FullyConnectedLayer(32, weight_scale=0.125),
ActivationLayer('leakyReLU'),
FullyConnectedLayer(32, weight_scale=0.125),
ActivationLayer('leakyReLU'),
FullyConnectedLayer(6, weight_scale=0.17),
SoftmaxLayer()],
DataProvider(num_genres=6))
neural_net.setup_layers((128, 599), (6, ))
neural_net.init_params_from_file()
genres = ['classical', 'metal', 'blues', 'disco', 'hiphop', 'reggae']
for genre in genres:
for layer_id in range(3):
activations_for_test_data = neural_net.test_data_activations_for_conv_layer(
layer_id + 1, genre)
for result in activations_for_test_data:
for filter_idx in range(result['filter_activations'].shape[0]):
x = np.arange(0, result['filter_activations'].shape[1], 1)
y = result['filter_activations'][filter_idx]
dir_path = './activations/6genres/conv_layer' + str(layer_id + 1) + '/filter' +\
str(filter_idx + 1)
if not os.path.exists(dir_path):
os.makedirs(dir_path)
plt.plot(x, y)
plt.gcf().set_size_inches(7.72903226, 1.6)
plt.axis('off')
plt.gca().set_axis_off()
