Update ch9

Author: fchollet

chapter09_part02_modern-convnet-architecture-patterns.ipynb

@@ -150,6 +150,74 @@
     "### Putting it together: a mini Xception-like model"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab_type": "code"
+   },
+   "outputs": [],
+   "source": [
+    "from google.colab import files\n",
+    "files.upload()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab_type": "code"
+   },
+   "outputs": [],
+   "source": [
+    "!mkdir ~/.kaggle\n",
+    "!cp kaggle.json ~/.kaggle/\n",
+    "!chmod 600 ~/.kaggle/kaggle.json\n",
+    "!kaggle competitions download -c dogs-vs-cats\n",
+    "!unzip -qq train.zip"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab_type": "code"
+   },
+   "outputs": [],
+   "source": [
+    "import os, shutil, pathlib\n",
+    "from tensorflow.keras.preprocessing import image_dataset_from_directory\n",
+    "\n",
+    "original_dir = pathlib.Path(\"train\")\n",
+    "new_base_dir = pathlib.Path(\"cats_vs_dogs_small\")\n",
+    "\n",
+    "def make_subset(subset_name, start_index, end_index):\n",
+    "    for category in (\"cat\", \"dog\"):\n",
+    "        dir = new_base_dir / subset_name / category\n",
+    "        os.makedirs(dir)\n",
+    "        fnames = [f\"{category}.{i}.jpg\" for i in range(start_index, end_index)]\n",
+    "        for fname in fnames:\n",
+    "            shutil.copyfile(src=original_dir / fname,\n",
+    "                            dst=dir / fname)\n",
+    "\n",
+    "make_subset(\"train\", start_index=0, end_index=1000)\n",
+    "make_subset(\"validation\", start_index=1000, end_index=1500)\n",
+    "make_subset(\"test\", start_index=1500, end_index=2500)\n",
+    "\n",
+    "train_dataset = image_dataset_from_directory(\n",
+    "    new_base_dir / \"train\",\n",
+    "    image_size=(180, 180),\n",
+    "    batch_size=32)\n",
+    "validation_dataset = image_dataset_from_directory(\n",
+    "    new_base_dir / \"validation\",\n",
+    "    image_size=(180, 180),\n",
+    "    batch_size=32)\n",
+    "test_dataset = image_dataset_from_directory(\n",
+    "    new_base_dir / \"test\",\n",
+    "    image_size=(180, 180),\n",
+    "    batch_size=32)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 0,
@@ -203,6 +271,23 @@
     "outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
     "model = keras.Model(inputs=inputs, outputs=outputs)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab_type": "code"
+   },
+   "outputs": [],
+   "source": [
+    "model.compile(loss=\"binary_crossentropy\",\n",
+    "              optimizer=\"rmsprop\",\n",
+    "              metrics=[\"accuracy\"])\n",
+    "history = model.fit(\n",
+    "    train_dataset,\n",
+    "    epochs=100,\n",
+    "    validation_data=validation_dataset)"
+   ]
   }
  ],
  "metadata": {