{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise 1\n", "\n", "Set up a ConvNet for the CIFAR-10 dataset. Try to achieve a test accuracy of more than 90%" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "FE7KNzPPVrVV" }, "source": [ "# Exercise 2 - Image Classification using tf.keras" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "gN7G9GFmVrVY" }, "source": [ "In this Colab you will classify images of flowers. You will build an image classifier using `tf.keras.Sequential` model and load data using `tf.keras.preprocessing.image.ImageDataGenerator`.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "zF9uvbXNVrVY" }, "source": [ "### Importing Packages" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "VddxeYBEVrVZ" }, "source": [ "Let's start by importing required packages. **os** package is used to read files and directory structure, **numpy** is used to convert python list to numpy array and to perform required matrix operations and **matplotlib.pyplot** is used to plot the graph and display images in our training and validation data." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": {}, "colab_type": "code", "id": "rtPGh2MAVrVa" }, "outputs": [], "source": [ "from __future__ import absolute_import, division, print_function, unicode_literals\n", "\n", "import os\n", "import numpy as np\n", "import glob\n", "import shutil\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Jlchl4x2VrVg" }, "source": [ "### TODO: Import TensorFlow and Keras Layers\n", "\n", "In the cell below, import Tensorflow as `tf` and the Keras layers and models you will use to build your CNN. Also, import the `ImageDataGenerator` from Keras so that you can perform image augmentation." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": {}, "colab_type": "code", "id": "L1WtoaOHVrVh" }, "outputs": [], "source": [ "#import packages" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "UZZI6lNkVrVm" }, "source": [ "## Data Loading" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "DPHx8-t-VrVo" }, "source": [ "In order to build our image classifier, we can begin by downloading the flowers dataset. We first need to download the archive version of the dataset and after the download we are storing it to \"/tmp/\" directory." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "_lPjfOmNVrVs" }, "source": [ "After downloading the dataset, we need to extract its contents." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "_URL = \"https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz\"\n", "\n", "zip_file = tf.keras.utils.get_file(origin=_URL,\n", " fname=\"flower_photos.tgz\",\n", " extract=True)\n", "\n", "base_dir = os.path.join(os.path.dirname(zip_file), 'flower_photos')" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "2yge5MKnnjMd" }, "source": [ "The dataset we downloaded contains images of 5 types of flowers:\n", "\n", "1. Rose\n", "2. Daisy\n", "3. Dandelion\n", "4. Sunflowers\n", "5. Tulips\n", "\n", "So, let's create the labels for these 5 classes: " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": {}, "colab_type": "code", "id": "FiYVs1MEmNHf" }, "outputs": [], "source": [ "classes = ['roses', 'daisy', 'dandelion', 'sunflowers', 'tulips']" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "G1ymuCPS0_eu" }, "source": [ "Also, the dataset we have downloaded has following directory structure.\n", "\n", "<pre style=\"font-size: 10.0pt; font-family: Arial; line-height: 2; letter-spacing: 1.0pt;\" >\n", "<b>flower_photos</b>\n", "|__ <b>diasy</b>\n", "|__ <b>dandelion</b>\n", "|__ <b>roses</b>\n", "|__ <b>sunflowers</b>\n", "|__ <b>tulips</b>\n", "</pre>\n", "\n", "As you can see there are no folders containing training and validation data. Therefore, we will have to create our own training and validation set. Let's write some code that will do this.\n", "\n", "\n", "The code below creates a `train` and a `val` folder each containing 5 folders (one for each type of flower). It then moves the images from the original folders to these new folders such that 80% of the images go to the training set and 20% of the images go into the validation set. In the end our directory will have the following structure:\n", "\n", "\n", "<pre style=\"font-size: 10.0pt; font-family: Arial; line-height: 2; letter-spacing: 1.0pt;\" >\n", "<b>flower_photos</b>\n", "|__ <b>diasy</b>\n", "|__ <b>dandelion</b>\n", "|__ <b>roses</b>\n", "|__ <b>sunflowers</b>\n", "|__ <b>tulips</b>\n", "|__ <b>train</b>\n", " |______ <b>daisy</b>: [1.jpg, 2.jpg, 3.jpg ....]\n", " |______ <b>dandelion</b>: [1.jpg, 2.jpg, 3.jpg ....]\n", " |______ <b>roses</b>: [1.jpg, 2.jpg, 3.jpg ....]\n", " |______ <b>sunflowers</b>: [1.jpg, 2.jpg, 3.jpg ....]\n", " |______ <b>tulips</b>: [1.jpg, 2.jpg, 3.jpg ....]\n", " |__ <b>val</b>\n", " |______ <b>daisy</b>: [507.jpg, 508.jpg, 509.jpg ....]\n", " |______ <b>dandelion</b>: [719.jpg, 720.jpg, 721.jpg ....]\n", " |______ <b>roses</b>: [514.jpg, 515.jpg, 516.jpg ....]\n", " |______ <b>sunflowers</b>: [560.jpg, 561.jpg, 562.jpg .....]\n", " |______ <b>tulips</b>: [640.jpg, 641.jpg, 642.jpg ....]\n", "</pre>\n", "\n", "Since we don't delete the original folders, they will still be in our `flower_photos` directory, but they will be empty. The code below also prints the total number of flower images we have for each type of flower. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": {}, "colab_type": "code", "id": "a-AL030LmcdD" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "roses: 641 Images\n", "daisy: 633 Images\n", "dandelion: 898 Images\n", "sunflowers: 699 Images\n", "tulips: 799 Images\n" ] } ], "source": [ "for cl in classes:\n", " img_path = os.path.join(base_dir, cl)\n", " images = glob.glob(img_path + '/*.jpg')\n", " print(\"{}: {} Images\".format(cl, len(images)))\n", " train, val = images[:round(len(images)*0.8)], images[round(len(images)*0.8):]\n", "\n", " for t in train:\n", " if not os.path.exists(os.path.join(base_dir, 'train', cl)):\n", " os.makedirs(os.path.join(base_dir, 'train', cl))\n", " shutil.move(t, os.path.join(base_dir, 'train', cl))\n", "\n", " for v in val:\n", " if not os.path.exists(os.path.join(base_dir, 'val', cl)):\n", " os.makedirs(os.path.join(base_dir, 'val', cl))\n", " shutil.move(v, os.path.join(base_dir, 'val', cl))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "8Lp-0ejxOtP1" }, "source": [ "For convenience, let us set up the path for the training and validation sets" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": {}, "colab_type": "code", "id": "uh68rmWspp0U" }, "outputs": [], "source": [ "train_dir = os.path.join(base_dir, 'train')\n", "val_dir = os.path.join(base_dir, 'val')" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "UOoVpxFwVrWy" }, "source": [ "## Data Augmentation" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Wn_QLciWVrWy" }, "source": [ "Overfitting generally occurs when we have small number of training examples. One way to fix this problem is to augment our dataset so that it has sufficient number of training examples. Data augmentation takes the approach of generating more training data from existing training samples, by augmenting the samples via a number of random transformations that yield believable-looking images. The goal is that at training time, your model will never see the exact same picture twice. This helps expose the model to more aspects of the data and generalize better.\n", "\n", "In **tf.keras** we can implement this using the same **ImageDataGenerator** class we used before. We can simply pass different transformations we would want to our dataset as a form of arguments and it will take care of applying it to the dataset during our training process. " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "2uJ1G030VrWz" }, "source": [ "## Experiment with Various Image Transformations\n", "\n", "In this section you will get some practice doing some basic image transformations. Before we begin making transformations let's define our `batch_size` and our image size. Remember that the input to our CNN are images of the same size. We therefore have to resize the images in our dataset to the same size.\n", "\n", "### TODO: Set Batch and Image Size\n", "\n", "In the cell below, create a `batch_size` of 100 images and set a value to `IMG_SHAPE` such that our training data consists of images with width of 150 pixels and height of 150 pixels." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": {}, "colab_type": "code", "id": "QyPkET61yMMX" }, "outputs": [], "source": [ "batch_size = \n", "IMG_SHAPE = " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "rlVj6VqaVrW0" }, "source": [ "### TODO: Apply Random Horizontal Flip\n", "\n", "In the cell below, use ImageDataGenerator to create a transformation that rescales the images by 255 and then applies a random horizontal flip. Then use the `.flow_from_directory` method to apply the above transformation to the images in our training set. Make sure you indicate the batch size, the path to the directory of the training images, the target size for the images, and to shuffle the images. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": {}, "colab_type": "code", "id": "Bi1_vHyBVrW2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 2935 images belonging to 5 classes.\n" ] } ], "source": [ "image_gen = \n", "\n", "train_data_gen = " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "zJpRSxJ-VrW7" }, "source": [ "Let's take 1 sample image from our training examples and repeat it 5 times so that the augmentation can be applied to the same image 5 times over randomly, to see the augmentation in action." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": {}, "colab_type": "code", "id": "jqb9OGoVKIOi" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1440x1440 with 5 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# This function will plot images in the form of a grid with 1 row and 5 columns where images are placed in each column.\n", "def plotImages(images_arr):\n", " fig, axes = plt.subplots(1, 5, figsize=(20,20))\n", " axes = axes.flatten()\n", " for img, ax in zip( images_arr, axes):\n", " ax.imshow(img)\n", " plt.tight_layout()\n", " plt.show()\n", "\n", "\n", "augmented_images = [train_data_gen[0][0][0] for i in range(5)]\n", "plotImages(augmented_images)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "qXnwkzFuVrXB" }, "source": [ "### TODO: Apply Random Rotation\n", "\n", "In the cell below, use ImageDataGenerator to create a transformation that rescales the images by 255 and then applies a random 45 degree rotation. Then use the `.flow_from_directory` method to apply the above transformation to the images in our training set. Make sure you indicate the batch size, the path to the directory of the training images, the target size for the images, and to shuffle the images. " ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "1zip35pDVrXB" }, "outputs": [], "source": [ "image_gen = \n", "\n", "train_data_gen = " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "m2lNPWFc3Bre" }, "source": [ "Let's take 1 sample image from our training examples and repeat it 5 times so that the augmentation can be applied to the same image 5 times over randomly, to see the augmentation in action." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "wmBx8NhrVrXK" }, "outputs": [], "source": [ "augmented_images = [train_data_gen[0][0][0] for i in range(5)]\n", "plotImages(augmented_images)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "NvqXaD8BVrXN" }, "source": [ "### TODO: Apply Random Zoom\n", "\n", "In the cell below, use ImageDataGenerator to create a transformation that rescales the images by 255 and then applies a random zoom of up to 50%. Then use the `.flow_from_directory` method to apply the above transformation to the images in our training set. Make sure you indicate the batch size, the path to the directory of the training images, the target size for the images, and to shuffle the images. " ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "tGNKLa_YVrXR" }, "outputs": [], "source": [ "image_gen = \n", "\n", "train_data_gen = " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "XuoAX0Za4zTk" }, "source": [ "Let's take 1 sample image from our training examples and repeat it 5 times so that the augmentation can be applied to the same image 5 times over randomly, to see the augmentation in action." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "-KQWw8IZVrXZ" }, "outputs": [], "source": [ "augmented_images = [train_data_gen[0][0][0] for i in range(5)]\n", "plotImages(augmented_images)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "OC8fIsalVrXd" }, "source": [ "### TODO: Put It All Together\n", "\n", "In the cell below, use ImageDataGenerator to create a transformation that rescales the images by 255 and that applies:\n", "\n", "- random 45 degree rotation\n", "- random zoom of up to 50%\n", "- random horizontal flip\n", "- width shift of 0.15\n", "- height shift of 0.15\n", "\n", "Then use the `.flow_from_directory` method to apply the above transformation to the images in our training set. Make sure you indicate the batch size, the path to the directory of the training images, the target size for the images, to shuffle the images, and to set the class mode to `sparse`." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "gnr2xujaVrXe" }, "outputs": [], "source": [ "image_gen_train = \n", "\n", "\n", "train_data_gen = " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "AW-pV5awVrXl" }, "source": [ "Let's visualize how a single image would look like 5 different times, when we pass these augmentations randomly to our dataset. " ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "z2m68eMhVrXm" }, "outputs": [], "source": [ "augmented_images = [train_data_gen[0][0][0] for i in range(5)]\n", "plotImages(augmented_images)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "a99fDBt7VrXr" }, "source": [ "### TODO: Create a Data Generator for the Validation Set\n", "\n", "Generally, we only apply data augmentation to our training examples. So, in the cell below, use ImageDataGenerator to create a transformation that only rescales the images by 255. Then use the `.flow_from_directory` method to apply the above transformation to the images in our validation set. Make sure you indicate the batch size, the path to the directory of the validation images, the target size for the images, and to set the class mode to `sparse`. Remember that it is not necessary to shuffle the images in the validation set. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "image_gen_val = \n", "val_data_gen = " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "wEgW4i18VrWZ" }, "source": [ "### TODO: Create the CNN\n", "\n", "In the cell below, create a convolutional neural network that consists of 3 convolution blocks. Each convolutional block contains a `Conv2D` layer followed by a max pool layer. The first convolutional block should have 16 filters, the second one should have 32 filters, and the third one should have 64 filters. All convolutional filters should be 3 x 3. All max pool layers should have a `pool_size` of `(2, 2)`.\n", "\n", "After the 3 convolutional blocks you should have a flatten layer followed by a fully connected layer with 512 units. The CNN should output class probabilities based on 5 classes which is done by the **softmax** activation function. All other layers should use a **relu** activation function. You should also add Dropout layers with a probability of 20%, where appropriate. " ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "Evjf8jZk2zi-" }, "outputs": [], "source": [ "model = " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "DADWLqMSJcH3" }, "source": [ "### TODO: Compile the Model\n", "\n", "In the cell below, compile your model using the ADAM optimizer, the sparse cross entropy function as a loss function. We would also like to look at training and validation accuracy on each epoch as we train our network, so make sure you also pass the metrics argument." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "08rRJ0sn3Tb1" }, "outputs": [], "source": [ "# Compile the model" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "oub9RtoFVrWk" }, "source": [ "### TODO: Train the Model\n", "\n", "In the cell below, train your model using the **fit_generator** function instead of the usual **fit** function. We have to use the `fit_generator` function because we are using the **ImageDataGenerator** class to generate batches of training and validation data for our model. Train the model for 80 epochs and make sure you use the proper parameters in the `fit_generator` function." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "tk5NT1PW3j_P" }, "outputs": [], "source": [ "epochs = \n", "\n", "history = " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "LZPYT-EmVrWo" }, "source": [ "### TODO: Plot Training and Validation Graphs.\n", "\n", "In the cell below, plot the training and validation accuracy/loss graphs." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "8CfngybnFHQR" }, "outputs": [], "source": [ "acc = \n", "val_acc = \n", "\n", "loss = \n", "val_loss = \n", "\n", "epochs_range = \n", "\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "E9FROGm-KGEG" }, "source": [ "### TODO: Experiment with Different Parameters\n", "\n", "So far you've created a CNN with 3 convolutional layers and followed by a fully connected layer with 512 units. In the cells below create a new CNN with a different architecture. Feel free to experiment by changing as many parameters as you like. For example, you can add more convolutional layers, or more fully connected layers. You can also experiment with different filter sizes in your convolutional layers, different number of units in your fully connected layers, different dropout rates, etc... You can also experiment by performing image augmentation with more image transformations that we have seen so far. Take a look at the [ImageDataGenerator Documentation](https://keras.io/preprocessing/image/) to see a full list of all the available image transformations. For example, you can add shear transformations, or you can vary the brightness of the images, etc... Experiment as much as you can and compare the accuracy of your various models. Which parameters give you the best result?" ] }, { "cell_type": "markdown", "metadata": { "colab": {}, "colab_type": "code", "id": "XeBOUPw7M6dz" }, "source": [ "### TODO: Visualize the first layer weights and the first layer activation maps\n", "\n", "Download a picture of a sunflower, resize it and visualize the activation maps of the first convolutional layer. Try to understand what filters may be detecting in your image." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### TODO: Load a VGG16 model and apply it to your downloaded picture of a sunflower" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Defining the loss tensor for filter visualization\n", "\n", "model_vgg16 = tf.keras.applications.VGG16(weights='imagenet',\n", " include_top=True)\n", "\n", "# Resize your downloaded image to size 224x224\n", "result = model_vgg16.predict(your_sunflower_image)\n", "\n", "# Predict class\n", "predicted_class = np.argmax(result[0], axis=-1)\n", "print(predicted_class)\n", "\n" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "name": "l05c03_exercise_flowers_with_data_augmentation.ipynb", "private_outputs": true, "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 1 }