{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastbook import *\n",
    "from fastai.vision.all import *\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Path('data2')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = Path('data2')\n",
    "path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.220044</td>\n",
       "      <td>0.910795</td>\n",
       "      <td>0.483412</td>\n",
       "      <td>00:25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.037957</td>\n",
       "      <td>0.840327</td>\n",
       "      <td>0.487204</td>\n",
       "      <td>00:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.922677</td>\n",
       "      <td>0.777105</td>\n",
       "      <td>0.472986</td>\n",
       "      <td>00:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.822957</td>\n",
       "      <td>0.745550</td>\n",
       "      <td>0.496682</td>\n",
       "      <td>00:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.751167</td>\n",
       "      <td>0.711318</td>\n",
       "      <td>0.497630</td>\n",
       "      <td>00:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.720383</td>\n",
       "      <td>0.703557</td>\n",
       "      <td>0.472038</td>\n",
       "      <td>00:31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def is_buy(x): return x[0]==\"T\"\n",
    "dls = ImageDataLoaders.from_name_func(\n",
    "    path, get_image_files(path), valid_pct=0.2, seed=42,\n",
    "    label_func=is_buy, item_tfms=Resize(224))\n",
    "\n",
    "learn = cnn_learner(dls, resnet34, metrics=error_rate)\n",
    "learn.fine_tune(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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