def get_config(self): config = {'units': self.units, 'projection_units': self.projection_units, 'activation': activations.serialize(self.activation), 'recurrent_activation': activations.serialize(self.recurrent_activation), 'projection_activation': activations.serialize(self.projection_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), 'projection_initializer': initializers.serialize(self.projection_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), 'projection_regularizer': regularizers.serialize(self.projection_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), 'projection_constraint': constraints.serialize(self.projection_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout, 'implementation': self.implementation} base_config = super(NASRNN, self).get_config() del base_config['cell'] 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), 'input_dim': self.input_dim, 'learnedKernel': self.tied_to.get_weights()[0], 'input_length': self.input_length} config2 = {'layer_inner': {'bias': np.asarray(self.tied_to.get_weights()[1]), 'weights': np.asarray(self.tied_to.get_weights()[0]), 'class_name': self.tied_to.__class__.__name__, 'config': self.tied_to.get_config()}} base_config = super(Convolution1D_tied, self).get_config() return dict(list(base_config.items()) + list(config.items()) + list(config2.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): base_config = super(LayerNormalization, self).get_config() base_config.update({"center": self.center, "scale": self.scale, "epsilon": self.epsilon, "conditional": self.conditional, "condition_hidden_units": self.condition_hidden_units, "condition_hidden_activation": activations.serialize(self.condition_hidden_activation), "condition_hidden_initializer": initializers.serialize(self.condition_hidden_initializer)}) return base_config
def get_config(self): config = { 'units': self.units, 'activation': activations.serialize(self.activation), 'recurrent_activation': activations.serialize(self.recurrent_activation), 'beta': self.beta, '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), 'use_chrono_initialization': self.use_chrono_initialization, '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, 'max_timesteps': self.max_timesteps } base_config = super(JANetCell, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = super().get_config() return dict( config, activation=activations.serialize(self.activation), add_biases=self.add_biases, projection_regularizer=regularizers.serialize( self.projection_regularizer), projection_dropout=self.projection_dropout, scaled_attention=self.scaled_attention)
def get_config(self): config = super().get_config() config.update({ 'mid_features': self.mid_features, 'out_features': self.out_features, 'stride': self.stride, 'activation': activations.serialize(self.activation) }) return config
def get_config(self): config = { 'output_dim': self.output_dim, 'activation': activations.serialize(self.activation), 'use_bias': self.use_bias, 'l2_normalize': self.l2_normalize, 'output_raw_logits': self.output_raw_logits, } base_config = super(HadamardClassifier, 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), 'recurrent_activation': activations.serialize(self.recurrent_activation), 'features_initializer': initializers.serialize(self.features_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'average_initializer': initializers.serialize(self.average_initializer), 'initial_attention_initializer': initializers.serialize(self.initial_attention_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'features_regularizer': regularizers.serialize(self.features_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'average_regularizer': regularizers.serialize(self.average_regularizer), 'initial_attention_regularizer': regularizers.serialize(self.initial_attention_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'features_constraint': constraints.serialize(self.features_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'average_constraint': constraints.serialize(self.average_constraint), 'initial_attention_constraint': constraints.serialize(self.initial_attention_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), # 'dropout': self.dropout, # 'recurrent_dropout': self.recurrent_dropout } base_config = super(RWA, 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), '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, 'clock_periods' : self.clock_periods} base_config = super(CWRNNCell, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'conditional': self.conditional, 'hidden_units': self.hidden_units, 'hidden_activation': activations.serialize(self.hidden_activation), 'hidden_initializer': initializers.serialize(self.hidden_initializer), } 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), "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), "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, } config.update(rnn_utils.config_for_enable_caching_device(self.cell)) base_config = super().get_config() del base_config["cell"] return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'units': self.units, 'groups': self.groups, 'activation': activations.serialize(self.activation), 'kernel_initializer': initializers.serialize(self.kernel_initializer), } base_config = super(GroupDense, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'units': self.units, 'weight_dim': self.weight_dim, 'interval_dim': self.interval_dim, '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 } base_config = super(BLSTMCell, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'inner_dim': self.inner_dim, 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'use_bias': self.use_bias, 'activation': activations.serialize(self.activation) } base_config = super(Switch, 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), } base_config = super(FeedForward, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'units': self.units, 'n_experts': self.n_experts, '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(DenseMoE, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = super().get_config() config.update({ 'filters': self.filters, 'sizes': self.sizes, 'activation': activations.serialize(self.activation), 'standardized': self.standardized }) return config
def get_config(self): config = { 'pooling': "s2s", 'output_dim': self.output_dim, 'step': self.step, 'activation_lstm': activations.serialize(self.activation_lstm), 'activation_recurrent': activations.serialize(self.activation_recurrent), 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer) } base_config = super(Set2SetV, self).get_config() return {**base_config, **config}
def get_config(self): config = { '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), } return dict(config)
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), '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), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout} base_config = super(GraphConvLSTM, 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), '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) } base_config = super(BottleneckLSTM2DCell, 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), '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 } config.update(rnn_utils.config_for_enable_caching_device(self.cell)) base_config = super().get_config() del base_config['cell'] return dict(list(base_config.items()) + list(config.items()))
def get_config(self): linear_config = generic_utils.serialize_keras_object(self.linear_model) dnn_config = generic_utils.serialize_keras_object(self.dnn_model) config = { 'linear_model': linear_config, 'dnn_model': dnn_config, 'activation': activations.serialize(self.activation), } base_config = base_layer.Layer.get_config(self) return dict(list(base_config.items()) + list(config.items()))
def get_config(self): if self.kernel_initializer in {'quaternion'}: ki = self.kernel_initializer else: ki = initializers.serialize(self.kernel_initializer) 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, 'kernel_initializer': ki, 'bias_initializer': initializers.serialize(self.bias_initializer), 'gamma_diag_initializer': initializers.serialize(self.gamma_diag_initializer), 'gamma_off_initializer': initializers.serialize(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, } base_config = super(QuaternionConv, 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, 'preslice': self.preslice, '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), 'attention_function': self.attention_function, 'mu1_regularizer': regularizers.serialize(self.mu1_regularizer), 'sig1_regularizer': regularizers.serialize(self.sig1_regularizer), 'mu2_regularizer': regularizers.serialize(self.mu2_regularizer), 'sig2_regularizer': regularizers.serialize(self.sig2_regularizer), 'mu1_constraint': constraints.serialize(self.mu1_constraint), 'sig1_constraint': constraints.serialize(self.sig1_constraint), 'mu2_constraint': constraints.serialize(self.mu2_constraint), 'sig2_constraint': constraints.serialize(self.sig2_constraint) } base_config = super(Target2D, 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 = { '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), '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), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout, 'implementation': self.implementation } base_config = super(LSTMPEEP, self).get_config() del base_config['cell'] return dict(list(base_config.items()) + list(config.items()))
def test_serialization(): all_activations = ['softmax', 'relu', 'elu', 'tanh', 'sigmoid', 'hard_sigmoid', 'linear', 'softplus', 'softsign', 'selu'] for name in all_activations: fn = activations.get(name) ref_fn = getattr(activations, name) assert fn == ref_fn config = activations.serialize(fn) fn = activations.deserialize(config) assert fn == ref_fn
def get_config(self): config = { 'tt_input_shape': self.tt_input_shape, 'tt_output_shape': self.tt_output_shape, 'tt_ranks': self.tt_ranks, '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(TT_GRU, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'hidden': self.hidden, 'activation': activations.serialize(self.activation), 'init': initializers.serialize(self.init), 'W_regularizer': regularizers.serialize(self.W_regularizer), 'W0_regularizer': regularizers.serialize(self.W0_regularizer), 'W_constraint': constraints.serialize(self.W_constraint), 'W0_constraint': constraints.serialize(self.W0_constraint) } base_config = super(Attention, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'filters': self.filters, 'data_format': self.data_format, 'activation': activations.serialize(self.activation), 'use_bias': self.use_bias, #'bias_initializer': initializers.serialize(self.bias_initializer), #'bias_regularizer': regularizers.serialize(self.bias_regularizer), #'bias_constraint': constraints.serialize(self.bias_constraint), } return config
def get_config(self): config = {'init': initializers.serialize(self.init), 'activation': activations.serialize(self.activation), 'W_regularizer': regularizers.serialize(self.W_regularizer), 'b_regularizer': regularizers.serialize(self.b_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'W_constraint': constraints.serialize(self.W_constraint), 'b_constraint': constraints.serialize(self.b_constraint), 'bias': self.bias, 'input_dim': self.input_dim} base_config = super(Highway, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = {'output_dim': self.output_dim, 'W_initializer':initializers.serialize(self.W_initializer), 'b_initializer':initializers.serialize(self.W_initializer), 'activation': activations.serialize(self.activation), 'W_regularizer': self.W_regularizer.get_config() if self.W_regularizer else None, 'b_regularizer': self.b_regularizer.get_config() if self.b_regularizer else None, 'activity_regularizer': self.activity_regularizer.get_config() if self.activity_regularizer else None, 'W_constraint': self.W_constraint.get_config() if self.W_constraint else None, 'b_constraint': self.b_constraint.get_config() if self.b_constraint else None, 'input_dim': self.input_dim} base_config = super(SparseFullyConnectedLayer, 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) } base_config = super(LocallyConnected2D, self).get_config() return dict(list(base_config.items()) + list(config.items()))