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Deep Learning with Theano

You're reading from   Deep Learning with Theano Perform large-scale numerical and scientific computations efficiently

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781786465825
Length 300 pages
Edition 1st Edition
Tools
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Author (1):
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Christopher Bourez Christopher Bourez
Author Profile Icon Christopher Bourez
Christopher Bourez
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Table of Contents (15) Chapters Close

Preface 1. Theano Basics FREE CHAPTER 2. Classifying Handwritten Digits with a Feedforward Network 3. Encoding Word into Vector 4. Generating Text with a Recurrent Neural Net 5. Analyzing Sentiment with a Bidirectional LSTM 6. Locating with Spatial Transformer Networks 7. Classifying Images with Residual Networks 8. Translating and Explaining with Encoding – decoding Networks 9. Selecting Relevant Inputs or Memories with the Mechanism of Attention 10. Predicting Times Sequences with Advanced RNN 11. Learning from the Environment with Reinforcement 12. Learning Features with Unsupervised Generative Networks 13. Extending Deep Learning with Theano 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...

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