Reshaping numpy 3D array

Question

I have a dataset with dimensions: (32, 32, 73257) where 32x32 are pixels of a single image.

How do I reshape it to (73257, 1024) so that every image is unrolled in a row?

So far, I did:

self.train_data = self.train_data.reshape(n_training_examples, number_of_pixels*number_of_pixels)

and it looks like I got garbage instead of normal pictures. I am assuming that reshaping was performed across wrong dimension...??

You need to swap axes, moving the 73257 dimension to the front, then reshape. — hpaulj
– hpaulj, Commented Nov 10, 2015 at 4:59
@hpaulj and how do I do that? I am not so proficient with numpy. — Uylenburgh
– Uylenburgh, Commented Nov 10, 2015 at 14:56

Jaime · Accepted Answer · 2015-11-10 05:31:19Z

1

As suggested in the comments, first get every image in a column, then transpose:

self.train_data = self.train_data.reshape(-1, n_training_examples).T

The memory layout of your array will not be changed by any of these operations, so two contiguous pixels of any image will lay 73257 bytes apart (assuming a uint8 image), which may not be the best of options if you want to process your data one image at a time. You will need to time and validate this, but creating a copy of the array may prove advantageous performance-wise:

self.train_data = self.train_data.reshape(-1, n_training_examples).T.copy()

answered Nov 10, 2015 at 5:31

Jaime

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1 Comment

hpaulj Over a year ago

I was thinking of something more like data.transpose(2,0,1).reshape(N,32*32), but don't know if the order of the flattened 32x32 part is right or not.

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