE4EVWvR1DPDBmk0LKHcjvJmStFkL2IryxZBWfXXRGsBvKK/pCeCPwO2U981L6tquDSxfrd9JSUreAzxetd+EUsA6KO+RSyJiq8y8v9nJO3m92xERb6YkVfqmo80DLqMkqxZSkq07UD4rLwEujYjtMvPeZ/bGV1j8c1T7uRtHubab1r22obiC8rkC2AV4QbX+e+DvdW1vrH0SEftSXu8y1aYFlJ/xPyk/sx2Bdat9bwOmRsQrM/OJgYKKiI8Dh1dPb6nifJzyfh72DQ06/dmu81JKcmA54H7gEmAO5T2+dV0cb6X8DqudwngT5Xfjw5T35WbVMo6B36PLAmdQfpbzKcmCW6rjdqS83wFeC3wdeN8gXxvAI/S/Z95J+Z1M9ToebdD+rvoNEfFN4EM1m+ZQErD3VLFuDWxO+by/C1g3It6QmQ2nd0YZvfk7YJ2azf+hvP77qj6fW/W7Iotfx7tqXs/7a7Z/h8YeabK9KwbzWejUda3+7f815Xdjn+ur5aGqr8mU38W111xa8o10tsvFxcXFxaVXC4MY2QRsR5mG1tf+FwO0P7Km7d2U0SfPGLlEKTr+UE3btzRo86Ga/X8Dnt/knFHFeSywQYP9U2r6md3la3tCzbl+XrdvfcqX6779Lxygr2UpX8T72j9O+WKzXIO24ynFic9s0tehLP4X5Z81ulZV282Bo4FXD/DemdbG9Zg+0Hutrs1TfdcOWKuu3bja11693v+lZjRbg76nUpJXff1/v0fXe3ZNP1OatNmsOkffqIGvAKs1aPccSoK3r79zG7RZo+baPU1JeDQcMUj5ovdB4N0deL/PqInrwAHaPpeSWOhrf3n9z676GX+07nNyTIs+a9/TT1F+p+zRoN3yHXitHftsN/gsPVW9Bz5d/36rjZ3yxb92xOjVwIub9D8Z+BjwPw32HVjTR9/owHOpGzlXfSa+UtN2YYv385SadrNbvO4BPxsNjnlXzTEPA++pv05Vu50piee+ts947VW7iZQEXV+7+4D9Gn1mKNPG3wb8cKD3YJuvZVrNMTPbPGbAcwzls9DJ60oZmde3/9/N3pdV280oydnth/u5dHEZC8uIB+Di4uLi4tKrpe5LzhXAt+uWYymjD66r+w/sL2gxraX6stE3bP9+4LkDxLFzTd831P9HHzi9Zv+Qh9u3+yWoA9d1xeo/7H3nemODNr+v2f+1Afp7T03b+cCOQ4xrEuUv6319Hdeh9860NtpPr2k/vY02CZwPjOvgz2U54K9V3/OASd283lVfs2v6mtKkTe174SMD9LcyJeHa1/7Fdft3q9l3arfe4w3imlFz3gMHaHtSTdubgVVbtP1ITdsFNJkCWfe+WQC8okuvs6Of7ar9zLr4/18bx9QmQ2cBE4b4eg6sO/clwLJN2gbl34m+tp9o0m5KTZvZLc494Gejrv0qwINV+yfr3/sN2m9Cf0JuDg3+zQL+ryaGh2jyh4w2r+WAiaC69tNqjpnZqXMM9rPQ6esKfLXm/O8Z6vV0cVkSF4uTSZKWVttRRm/ULu+j/CV386rN3cCemblXZj7eoq8P0T895sjMvKXViTPzIkpiAcp/ZLeuazKxZv2+AV7
|
2021-04-29 00:53:46 +01:00
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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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|
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"source": [
|
|
|
|
|
"best_results = [standard_res]\n",
|
|
|
|
|
"best_labels = ['Standard']\n",
|
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|
|
"\n",
|
|
|
|
|
"# Dense\n",
|
|
|
|
|
"b_dense = fc_results[np.argmax(fc_results[:, 2])]\n",
|
|
|
|
|
"best_results.append(b_dense[2:])\n",
|
|
|
|
|
"best_labels.append(f'Dense: {int(b_dense[0])} Layers\\nNodes: {int(b_dense[1])}')\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"# Convolutional Kernel\n",
|
|
|
|
|
"b_kernel = kernel_results[np.argmax(kernel_results[:, 2])]\n",
|
|
|
|
|
"best_results.append(b_kernel[2:])\n",
|
|
|
|
|
"best_labels.append(f'Kernel: Layer {int(b_kernel[0])}\\nSize: {int(b_kernel[1])}')\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"# Layer 1.5 + 3.5\n",
|
|
|
|
|
"for layer in [layer1, layer3]:\n",
|
|
|
|
|
" b_layer35 = layer[np.argmax(layer[:, 3])]\n",
|
|
|
|
|
" best_results.append(b_layer35[3:])\n",
|
|
|
|
|
" best_labels.append(f'Layer {b_layer35[0]}\\nK: {int(b_layer35[1])}, F: {int(b_layer35[2])}')\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"best_results = best_results[::-1]\n",
|
|
|
|
|
"best_labels = best_labels[::-1]\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"fig = plt.figure(figsize=(6, 4))\n",
|
|
|
|
|
"fig.set_dpi(fig_dpi)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"plt.barh(range(len(best_labels)), [i[0] for i in best_results], tick_label=best_labels, label='Top-1')\n",
|
|
|
|
|
"plt.barh(range(len(best_labels)), [i[1] - i[0] for i in best_results], tick_label=best_labels, label='Top-5', left=[i[0] for i in best_results])\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"plt.legend()\n",
|
|
|
|
|
"plt.grid(axis='x')\n",
|
|
|
|
|
"plt.title('Best Accuracies for Architectures')\n",
|
|
|
|
|
"plt.xlabel('% Test Accuracy')\n",
|
|
|
|
|
"plt.ylabel('Architecture Accuracies')\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"plt.xlim(0, 100)\n",
|
|
|
|
|
"plt.xticks(np.linspace(0, 100, 11))\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"plt.tight_layout()\n",
|
|
|
|
|
"plt.savefig('best-barh.png')\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"plt.show()"
|
|
|
|
|
]
|
|
|
|
|
},
|
2021-04-27 23:46:23 +01:00
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": []
|
2021-04-10 12:20:26 +01:00
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"metadata": {
|
|
|
|
|
"kernelspec": {
|
2021-04-27 23:46:23 +01:00
|
|
|
"name": "pythonjvsc74a57bd0333605e348ea7c6bf4ca805dbc845da062650cb5bf1d8f33f5f4a9d3bca7d68b",
|
|
|
|
|
"display_name": "Python 3.9.3 ('.venv': venv)"
|
2021-04-10 12:20:26 +01:00
|
|
|
},
|
|
|
|
|
"language_info": {
|
|
|
|
|
"codemirror_mode": {
|
|
|
|
|
"name": "ipython",
|
|
|
|
|
"version": 3
|
|
|
|
|
},
|
|
|
|
|
"file_extension": ".py",
|
|
|
|
|
"mimetype": "text/x-python",
|
|
|
|
|
"name": "python",
|
|
|
|
|
"nbconvert_exporter": "python",
|
|
|
|
|
"pygments_lexer": "ipython3",
|
2021-04-27 23:46:23 +01:00
|
|
|
"version": "3.9.3"
|
|
|
|
|
},
|
|
|
|
|
"metadata": {
|
|
|
|
|
"interpreter": {
|
|
|
|
|
"hash": "333605e348ea7c6bf4ca805dbc845da062650cb5bf1d8f33f5f4a9d3bca7d68b"
|
|
|
|
|
}
|
2021-04-10 12:20:26 +01:00
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"nbformat": 4,
|
|
|
|
|
"nbformat_minor": 5
|
2021-04-21 23:43:46 +01:00
|
|
|
}
|
