You're reading from Deep Reinforcement Learning Hands-On. - Second Edition

Product typeBook

Published inJan 2020

Reading LevelIntermediate

PublisherPackt

ISBN-139781838826994

Edition2nd Edition

Languages

Python

Tools

Keras TensorFlow

Concepts

Deep Reinforcement Learning

Author (1)

Maxim Lapan

Models tested on data

Once we've got our models ready, we can check them against our dataset and free-form sentences. During the training, both training tools (train_crossent.py and train_scst.py) periodically save the model, which is done in two different situations: when the BLEU score on the test dataset updates the maximum and every 10 epochs. Both kinds of models have the same format (produced by the torch.save() method) and contain the model's weights. Except the weights, I save the token to integer ID mapping, which will be used by tools to preprocess the phrases.

To experiment with models, two utilities exist: data_test.py and use_model.py. data_test.py loads the model, applies it to all phrases from the given genre, and reports the average BLEU score. Before the testing, phrase pairs are grouped by the first phrase. For example, the following is the result for two models, trained on the comedy genre. The first one was trained by the cross-entropy method and the...

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Deep Reinforcement Learning Hands-On. - Second Edition

Published in: Jan 2020Publisher: PacktISBN-13: 9781838826994

Author (1)

Maxim Lapan

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
Read more about Maxim Lapan

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You're reading from Deep Reinforcement Learning Hands-On. - Second Edition

Models tested on data

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Author (1)

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