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

NN building blocks

In the torch.nn package, you will find tons of predefined classes providing you with the basic functionality blocks. All of them are designed with practice in mind (for example, they support mini-batches, they have sane default values, and the weights are properly initialized). All modules follow the convention of callable, which means that the instance of any class can act as a function when applied to its arguments. For example, the Linear class implements a feed-forward layer with optional bias:

>>> import torch.nn as nn
>>> l = nn.Linear(2, 5)
>>> v = torch.FloatTensor([1, 2])
>>> l(v)
tensor([ 1.0532,  0.6573, -0.3134,  1.1104, -0.4065], grad_fn=<AddBackward0>)

Here, we created a randomly initialized feed-forward layer, with two inputs and five outputs, and applied it to our float tensor. All classes in the torch.nn packages inherit from the nn.Module base class, which you can use to implement...

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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

NN building blocks

Unlock this book and the full library FREE for 7 days

Author (1)

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