在 Python 中的数组索引中使用 None [英] Use of None in Array indexing in Python

问题描述

我正在使用 Theano 的 LSTM 教程 (http://deeplearning.net/tutorial/lstm.html).在 lstm.py (http://deeplearning.net/tutorial/code/lstm.py) 文件中，我不明白以下行:

I am using the LSTM tutorial for Theano (http://deeplearning.net/tutorial/lstm.html). In the lstm.py (http://deeplearning.net/tutorial/code/lstm.py) file, I don't understand the following line:

c = m_[:, None] * c + (1. - m_)[:, None] * c_

m_[:, None] 是什么意思?在这种情况下，m_ 是 theano 向量，而 c 是一个矩阵.

What does m_[:, None] mean? In this case m_ is the theano vector while c is a matrix.

推荐答案

这个问题已经在 Theano 邮件列表中提出和回答，但实际上是关于 numpy 索引的基础知识.

This question has been asked and answered on the Theano mailing list, but is actually about the basics of numpy indexing.

这是问题和答案https://groups.google.com/forum/#!topic/theano-users/jq92vNtkYUI

为了完整起见，这里有另一种解释:使用 None 切片会向数组添加一个轴，请参阅相关的 numpy 文档，因为它在 numpy 和 Theano 中的行为相同:

For completeness, here is another explanation: slicing with None adds an axis to your array, see the relevant numpy documentation, because it behaves the same in both numpy and Theano:

http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#numpy.newaxis

注意np.newaxis是None:

import numpy as np
a = np.arange(30).reshape(5, 6)

print a.shape  # yields (5, 6)
print a[np.newaxis, :, :].shape  # yields (1, 5, 6)
print a[:, np.newaxis, :].shape  # yields (5, 1, 6)
print a[:, :, np.newaxis].shape  # yields (5, 6, 1)

这通常用于调整形状，以便能够广播到更高的维度.例如.中轴平铺7次可以实现为

Typically this is used to adjust shapes to be able to broadcast to higher dimensions. E.g. tiling 7 times in the middle axis can be achieved as

b = a[:, np.newaxis] * np.ones((1, 7, 1))

print b.shape  # yields (5, 7, 6), 7 copies of a along the second axis

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