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 = { 'alpha_initializer': initializers.serialize(self.alpha_initializer), 'alpha_regularizer': regularizers.serialize(self.alpha_regularizer), 'alpha_constraint': constraints.serialize(self.alpha_constraint), 'beta_initializer': initializers.serialize(self.beta_initializer), 'beta_regularizer': regularizers.serialize(self.beta_regularizer), 'beta_constraint': constraints.serialize(self.beta_constraint), 'shared_axes': self.shared_axes } base_config = super(ParametricSoftplus, self).get_config() return dict(list(base_config.items()) + list(config.items()))
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 = { '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(InstanceNormalization, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = {'name': self.__class__.__name__, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'downsampling_factor': self.downsampling_factor} base_config = super().get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'units': self.units, 'bond_classes': self.bond_classes, '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(EdgeNetwork, self).get_config() return dict(list(base_config.items()) + list(config.items()))
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 = {'epsilon': self.epsilon, 'axis': self.axis, 'center': self.center, 'scale': self.scale, 'momentum': self.momentum, 'gamma_regularizer': initializers.serialize(self.gamma_regularizer), 'beta_regularizer': initializers.serialize(self.beta_regularizer), 'moving_mean_initializer': initializers.serialize(self.moving_mean_initializer), 'moving_variance_initializer': initializers.serialize(self.moving_variance_initializer), 'beta_constraint': constraints.serialize(self.beta_constraint), 'gamma_constraint': constraints.serialize(self.gamma_constraint), 'r_max_value': self.r_max_value, 'd_max_value': self.d_max_value, 't_delta': self.t_delta} base_config = super(BatchRenormalization, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = {'nb_complex': self.nb_complex, 'filter_delays':self.filter_delays, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint)} base_config = super(SpatioTemporalFilterComplex, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'units': self.units, # '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 = { '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), 'is_placeholder': False } base_config = super(Smooth, 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), '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), '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, 'max_timesteps': self.max_timesteps } base_config = super(JANet, self).get_config() del base_config['cell'] return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = {'kernel_initializer': initializers.serialize(self.kernel_initializer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'axis': self.axis} base_config = super(AutoPool, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'activation': self.activation, 'model_size': self.model_size, 'W1_regularizer': regularizers.serialize(self.W1_regularizer), 'W2_regularizer': regularizers.serialize(self.W2_regularizer), 'b1_regularizer': regularizers.serialize(self.b1_regularizer), 'b2_regularizer': regularizers.serialize(self.b2_regularizer), 'W1_constraint': constraints.serialize(self.W1_constraint), 'W2_constraint': constraints.serialize(self.W2_constraint), 'b1_constraint': constraints.serialize(self.b1_constraint), 'b2_constraint': constraints.serialize(self.b2_constraint), 'bias1': self.bias1, 'bias2': self.bias2 } base_config = super(RemappedCoAttentionWeight, 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), '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(BottleneckLSTM2D, self).get_config() del base_config['cell'] 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().get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'tau_initializer': initializers.serialize(self.tau_initializer), 'tau_regularizer': regularizers.serialize(self.tau_regularizer), 'tau_constraint': constraints.serialize(self.tau_constraint) } base_config = super(TLU, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'output_dim': self.output_dim, 'num_filters': self.num_filters, 'graph_conv_filters': self.graph_conv_filters, '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(GraphCNN, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { "output_shape": self.partial_output_shape, "equation": self.equation, "activation": activations.serialize(self.activation), "bias_axes": self.bias_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), } base_config = super(EinsumDense, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = {'kernel_initializer': initializers.serialize(self.kernel_initializer), 'activation': activations.serialize(self.activation), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), } base_config = super(O2Transform, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'units': self.units, 'interval_dim': self.interval_dim, 'weight_dim': self.weight_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), '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(BLSTM, self).get_config() del base_config['cell'] return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'input_dim': self.input_dim, 'output_dim': self.output_dim, 'data_format': self.data_format, '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), '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(TensorProd2D, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'units': self.units, 'initializer': initializers.serialize(self.initializer), 'regularizer': regularizers.serialize(self.regularizer), 'constraint': constraints.serialize(self.constraint) } base_config = super(Constant, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'a_initializer': initializers.serialize(self.a_initializer), 'a_regularizer': regularizers.serialize(self.a_regularizer), 'a_constraint': constraints.serialize(self.a_constraint), 'k_initializer': initializers.serialize(self.k_initializer), 'k_regularizer': regularizers.serialize(self.k_regularizer), 'k_constraint': constraints.serialize(self.k_constraint), 'n_initializer': initializers.serialize(self.n_initializer), 'n_regularizer': regularizers.serialize(self.n_regularizer), 'n_constraint': constraints.serialize(self.n_constraint), 'z_initializer': initializers.serialize(self.z_initializer), 'z_regularizer': regularizers.serialize(self.z_regularizer), 'z_constraint': constraints.serialize(self.z_constraint), 'shared_axes': self.shared_axes } base_config = super(Hill, 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 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 = { 'axis': self.axis, 'momentum': self.momentum, 'epsilon': self.epsilon, 'center': self.center, 'scale': self.scale, 'beta_initializer': initializers.serialize(self.beta_initializer), 'gamma_initializer': initializers.serialize(self.gamma_initializer), 'moving_mean_initializer': initializers.serialize(self.moving_mean_initializer), 'moving_variance_initializer': initializers.serialize(self.moving_variance_initializer), 'beta_regularizer': regularizers.serialize(self.beta_regularizer), 'gamma_regularizer': regularizers.serialize(self.gamma_regularizer), 'beta_constraint': constraints.serialize(self.beta_constraint), 'gamma_constraint': constraints.serialize(self.gamma_constraint) } base_config = super(BatchNormGAN, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'gamma_initializer': initializers.serialize(self.gamma_initializer), 'gamma_regularizer': regularizers.serialize(self.gamma_regularizer), 'gamma_constraint': constraints.serialize(self.gamma_constraint), 'epsilon': self.epsilon } base_config = super(WeightNorm_Conv, 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), '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_RNN, 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 get_config(self): config = {'output_dim': self.output_dim, 'window_size': self.window_size, 'init': self.init.get_config(), 'stride': self.strides[0], 'activation': activations.serialize(self.activation), 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activy_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'use_bias': self.use_bias, 'input_dim': self.input_dim, 'input_length': self.input_length} base_config = super(GCNN, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def test_serialization(): 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 = { 'filters': self.filters, 'kernel_size': self.kernel_size, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'activation': activations.serialize(self.activation), 'padding': self.padding, 'strides': self.strides, 'data_format': self.data_format, '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), 'use_bias': self.use_bias} base_config = super(CosineConvolution2D, 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().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()))
def get_config(self): config = { 'vv_theta': self.vv, 'axis': self.axis, 'momentum': self.momentum, 'epsilon': self.epsilon, 'center': self.center, 'scale': self.scale, 'beta_initializer': initializers.serialize(self.beta_initializer), 'gamma_initializer': initializers.serialize(self.gamma_initializer), 'moving_mean_initializer': initializers.serialize(self.moving_mean_initializer), 'moving_variance_initializer': initializers.serialize(self.moving_variance_initializer), 'beta_regularizer': regularizers.serialize(self.beta_regularizer), 'gamma_regularizer': regularizers.serialize(self.gamma_regularizer), 'beta_constraint': constraints.serialize(self.beta_constraint), 'gamma_constraint': constraints.serialize(self.gamma_constraint) } base_config = super(RProjFWH_BatchNormalization, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = super(DepthwiseConv2D, 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 = { 'alpha_pos_initializer': initializers.serialize(self.alpha_pos_initializer), 'alpha_neg_initializer': initializers.serialize(self.alpha_neg_initializer), 'beta_pos_initializer': initializers.serialize(self.beta_pos_initializer), 'beta_neg_initializer': initializers.serialize(self.beta_neg_initializer), 'rho_pos_initializer': initializers.serialize(self.rho_pos_initializer), 'rho_neg_initializer': initializers.serialize(self.rho_neg_initializer), 'alpha_pos_constraint': constraints.serialize(self.alpha_pos_constraint), 'alpha_neg_constraint': constraints.serialize(self.alpha_neg_constraint), 'beta_pos_constraint': constraints.serialize(self.beta_pos_constraint), 'beta_neg_constraint': constraints.serialize(self.beta_neg_constraint), 'rho_pos_constraint': constraints.serialize(self.rho_pos_constraint), 'rho_neg_constraint': constraints.serialize(self.rho_neg_constraint), 'alpha_pos_regularizer': regularizers.serialize(self.alpha_pos_regularizer), 'alpha_neg_regularizer': regularizers.serialize(self.alpha_neg_regularizer), 'beta_pos_regularizer': regularizers.serialize(self.beta_pos_regularizer), 'beta_neg_regularizer': regularizers.serialize(self.beta_neg_regularizer), 'rho_pos_regularizer': regularizers.serialize(self.rho_pos_regularizer), 'rho_neg_regularizer': regularizers.serialize(self.rho_neg_regularizer), } base_config = super(PowerPReLU, self).get_config() return dict(list(base_config.items()) + list(config.items()))