def eight_class():
neural_net = ConvNet([ConvLayer(32, (128, 4), weight_scale=0.044, padding_mode=False),
ActivationLayer('leakyReLU'),
MaxPoolingLayerCUDA((1, 4)),
ConvLayer(32, (32, 4), weight_scale=0.088, padding_mode=False),
ActivationLayer('leakyReLU'),
MaxPoolingLayerCUDA((1, 2)),
ConvLayer(32, (32, 4), weight_scale=0.088, padding_mode=False),
ActivationLayer('leakyReLU'),
GlobalPoolingLayer(),
FullyConnectedLayer(32, weight_scale=0.125),
ActivationLayer('leakyReLU'),
FullyConnectedLayer(32, weight_scale=0.125),
ActivationLayer('leakyReLU'),
FullyConnectedLayer(8, weight_scale=0.17),
SoftmaxLayer()],
DataProvider(num_genres=8))
neural_net.init_params_from_file(conv_only=True)
time1 = time.time()
neural_net.train(learning_rate=0.005, num_iters=80, lrate_schedule=True)
time2 = time.time()
print('Time taken: %.1fs' % (time2 - time1))
print "\nRESULTS:\n"
for result in neural_net.results:
print result
for val in neural_net.results[result]:
print val
neural_net.serialise_params()
