performance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.391049</td>\n",
       "      <td>0.833577</td>\n",
       "      <td>0.507310</td>\n",
       "      <td>0.744904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.287408</td>\n",
       "      <td>0.885414</td>\n",
       "      <td>0.529939</td>\n",
       "      <td>0.830370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.246776</td>\n",
       "      <td>0.904918</td>\n",
       "      <td>0.391198</td>\n",
       "      <td>0.836678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.222888</td>\n",
       "      <td>0.916392</td>\n",
       "      <td>0.491499</td>\n",
       "      <td>0.823837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.201395</td>\n",
       "      <td>0.925193</td>\n",
       "      <td>0.253799</td>\n",
       "      <td>0.905195</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       loss  accuracy  val_loss  val_accuracy\n",
       "0  0.391049  0.833577  0.507310      0.744904\n",
       "1  0.287408  0.885414  0.529939      0.830370\n",
       "2  0.246776  0.904918  0.391198      0.836678\n",
       "3  0.222888  0.916392  0.491499      0.823837\n",
       "4  0.201395  0.925193  0.253799      0.905195"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist1 = pd.read_csv('history.csv', index_col=0)\n",
    "hist1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.437181</td>\n",
       "      <td>0.808077</td>\n",
       "      <td>0.785448</td>\n",
       "      <td>0.615526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.357403</td>\n",
       "      <td>0.849384</td>\n",
       "      <td>0.375105</td>\n",
       "      <td>0.843077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.320148</td>\n",
       "      <td>0.869315</td>\n",
       "      <td>1.478714</td>\n",
       "      <td>0.500045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.285857</td>\n",
       "      <td>0.884433</td>\n",
       "      <td>0.439844</td>\n",
       "      <td>0.785471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.262975</td>\n",
       "      <td>0.895847</td>\n",
       "      <td>0.640183</td>\n",
       "      <td>0.723599</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       loss  accuracy  val_loss  val_accuracy\n",
       "0  0.437181  0.808077  0.785448      0.615526\n",
       "1  0.357403  0.849384  0.375105      0.843077\n",
       "2  0.320148  0.869315  1.478714      0.500045\n",
       "3  0.285857  0.884433  0.439844      0.785471\n",
       "4  0.262975  0.895847  0.640183      0.723599"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist2 = pd.read_csv('history2.csv', index_col=0)\n",
    "hist2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa8bb1b2550>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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(hist1.index+1, hist1['accuracy'], color='red')\n",
    "plt.plot(hist1.index+1, hist1['val_accuracy'], color='red', linestyle='dashed')\n",
    "plt.plot(hist2.index+1, hist2['accuracy'], color='blue')\n",
    "plt.plot(hist2.index+1, hist2['val_accuracy'], color='blue', linestyle='dashed')"
   ]
  }
 ],
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