Book Image

Deep Learning with Theano

By : Christopher Bourez
Book Image

Deep Learning with Theano

By: Christopher Bourez

Overview of this book

This book offers a complete overview of Deep Learning with Theano, a Python-based library that makes optimizing numerical expressions and deep learning models easy on CPU or GPU. The book provides some practical code examples that help the beginner understand how easy it is to build complex neural networks, while more experimented data scientists will appreciate the reach of the book, addressing supervised and unsupervised learning, generative models, reinforcement learning in the fields of image recognition, natural language processing, or game strategy. The book also discusses image recognition tasks that range from simple digit recognition, image classification, object localization, image segmentation, to image captioning. Natural language processing examples include text generation, chatbots, machine translation, and question answering. The last example deals with generating random data that looks real and solving games such as in the Open-AI gym. At the end, this book sums up the best -performing nets for each task. While early research results were based on deep stacks of neural layers, in particular, convolutional layers, the book presents the principles that improved the efficiency of these architectures, in order to help the reader build new custom nets.
Table of Contents (22 chapters)
Deep Learning with Theano
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Index

Example of predictions


Let's predict a sentence with the generated model:

sentence = [0]
while sentence[-1] != 1:
    pred = predict_model(sentence)[-1]
    sentence.append(pred)
print(" ".join([ index_[w] for w in sentence[1:-1]]))

Note that we take the most probable next word (argmax), while we must, in order to get some randomness, draw the next word following the predicted probabilities.

At 150 epochs, while the model has still not converged entirely with learning our Shakespeare writings, we can play with the predictions, initiating it with a few words, and see the network generate the end of the sentences:

  • First citizen: A word , i know what a word

  • How now!

  • Do you not this asleep , i say upon this?

  • Sicinius: What, art thou my master?

  • Well, sir, come.

  • I have been myself

  • A most hose, you in thy hour, sir

  • He shall not this

  • Pray you, sir

  • Come, come, you

  • The crows?

  • I'll give you

  • What, ho!

  • Consider you, sir

  • No more!

  • Let us be gone, or your UNKNOWN UNKNOWN, i do me to do

  • We are not now

From these...