We finally initiate the training session with the respective arguments. You will notice the tqdm module displaying a percentage bar indicating the number of processed batches per epoch. At the end of the epoch, you will be able to visualize a 4 x 4 grid (shown next) of samples generated from the GAN network. And there you have it, now you know how to implement a GAN in Keras. On a side note, it can be very beneficial to have tensorflow-gpu along with CUDA set up, if you're running the code on a local machine with access to a GPU. We ran this code for 200 epochs, yet it would not be uncommon to let it run for thousands of epochs, given the resources and time. Ideally, the longer the two networks battle, the better the results should get. Yet, this may not always be the case, and hence, such attempts may also require careful monitoring of the...
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Table Of Contents
Hands-On Neural Networks with Keras
By :
Hands-On Neural Networks with Keras
By:
Overview of this book
Neural networks are used to solve a wide range of problems in different areas of AI and deep learning.
Hands-On Neural Networks with Keras will start with teaching you about the core concepts of neural networks. You will delve into combining different neural network models and work with real-world use cases, including computer vision, natural language understanding, synthetic data generation, and many more. Moving on, you will become well versed with convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, autoencoders, and generative adversarial networks (GANs) using real-world training datasets. We will examine how to use CNNs for image recognition, how to use reinforcement learning agents, and many more. We will dive into the specific architectures of various networks and then implement each of them in a hands-on manner using industry-grade frameworks.
By the end of this book, you will be highly familiar with all prominent deep learning models and frameworks, and the options you have when applying deep learning to real-world scenarios and embedding artificial intelligence as the core fabric of your organization.
Table of Contents (16 chapters)
Preface
Overview of Neural Networks
A Deeper Dive into Neural Networks
Signal Processing - Data Analysis with Neural Networks
Section 2: Advanced Neural Network Architectures
Convolutional Neural Networks
Recurrent Neural Networks
Long Short-Term Memory Networks
Reinforcement Learning with Deep Q-Networks
Section 3: Hybrid Model Architecture
Autoencoders
Generative Networks
Section 4: Road Ahead
Contemplating Present and Future Developments
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