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(FRAME_HEIGHT - 1)
self.play(FadeIn(group))
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(FRAME_HEIGHT - 1)
self.add(groups)
def get_set(self, network, test):
test_in, test_out = test
activations = network.get_activation_of_all_layers(test_in)
group = Group(*map(MNistMobject, activations))
group.arrange_submobjects(RIGHT, buff=LARGE_BUFF)
return group