为什么我不能评估theano中的重塑张量? [英] Why can't I evaluate the reshaped tensorvariable in theano?

问题描述

为什么我不能通过下面编写的代码来评估重塑后的张量变量?

Why can't I evaluate the reshaped tensorvariable through the code I wrote below?

from theano import shared
from theano import tensor as T
import numpy

x = T.matrix('x') # the input data

# input = (nImages, nChannel(nFeatureMaps), nDim1, nDim2, nDim3)

layer1_input = T.reshape(x, xTrain.shape, ndim=5)
layer1_input.eval({x:xTrain})

由于我已经调整了张量变量x的形状，并向其传递了一个相同维的numpy数组，所以它只是报告，

Since I have reshape the tensorvariable x, and pass a numpy array of same dimension to it, it simply reports,

TypeError:('theano函数的输入参数名称错误 :17"位于索引0(从0开始)'，'错误 尺寸数量:预期2，得到5个形状(2592、1、51、61， 23).')

TypeError: ('Bad input argument to theano function with name ":17" at index 0(0-based)', 'Wrong number of dimensions: expected 2, got 5 with shape (2592, 1, 51, 61, 23).')

推荐答案

我认为问题是因为您使用matrix(二维)作为x的数据类型而接收到五维输入xTrain.如此处所述，对于五维输入，您应该创建一个自定义数据类型.

I think the problem is because you are using matrix (two dimensional) as data type of x that receive a five dimensional input xTrain. As said here, for five dimensional input, you should create a custom data type.

示例代码:

from theano import tensor as T
import numpy as np
xTrain = np.random.rand(1,1,2,3,3).astype('float32')

dtensor5 = T.TensorType('float32', (False,)*5)
x = dtensor5('x')

layer1_input = x
print layer1_input.eval({x:xTrain})

关于

由于我已经调整了张量变量x的值，并传递了一个numpy数组 尺寸相同

Since I have reshape the tensorvariable x, and pass a numpy array of same dimension to it

我认为实际发生的情况是变量x首先接收输入(引发错误)，然后为layer1_input对其进行整形

I think what actually happen is variable x recieve the input first (raise an error) and then you reshape it for layer1_input

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