How to Reshape a 4D NumPy Array to a 2D Array?

Intuition and Idea Behind Reshaping 4D Array to 2D Array in NumPy

In NumPy, reshaping multidimensional arrays requires an understanding of axis permutations and the reshape function. To reshape a 4D array to a 2D array, we typically follow a three-step process:

1. Permute Axes: Rearrange the dimensions of the array to match the desired output shape. This is done using functions like transpose, moveaxis, rollaxis, or swapaxes.
2. Split or Merge Axes: Adjust the dimensionality of the array by creating new axes (splitting) or combining existing axes (merging).
3. Reshape: Use the reshape function to adjust the shape of the array to the desired output size.

Example

Consider the following 4D array:

array([[[[ 0,  0],
         [ 0,  0]],

        [[ 5, 10],
         [15, 20]]],


       [[[ 6, 12],
         [18, 24]],

        [[ 7, 14],
         [21, 28]]]])

To reshape it to (4,4):

1. Permute Axes: We want the axes order to be (0, 1, 3, 2). This can be achieved using transpose:

a = a.transpose((2, 0, 3, 1))

2. Split Axes: Perform no splitting, as we already have the correct number of dimensions.
3. Reshape: Reshape to the desired shape:

a = a.reshape(4,4)

Result:

array([[ 0,  5,  0, 10],
       [ 6,  7, 12, 14],
       [ 0, 15,  0, 20],
       [18, 21, 24, 28]])

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