memoryimagelist.ipynb

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   "cell_type": "code",
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   "source": [
    "from fastai.vision import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = untar_data(URLs.MNIST)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MemoryImageList(ImageList):\n",
    "    _map = {}\n",
    "    def open(self, i):\n",
    "        item = self._map.get(str(i))\n",
    "        if isinstance(item, Image):\n",
    "            return item\n",
    "        item = super().open(i)\n",
    "        self._map[str(i)] = item\n",
    "        return item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<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>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.644401</td>\n",
       "      <td>0.465631</td>\n",
       "      <td>0.849500</td>\n",
       "      <td>00:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.323800</td>\n",
       "      <td>0.243660</td>\n",
       "      <td>0.922250</td>\n",
       "      <td>00:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.261255</td>\n",
       "      <td>0.216338</td>\n",
       "      <td>0.930250</td>\n",
       "      <td>00:16</td>\n",
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   "source": [
    "data = MemoryImageList.from_folder(p/'training').split_by_rand_pct(.2, seed=1).label_from_folder().databunch(bs=128).normalize(imagenet_stats)\n",
    "learn = cnn_learner(data, models.resnet18, metrics=accuracy)\n",
    "learn.fit_one_cycle(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.660670</td>\n",
       "      <td>0.458675</td>\n",
       "      <td>0.852833</td>\n",
       "      <td>00:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.318554</td>\n",
       "      <td>0.245979</td>\n",
       "      <td>0.921667</td>\n",
       "      <td>00:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.260065</td>\n",
       "      <td>0.218972</td>\n",
       "      <td>0.929917</td>\n",
       "      <td>00:16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
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       "<IPython.core.display.HTML object>"
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    }
   ],
   "source": [
    "data = ImageList.from_folder(p/'training').split_by_rand_pct(.2, seed=1).label_from_folder().databunch(bs=128).normalize(imagenet_stats)\n",
    "learn = cnn_learner(data, models.resnet18, metrics=accuracy)\n",
    "learn.fit_one_cycle(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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