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

Training: cross-entropy

To train the first approximation of the model, the cross-entropy method is used and implemented in train_crossent.py. During the training, we randomly switch between the teacher-forcing mode (when we give the target sequence on the decoder's input) and argmax chain decoding (when we decode the sequence one step at a time, choosing the token with the highest probability in the output distribution). The decision between those two training modes is taken randomly with the fixed probability of 50%. This allows for combining the characteristics of both methods: fast convergence from teacher forcing and stable decoding from curriculum learning.

Implementation

What follows is the implementation of the cross-entropy method training from train_crossent.py.

SAVES_DIR = "saves"
BATCH_SIZE = 32
LEARNING_RATE = 1e-3
MAX_EPOCHES = 100
log = logging.getLogger("train")
TEACHER_PROB = 0.5

In the beginning, we define hyperparameters...

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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

Training: cross-entropy

Implementation

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