{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "data = pd.read_csv(\"result/mnist/07-13-02-05_75_200_0.25_0.4_0.3_acc.csv\", header=None)\n",
    "print(data.shape)\n",
    "\n",
    "loss = pd.DataFrame()\n",
    "acc = pd.DataFrame()\n",
    "\n",
    "for i in range(len(data.iloc[0])):\n",
    "    if i % 2 == 0:\n",
    "        loss[i] = data[i]\n",
    "    else:\n",
    "        acc[i] = data[i]\n",
    "\n",
    "print(loss.shape, acc.shape)\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2, sharey=False, sharex=True, figsize=(6, 6))\n",
    "\n",
    "loss.replace('nan' ,np.inf ,inplace=True)\n",
    "loss = loss.fillna(np.inf)\n",
    "\n",
    "loss.plot(kind=\"line\", ax=axes[0], legend=False, alpha=0.5, ylabel=\"loss\", grid=True, title=f\"best loss {loss.min(numeric_only=True).min()}\")\n",
    "\n",
    "acc.plot(kind=\"line\", ax=axes[1], legend=False, alpha=0.5, ylabel=\"acc\", xlabel=\"epoch\", grid=True, title=f\"best acc {acc.max(numeric_only=True).max()}\")\n",
    "\n",
    "plt.show()\n",
    "plt.clf()\n",
    "plt.clf()\n",
    "plt.close()\n",
    "plt.close()\n",
    "\n"
   ]
  }
 ],
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