def get_strongest_filter(img_id, layer):
filters = AlexNet.channels[layer]
path = get_path_from_id(img_id)
# Get activations for shortened model
activation_img = AlexNet(layer).predict(path)
# Make sure that dimensions 2 and 3 are spacial (Image is square)
assert activation_img.shape[2] == activation_img.shape[3], "Index ordering incorrect"
assert activation_img.shape[1] == filters
# Find maximum activation for each filter for a given image
activation_img = np.nanmax(activation_img, axis=3)
activation_img = np.nanmax(activation_img, axis=2)
# Remove batch size dimension
assert activation_img.shape[0] == 1
activation_img = activation_img.sum(0)
# Make activations 1-based indexing
activation_img = np.insert(activation_img, 0, 0.0)
# activation_image is now a vector of length equal to number of filters (plus one for one-based indexing)
# each entry corresponds to the maximum/summed activation of each filter for a given image
return activation_img.argmax()
