import numpy as np

height = 2
width = 2
channels = 3

# create a fake convolutional feature tensor
in_arr_A = []
for i in range(height*width):
    feature_vector = [i]*channels
    in_arr_A.append(feature_vector)
in_arr_A = np.array(in_arr_A)
in_arr_A = np.reshape(in_arr_A, (height, width, channels))

# and make a copy of it
in_arr_B = np.copy(in_arr_A)

# create the output array
heatmaps = np.zeros((height, width, height*width))

# for each feature vector f in in_arr_A:
for i in range(height):
    for j in range(width):
        f = in_arr_A[i,j,:]

        heatmap = np.zeros((height, width))
        # slide f over the entireity of in_arr_B, registering similarity:
        for k in range(height):
            for l in range(width):
                heatmap[k, l] = np.dot(f, in_arr_B[k, l]) # might want to L2 normalize each vector first...

        heatmaps[:,:,i*width+j] = heatmap

print heatmaps