def get_config(self): config = { 'units': self.units, 'learn_mode': self.learn_mode, 'test_mode': self.test_mode, 'use_boundary': self.use_boundary, 'use_bias': self.use_bias, 'sparse_target': self.sparse_target, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'chain_initializer': initializers.serialize(self.chain_initializer), 'boundary_initializer': initializers.serialize(self.boundary_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'activation': activations.serialize(self.activation), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'chain_regularizer': regularizers.serialize(self.chain_regularizer), 'boundary_regularizer': regularizers.serialize(self.boundary_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'chain_constraint': constraints.serialize(self.chain_constraint), 'boundary_constraint': constraints.serialize(self.boundary_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'input_dim': self.input_dim, 'unroll': self.unroll} base_config = super(CRF, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { "filters": self.filters, "kernel_size": self.kernel_size, "strides": self.strides, "padding": self.padding, "dilation_rate": self.dilation_rate, "kernel_initializer": initializers.serialize(self.kernel_initializer), "kernel_regularizer": regularizers.serialize(self.kernel_regularizer), "activity_regularizer": regularizers.serialize(self.activity_regularizer), "kernel_constraint": constraints.serialize(self.kernel_constraint), "demod": self.demod, } base_config = super(Conv2DMod, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'units': self.units, 'use_gating': self.use_gating, 'kernel_W_initializer': initializers.serialize(self.kernel_W_initializer), 'kernel_M_initializer': initializers.serialize(self.kernel_M_initializer), 'gate_initializer': initializers.serialize(self.gate_initializer), 'kernel_W_regularizer': regularizers.serialize(self.kernel_W_regularizer), 'kernel_M_regularizer': regularizers.serialize(self.kernel_M_regularizer), 'gate_regularizer': regularizers.serialize(self.gate_regularizer), 'kernel_W_constraint': constraints.serialize(self.kernel_W_constraint), 'kernel_M_constraint': constraints.serialize(self.kernel_M_constraint), 'gate_constraint': constraints.serialize(self.gate_constraint), 'epsilon': self.epsilon } base_config = super(NALU, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'norm_method': self.norm_method, 'filter_size': self.filter_size, 'data_format': self.data_format, 'activation': activations.serialize(self.activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint) } base_config = super(ImageNormalization3D, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): """ Gets class configuration for Keras serialization """ config = { "units": self.units, "attn_heads": self.attn_heads, "attn_heads_reduction": self.attn_heads_reduction, "in_dropout_rate": self.in_dropout_rate, "attn_dropout_rate": self.attn_dropout_rate, "activation": activations.serialize(self.activation), "use_bias": self.use_bias, "final_layer": self.final_layer, "saliency_map_support": self.saliency_map_support, "kernel_initializer": initializers.serialize(self.kernel_initializer), "kernel_regularizer": regularizers.serialize(self.kernel_regularizer), "kernel_constraint": constraints.serialize(self.kernel_constraint), "bias_initializer": initializers.serialize(self.bias_initializer), "bias_regularizer": regularizers.serialize(self.bias_regularizer), "bias_constraint": constraints.serialize(self.bias_constraint), "attn_kernel_initializer": initializers.serialize( self.attn_kernel_initializer ), "attn_kernel_regularizer": regularizers.serialize( self.attn_kernel_regularizer ), "attn_kernel_constraint": constraints.serialize( self.attn_kernel_constraint ), } base_config = super().get_config() return {**base_config, **config}
def get_config(self): config = { 'filters': self.filters, 'kernel_size': self.kernel_size, 'strides': self.strides, 'padding': self.padding, 'data_format': self.data_format, 'activation': activations.serialize(self.activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'spectral_normalization': self.spectral_normalization } base_config = super(Conv2DTranspose_Spectral, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'rank': self.rank, 'filters': self.filters, 'kernel_size': self.kernel_size, 'strides': self.strides, 'padding': self.padding, 'data_format': self.data_format, 'dilation_rate': self.dilation_rate, 'activation': activations.serialize(self.activation), 'use_bias': self.use_bias, 'normalize_weight': self.normalize_weight, 'kernel_initializer': sanitizedInitSer(self.kernel_initializer), 'bias_initializer': sanitizedInitSer(self.bias_initializer), 'gamma_diag_initializer': sanitizedInitSer(self.gamma_diag_initializer), 'gamma_off_initializer': sanitizedInitSer(self.gamma_off_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'gamma_diag_regularizer': regularizers.serialize(self.gamma_diag_regularizer), 'gamma_off_regularizer': regularizers.serialize(self.gamma_off_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'gamma_diag_constraint': constraints.serialize(self.gamma_diag_constraint), 'gamma_off_constraint': constraints.serialize(self.gamma_off_constraint), 'init_criterion': self.init_criterion, 'spectral_parametrization': self.spectral_parametrization, } base_config = super(ComplexConv, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'axis': self.axis, 'epsilon': self.epsilon, 'center': self.center, 'scale': self.scale, 'beta_initializer': initializers.serialize(self.beta_initializer), 'gamma_diag_initializer': initializers.serialize(self.gamma_diag_initializer), 'gamma_off_initializer': initializers.serialize(self.gamma_off_initializer), 'beta_regularizer': regularizers.serialize(self.beta_regularizer), 'gamma_diag_regularizer': regularizers.serialize(self.gamma_diag_regularizer), 'gamma_off_regularizer': regularizers.serialize(self.gamma_off_regularizer), 'beta_constraint': constraints.serialize(self.beta_constraint), 'gamma_diag_constraint': constraints.serialize(self.gamma_diag_constraint), 'gamma_off_constraint': constraints.serialize(self.gamma_off_constraint), } base_config = super(ComplexLayerNorm, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): """ Gets class configuration for Keras serialization. Used by keras model serialization. Returns: A dictionary that contains the config of the layer """ config = { "units": self.units, "use_bias": self.use_bias, "final_layer": self.final_layer, "activation": activations.serialize(self.activation), "kernel_initializer": initializers.serialize(self.kernel_initializer), "kernel_regularizer": regularizers.serialize(self.kernel_regularizer), "kernel_constraint": constraints.serialize(self.kernel_constraint), "bias_initializer": initializers.serialize(self.bias_initializer), "bias_regularizer": regularizers.serialize(self.bias_regularizer), "bias_constraint": constraints.serialize(self.bias_constraint), } base_config = super().get_config() return {**base_config, **config}
def get_config(self): config = {'filters': self.filters, 'kernel_size': self.kernel_size, 'strides': self.strides, 'padding': self.padding, 'data_format': self.data_format, 'dilation_rate': self.dilation_rate, 'activation': activations.serialize(self.activation), 'recurrent_activation': activations.serialize(self.recurrent_activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout} base_config = super(ConvGRU2D, self).get_config() del base_config['cell'] return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = super().get_config() config.update({ "blade_indices_kernel": self.blade_indices_kernel.numpy(), "blade_indices_bias": self.blade_indices_bias.numpy(), "activation": activations.serialize(self.activation), "use_bias": self.use_bias, "kernel_initializer": initializers.serialize(self.kernel_initializer), "bias_initializer": initializers.serialize(self.bias_initializer), "kernel_regularizer": regularizers.serialize(self.kernel_regularizer), "bias_regularizer": regularizers.serialize(self.bias_regularizer), "activity_regularizer": regularizers.serialize(self.activity_regularizer), "kernel_constraint": constraints.serialize(self.kernel_constraint), "bias_constraint": constraints.serialize(self.bias_constraint) }) return config
def get_config(self): config = { 'filters': self.filters, 'kernel_size': self.kernel_size, 'strides': self.strides, 'padding': self.padding, 'dilation_rate': self.dilation_rate, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'demod': self.demod } base_config = super(Conv2DMod, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'activation': activations.serialize(self.activation), 'recurrent_activation': activations.serialize(self.recurrent_activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint) } base_config = super(ExtendedRNNCell, self).get_config() config.update(base_config) return config
def get_config(self): if self.kernel_initializer in {'complex'}: ki = self.kernel_initializer else: ki = initializers.serialize(self.kernel_initializer) config = { 'units': self.units, 'activation': activations.serialize(self.activation), 'use_bias': self.use_bias, 'init_criterion': self.init_criterion, 'kernel_initializer': ki, 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'seed': self.seed, } base_config = super(ComplexDense, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'filters': self.filters, 'kernel_size': self.kernel_size, 'use_bias': self.use_bias, 'activation': activations.serialize(self.activation), 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint) } base_config = super().get_config() return {**base_config, **config}
def get_config(self): config = { 'units': self.units, 'x_imputation': self.x_imputation, 'input_decay': serialize_keras_object(self.input_decay), 'hidden_decay': serialize_keras_object(self.hidden_decay), 'use_decay_bias': self.use_decay_bias, 'feed_masking': self.feed_masking, 'masking_decay': serialize_keras_object(self.masking_decay), 'decay_initializer': initializers.get(self.decay_initializer), 'decay_regularizer': regularizers.get(self.decay_regularizer), 'decay_constraint': constraints.get(self.decay_constraint), 'activation': activations.serialize(self.activation), 'recurrent_activation': activations.serialize(self.recurrent_activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout, 'implementation': self.implementation, 'reset_after': self.reset_after } base_config = super().get_config() del base_config['cell'] return dict(list(base_config.items()) + list(config.items()))
def get_config(self): """ Part of keras layer interface, where the signature is converted into a dict :return: """ config = { 'activation': activations.serialize(self.activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint) } base_config = super().get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { "filters": self.filters, "kernel_size": self.kernel_size, "strides": self.strides, "padding": self.padding, "data_format": self.data_format, "dilation_rate": self.dilation_rate, "activation": activations.serialize(self.activation), "use_bias": self.use_bias, "kernel_initializer": initializers.serialize(self.kernel_initializer), "bias_initializer": initializers.serialize(self.bias_initializer), "kernel_regularizer": regularizers.serialize(self.kernel_regularizer), "bias_regularizer": regularizers.serialize(self.bias_regularizer), "activity_regularizer": regularizers.serialize(self.activity_regularizer), "kernel_constraint": constraints.serialize(self.kernel_constraint), "bias_constraint": constraints.serialize(self.bias_constraint), } base_config = super(Conv, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): """ Method for returning the configuration of the MMoE layer. :return: Config dictionary """ config = { 'units': self.units, 'num_experts': self.num_experts, 'num_tasks': self.num_tasks, 'use_expert_bias': self.use_expert_bias, 'use_gate_bias': self.use_gate_bias, 'expert_activation': activations.serialize(self.expert_activation), 'gate_activation': activations.serialize(self.gate_activation), 'expert_bias_initializer': initializers.serialize(self.expert_bias_initializer), 'gate_bias_initializer': initializers.serialize(self.gate_bias_initializer), 'expert_bias_regularizer': regularizers.serialize(self.expert_bias_regularizer), 'gate_bias_regularizer': regularizers.serialize(self.gate_bias_regularizer), 'expert_bias_constraint': constraints.serialize(self.expert_bias_constraint), 'gate_bias_constraint': constraints.serialize(self.gate_bias_constraint), 'expert_kernel_initializer': initializers.serialize(self.expert_kernel_initializer), 'gate_kernel_initializer': initializers.serialize(self.gate_kernel_initializer), 'expert_kernel_regularizer': regularizers.serialize(self.expert_kernel_regularizer), 'gate_kernel_regularizer': regularizers.serialize(self.gate_kernel_regularizer), 'expert_kernel_constraint': constraints.serialize(self.expert_kernel_constraint), 'gate_kernel_constraint': constraints.serialize(self.gate_kernel_constraint), 'activity_regularizer': regularizers.serialize(self.activity_regularizer) } base_config = super(MMoE, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'filters': self.filters, 'kernel_size': self.kernel_size, 'octave': self.octave, 'ratio_out': self.ratio_out, 'strides': self.strides, 'dilation_rate': self.dilation_rate, 'activation': activations.serialize(self.activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint) } base_config = super(OctaveConv1D, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): """ Part of keras layer interface, where the signature is converted into a dict Returns: configurational dictionary """ config = { "T": self.T, "n_hidden": self.n_hidden, "activation": activations.serialize(self.activation), "activation_lstm": activations.serialize(self.activation_lstm), "recurrent_activation": activations.serialize(self.recurrent_activation), "kernel_initializer": initializers.serialize(self.kernel_initializer), "recurrent_initializer": initializers.serialize(self.recurrent_initializer), "bias_initializer": initializers.serialize(self.bias_initializer), "use_bias": self.use_bias, "unit_forget_bias": self.unit_forget_bias, "kernel_regularizer": regularizers.serialize(self.kernel_regularizer), "recurrent_regularizer": regularizers.serialize(self.recurrent_regularizer), "bias_regularizer": regularizers.serialize(self.bias_regularizer), "kernel_constraint": constraints.serialize(self.kernel_constraint), "recurrent_constraint": constraints.serialize(self.recurrent_constraint), "bias_constraint": constraints.serialize(self.bias_constraint), } base_config = super().get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'units': self.units, 'activation': activations.serialize(self.activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout } # config.update(_config_for_enable_caching_device(self)) base_config = super(IndRNNCell, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'axis': self.axis, 'momentum': self.momentum, 'epsilon': self.epsilon, 'center': self.center, 'scale': self.scale, 'beta_initializer': initializers.serialize(self.beta_initializer), 'gamma_initializer': initializers.serialize(self.gamma_initializer), 'moving_mean_initializer': initializers.serialize(self.moving_mean_initializer), 'moving_variance_initializer': initializers.serialize(self.moving_variance_initializer), 'beta_regularizer': regularizers.serialize(self.beta_regularizer), 'gamma_regularizer': regularizers.serialize(self.gamma_regularizer), 'beta_constraint': constraints.serialize(self.beta_constraint), 'gamma_constraint': constraints.serialize(self.gamma_constraint) } base_config = super(BatchNormalizationF16, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): # TODO(WindQAQ): Get rid of this hacky way. config = super(tf.keras.layers.Dense, self).get_config() config.update({ "units": self.units, "sigma": self.sigma, "use_factorised": self.use_factorised, "activation": activations.serialize(self.activation), "use_bias": self.use_bias, "kernel_regularizer": regularizers.serialize(self.kernel_regularizer), "bias_regularizer": regularizers.serialize(self.bias_regularizer), "activity_regularizer": regularizers.serialize(self.activity_regularizer), "kernel_constraint": constraints.serialize(self.kernel_constraint), "bias_constraint": constraints.serialize(self.bias_constraint), }) return config
def get_config(self): config = { "units": self.units, "activation": activations.serialize(self.activation), "use_bias": self.use_bias, "kernel_quantizer": constraints.serialize(self.kernel_quantizer_internal), "bias_quantizer": constraints.serialize(self.bias_quantizer_internal), "kernel_initializer": initializers.serialize(self.kernel_initializer), "bias_initializer": initializers.serialize(self.bias_initializer), "kernel_regularizer": regularizers.serialize(self.kernel_regularizer), "bias_regularizer": regularizers.serialize(self.bias_regularizer), "activity_regularizer": regularizers.serialize(self.activity_regularizer), "kernel_constraint": constraints.serialize(self.kernel_constraint), "bias_constraint": constraints.serialize(self.bias_constraint), "kernel_range": self.kernel_range, "bias_range": self.bias_range } base_config = super(QDense, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'units': self.units, 'activation': activations.serialize(self.activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint) } base_config = super(MDense, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self) -> Dict: """ Part of keras layer interface, where the signature is converted into a dict Returns: configurational dictionary """ config = { "activation": activations.serialize(self.activation), "use_bias": self.use_bias, "kernel_initializer": initializers.serialize(self.kernel_initializer), "bias_initializer": initializers.serialize(self.bias_initializer), "kernel_regularizer": regularizers.serialize(self.kernel_regularizer), "bias_regularizer": regularizers.serialize(self.bias_regularizer), "activity_regularizer": regularizers.serialize(self.activity_regularizer), "kernel_constraint": constraints.serialize(self.kernel_constraint), "bias_constraint": constraints.serialize(self.bias_constraint), } base_config = super().get_config() return dict(list(base_config.items()) + list(config.items())) # noqa
def get_config(self): config = { 'units': self.units, 'attention_width': self.attention_width, 'attention_type': self.attention_type, 'return_attention': self.return_attention, 'history_only': self.history_only, 'use_additive_bias': self.use_additive_bias, 'use_attention_bias': self.use_attention_bias, 'kernel_initializer': regularizers.serialize(self.kernel_initializer), 'bias_initializer': regularizers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'attention_activation': activations.serialize(self.attention_activation), 'attention_regularizer_weight': self.attention_regularizer_weight, } base_config = super(SeqSelfAttention, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = super(FRN, self).get_config() config.update({ 'reg_epsilon': self.reg_epsilon, 'tau_regularizer': regularizers.serialize(self.tau_regularizer), 'beta_regularizer': regularizers.serialize(self.beta_regularizer), 'gamma_regularizer': regularizers.serialize(self.gamma_regularizer), }) return config
def get_config(self): config = { 'rank': self.rank, 'n_filters': self.n_filters, 'n_experts_per_filter': self.n_experts_per_filter, 'kernel_size': self.kernel_size, 'strides': self.strides, 'padding': self.padding, 'data_format': self.data_format, 'dilation_rate': self.dilation_rate, 'expert_activation': activations.serialize(self.expert_activation), 'gating_activation': activations.serialize(self.gating_activation), 'use_expert_bias': self.use_expert_bias, 'use_gating_bias': self.use_gating_bias, 'expert_kernel_initializer_scale': self.expert_kernel_initializer_scale, 'gating_kernel_initializer_scale': self.gating_kernel_initializer_scale, 'expert_bias_initializer': initializers.serialize(self.expert_bias_initializer), 'gating_bias_initializer': initializers.serialize(self.gating_bias_initializer), 'expert_kernel_regularizer': regularizers.serialize(self.expert_kernel_regularizer), 'gating_kernel_regularizer': regularizers.serialize(self.gating_kernel_regularizer), 'expert_bias_regularizer': regularizers.serialize(self.expert_bias_regularizer), 'gating_bias_regularizer': regularizers.serialize(self.gating_bias_regularizer), 'expert_kernel_constraint': constraints.serialize(self.expert_kernel_constraint), 'gating_kernel_constraint': constraints.serialize(self.gating_kernel_constraint), 'expert_bias_constraint': constraints.serialize(self.expert_bias_constraint), 'gating_bias_constraint': constraints.serialize(self.gating_bias_constraint), 'activity_regularizer': regularizers.serialize(self.activity_regularizer) } base_config = super(_ConvMoE, self).get_config() return dict(list(base_config.items()) + list(config.items()))