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)

7. Generative Adversarial Networks

Activity 7.01: Implementing a DCGAN for the MNIST Fashion Dataset


  1. Open a new Jupyter Notebook and name it Activity 7.01. Import the following library packages:
    # Import the required library functions
    import numpy as np
    import matplotlib.pyplot as plt
    from matplotlib import pyplot
    import tensorflow as tf
    from tensorflow.keras.layers import Input
    from tensorflow.keras.initializers import RandomNormal
    from tensorflow.keras.models import Model, Sequential
    from tensorflow.keras.layers \
    import Reshape, Dense, Dropout, Flatten,Activation
    from tensorflow.keras.layers import LeakyReLU,BatchNormalization
    from tensorflow.keras.layers import Conv2D, UpSampling2D,Conv2DTranspose
    from tensorflow.keras.datasets import fashion_mnist
    from tensorflow.keras.optimizers import Adam
  2. Create a function that will generate real data samples from the fashion MNIST data:
    # Function to generate real data samples
    def realData(batch):