DIGITS-CNN/cars/lr-investigations/lr.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5c2606e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26bf4aa1",
   "metadata": {},
   "source": [
    "# Fixed Learning Rate\n",
    "80/10/10 Split, 200 epochs\n",
    "\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",
   "execution_count": 2,
   "id": "4ceac619",
   "metadata": {},
   "outputs": [],
   "source": [
    "fixed_results = np.array([\n",
    "    [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",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "071aaf45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fixed_results[:, 0], fixed_results[:, 1], 'x-', label=\"Top-1 Accuracy\")\n",
    "plt.plot(fixed_results[:, 0], fixed_results[:, 2], 'x-', label=\"Top-5 Accuracy\")\n",
    "plt.plot(fixed_results[:, 0], fixed_results[:, 4], 'x-', label=\"Final Val. Accuracy\")\n",
    "\n",
    "plt.ylim(0)\n",
    "\n",
    "plt.title('Model Accuracy for Fixed Learning Rates')\n",
    "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",
   "execution_count": 4,
   "id": "80114709",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fixed_results[:, 0], fixed_results[:, 3], 'x-', label=\"Final Validation Loss\")\n",
    "\n",
    "# plt.ylim(0)\n",
    "\n",
    "plt.title('Final Validation Loss for Fixed Learning Rates')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Learning Rate')\n",
    "\n",
    "# plt.legend()\n",
    "plt.xscale('log')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ae7559e",
   "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",
   "execution_count": 5,
   "id": "a1fea30c",
   "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",
    "    [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",
   "execution_count": 6,
   "id": "7c134854",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(step_down_results[:, 2], step_down_results[:, 3], 'x-', label=\"Top-1 Accuracy\")\n",
    "plt.plot(step_down_results[:, 2], step_down_results[:, 4], 'x-', label=\"Top-5 Accuracy\")\n",
    "plt.plot(step_down_results[:, 2], step_down_results[:, 6], 'x-', label=\"Final Val. Accuracy\")\n",
    "\n",
    "plt.ylim(0)\n",
    "\n",
    "plt.title('Model Accuracy for Step Down Learning Rates')\n",
    "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",
   "execution_count": null,
   "id": "914e9081",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}