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,
32,
ksize=4,
stride=2,
pad=1,
use_weightnorm=config.use_weightnorm))
discriminator.add(BatchNormalization(32))
discriminator.add(Activation(config.nonlinearity))
discriminator.add(
Convolution2D(32,
64,
ksize=4,
stride=2,
pad=1,
use_weightnorm=config.use_weightnorm))
discriminator.add(BatchNormalization(64))
discriminator.add(Activation(config.nonlinearity))
discriminator.add(
Convolution2D(64,
128,
ksize=4,
stride=2,
config.use_weightnorm = False
config.weight_init_std = 0.1
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
# generator
generator = Sequential(weight_initializer=config.weight_initializer,
weight_init_std=config.weight_init_std)
generator.add(
Linear(config.ndim_input, 500, use_weightnorm=config.use_weightnorm))
generator.add(BatchNormalization(500))
generator.add(Activation(config.nonlinearity))
generator.add(Linear(None, 500, use_weightnorm=config.use_weightnorm))
generator.add(BatchNormalization(500))
generator.add(Activation(config.nonlinearity))
generator.add(
Linear(None, config.ndim_output, use_weightnorm=config.use_weightnorm))
if config.distribution_output == "sigmoid":
generator.add(Activation("sigmoid"))
if config.distribution_output == "tanh":
generator.add(Activation("tanh"))
params = {
"config": config.to_dict(),
"model": generator.to_dict(),
}
config.nonlinearity = "elu"
config.optimizer = "Adam"
config.learning_rate = 0.0003
config.momentum = 0.9
config.gradient_clipping = 10
config.weight_decay = 0
config.use_weightnorm = False
config.num_mc_samples = 1
# p(x|y,z) - x ~ Bernoulli
p_x_ayz = Sequential(weight_initializer=config.weight_initializer,
weight_init_std=config.weight_init_std)
p_x_ayz.add(
Merge(num_inputs=3, out_size=500,
use_weightnorm=config.use_weightnorm))
p_x_ayz.add(BatchNormalization(500))
p_x_ayz.add(Activation(config.nonlinearity))
p_x_ayz.add(Linear(None, 500, use_weightnorm=config.use_weightnorm))
p_x_ayz.add(BatchNormalization(500))
p_x_ayz.add(Activation(config.nonlinearity))
p_x_ayz.add(Linear(None, 500, use_weightnorm=config.use_weightnorm))
p_x_ayz.add(BatchNormalization(500))
p_x_ayz.add(Activation(config.nonlinearity))
p_x_ayz.add(
Linear(None, config.ndim_x, use_weightnorm=config.use_weightnorm))
# p(a|x,y,z) - a ~ Gaussian
p_a_yz = Sequential(weight_initializer=config.weight_initializer,
weight_init_std=config.weight_init_std)
p_a_yz.add(
Merge(num_inputs=2, out_size=500,
raise Exception("could not load {}".format(sequence_filename))
else:
config = Params()
config.num_classes = 10
config.weight_std = 0.1
config.weight_initializer = "Normal"
config.nonlinearity = "relu"
config.optimizer = "adam"
config.learning_rate = 0.0001
config.momentum = 0.9
config.gradient_clipping = 1
config.weight_decay = 0
model = Sequential()
model.add(Convolution2D(1, 32, ksize=4, stride=2, pad=1))
model.add(BatchNormalization(32))
model.add(Activation(config.nonlinearity))
model.add(Convolution2D(32, 64, ksize=4, stride=2, pad=1))
model.add(BatchNormalization(64))
model.add(Activation(config.nonlinearity))
model.add(Convolution2D(64, 128, ksize=3, stride=2, pad=1))
model.add(BatchNormalization(128))
model.add(Activation(config.nonlinearity))
model.add(Linear(None, config.num_classes))
params = {
"config": config.to_dict(),
"model": model.to_dict(),
}
with open(sequence_filename, "w") as f:
else:
config = Params()
config.num_classes = 10
config.weight_std = 0.1
config.weight_initializer = "Normal"
config.nonlinearity = "relu"
config.optimizer = "adam"
config.learning_rate = 0.0001
config.momentum = 0.9
config.gradient_clipping = 1
config.weight_decay = 0
model = Sequential()
model.add(Linear(None, 500))
model.add(Activation(config.nonlinearity))
model.add(BatchNormalization(500))
model.add(Linear(None, 500))
model.add(Activation(config.nonlinearity))
model.add(BatchNormalization(500))
model.add(Linear(None, config.num_classes))
params = {
"config": config.to_dict(),
"model": model.to_dict(),
}
with open(sequence_filename, "w") as f:
json.dump(params, f, indent=4, sort_keys=True, separators=(',', ': '))
model = Discriminator(params)
model.load(args.model_dir)
config.ndim_y = 10
config.weight_init_std = 0.01
config.weight_initializer = "Normal"
config.nonlinearity = "relu"
config.optimizer = "Adam"
config.learning_rate = 0.0002
config.momentum = 0.9
config.gradient_clipping = 10
config.weight_decay = 0
config.lambda_ = 1
config.Ip = 1
model = Sequential(weight_initializer=config.weight_initializer, weight_init_std=config.weight_init_std)
model.add(Linear(None, 1200))
model.add(Activation(config.nonlinearity))
model.add(BatchNormalization(1200))
model.add(Linear(None, 600))
model.add(Activation(config.nonlinearity))
model.add(BatchNormalization(600))
model.add(Linear(None, config.ndim_y))
params = {
"config": config.to_dict(),
"model": model.to_dict(),
}
with open(model_filename, "w") as f:
json.dump(params, f, indent=4, sort_keys=True, separators=(',', ': '))
vat = VAT(params)
vat.load(args.model_dir)
config.ndim_h = ndim_h
config.weight_std = 0.01
config.weight_initializer = "Normal"
config.nonlinearity_d = "elu"
config.nonlinearity_g = "elu"
config.optimizer = "adam"
config.learning_rate = 0.0001
config.momentum = 0.5
config.gradient_clipping = 1
config.weight_decay = 0
# Discriminator
encoder = Sequential()
encoder.add(gaussian_noise(std=0.3))
encoder.add(Convolution2D(3, 32, ksize=4, stride=2, pad=1))
encoder.add(BatchNormalization(32))
encoder.add(Activation(config.nonlinearity_d))
encoder.add(Convolution2D(32, 64, ksize=4, stride=2, pad=1))
encoder.add(BatchNormalization(64))
encoder.add(Activation(config.nonlinearity_d))
encoder.add(Convolution2D(64, 128, ksize=4, stride=2, pad=1))
encoder.add(BatchNormalization(128))
encoder.add(Activation(config.nonlinearity_d))
encoder.add(Convolution2D(128, 256, ksize=4, stride=2, pad=1))
encoder.add(BatchNormalization(256))
encoder.add(Activation(config.nonlinearity_d))
encoder.add(Linear(None, ndim_h))
projection_size = 6
# Decoder
config = EnergyModelParams() config.num_experts = 512 config.weight_init_std = 0.05 config.weight_initializer = "Normal" config.use_weightnorm = False config.nonlinearity = "elu" config.optimizer = "Adam" config.learning_rate = 0.0002 config.momentum = 0.5 config.gradient_clipping = 10 config.weight_decay = 0 # feature extractor feature_extractor = Sequential(weight_initializer=config.weight_initializer, weight_init_std=config.weight_init_std) feature_extractor.add(Convolution2D(3, 32, ksize=4, stride=2, pad=1, use_weightnorm=config.use_weightnorm)) feature_extractor.add(BatchNormalization(32)) feature_extractor.add(Activation(config.nonlinearity)) feature_extractor.add(dropout()) feature_extractor.add(Convolution2D(32, 64, ksize=4, stride=2, pad=1, use_weightnorm=config.use_weightnorm)) feature_extractor.add(BatchNormalization(64)) feature_extractor.add(Activation(config.nonlinearity)) feature_extractor.add(dropout()) feature_extractor.add(Convolution2D(64, 192, ksize=4, stride=2, pad=1, use_weightnorm=config.use_weightnorm)) feature_extractor.add(BatchNormalization(192)) feature_extractor.add(Activation(config.nonlinearity)) feature_extractor.add(dropout()) feature_extractor.add(Convolution2D(192, 256, ksize=4, stride=2, pad=1, use_weightnorm=config.use_weightnorm)) feature_extractor.add(reshape_1d()) feature_extractor.add(MinibatchDiscrimination(None, num_kernels=50, ndim_kernel=5, train_weights=True)) feature_extractor.add(tanh())