# specify discriminator
discriminator_sequence_filename = args.model_dir + "/discriminator.json"
if os.path.isfile(discriminator_sequence_filename):
print "loading", discriminator_sequence_filename
with open(discriminator_sequence_filename, "r") as f:
try:
params = json.load(f)
except Exception as e:
raise Exception(
"could not load {}".format(discriminator_sequence_filename))
else:
config = DiscriminatorParams()
config.weight_init_std = 0.001
config.weight_initializer = "Normal"
config.use_weightnorm = False
config.nonlinearity = "elu"
config.optimizer = "Adam"
config.learning_rate = 0.0001
config.momentum = 0.5
config.gradient_clipping = 10
config.weight_decay = 0
config.use_feature_matching = False
config.use_minibatch_discrimination = False
discriminator = Sequential(weight_initializer=config.weight_initializer,
weight_init_std=config.weight_init_std)
discriminator.add(gaussian_noise(std=0.3))
discriminator.add(
Convolution2D(3,
discriminator_sequence_filename = args.model_dir + "/discriminator.json"
if os.path.isfile(discriminator_sequence_filename):
print "loading", discriminator_sequence_filename
with open(discriminator_sequence_filename, "r") as f:
try:
params = json.load(f)
except Exception as e:
raise Exception(
"could not load {}".format(discriminator_sequence_filename))
else:
config = DiscriminatorParams()
config.ndim_input = image_width * image_height
config.ndim_output = 10
config.weight_init_std = 1
config.weight_initializer = "GlorotNormal"
config.use_weightnorm = False
config.nonlinearity = "softplus"
config.optimizer = "Adam"
config.learning_rate = 0.001
config.momentum = 0.5
config.gradient_clipping = 10
config.weight_decay = 0
config.use_feature_matching = False
config.use_minibatch_discrimination = False
discriminator = Sequential(weight_initializer=config.weight_initializer,
weight_init_std=config.weight_init_std)
discriminator.add(gaussian_noise(std=0.3))
discriminator.add(
Linear(config.ndim_input, 1000, use_weightnorm=config.use_weightnorm))