seq.add(link.Linear(500, 500))
seq.add(function.Activation("leaky_relu"))
seq.add(link.Linear(500, 500, use_weightnorm=True))
seq.add(function.Activation("relu"))
seq.add(link.BatchNormalization(500))
seq.add(link.Linear(500, 500))
seq.add(function.Activation("sigmoid"))
seq.add(link.Linear(500, 500, use_weightnorm=True))
seq.add(function.Activation("softmax"))
seq.add(link.BatchNormalization(500))
seq.add(link.Linear(500, 500))
seq.add(function.Activation("softplus"))
seq.add(link.Linear(500, 500, use_weightnorm=True))
seq.add(function.Activation("tanh"))
seq.add(link.Linear(500, 10))
seq.build()
y = seq(x)
print y.data.shape
# Conv test
x = np.random.normal(scale=1, size=(2, 3, 96, 96)).astype(np.float32)
x = Variable(x)
seq = Sequential(weight_initializer="GlorotNormal", weight_init_std=0.05)
seq.add(link.Convolution2D(3, 64, ksize=4, stride=2, pad=0))
seq.add(link.BatchNormalization(64))
seq.add(function.Activation("relu"))
seq.add(link.Convolution2D(64, 128, ksize=4, stride=2, pad=0))
seq.add(link.BatchNormalization(128))
seq.add(function.Activation("relu"))
# discriminator.add(BatchNormalization(1000))
discriminator.add(Linear(None, 500, use_weightnorm=config.use_weightnorm))
discriminator.add(gaussian_noise(std=0.5))
discriminator.add(Activation(config.nonlinearity))
# discriminator.add(BatchNormalization(500))
discriminator.add(Linear(None, 250, use_weightnorm=config.use_weightnorm))
discriminator.add(gaussian_noise(std=0.5))
discriminator.add(Activation(config.nonlinearity))
# discriminator.add(BatchNormalization(250))
if config.use_minibatch_discrimination:
discriminator.add(
MinibatchDiscrimination(None, num_kernels=50, ndim_kernel=5))
discriminator.add(
Linear(None, config.ndim_output, use_weightnorm=config.use_weightnorm))
# no need to add softmax() here
discriminator.build()
params = {
"config": config.to_dict(),
"model": discriminator.to_dict(),
}
with open(discriminator_sequence_filename, "w") as f:
json.dump(params, f, indent=4, sort_keys=True, separators=(',', ': '))
discriminator_params = params
# specify generator
generator_sequence_filename = args.model_dir + "/generator.json"
if os.path.isfile(generator_sequence_filename):