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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

Training the model

Now we can start training the model. In this example, we chose to train the model using SGD with a batch size of 64 and 100 epochs. To validate the model, we randomly selected 16 words and used the similarity measure as an evaluation metric:

  1. Let's begin training:
    valid_size = 16     # Random set of words to evaluate similarity on.
    valid_window = 100  # Only pick dev samples in the head of the distribution.
    valid_examples = np.array(np.random.choice(valid_window, valid_size, replace=False), dtype='int32')
    
    n_epochs = 100
    n_train_batches = data_size // batch_size
    n_iters = n_epochs * n_train_batches
    train_loss = np.zeros(n_iters)
    average_loss = 0
    
    for epoch in range(n_epochs):
        for minibatch_index in range(n_train_batches):
    
            iteration = minibatch_index + n_train_batches * epoch
            loss = train_model(minibatch_index)
            train_loss[iteration] = loss
            average_loss += loss
    
    
            if iteration % 2000 == 0:
    
              if iteration ...
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