Is there a way to wrap every single entry of an numpy.ndarray into a separate array?

I’m facing some problems getting an array into the right shape to use it as an input into a convolutional neural net:

My array has the shape (100,64,64), but I’d need it to be (100,64,64,1). I realize it looks a bit odd, but I basically want to pack every single entry into a separate array.

A simplified example, with a 2D array, where the analogous would be from (3,3) to (3,3,1):

[[0,1,0],        [[[0],[1],[0]],
 [1,1,1],         [[1],[1],[1]],
 [0,0,1]]         [[0],[0],[1]]]

Is there a convenient way to do this using numpy?

I’ve tried to use the function numpy.reshape: With which I know, how to “add” another array wrapping the original one.

import numpy as np

data = data.reshape((1,)+data.shape)

This gives the output for data.shape: (1,100,64,64). Is there a way to add a dimension at the “inner end”?

If I try data.reshape(data.shape+(,1)), I get an invalid syntax error.

Answer

You can reshape using:

a[:,:,None]

Or, programmatically (works for any number of dimensions):

a.reshape((*a.shape,1))

example

a = np.array([[0,1,0],
              [1,1,1],
              [0,0,1]])

# array([[0, 1, 0],
#        [1, 1, 1],
#        [0, 0, 1]])


a[:,:,None]  # or a.reshape((*a.shape,1))

# array([[[0], [1], [0]],
#        [[1], [1], [1]],
#        [[0], [0], [1]]])