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added initial rotation augmentation data
2021-04-20 07:49:45 +01:00
{
"cells": [
{
"cell_type": "code",
adding epochs data, writing
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"execution_count": 73,
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"id": "3c568ab9",
"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"
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]
},
{
"cell_type": "markdown",
"id": "7ecc547f",
"metadata": {},
"source": [
"# Rotations\n",
"\n",
"80/10/10 Split, 30 epochs\n",
"\n",
"Expansion Factor: 2\n",
"\n",
"## Index\n",
"0. degrees\n",
"1. top-1 accuracy\n",
"2. top-5 accuracy\n",
"3. last val loss\n",
"4. last val accuracy"
]
},
{
"cell_type": "code",
adding epochs data, writing
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"execution_count": 74,
added initial rotation augmentation data
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"id": "1b2471d2",
"metadata": {},
"outputs": [],
"source": [
"standard_res = [20.88, 48.24, 3.34, 25.25]\n",
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"\n",
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"flipped_res = [44.84, 72.45, 2.84, 49.02]\n",
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"flipped_batch256_res = [31.5, 60.59, 3.44, 36.33]\n",
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"\n",
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"# default batch size (128)\n",
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"rot_results = np.array([\n",
" [1, 44.6, 71.34, 2.84, 48.71],\n",
" [5, 46.45, 73.5, 2.85, 47.61],\n",
" [10, 45.71, 72.64, 2.93, 45.89],\n",
" [20, 42.12, 70.41, 2.95, 40.99],\n",
" [30, 28.17, 56.52, 3.44, 30.58],\n",
" [40, 25.08, 51.33, 3.46, 28.37]\n",
"])\n",
"\n",
"rot_batch256_results = np.array([\n",
batch256 results, exporting figures, began writing
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" [1, 34.22, 62.08, 3.45, 38.23],\n",
" [5, 32.43, 60.90, 3.35, 34.77],\n",
" [10, 30.27, 58.62, 3.47, 30.86],\n",
" [20, 26.87, 53.80, 3.36, 28.96],\n",
" [30, 18.47, 41.2, 3.79, 21.82],\n",
" [40, 15.19, 37.37, 3.87, 18.19],\n",
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"])"
]
},
{
"cell_type": "code",
adding epochs data, writing
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"execution_count": 75,
added initial rotation augmentation data
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"id": "c664a31c",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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batch256 results, exporting figures, began writing
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added initial rotation augmentation data
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},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
batch256 results, exporting figures, began writing
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"fig = plt.figure(figsize=(5, 4))\n",
"fig.set_dpi(fig_dpi)\n",
"\n",
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"plt.plot(rot_results[:, 0], rot_results[:, 1], 'x-', label=\"Top-1 Accuracy\")\n",
"plt.plot(rot_results[:, 0], rot_results[:, 2], 'x-', label=\"Top-5 Accuracy\")\n",
"plt.plot(rot_results[:, 0], rot_results[:, 4], 'x-', label=\"Final Val. Accuracy\")\n",
"\n",
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"plt.ylim(10, 80)\n",
"\n",
"plt.title('Accuracy for Rotated Training Data')\n",
"plt.ylabel('% Accuracy')\n",
"plt.xlabel('Degrees Rotation')\n",
"\n",
"plt.legend()\n",
"plt.grid()\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig('rot-accuracy.png')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
adding epochs data, writing
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"execution_count": 76,
batch256 results, exporting figures, began writing
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"id": "1d3ecda0",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<Figure size 1000x800 with 1 Axes>",
adding epochs data, writing
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batch256 results, exporting figures, began writing
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},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"fig = plt.figure(figsize=(5, 4))\n",
"fig.set_dpi(fig_dpi)\n",
"\n",
"plt.plot(rot_batch256_results[:, 0], rot_batch256_results[:, 1], 'x-', label=\"Top-1 Accuracy\")\n",
"plt.plot(rot_batch256_results[:, 0], rot_batch256_results[:, 2], 'x-', label=\"Top-5 Accuracy\")\n",
"plt.plot(rot_batch256_results[:, 0], rot_batch256_results[:, 4], 'x-', label=\"Final Val. Accuracy\")\n",
"\n",
"plt.ylim(10, 80)\n",
added initial rotation augmentation data
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"\n",
batch256 results, exporting figures, began writing
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"plt.title('Accuracy for Rotated Training Data')\n",
added initial rotation augmentation data
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"plt.ylabel('% Accuracy')\n",
"plt.xlabel('Degrees Rotation')\n",
"\n",
"plt.legend()\n",
"plt.grid()\n",
batch256 results, exporting figures, began writing
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"\n",
"plt.tight_layout()\n",
"plt.savefig('rot-accuracy-256batch.png')\n",
"\n",
added initial rotation augmentation data
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"plt.show()"
]
},
adding kernel size data
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{
batch256 results, exporting figures, began writing
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"cell_type": "markdown",
"id": "c75a7fcb",
"metadata": {},
adding kernel size data
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"source": [
"# All\n",
"\n",
"Flip, rotate both ways, flip both rotations\n",
"\n",
"Expansion Factor: 6\n",
"\n",
"## Index\n",
"0. degrees\n",
"1. top-1 accuracy\n",
"2. top-5 accuracy\n",
"3. last val loss\n",
"4. last val accuracy"
batch256 results, exporting figures, began writing
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]
adding kernel size data
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},
{
"cell_type": "code",
adding epochs data, writing
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"execution_count": 77,
batch256 results, exporting figures, began writing
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"id": "dd63f155",
adding kernel size data
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"metadata": {},
"outputs": [],
"source": [
"all_results = np.array([\n",
" [5, 53.24, 79.49, 2.69, 55.64]\n",
adding layers results
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"])\n",
"\n",
"all_results_ba = np.array([\n",
" [5, 26.37, 48.05, 5.07, 27.39]\n",
adding kernel size data
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"])"
]
},
added initial rotation augmentation data
2021-04-20 07:49:45 +01:00
{
"cell_type": "code",
adding epochs data, writing
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"execution_count": 78,
batch256 results, exporting figures, began writing
2021-04-27 23:46:23 +01:00
"id": "3d6e9057",
added initial rotation augmentation data
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"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
adding epochs data, writing
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added initial rotation augmentation data
2021-04-20 07:49:45 +01:00
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"best_results = [standard_res, flipped_res]\n",
batch256 results, exporting figures, began writing
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"best_labels = ['Unaugmented\\nE: 1', 'Flipped\\nE: 2']\n",
added initial rotation augmentation data
2021-04-20 07:49:45 +01:00
"\n",
"# Clockwise Rotation\n",
"b_clock = rot_results[np.argmax(rot_results[:, 1])]\n",
"best_results.append(b_clock[1:])\n",
batch256 results, exporting figures, began writing
2021-04-27 23:46:23 +01:00
"best_labels.append(f'Clockwise {b_clock[0]}°\\nE: 2')\n",
added initial rotation augmentation data
2021-04-20 07:49:45 +01:00
"\n",
"best_results.append(all_results[0, 1:])\n",
adding layers results
2021-04-29 00:53:46 +01:00
"best_labels.append(f'Full {all_results[0, 0]}°\\nE: 6')\n",
added initial rotation augmentation data
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"\n",
"best_results = best_results[::-1]\n",
"best_labels = best_labels[::-1]\n",
"\n",
adding epochs data, writing
2021-04-30 19:47:47 +01:00
"fig = plt.figure(figsize=(6, 3))\n",
batch256 results, exporting figures, began writing
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"fig.set_dpi(fig_dpi)\n",
"\n",
added initial rotation augmentation data
2021-04-20 07:49:45 +01:00
"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",
batch256 results, exporting figures, began writing
2021-04-27 23:46:23 +01:00
"plt.title('Best Accuracy for Data Augmentations')\n",
"plt.xlabel('% Test Accuracy')\n",
"plt.ylabel('Data Augmentation Methods')\n",
"\n",
adding layers results
2021-04-29 00:53:46 +01:00
"plt.xlim(0, 100)\n",
"plt.xticks(np.linspace(0, 100, 11))\n",
batch256 results, exporting figures, began writing
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"\n",
"plt.tight_layout()\n",
"plt.savefig('best-barh.png')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
adding epochs data, writing
2021-04-30 19:47:47 +01:00
"execution_count": 79,
batch256 results, exporting figures, began writing
2021-04-27 23:46:23 +01:00
"id": "bcdf6d2a",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
adding epochs data, writing
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batch256 results, exporting figures, began writing
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},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"best_results_256 = [standard_res, flipped_batch256_res]\n",
"best_labels_256 = ['Unaugmented\\nE: 1', 'Flipped\\nE: 2']\n",
"\n",
"# Clockwise Rotation\n",
"b_clock_256 = rot_batch256_results[np.argmax(rot_batch256_results[:, 1])]\n",
"best_results_256.append(b_clock_256[1:])\n",
"best_labels_256.append(f'Clockwise {b_clock_256[0]}°\\nE: 2')\n",
"\n",
adding layers results
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"best_results_256.append(all_results_ba[0, 1:])\n",
"best_labels_256.append(f'Full {all_results_ba[0, 0]}°\\nE: 6')\n",
batch256 results, exporting figures, began writing
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"\n",
"best_results_256 = best_results_256[::-1]\n",
"best_labels_256 = best_labels_256[::-1]\n",
"\n",
adding epochs data, writing
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"fig = plt.figure(figsize=(6, 3))\n",
batch256 results, exporting figures, began writing
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"fig.set_dpi(fig_dpi)\n",
"\n",
"plt.barh(range(len(best_labels_256)), [i[0] for i in best_results_256], tick_label=best_labels_256, label='Top-1')\n",
"plt.barh(range(len(best_labels_256)), [i[1] - i[0] for i in best_results_256], tick_label=best_labels_256, label='Top-5', left=[i[0] for i in best_results_256])\n",
"\n",
"plt.legend()\n",
"plt.grid(axis='x')\n",
"plt.title('Best Accuracy for Data Augmentations')\n",
added initial rotation augmentation data
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"plt.xlabel('% Test Accuracy')\n",
"plt.ylabel('Data Augmentation Methods')\n",
batch256 results, exporting figures, began writing
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"\n",
adding layers results
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"plt.xlim(0, 100)\n",
"plt.xticks(np.linspace(0, 100, 11))\n",
batch256 results, exporting figures, began writing
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"\n",
"plt.tight_layout()\n",
"plt.savefig('best-barh-256batch.png')\n",
"\n",
added initial rotation augmentation data
2021-04-20 07:49:45 +01:00
"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
}
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