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)