Book Image

TensorFlow Machine Learning Projects

By : Ankit Jain, Amita Kapoor
Book Image

TensorFlow Machine Learning Projects

By: Ankit Jain, Amita Kapoor

Overview of this book

TensorFlow has transformed the way machine learning is perceived. TensorFlow Machine Learning Projects teaches you how to exploit the benefits—simplicity, efficiency, and flexibility—of using TensorFlow in various real-world projects. With the help of this book, you’ll not only learn how to build advanced projects using different datasets but also be able to tackle common challenges using a range of libraries from the TensorFlow ecosystem. To start with, you’ll get to grips with using TensorFlow for machine learning projects; you’ll explore a wide range of projects using TensorForest and TensorBoard for detecting exoplanets, TensorFlow.js for sentiment analysis, and TensorFlow Lite for digit classification. As you make your way through the book, you’ll build projects in various real-world domains, incorporating natural language processing (NLP), the Gaussian process, autoencoders, recommender systems, and Bayesian neural networks, along with trending areas such as Generative Adversarial Networks (GANs), capsule networks, and reinforcement learning. You’ll learn how to use the TensorFlow on Spark API and GPU-accelerated computing with TensorFlow to detect objects, followed by how to train and develop a recurrent neural network (RNN) model to generate book scripts. By the end of this book, you’ll have gained the required expertise to build full-fledged machine learning projects at work.
Table of Contents (23 chapters)
Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
Index

Generating book scripts


Now that the model has been trained, we can have some fun with it. In this section, we will see how we can use the model to generate book scripts. Use the following parameters:

  • Script Length = 200 words
  • Starting word = postgresql

Follow these steps to generate the model:

  1. Load the graph of the trained model.
  2. Extract four tensors, as follows:
    • Input/input:0
    • Network/initial_state:0
    • Network/final_state:0
    • Network/probs:0

Extract the four tensors using the following code:

 def extract_tensors(tf_graph):
 """
 Get input, initial state, final state, and probabilities tensor from the graph
 :param loaded_graph: TensorFlow graph loaded from file
 :return: Tuple (tensor_input,tensor_initial_state,tensor_final_state, tensor_probs)
 """
 tensor_input = tf_graph.get_tensor_by_name("Input/input:0")
 tensor_initial_state = tf_graph.get_tensor_by_name("Network/initial_state:0")
 tensor_final_state = tf_graph.get_tensor_by_name("Network/final_state:0")
 tensor_probs = tf_graph.get_tensor_by_name...