将大小为（n，n，m）的数组转换为（None，n，n，m）

Question

I am trying to reshape an array of size (14,14,3) to (None, 14,14,3). I have seen that the output of each layer in convolutional neural network has shape in the format(None, n, n, m).

Consider that the name of my array is arr

I tried arr[None,:,:] but it converts it to a dimension of (1,14,14,3).

How should I do it?

Answer 1

https://www.tensorflow.org/api_docs/python/tf/TensorShape

A TensorShape represents a possibly-partial shape specification for a Tensor. It may be one of the following:

Partially-known shape: has a known number of dimensions, and an unknown size for one or more dimension. eg TensorShape([None, 256])

That is not possible in numpy . All dimensions of a ndarray are known.

arr[None,:,:] notation adds a new size 1 dimension, (1,14,14,3) . Under broadcasting rules, such a dimension may be changed to match a dimension of another array. In that sense we often treat the None as a flexible dimension.

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I haven't worked with tensorflow though I see a lot of questions with both tags. tensorflow should have mechanisms for transfering values to and from tensors. It knows about numpy , but numpy does not 'know' anything about tensorflow .

A ndarray is an object with known values, and its shape is used to access those values in a multidimensional way. In contrast a tensor does not have values:

https://www.tensorflow.org/api_docs/python/tf/Tensor

It does not hold the values of that operation's output, but instead provides a means of computing those values

Looks like you can create a TensorProt from an array (and return an array from one as well):

https://www.tensorflow.org/api_docs/python/tf/make_tensor_proto

and to make a Tensor from an array:

https://www.tensorflow.org/api_docs/python/tf/convert_to_tensor

Answer 2

The shape (None, 14,14,3) represent ,(batch_size,imgH,imgW,imgChannel) now imgH and imgW can be use interchangeably depends on the network and the problem. But the batchsize is given as "None" in the neural network because we don't want to restrict our batchsize to some specific value as our batchsize depends on a lot of factors like memory available for our model to run etc.

So lets say you have 4 images of size 14x14x3 then you can append each image into the array say L1, and now the L1 will have the shape 4x14x14x3 ie you made a batch of 4 images and now you can feed this to your neural network.

NOTE here None will be replaced by 4 and for the whole training process it will be 4. Similarly when you feed your network only one image it assumes the batchsize of 1 and set None equal to 1 giving you the shape (1X14X14X3)