deep-learning-with-python-notebooks/chapter09_part02_modern-convnet-architecture-patterns.ipynb

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 "cells": [
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   "source": [
    "This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\n\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\n\nThis notebook was generated for TensorFlow 2.6."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "## Modern convnet architecture patterns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Modularity, hierarchy, and reuse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Residual connections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Case where the target block changes the number of output filters**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "inputs = keras.Input(shape=(32, 32, 3))\n",
    "x = layers.Conv2D(32, 3, activation=\"relu\")(inputs)\n",
    "residual = x\n",
    "x = layers.Conv2D(64, 3, activation=\"relu\", padding=\"same\")(x)\n",
    "residual = layers.Conv2D(64, 1)(residual)\n",
    "x = layers.add([x, residual])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Case where the target block includes a max pooling layer**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "inputs = keras.Input(shape=(32, 32, 3))\n",
    "x = layers.Conv2D(32, 3, activation=\"relu\")(inputs)\n",
    "residual = x\n",
    "x = layers.Conv2D(64, 3, activation=\"relu\", padding=\"same\")(x)\n",
    "x = layers.MaxPooling2D(2, padding=\"same\")(x)\n",
    "residual = layers.Conv2D(64, 1, strides=2)(residual)\n",
    "x = layers.add([x, residual])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
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   "outputs": [],
   "source": [
    "inputs = keras.Input(shape=(32, 32, 3))\n",
    "x = layers.experimental.preprocessing.Rescaling(1./255)(inputs)\n",
    "\n",
    "def residual_block(x, filters, pooling=False):\n",
    "    residual = x\n",
    "    x = layers.Conv2D(filters, 3, activation=\"relu\", padding=\"same\")(x)\n",
    "    x = layers.Conv2D(filters, 3, activation=\"relu\", padding=\"same\")(x)\n",
    "    if pooling:\n",
    "        x = layers.MaxPooling2D(2, padding=\"same\")(x)\n",
    "        residual = layers.Conv2D(filters, 1, strides=2)(residual)\n",
    "    elif filters != residual.shape[-1]:\n",
    "        residual = layers.Conv2D(filters, 1)(residual)\n",
    "    x = layers.add([x, residual])\n",
    "    return x\n",
    "\n",
    "x = residual_block(x, filters=32, pooling=True)\n",
    "x = residual_block(x, filters=64, pooling=True)\n",
    "x = residual_block(x, filters=128, pooling=False)\n",
    "\n",
    "x = layers.GlobalAveragePooling2D()(x)\n",
    "outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
    "model = keras.Model(inputs=inputs, outputs=outputs)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Batch normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Depthwise separable convolutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Putting it together: a mini Xception-like model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "data_augmentation = keras.Sequential(\n",
    "    [\n",
    "        layers.experimental.preprocessing.RandomFlip(\"horizontal\"),\n",
    "        layers.experimental.preprocessing.RandomRotation(0.1),\n",
    "        layers.experimental.preprocessing.RandomZoom(0.2),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "inputs = keras.Input(shape=(180, 180, 3))\n",
    "x = data_augmentation(inputs)\n",
    "\n",
    "x = layers.experimental.preprocessing.Rescaling(1./255)(x)\n",
    "x = layers.Conv2D(filters=32, kernel_size=5)(x)\n",
    "\n",
    "for size in [32, 64, 128, 256, 512]:\n",
    "    residual = x\n",
    "\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.SeparableConv2D(size, 3, padding=\"same\")(x)\n",
    "    x = layers.Activation(\"relu\")(x)\n",
    "\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.SeparableConv2D(size, 3, padding=\"same\")(x)\n",
    "    x = layers.Activation(\"relu\")(x)\n",
    "\n",
    "    x = layers.MaxPooling2D(3, strides=2, padding=\"same\")(x)\n",
    "\n",
    "    residual = layers.Conv2D(\n",
    "        size, 1, strides=2, padding=\"same\")(residual)\n",
    "    x = layers.add([x, residual])\n",
    "\n",
    "x = layers.GlobalAveragePooling2D()(x)\n",
    "x = layers.Dropout(0.5)(x)\n",
    "outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
    "model = keras.Model(inputs=inputs, outputs=outputs)"
   ]
  }
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