discriminator_params = params
# specify generator
generator_sequence_filename = args.model_dir + "/generator.json"
if os.path.isfile(generator_sequence_filename):
print "loading", generator_sequence_filename
with open(generator_sequence_filename, "r") as f:
try:
params = json.load(f)
except:
raise Exception(
"could not load {}".format(generator_sequence_filename))
else:
config = GeneratorParams()
config.ndim_input = ndim_z
config.distribution_output = "tanh"
config.use_weightnorm = False
config.weight_init_std = 0.001
config.weight_initializer = "Normal"
config.nonlinearity = "relu"
config.optimizer = "Adam"
config.learning_rate = 0.0001
config.momentum = 0.5
config.gradient_clipping = 10
config.weight_decay = 0
# model
# compute projection width
input_size = get_in_size_of_deconv_layers(image_width,
num_layers=4,
ndim_latent_code = 50
# 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.ndim_input = image_width * image_height
config.clamp_lower = -0.01
config.clamp_upper = 0.01
config.num_critic = 5
config.weight_init_std = 0.001
config.weight_initializer = "Normal"
config.use_weightnorm = False
config.nonlinearity = "leaky_relu"
config.optimizer = "rmsprop"
config.learning_rate = 0.0001
config.momentum = 0.5
config.gradient_clipping = 1
config.weight_decay = 0
config.use_feature_matching = False
config.use_minibatch_discrimination = False
separators=(',', ': '))
# specify generator
generator_sequence_filename = args.model_dir + "/generator.json"
if os.path.isfile(generator_sequence_filename):
print "loading", generator_sequence_filename
with open(generator_sequence_filename, "r") as f:
try:
generator_params = json.load(f)
except:
raise Exception(
"could not load {}".format(generator_sequence_filename))
else:
config = GeneratorParams()
config.ndim_input = 256
config.ndim_output = 2
config.num_mixture = args.num_mixture
config.distribution_output = "universal"
config.use_weightnorm = False
config.weight_std = 0.01
config.weight_initializer = "Normal"
config.nonlinearity = "relu"
config.optimizer = "adam"
config.learning_rate = 0.0001
config.momentum = 0.5
config.gradient_clipping = 1
config.weight_decay = 0
# generator
generator = Sequential()
separators=(',', ': '))
# specify generator
generator_sequence_filename = args.model_dir + "/generator.json"
if os.path.isfile(generator_sequence_filename):
print "loading", generator_sequence_filename
with open(generator_sequence_filename, "r") as f:
try:
generator_params = json.load(f)
except:
raise Exception(
"could not load {}".format(generator_sequence_filename))
else:
config = GeneratorParams()
config.ndim_input = ndim_latent_code
config.ndim_output = image_width * image_height
config.distribution_output = "tanh"
config.use_weightnorm = False
config.weight_std = 0.01
config.weight_initializer = "Normal"
config.nonlinearity = "relu"
config.optimizer = "adam"
config.learning_rate = 0.0001
config.momentum = 0.5
config.gradient_clipping = 1
config.weight_decay = 0
# generator
generator = Sequential()
generator.add(