Runtime error during backward_pass() of PoolingLayer

[krworks]

I greatly appreciate your work and clearly written code which gives incredible insights into the back propagation technique. I've encountered a bit of a bug which is pretty solvable, but I don't want to make a pull request as I'm not sure of default values here.

It's at layers.py:400 (at the end of the line, last param):

ML-From-Scratch/mlfromscratch/deep_learning/layers.py

Line 400 in a2806c6

accum_grad = column_to_image(accum_grad_col, (batch_size * channels, 1, height, width), self.pool_shape, self.stride, 0)

The last param is supposed to be in the string-style enum format of padding type. It's passing a literal 0 when it should be passing self.padding. PoolingLayer should also have a valid default value for self.padding which is also 0 (which of course causes this same error). In the case of 0 being an acceptable default, that value should be acceptable by the receiving function determine_padding, which is where the error is raised:

ML-From-Scratch/mlfromscratch/deep_learning/layers.py

Line 718 in a2806c6

pad_h, pad_w = determine_padding(filter_shape, output_shape)

Again, thank you for this repository. Amazing work.

[krworks]

Just to give a bit more context; I'm instantiating the PoolingLayer via:
clf.add(MaxPooling2D(pool_shape=(2,2), stride=2, padding='same'))