The most interesting part is in the folder neural_networks/layers.py: Code for a convolutional neural network, based on only numpy (no PyTorch or TensorFlow). It is therefore very foundational and illustrates how CNNs work mathematically.

The CNNs is compatible with colour images (3-channel rgb), includes pooling layers (class Pool2D) and works with any given (valid) stride.

neural_networks/activations.py contains basic activation functions, like ReLu or SoftMax with the appropriate forward / backward implementations calculating the jacobian, etc., needed for backpropagation.

Many functions make heavy use of slicing, to speed up the training process significantly. See e.g. Conv2D.forward:

for x in range(out_rows):
    for y in range(out_cols):
        out[:,x,y,:] = np.apply_over_axes(np.sum, W[None]*X_pad[:,x*s:x*s+kernel_height,y*s:y*s+kernel_width,:][...,None], [1,2,3])[:,0,0,0,:]

which is the sliced version of a depth-6 nested for loop -- and thus allows for significant speedup (on my computer, more than 20x speedup for the given training data).

In losses.py, CrossEntropy is the most important function. To allow for speed-up, we simplified mathematically as much as possible, yielding

loss = -1.0/m *np.trace(np.matmul(Y,np.log(Y_hat.T)))

for the forward pass and

-1/m*(np.divide(Y,Y_hat))

for the backward pass.

This is based on a project for CS289 at UC Berkeley.