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_random_results(self):
group = Group(*[
Group(*[
MNistMobject(a) for a in network.get_activation_of_all_layers(
np.random.randn(784, 1))
]).arrange_submobjects(RIGHT) for x in range(10)
]).arrange_submobjects(DOWN)
group.scale_to_fit_height(2 * SPACE_HEIGHT - 1)
self.add(group)
def show_test_input(self, network):
training_data, validation_data, test_data = load_data_wrapper()
group = Group(*[
self.get_set(network, test) for test in test_data[3:20]
if test[1] in [4, 9]
])
group.arrange_submobjects(DOWN, buff=MED_LARGE_BUFF)
group.scale_to_fit_height(2 * SPACE_HEIGHT - 1)
self.play(FadeIn(group))
def get_activation_images(self, digit, network, test_data, n_examples=8):
input_vectors = [data[0] for data in test_data if data[1] == digit]
activation_iamges = Group(*[
Group(*[
MNistMobject(a)
for a in network.get_activation_of_all_layers(vect)
]).arrange_submobjects(RIGHT)
for vect in input_vectors[:n_examples]
]).arrange_submobjects(DOWN)
activation_iamges.scale_to_fit_height(2 * SPACE_HEIGHT - 1)
return activation_iamges
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)
