03.1_augmentation.ipynb

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   "source": [
    "![Practicum AI Logo image](https://github.com/PracticumAI/practicumai.github.io/blob/main/images/logo/PracticumAI_logo_250x50.png?raw=true)\n",
    "***\n",
    "# *Practicum AI:* CNN - CIFAR Augmentation\n",
    "\n",
    "This exercise adapted from Baig et al. (2020) <i>The Deep Learning Workshop</i> from <a href=\"https://www.packtpub.com/product/the-deep-learning-workshop/9781839219856\">Packt Publishers</a> (Exercise 3.04, page 140)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# Import the cifar dataset\n",
    "from tensorflow.keras.datasets import cifar10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Load the dataset\n",
    "\n",
    "```python\n",
    "(features_train, label_train), (features_test, label_test) = cifar10.load_data()\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code it!"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "***\n",
    "#### Displaying Images"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Best practice is to view data as the first step in any AI project.  Click [here](https://machinelearningmastery.com/how-to-load-and-visualize-standard-computer-vision-datasets-with-keras/) to view code for displaying cifar10 images.  Then copy and modify it for this exercise.  You may decide to display a single image or multiple images using a for loop."
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [],
   "source": [
    "# Code it -- Display a single image at a specific index!  Click ... for answer"
   ]
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   "source": [
    "some_image = features_train[8]\n",
    "\n",
    "plt.subplot(332)\n",
    "plt.imshow(some_image)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code it -- Display the first 9 images in the dataset. Click ... for answer"
   ]
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   "source": [
    "for i in range(9):\n",
    "    # Define subplot\n",
    "    plt.subplot(330 + 1 + i)\n",
    "    # Plot raw pixel data\n",
    "    plt.imshow(features_train[i])\n",
    "\n",
    "# Show the figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "***\n",
    "#### 3. Examine the training dataset\n",
    "\n",
    "```python\n",
    "features_train.shape\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code it!"
   ]
  },
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   "source": [
    "#### 4. Create parameter variables\n",
    "\n",
    "<div style=\"padding: 10px;margin-bottom: 20px;border: thin solid #E5C250;border-left-width: 10px;background-color: #fff\"><strong>Tip:</strong> Use upper-case for global constants.  Example: BATCH_SIZE = 16 </div>\n",
    "\n",
    "```python\n",
    "batch_size = 16\n",
    "img_height = 32\n",
    "img_width  = 32\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code it!"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "#### 5. Instantiate a training image data generator\n",
    "\n",
    "Augment, rescale, and horizontally flip the images.  Also, set the width_shift_range and height_shift_range parameters to 0.1.\n",
    "\n",
    "```python\n",
    "train_img_gen = ImageDataGenerator(rescale            = 1./255, \n",
    "                                   width_shift_range  = 0.1,\n",
    "                                   height_shift_range = 0.1, \n",
    "                                   horizontal_flip    = True)\n",
    "```"
   ]
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  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code it!"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6. Instantiate a validation image data generator\n",
    "\n",
    "Rescale the images.\n",
    "\n",
    "```python\n",
    "val_img_gen = ImageDataGenerator(rescale = 1./255)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code it!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7. Instantiate a training data generator\n",
    "\n",
    "```python\n",
    "train_data_gen = train_img_gen.flow (features_train, label_train, batch_size = batch_size)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code it!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8. Instantiate a validation data generator\n",
    "\n",
    "```python\n",
    "val_data_gen = train_img_gen.flow (features_test, label_test, batch_size = batch_size)\n",
    "```"
   ]
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  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code it!"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 9. Set a random seed for reproducibility\n",
    "\n",
    "```python\n",
    "np.random.seed(8)\n",
    "tf.random.set_seed(8)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code it!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 10. Define the model\n",
    "\n",
    "```python\n",
    "model = tf.keras.Sequential([\n",
    "        layers.Conv2D(64, 3, activation = 'relu', \n",
    "                      input_shape = (img_height, img_width ,3)), \n",
    "        layers.MaxPooling2D(), \n",
    "        layers.Conv2D(128, 3, activation = 'relu'), \n",
    "        layers.MaxPooling2D(), \n",
    "        layers.Flatten(), \n",
    "        layers.Dense(128, activation = 'relu'), \n",
    "        layers.Dense(10, activation = 'softmax')])\n",
    "```"
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code it!"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 11. Instantiate an Adam optimizer\n",
    "\n",
    "```python\n",
    "optimizer = tf.keras.optimizers.Adam(0.001)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code it!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 12. Compile the neural network\n",
    "\n",
    "```python\n",
    "model.compile(loss = 'sparse_categorical_crossentropy', \n",
    "              optimizer = optimizer, metrics = ['accuracy'])\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code it!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 13. Fit (train) the model\n",
    "\n",
    "```python\n",
    "model.fit(train_data_gen, \n",
    "          steps_per_epoch  = len(features_train) // batch_size, \n",
    "          epochs           = 5, \n",
    "          validation_data  = val_data_gen, \n",
    "          validation_steps = len(features_test) // batch_size)\n",
    "```"
   ]
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   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code it!"
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