Rename chapters

Author: mattdangerw

chapter02_mathematical-building-blocks.ipynb

@@ -17,7 +17,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip install keras-nightly --upgrade -q"
+    "!pip install keras-nightly keras-hub-nightly --upgrade -q"
    ]
   },
   {

chapter03_introduction-to-ml-frameworks.ipynb

@@ -17,7 +17,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip install keras-nightly --upgrade -q"
+    "!pip install keras-nightly keras-hub-nightly --upgrade -q"
    ]
   },
   {

chapter04_getting-started-with-neural-networks.ipynb renamed to chapter04_classification-and-regression.ipynb

@@ -17,7 +17,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip install keras-nightly --upgrade -q"
+    "!pip install keras-nightly keras-hub-nightly --upgrade -q"
    ]
   },
   {
@@ -1252,7 +1252,7 @@
   "accelerator": "GPU",
   "colab": {
    "collapsed_sections": [],
-   "name": "chapter04_getting-started-with-neural-networks",
+   "name": "chapter04_classification-and-regression",
    "private_outputs": false,
    "provenance": [],
    "toc_visible": true

chapter05_fundamentals-of-ml.ipynb

@@ -17,7 +17,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip install keras-nightly --upgrade -q"
+    "!pip install keras-nightly keras-hub-nightly --upgrade -q"
    ]
   },
   {

chapter07_working-with-keras.ipynb renamed to chapter07_deep-dive-keras.ipynb

@@ -17,7 +17,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip install keras-nightly --upgrade -q"
+    "!pip install keras-nightly keras-hub-nightly --upgrade -q"
    ]
   },
   {
@@ -1841,7 +1841,7 @@
   "accelerator": "GPU",
   "colab": {
    "collapsed_sections": [],
-   "name": "chapter07_working-with-keras",
+   "name": "chapter07_deep-dive-keras",
    "private_outputs": false,
    "provenance": [],
    "toc_visible": true

chapter08_image-classification.ipynb

@@ -17,7 +17,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip install keras-nightly --upgrade -q"
+    "!pip install keras-nightly keras-hub-nightly --upgrade -q"
    ]
   },
   {
@@ -564,7 +564,8 @@
     "    for i in range(9):\n",
     "        ax = plt.subplot(3, 3, i + 1)\n",
     "        augmented_image, _ = data_augmentation(image, None)\n",
-    "        plt.imshow(np.array(augmented_image).astype(\"uint8\"))\n",
+    "        augmented_image = keras.ops.convert_to_numpy(augmented_image)\n",
+    "        plt.imshow(augmented_image.astype(\"uint8\"))\n",
     "        plt.axis(\"off\")"
    ]
   },
@@ -663,10 +664,22 @@
    },
    "outputs": [],
    "source": [
-    "conv_base = keras.applications.Xception(\n",
-    "    weights=\"imagenet\",\n",
-    "    include_top=False,\n",
-    "    input_shape=(180, 180, 3),\n",
+    "import keras_hub\n",
+    "\n",
+    "conv_base = keras_hub.models.Backbone.from_preset(\"xception_41_imagenet\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab_type": "code"
+   },
+   "outputs": [],
+   "source": [
+    "preprocessor = keras_hub.layers.ImageConverter.from_preset(\n",
+    "    \"xception_41_imagenet\",\n",
+    "    image_size=(180, 180),\n",
     ")"
    ]
   },
@@ -687,14 +700,11 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "from keras.applications.xception import preprocess_input\n",
-    "\n",
     "def get_features_and_labels(dataset):\n",
     "    all_features = []\n",
     "    all_labels = []\n",
     "    for images, labels in dataset:\n",
-    "        preprocessed_images = preprocess_input(images)\n",
+    "        preprocessed_images = preprocessor(images)\n",
     "        features = conv_base.predict(preprocessed_images, verbose=0)\n",
     "        all_features.append(features)\n",
     "        all_labels.append(labels)\n",
@@ -809,8 +819,12 @@
    },
    "outputs": [],
    "source": [
-    "conv_base = keras.applications.Xception(weights=\"imagenet\", include_top=False)\n",
-    "conv_base.trainable = False"
+    "import keras_hub\n",
+    "\n",
+    "conv_base = keras_hub.models.Backbone.from_preset(\n",
+    "    \"xception_41_imagenet\",\n",
+    "    trainable=False,\n",
+    ")"
    ]
   },
   {
@@ -846,7 +860,7 @@
    "outputs": [],
    "source": [
     "inputs = keras.Input(shape=(180, 180, 3))\n",
-    "x = preprocess_input(inputs)\n",
+    "x = preprocessor(inputs)\n",
     "x = conv_base(x)\n",
     "x = layers.GlobalAveragePooling2D()(x)\n",
     "x = layers.Dense(256)(x)\n",
@@ -907,19 +921,6 @@
     "#### Fine-tuning a pretrained model"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 0,
-   "metadata": {
-    "colab_type": "code"
-   },
-   "outputs": [],
-   "source": [
-    "conv_base.trainable = True\n",
-    "for layer in conv_base.layers[:-4]:\n",
-    "    layer.trainable = False"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 0,

chapter09_convnet-architecture-best-practices.ipynb renamed to chapter09_convnet-architecture-patterns.ipynb

@@ -17,7 +17,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip install keras-nightly --upgrade -q"
+    "!pip install keras-nightly keras-hub-nightly --upgrade -q"
    ]
   },
   {
@@ -328,7 +328,7 @@
   "accelerator": "GPU",
   "colab": {
    "collapsed_sections": [],
-   "name": "chapter09_convnet-architecture-best-practices",
+   "name": "chapter09_convnet-architecture-patterns",
    "private_outputs": false,
    "provenance": [],
    "toc_visible": true