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

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "fig_dpi = 200"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Different Train/Validation/Test Splits\n",
    "\n",
    "## Index\n",
    "0. train prop\n",
    "1. val prop\n",
    "2. test prop\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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "split_results = np.array([\n",
    "    [50, 5, 45, 8.14, 20.7, 5.77, 8.29],\n",
    "    [50, 25, 25, 7.91, 20.9, 5.59, 8.41],\n",
    "    [70, 15, 15, 15.81, 34.62, 4.92, 16.82],\n",
    "    [80, 10, 10, 20.94, 44.1, 4.39, 24.45],\n",
    "    [90, 5, 5, 20.62, 45.19, 4.17, 30.29]\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     "metadata": {
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     }
    }
   ],
   "source": [
    "split_labels = [f\"{int(i[0])}/{int(i[1])}/{int(i[2])}\" for i in split_results]\n",
    "\n",
    "fig = plt.figure(figsize=(5, 3))\n",
    "fig.set_dpi(fig_dpi)\n",
    "\n",
    "plt.barh(range(len(split_labels)), [i[3] for i in split_results], tick_label=split_labels, label='Top-1')\n",
    "plt.barh(range(len(split_labels)), [i[4] - i[3] for i in split_results], tick_label=split_labels, label='Top-5', left=[i[3] for i in split_results])\n",
    "\n",
    "plt.legend()\n",
    "plt.grid(axis='x')\n",
    "plt.title('Accuracy for Dataset Split Proportions')\n",
    "plt.xlabel('% Test Accuracy')\n",
    "plt.ylabel('Dataset Split (Train/Val/Test)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('split-barh.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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