2021-04-20 07:49:45 +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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2021-04-21 23:43:46 +01:00
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"execution_count": 1,
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2021-04-20 07:49:45 +01:00
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"id": "3c568ab9",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib as mpl\n",
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"from matplotlib import pyplot as plt"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7ecc547f",
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"metadata": {},
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"source": [
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"# Rotations\n",
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"\n",
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"80/10/10 Split, 30 epochs\n",
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"\n",
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"Expansion Factor: 2\n",
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"\n",
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"## Index\n",
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"0. degrees\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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2021-04-21 23:43:46 +01:00
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"execution_count": 2,
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2021-04-20 07:49:45 +01:00
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"id": "1b2471d2",
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"metadata": {},
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"outputs": [],
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"source": [
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"standard_res = [20.88, 48.24, 3.34, 25.25]\n",
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"flipped_res = [44.84, 72.45, 2.84, 49.02]\n",
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"\n",
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2021-04-25 19:56:49 +01:00
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"# default batch size\n",
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2021-04-20 07:49:45 +01:00
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"rot_results = np.array([\n",
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" [1, 44.6, 71.34, 2.84, 48.71],\n",
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" [5, 46.45, 73.5, 2.85, 47.61],\n",
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" [10, 45.71, 72.64, 2.93, 45.89],\n",
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" [20, 42.12, 70.41, 2.95, 40.99],\n",
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" [30, 28.17, 56.52, 3.44, 30.58],\n",
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" [40, 25.08, 51.33, 3.46, 28.37]\n",
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"])\n",
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"\n",
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"rot_batch256_results = np.array([\n",
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" [5, 32.43, 60.9, 3.35, 34.77]\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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2021-04-21 23:43:46 +01:00
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"execution_count": 4,
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2021-04-20 07:49:45 +01:00
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"id": "c664a31c",
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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 432x288 with 1 Axes>",
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2021-04-21 23:43:46 +01:00
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2021-04-20 07:49:45 +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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"plt.plot(rot_results[:, 0], rot_results[:, 1], 'x-', label=\"Top-1 Accuracy\")\n",
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"plt.plot(rot_results[:, 0], rot_results[:, 2], 'x-', label=\"Top-5 Accuracy\")\n",
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"plt.plot(rot_results[:, 0], rot_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 Rotated Training Data')\n",
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"plt.ylabel('% Accuracy')\n",
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"plt.xlabel('Degrees Rotation')\n",
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"\n",
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"plt.legend()\n",
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"plt.grid()\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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"source": [
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"# All\n",
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"\n",
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"Flip, rotate both ways, flip both rotations\n",
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"\n",
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"Expansion Factor: 6\n",
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"\n",
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"## Index\n",
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"0. degrees\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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"cell_type": "markdown",
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"metadata": {}
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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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"metadata": {},
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"outputs": [],
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"source": [
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|
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"all_results = np.array([\n",
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" [5, 53.24, 79.49, 2.69, 55.64]\n",
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"])"
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]
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},
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2021-04-20 07:49:45 +01:00
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{
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"cell_type": "code",
|
2021-04-21 23:43:46 +01:00
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"execution_count": 5,
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2021-04-20 07:49:45 +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 432x288 with 1 Axes>",
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2021-04-21 23:43:46 +01:00
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2021-04-20 07:49:45 +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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"best_results = [standard_res, flipped_res]\n",
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"best_labels = ['Unaugmented\\nExpansion Factor: 1', 'Flipped\\nExpansion Factor: 2']\n",
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"\n",
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"# Clockwise Rotation\n",
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"b_clock = rot_results[np.argmax(rot_results[:, 1])]\n",
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"best_results.append(b_clock[1:])\n",
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"best_labels.append(f'Clockwise Rotation\\n{b_clock[0]} Degrees\\nExpansion Factor: 2')\n",
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"\n",
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"best_results.append(all_results[0, 1:])\n",
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"best_labels.append(f'All Transforms\\n{all_results[0, 0]} Degrees\\nExpansion Factor: 6')\n",
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"\n",
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"best_results = best_results[::-1]\n",
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"best_labels = best_labels[::-1]\n",
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"\n",
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"plt.barh(range(len(best_labels)), [i[0] for i in best_results], tick_label=best_labels, label='Top-1')\n",
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"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",
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"\n",
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"plt.legend()\n",
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"plt.grid(axis='x')\n",
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"plt.title('Best Test Accuracy for Data Augmentations')\n",
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"plt.xlabel('% Test Accuracy')\n",
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"plt.ylabel('Data Augmentation Methods')\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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"name": "pythonjvsc74a57bd0333605e348ea7c6bf4ca805dbc845da062650cb5bf1d8f33f5f4a9d3bca7d68b",
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"display_name": "Python 3.9.3 ('.venv': venv)"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.3"
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},
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"metadata": {
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"interpreter": {
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"hash": "333605e348ea7c6bf4ca805dbc845da062650cb5bf1d8f33f5f4a9d3bca7d68b"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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