Book Image

The Deep Learning Workshop

By : Mirza Rahim Baig, Thomas V. Joseph, Nipun Sadvilkar, Mohan Kumar Silaparasetty, Anthony So
Book Image

The Deep Learning Workshop

By: Mirza Rahim Baig, Thomas V. Joseph, Nipun Sadvilkar, Mohan Kumar Silaparasetty, Anthony So

Overview of this book

Are you fascinated by how deep learning powers intelligent applications such as self-driving cars, virtual assistants, facial recognition devices, and chatbots to process data and solve complex problems? Whether you are familiar with machine learning or are new to this domain, The Deep Learning Workshop will make it easy for you to understand deep learning with the help of interesting examples and exercises throughout. The book starts by highlighting the relationship between deep learning, machine learning, and artificial intelligence and helps you get comfortable with the TensorFlow 2.0 programming structure using hands-on exercises. You’ll understand neural networks, the structure of a perceptron, and how to use TensorFlow to create and train models. The book will then let you explore the fundamentals of computer vision by performing image recognition exercises with convolutional neural networks (CNNs) using Keras. As you advance, you’ll be able to make your model more powerful by implementing text embedding and sequencing the data using popular deep learning solutions. Finally, you’ll get to grips with bidirectional recurrent neural networks (RNNs) and build generative adversarial networks (GANs) for image synthesis. By the end of this deep learning book, you’ll have learned the skills essential for building deep learning models with TensorFlow and Keras.
Table of Contents (9 chapters)
Preface

3. Image Classification with Convolutional Neural Networks (CNNs)

Activity 3.01: Building a Multiclass Classifier Based on the Fashion MNIST Dataset

Solution

  1. Open a new Jupyter Notebook.
  2. Import tensorflow.keras.datasets.fashion_mnist:
    from tensorflow.keras.datasets import fashion_mnist
  3. Load the Fashion MNIST dataset using fashion_mnist.load_data() and save the results to (features_train, label_train), (features_test, label_test):
    (features_train, label_train), (features_test, label_test) = \
    fashion_mnist.load_data()
  4. Print the shape of the training set:
    features_train.shape

    The output will be as follows:

    (60000, 28, 28)

    The training set is composed of 60000 images of size 28 by 28. We will need to reshape it and add the channel dimension.

  5. Print the shape of the testing set:
    features_test.shape

    The output will be as follows:

    (10000, 28, 28)

    The testing set is composed of 10000 images of size 28 by 28. We will need to reshape it and add the channel dimension

  6. ...