def show_all_activation_images(self, network, test_data):
image_samples = Group(*[
self.get_activation_images(digit, network, test_data)
for digit in range(10)
])
image_samples.arrange_submobjects_in_grid(n_rows=2, buff=LARGE_BUFF)
image_samples.scale_to_fit_height(2 * SPACE_HEIGHT - 1)
self.add(image_samples)
def show_maximizing_inputs(self, network):
training_data, validation_data, test_data = load_data_wrapper()
layer = 1
n_neurons = DEFAULT_LAYER_SIZES[layer]
groups = Group()
for k in range(n_neurons):
out = np.zeros(n_neurons)
out[k] = 1
in_vect = maximizing_input(network, layer, out)
new_out = network.get_activation_of_all_layers(in_vect)[layer]
group = Group(*map(MNistMobject, [in_vect, new_out]))
group.arrange_submobjects(DOWN+RIGHT, SMALL_BUFF)
groups.add(group)
groups.arrange_submobjects_in_grid()
groups.scale_to_fit_height(2*SPACE_HEIGHT - 1)
self.add(groups)