首页validX = numpy.reshape(validX, (validX.shape[0], validX.shape[1], 1))

validX = numpy.reshape(validX, (validX.shape[0], validX.shape[1], 1))

时间: 2024-06-04 13:11:14 浏览: 52

详解numpy.reshape中参数newshape出现-1的含义

前言 reshape函数是Numpy中用来修改数组形状的函数，其函数原型为： numpy.reshape(a, newshape, order='C') 在该函数中有一个用来指定数组新形状的参数newshape，该参数的取值可以为整数或整数构成的元组，在众多的取值情况中，存在一种特殊的情况：newshape取-1或(dim1,dim2,…,−1,…,dimn)(dim1,dim2,…,-1,…,dimn)(dim1,dim2,…,−1,…,dimn)，那这里的-1有什么含义呢？请看下文。详细解释在Numpy官方文档中对参数newshape的解释中存在这样几句话：One shape dim

This line of code is using the numpy library to reshape the validX array. The new shape of the array will have the same number of rows as the original but with an added dimension of size 1. The first argument in the reshape function specifies the number of rows, which is the same as the original validX array. The second argument is the number of columns, which is also the same as the original validX array. The third argument is the size of the new added dimension, which is 1 in this case. The result of this line of code will be a new array with the same number of rows and columns as the original validX array, but with an added dimension of size 1. This is often done when working with neural networks, where the third dimension is used to represent the number of input channels.

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