def init(self, params):
config = self.config
self.decoder = sequential.from_dict(params["decoder"])
self.chain_decoder.add_sequence(self.decoder)
self.chain_decoder.setup_optimizers(config.optimizer,
config.learning_rate,
config.momentum,
config.weight_decay,
config.gradient_clipping)
self.discriminator = sequential.from_dict(params["discriminator"])
self.chain_discriminator.add_sequence(self.discriminator)
self.chain_discriminator.setup_optimizers(config.optimizer,
config.learning_rate,
config.momentum,
config.weight_decay,
config.gradient_clipping)
self.generator = sequential.from_dict(params["generator"])
self.chain_generator.add_sequence(self.generator)
self.chain_generator.setup_optimizers(config.optimizer,
config.learning_rate,
config.momentum,
config.weight_decay,
config.gradient_clipping)
def __init__(self, params):
self.params = copy.deepcopy(params)
self.config = to_object(params["config"])
self.chain_discriminator = sequential.chain.Chain(
weight_initializer=self.config.weight_initializer,
weight_std=self.config.weight_std)
self.chain_generator = sequential.chain.Chain(
weight_initializer=self.config.weight_initializer,
weight_std=self.config.weight_std)
# add decoder
self.decoder = sequential.from_dict(self.params["decoder"])
self.chain_discriminator.add_sequence_with_name(
self.decoder, "decoder")
# add encoder
self.encoder = sequential.from_dict(self.params["encoder"])
self.chain_discriminator.add_sequence_with_name(
self.encoder, "encoder")
# add generator
self.generator = sequential.from_dict(self.params["generator"])
self.chain_generator.add_sequence_with_name(self.generator,
"generator")
# setup optimizer
self.chain_discriminator.setup_optimizers(self.config.optimizer,
self.config.learning_rate,
self.config.momentum)
self.chain_generator.setup_optimizers(self.config.optimizer,
self.config.learning_rate,
self.config.momentum)
self._gpu = False
def build_energy_model(self): params = self.params_energy_model self.energy_model = DeepEnergyModel() self.energy_model.add_feature_extractor(sequential.from_dict(params["feature_extractor"])) self.energy_model.add_experts(sequential.from_dict(params["experts"])) self.energy_model.add_b(sequential.from_dict(params["b"])) config = self.config_energy_model self.energy_model.setup_optimizers(config.optimizer, config.learning_rate, config.momentum, config.weight_decay, config.gradient_clipping)
def build_generator(self):
self.generator = sequential.chain.Chain()
self.generator.add_sequence(
sequential.from_dict(self.params_generator["model"]))
config = self.config_generator
self.generator.setup_optimizers(config.optimizer, config.learning_rate,
config.momentum)
def __init__(self, params): super(SDGM, self).__init__(params) params = self.params config = self.config self.p_a_yz = sequential.from_dict(params["p_a_yz"]) self.chain.add_sequence_with_name(self.p_a_yz, "p_a_yz") self.p_x_ayz = sequential.from_dict(params["p_x_ayz"]) self.chain.add_sequence_with_name(self.p_x_ayz, "p_x_ayz") self.q_a_x = sequential.from_dict(params["q_a_x"]) self.chain.add_sequence_with_name(self.q_a_x, "q_a_x") self.q_y_ax = sequential.from_dict(params["q_y_ax"]) self.chain.add_sequence_with_name(self.q_y_ax, "q_y_ax") self.q_z_axy = sequential.from_dict(params["q_z_axy"]) self.chain.add_sequence_with_name(self.q_z_axy, "q_z_axy") self.chain.setup_optimizers(config.optimizer, config.learning_rate, config.momentum, config.weight_decay, config.gradient_clipping)
def __init__(self, params): super(VAT, self).__init__() self.params = copy.deepcopy(params) self.config = to_object(params["config"]) config = self.config self.chain = sequential.chain.Chain() self.model = sequential.from_dict(params["model"]) self.chain.add_sequence(self.model) self.chain.setup_optimizers(config.optimizer, config.learning_rate, config.momentum, config.weight_decay, config.gradient_clipping) self._gpu = False
def build_discriminator(self):
config = self.config_discriminator
self.discriminator = sequential.chain.Chain(
weight_initializer=config.weight_initializer,
weight_std=config.weight_std)
self.discriminator.add_sequence(
sequential.from_dict(self.params_discriminator["model"]))
self.discriminator.setup_optimizers(config.optimizer,
config.learning_rate,
config.momentum)
def __init__(self, params):
super(SDGM, self).__init__(params)
params = self.params
config = self.config
self.p_a_yz = sequential.from_dict(params["p_a_yz"])
self.chain.add_sequence_with_name(self.p_a_yz, "p_a_yz")
self.p_x_ayz = sequential.from_dict(params["p_x_ayz"])
self.chain.add_sequence_with_name(self.p_x_ayz, "p_x_ayz")
self.q_a_x = sequential.from_dict(params["q_a_x"])
self.chain.add_sequence_with_name(self.q_a_x, "q_a_x")
self.q_y_ax = sequential.from_dict(params["q_y_ax"])
self.chain.add_sequence_with_name(self.q_y_ax, "q_y_ax")
self.q_z_axy = sequential.from_dict(params["q_z_axy"])
self.chain.add_sequence_with_name(self.q_z_axy, "q_z_axy")
self.chain.setup_optimizers(config.optimizer, config.learning_rate,
config.momentum, config.weight_decay,
config.gradient_clipping)
def __init__(self, params):
self.params = copy.deepcopy(params)
self.config = to_object(params["config"])
self.discriminator = sequential.chain.Chain(
self.config.weight_initializer, self.config.weight_std)
self.discriminator.add_sequence(sequential.from_dict(params["model"]))
self.discriminator.setup_optimizers(self.config.optimizer,
self.config.learning_rate,
self.config.momentum)
self._gpu = False
def __init__(self, params):
self.params = copy.deepcopy(params)
config = to_object(params["config"])
self.classifier = sequential.chain.Chain(
weight_initializer=config.weight_initializer,
weight_std=config.weight_std)
self.classifier.add_sequence(sequential.from_dict(
self.params["model"]))
self.classifier.setup_optimizers(config.optimizer,
config.learning_rate, config.momentum)
self.config = config
self._gpu = False
def init(self, params):
config = self.config
self.decoder = sequential.from_dict(params["decoder"])
self.chain_decoder.add_sequence_with_name(self.decoder, "decoder")
self.chain_decoder.setup_optimizers(config.optimizer,
config.learning_rate,
config.momentum,
config.weight_decay,
config.gradient_clipping)
self.discriminator_y = sequential.from_dict(params["discriminator_y"])
self.discriminator_z = sequential.from_dict(params["discriminator_z"])
self.chain_discriminator.add_sequence_with_name(
self.discriminator_y, "y")
self.chain_discriminator.add_sequence_with_name(
self.discriminator_z, "z")
self.chain_discriminator.setup_optimizers(config.optimizer,
config.learning_rate,
config.momentum,
config.weight_decay,
config.gradient_clipping)
self.generator_shared = sequential.from_dict(
params["generator_shared"])
self.generator_y = sequential.from_dict(params["generator_y"])
self.generator_z = sequential.from_dict(params["generator_z"])
self.chain_generator.add_sequence_with_name(self.generator_shared,
"shared")
self.chain_generator.add_sequence_with_name(self.generator_y, "y")
self.chain_generator.add_sequence_with_name(self.generator_z, "z")
self.chain_generator.setup_optimizers(config.optimizer,
config.learning_rate,
config.momentum,
config.weight_decay,
config.gradient_clipping)
self.chain_cluster_head = sequential.chain.Chain()
self.cluster_head = sequential.from_dict(params["cluster_head"])
self.chain_cluster_head.add_sequence_with_name(self.cluster_head,
"cluster_head")
self.chain_cluster_head.setup_optimizers(config.optimizer,
config.learning_rate,
config.momentum,
config.weight_decay,
config.gradient_clipping)
def build_generative_model(self): params = self.params_generative_model self.generative_model = DeepGenerativeModel() self.generative_model.add_sequence(sequential.from_dict(params["model"])) config = self.config_generative_model self.generative_model.setup_optimizers(config.optimizer, config.learning_rate, config.momentum, config.weight_decay, config.gradient_clipping)
