The confusion matrix for the sound dataset.

sound-confusion-matrix.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interp = ClassificationInterpretation.from_learner(learn)\n",
    "\n",
    "losses,idxs = interp.top_losses()\n",
    "\n",
    "len(data.valid_ds)==len(losses)==len(idxs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "interp.plot_confusion_matrix(figsize=(12,12), dpi=60)"
   ]
  }
 ],
 "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": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}