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 = {'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 = { '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 = { 'groups': self.groups, '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(GroupNormalization, 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 = { 'param_initializer': initializers.serialize(self.param_initializer), 'param_regularizer': regularizers.serialize(self.param_regularizer), 'param_constraint': constraints.serialize(self.param_constraint), 'shared_axes': self.shared_axes } base_config = super(ParameterBase, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def test_serialization(self): all_activations = ['max_norm', 'non_neg', 'unit_norm', 'min_max_norm'] for name in all_activations: fn = constraints.get(name) ref_fn = getattr(constraints, name)() assert fn.__class__ == ref_fn.__class__ config = constraints.serialize(fn) fn = constraints.deserialize(config) assert fn.__class__ == ref_fn.__class__
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, 'control_units': self.control_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(SCLSTM, self).get_config() del base_config['cell'] return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = super(ConditionalDepthwiseConv2D, self).get_config() config.pop('kernel_initializer') config.pop('kernel_regularizer') config.pop('kernel_constraint') config['depth_multiplier'] = 1 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 = { 'axis': self.axis, 'epsilon': self.epsilon, 'momentum': self.momentum, 'center': self.center, 'scale': self.scale, 'beta_initializer': initializers.serialize(self.beta_initializer), 'gamma_initializer': initializers.serialize(self.gamma_initializer), 'mean_weights_initializer': initializers.serialize(self.mean_weights_initializer), 'variance_weights_initializer': initializers.serialize(self.variance_weights_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), 'mean_weights_regularizer': regularizers.serialize(self.mean_weights_regularizer), 'variance_weights_regularizer': regularizers.serialize(self.variance_weights_regularizer), 'beta_constraint': constraints.serialize(self.beta_constraint), 'gamma_constraint': constraints.serialize(self.gamma_constraint), 'mean_weights_constraints': constraints.serialize(self.mean_weights_constraints), 'variance_weights_constraints': constraints.serialize(self.variance_weights_constraints), } base_config = super(SwitchNormalization, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): if self.arg_array: serial_low_bound = self.low_bound.tolist() serial_sup_bound = self.sup_bound.tolist() else: serial_low_bound, serial_sup_bound = self.low_bound, self.sup_bound config = { 'low_bound': serial_low_bound, 'sup_bound': serial_sup_bound, 'with_sum': self.with_sum, 'a_initializer': initializers.serialize(self.a_initializer), 'a_regularizer': regularizers.serialize(self.a_regularizer), 'a_constraint': constraints.serialize(self.a_constraint), 'b_initializer': initializers.serialize(self.b_initializer), 'b_regularizer': regularizers.serialize(self.b_regularizer), 'b_constraint': constraints.serialize(self.b_constraint) } base_config = super(Restrict, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'number_of_classes': self.number_of_classes, '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(ConditionalInstanceNormalization, 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), 'kernel_dropout': self.kernel_dropout, 'unit_dropout': self.unit_dropout, 'use_mc_dropout': self.use_mc_dropout } base_config = super(DropConnectDense, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = dict( n_group=self.n_group, 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)) return config
def get_config(self): config = { 'use_kernel': self.use_kernel, 'use_bias': self.use_bias, 'var_initializer': initializers.serialize(self.var_initializer), 'var_regularizer': regularizers.serialize(self.var_regularizer), 'var_constraint': constraints.serialize(self.var_constraint) } base_config = super(Ghost, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = \ { "rank": self.rank, "filters": self.filters, "depth": self.depth, "kernel_size": self.kernel_size, "strides": self.strides, "padding": self.padding, "data_format": self.data_format, "dilation_rate": self.dilation_rate, "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, '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), '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(CTRNN, self).get_config() del base_config['cell'] 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 = { '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 = { 'units': self.units, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), } base_config = super(AngularLinear, 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(ModConv2d_grouped, 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, '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 = { "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 = { '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, 'depthwise_initializer': initializers.serialize(self.depthwise_initializer), 'pointwise_initializer': initializers.serialize(self.pointwise_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'depthwise_regularizer': regularizers.serialize(self.depthwise_regularizer), 'pointwise_regularizer': regularizers.serialize(self.pointwise_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'depthwise_constraint': constraints.serialize(self.depthwise_constraint), 'pointwise_constraint': constraints.serialize(self.pointwise_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint) } base_config = super(SeparableConv2DKeras, 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), '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 = { '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 = { '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(_TimeDelayLayer, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'number_of_classes': self.number_of_classes, 'rank': 2, 'filters': self.filters, 'filters_emb': self.filters_emb, '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(FactorizedConv11, 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, "activity_regularizer": regularizers.serialize(self.activity_regularizer), "entropy_regularizer": regularizers.serialize(self.regularizers["entropy"], ), "kernel_regularizer": regularizers.serialize(self.regularizers["kernels"]), "bias_regularizer": regularizers.serialize(self.regularizers["biases"]), "kernel_constraint": constraints.serialize(self.constraints["kernels"]), "bias_constraint": constraints.serialize(self.constraints["biases"]), } base_config = super(ContextualDense, 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 = { '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 = { "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 = { '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()))