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), '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), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout} base_config = super(ConvLSTM2DCell, 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) } # Only add TensorFlow-specific parameters if they are set, so as to preserve # model compatibility with external Keras. if self.renorm: config['renorm'] = True config['renorm_clipping'] = self.renorm_clipping config['renorm_momentum'] = self.renorm_momentum if self.virtual_batch_size is not None: config['virtual_batch_size'] = self.virtual_batch_size # Note: adjustment is not serializable. if self.adjustment is not None: logging.warning('The `adjustment` function of this `BatchNormalization` ' 'layer cannot be serialized and has been omitted from ' 'the layer config. It will not be included when ' 're-creating the layer from the saved config.') base_config = super(BatchNormalizationBase, 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, '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), 'implementation': self.implementation } base_config = super(LocallyConnected2D, 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_initializer': initializers.serialize(self.gamma_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(LayerNormalization, 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(Dense, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'input_dim': self.input_dim, 'output_dim': self.output_dim, 'embeddings_initializer': initializers.serialize(self.embeddings_initializer), 'embeddings_regularizer': regularizers.serialize(self.embeddings_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'embeddings_constraint': constraints.serialize(self.embeddings_constraint), 'mask_zero': self.mask_zero, 'input_length': self.input_length } base_config = super(Embedding, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'alpha_initializer': initializers.serialize(self.alpha_initializer), 'alpha_regularizer': regularizers.serialize(self.alpha_regularizer), 'alpha_constraint': constraints.serialize(self.alpha_constraint), 'shared_axes': self.shared_axes } base_config = super(PReLU, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'units': self.units, '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(CuDNNGRU, 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, '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().get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'units': self.units, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), '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), '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(CuDNNLSTM, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'units': self.units * self.nRIM, '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), '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), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout, 'implementation': self.implementation } config.update(_config_for_enable_caching_device(self)) base_config = super(LSTM_cell_test, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'units': self.units, 'num_memory_slots': self.num_memory_slots, 'num_attention_heads': self.num_attention_heads, 'activation': wrap_activations_serialize(self.activation), 'recurrent_activation': wrap_activations_serialize(self.recurrent_activation), 'mlp_activation': wrap_activations_serialize(self.mlp_activation), 'forget_bias': self.forget_bias, 'input_bias': self.input_bias, 'sigma_bias': self.sigma_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'attention_initializer': initializers.serialize(self.attention_initializer), 'mlp_initializer': initializers.serialize(self.mlp_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'attention_regularizer': regularizers.serialize(self.attention_regularizer), 'mlp_regularizer': regularizers.serialize(self.mlp_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'attention_constraint': constraints.serialize(self.attention_constraint), 'mlp_constraint': constraints.serialize(self.mlp_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout } base_config = super(TmpHierRMCRNN, self).get_config() del base_config['cell'] return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'units': self.units, '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), '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(GRUCell, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { "num_heads": self._num_heads, "key_dim": self._key_dim, "value_dim": self._value_dim, "dropout": self._dropout, "use_bias": self._use_bias, "output_shape": self._output_shape, "attention_axes": self._attention_axes, "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), "query_shape": self._query_shape, "key_shape": self._key_shape, "value_shape": self._value_shape, } base_config = super(MultiHeadAttention, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'units': self.units, 'learning_rate': self.learning_rate, 'online': self.online, 'n_passes': self.n_passes, 'return_hidden': self.return_hidden, 'visible_activation': activations.serialize(self.visible_activation), 'hidden_activation': activations.serialize(self.hidden_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), 'optimizer': optimizers.serialize(self.optimizer) } base_config = super(OnlineBolzmannCell, 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, 'add_self_loops': self.add_self_loops, 'aggregation_method': self.aggregation_method, 'graph_regularization': self.graph_regularization, 'num_bases': self.num_bases, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'kernel_coef_initializer': initializers.serialize(self.kernel_coef_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'kernel_coef_regularizer': regularizers.serialize(self.kernel_coef_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'kernel_coef_constraint': constraints.serialize(self.kernel_coef_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint) } base_config = super(GraphConv, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'units': self.units, 'activation': 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) } base_config = super(FullyConnectv2, 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(DenseTied, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'units': self.units, 'tau': self.tau, '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), '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(SimpleCTRNN, self).get_config() del base_config['cell'] return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'activation': activations.serialize(self.activation), 'use_bias': self.use_bias, 'kernel_initializer': self.kernel_initializer, 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': self.kernel_regularizer, 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': self.kernel_constraint, 'bias_constraint': constraints.serialize(self.bias_constraint) } base_config = super(_MultiTimeDelayLayer, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'units': self.units, 'conv_units': self.conv_units, 'n_actions': self.n_actions, 'feature_units': self.feature_units, 'num_head': self.num_head, 'dropout': self.dropout, 'use_bias': self.use_bias, 'memory_size': self.memory_size, 'compression_rate': self.compression_rate, 'state_constraint': constraints.serialize(self.state_constraint), 'state_initializer': initializers.serialize(self.state_initializer), 'gate_initializer': initializers.serialize(self.gate_initializer), 'gate_regularizer': regularizers.serialize(self.gate_regularizer), 'gate_constraint': constraints.serialize(self.gate_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(OSAR, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = super(DepthwiseConv1D, self).get_config() config.pop('filters') config.pop('kernel_initializer') config.pop('kernel_regularizer') config.pop('kernel_constraint') config['depth_multiplier'] = self.depth_multiplier config['depthwise_initializer'] = initializers.serialize( self.depthwise_initializer) config['depthwise_regularizer'] = regularizers.serialize( self.depthwise_regularizer) config['depthwise_constraint'] = constraints.serialize( self.depthwise_constraint) return config
def get_config(self): config = { 'tied_layer': '', 'activation': activations.serialize(self.activation), 'use_bias': self.use_bias, 'bias_initializer': initializers.serialize(self.bias_initializer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'bias_constraint': constraints.serialize(self.bias_constraint), 'varName': self.varName, 'varShape': self.varShape } base_config = super(DenseTied, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'num_fields': self.num_fields, 'embedding_size': self.embedding_size, 'use_bias': self.use_bias, 'activation': self.activation, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer) } base_config = super(FieldWiseBiInteraction, 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, "depth": self.depth, "strides": self.strides, "padding": "same", "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(ResBasicBlockND, self).get_config() return {**base_config, **config}
def get_config(self): config = { 'units': self.units, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout, } base_config = super(KerasALIF, 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, "data_format": self.data_format, "dilation_rate": self.dilation_rate, "groups": self.groups, "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): 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) } # Only add TensorFlow-specific parameters if they are set, so as to preserve # model compatibility with external Keras. if self.renorm: config['renorm'] = True config['renorm_clipping'] = self.renorm_clipping config['renorm_momentum'] = self.renorm_momentum base_config = super(ShiftNormalization, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'units_vec': self.units_vec, 'modules': self.modules, 'tau_vec': self.tau_vec, '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 } base_config = super(CTRNNCell, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'out_channels': self.out_channels, 'kernel_size': self.kernel_size, 'strides': self.strides, 'padding': self.padding, 'use_linear_and_bias': self.use_linear_and_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint) } base_config = super(QuadraticConv2D, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'units': self.units, 'activation': 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), 'use_bn': self.use_bn, 'keep_prob': self.keep_prob } base_config = super(BiInteraction, 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), '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), } base_config = super(SimpleRNNCell, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): base_config = super(MultiHeadAttention, self).get_config() base_config.update({ 'output_dim': self.output_dim, 'num_heads': self.num_heads, 'negative_infinity': self.negative_infinity, 'padding_value': self.padding_value, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), }) return base_config
def get_config(self): config = { 'sub_units': self.sub_units, 'sub_lstms': self.sub_lstms, 'sub_activation': activations.serialize(self.sub_activation), 'cake_activation': activations.serialize(self.cake_activation), 'sub_use_bias': self.sub_use_bias, 'cake_use_bias': self.cake_use_bias, 'sub_kernel_initializer': initializers.serialize(self.sub_kernel_initializer), 'cake_kernel_initializer': initializers.serialize(self.cake_kernel_initializer), 'sub_recurrent_initializer': initializers.serialize(self.sub_recurrent_initializer), 'cake_recurrent_initializer': initializers.serialize(self.cake_recurrent_initializer), 'sub_bias_initializer': initializers.serialize(self.sub_bias_initializer), 'cake_bias_initializer': initializers.serialize(self.cake_bias_initializer), 'sub_unit_forget_bias': self.sub_unit_forget_bias, 'cake_unit_forget_bias': self.cake_unit_forget_bias, 'sub_kernel_regularizer': regularizers.serialize(self.sub_kernel_regularizer), 'cake_kernel_regularizer': regularizers.serialize(self.cake_kernel_regularizer), 'sub_recurrent_regularizer': regularizers.serialize(self.sub_recurrent_regularizer), 'cake_recurrent_regularizer': regularizers.serialize(self.cake_recurrent_regularizer), 'sub_bias_regularizer': regularizers.serialize(self.sub_bias_regularizer), 'cake_bias_regularizer': regularizers.serialize(self.cake_bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'sub_kernel_constraint': constraints.serialize(self.sub_kernel_constraint), 'cake_kernel_constraint': constraints.serialize(self.cake_kernel_constraint), 'sub_recurrent_constraint': constraints.serialize(self.sub_recurrent_constraint), 'cake_recurrent_constraint': constraints.serialize(self.cake_recurrent_constraint), 'sub_bias_constraint': constraints.serialize(self.sub_bias_constraint), 'cake_bias_constraint': constraints.serialize(self.cake_bias_constraint), 'sub_dropout': self.sub_dropout, 'cake_dropout': self.cake_dropout, 'sub_recurrent_dropout': self.sub_recurrent_dropout, 'cake_recurrent_dropout': self.cake_recurrent_dropout, 'implementation': self.implementation } base_config = super(JujubeCake, self).get_config() del base_config['cell'] return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'units': self.units, 'projection_units': self.projection_units, 'use_feedback': self.use_feedback, 'use_recurrent': self.use_recurrent, 'activation': activations.serialize(self.activation), 'projection_activation': activations.serialize(self.projection_activation), 'use_bias': self.use_bias, 'use_projection_bias': self.use_projection_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'projection_initializer': initializers.serialize(self.projection_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'recurrent_initializer': initializers.serialize(self.feedback_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'bias_initializer': initializers.serialize(self.projection_bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'projection_regularizer': regularizers.serialize(self.projection_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'feedback_regularizer': regularizers.serialize(self.feedback_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'projection_bias_regularizer': regularizers.serialize(self.projection_bias_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'projection_constraint': constraints.serialize(self.projection_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'feedback_constraint': constraints.serialize(self.feedback_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'projection_bias_constraint': constraints.serialize(self.projection_bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout } base_config = super(Cell, self).get_config() return dict(list(base_config.items()) + list(config.items()))