我们从Python开源项目中，提取了以下38个代码示例，用于说明如何使用keras.initializers.serialize()。
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 get_config(self): config = {'units': self.units, 'window_size': self.window_size, 'stride': self.strides[0], 'return_sequences': self.return_sequences, 'go_backwards': self.go_backwards, 'stateful': self.stateful, 'unroll': self.unroll, 'use_bias': self.use_bias, 'dropout': self.dropout, 'activation': activations.serialize(self.activation), 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'input_dim': self.input_dim, 'input_length': self.input_length} base_config = super(QRNN, 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} base_config = super(MultiplicativeLSTM, 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, '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 = {'filters_simple': self.filters_simple, 'filters_complex': self.filters_complex, 'kernel_size': self.kernel_size, 'data_format': self.data_format, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'activation': self.activation.__name__, 'dilation_rate': self.dilation_rate, 'padding': self.padding, 'strides': self.strides, 'kernel_regularizer': self.kernel_regularizer.get_config() if self.kernel_regularizer else None, 'bias_regularizer': self.bias_regularizer.get_config() if self.bias_regularizer else None, 'activity_regularizer': self.activity_regularizer.get_config() if self.activity_regularizer else None, 'kernel_constraint': self.kernel_constraint.get_config() if self.kernel_constraint else None, 'bias_constraint': self.bias_constraint.get_config() if self.bias_constraint else None, 'use_bias': self.use_bias} base_config = super(Convolution2DEnergy_Scatter, self).get_config() return dict(list(base_config.items()) + list(config.items())) # separate biases per channel
def get_config(self): config = {'filters_simple': self.filters_simple, 'filters_complex': self.filters_complex, 'kernel_size': self.kernel_size, 'data_format': self.data_format, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'activation': self.activation.__name__, 'dilation_rate': self.dilation_rate, 'padding': self.padding, 'strides': self.strides, 'kernel_regularizer': self.kernel_regularizer.get_config() if self.kernel_regularizer else None, 'bias_regularizer': self.bias_regularizer.get_config() if self.bias_regularizer else None, 'activity_regularizer': self.activity_regularizer.get_config() if self.activity_regularizer else None, 'kernel_constraint': self.W_constraint.get_config() if self.W_constraint else None, 'bias_constraint': self.bias_constraint.get_config() if self.bias_constraint else None, 'use_bias': self.bias} base_config = super(Convolution2DEnergy_Scatter2, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'rank': self.rank, 'kernel_size': self.kernel_size, 'padding': self.padding, 'data_format': self.data_format, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'bias_initializer': initializers.serialize(self.kernel_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(_ConvGDN, self).get_config() return dict(list(base_config.items()) + list(config.items()))
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()))
def get_config(self): config = { 'filters': self.filters, 'sum_axes': self.sum_axes, 'filter_axes': self.filter_axes, 'activation': activations.serialize(self.activation), 'kernel_activation': activations.serialize(self.kernel_activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'bias_initializer': initializers.serialize(self.kernel_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(FilterDims, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'filters_simple': self.filters_simple, 'filters_complex': self.filters_complex, 'sum_axes': self.sum_axes, 'filter_axes': self.filter_axes, 'activation': activations.serialize(self.activation), 'kernel_activation': activations.serialize(self.kernel_activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'bias_initializer': initializers.serialize(self.kernel_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(FilterDimsV1, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = {'units': self.units, 'learn_mode': self.learn_mode, 'test_mode': self.test_mode, 'use_boundary': self.use_boundary, 'use_bias': self.use_bias, 'sparse_target': self.sparse_target, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'chain_initializer': initializers.serialize(self.chain_initializer), 'boundary_initializer': initializers.serialize(self.boundary_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'activation': activations.serialize(self.activation), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'chain_regularizer': regularizers.serialize(self.chain_regularizer), 'boundary_regularizer': regularizers.serialize(self.boundary_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'chain_constraint': constraints.serialize(self.chain_constraint), 'boundary_constraint': constraints.serialize(self.boundary_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'input_dim': self.input_dim, 'unroll': self.unroll} base_config = super(CRF, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { '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): 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} base_config = super(PhasedLSTM, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = {'epsilon': self.epsilon, 'axis': self.axis, 'gamma_init': initializers.serialize(self.gamma_init), 'beta_init': initializers.serialize(self.beta_init), 'gamma_regularizer': regularizers.serialize(self.gamma_regularizer), 'beta_regularizer': regularizers.serialize(self.gamma_regularizer)} base_config = super(LayerNormalization, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'ratio': self.ratio, 'data_format': self.data_format, '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), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint) } base_config = super(SE, 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 = { 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'bias_initializer': initializers.serialize(self.bias_initializer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'bias_constraint': constraints.serialize(self.bias_constraint), 'context_initializer': initializers.serialize(self.context_initializer), 'context_regularizer': regularizers.serialize(self.context_regularizer), 'context_constraint': constraints.serialize(self.context_constraint) } base_config = super(AttentionLayer, 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 = {'filters_simple': self.filters_simple, 'filters_complex': self.filters_complex, 'filters_temporal': self.filters_temporal, 'spatial_kernel_size': self.spatial_kernel_size, 'temporal_frequencies': self.temporal_frequencies, 'temporal_frequencies_initial_max': self.temporal_frequencies_initial_max, 'temporal_frequencies_scaling': self.temporal_frequencies_scaling, '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, 'spatial_kernel_initializer': initializers.serialize(self.spatial_kernel_initializer), 'temporal_kernel_initializer': initializers.serialize(self.temporal_kernel_initializer), 'temporal_frequencies_initializer': initializers.serialize(self.temporal_frequencies_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'spatial_kernel_regularizer': regularizers.serialize(self.spatial_kernel_regularizer), 'temporal_kernel_regularizer': regularizers.serialize(self.temporal_kernel_regularizer), 'temporal_frequencies_regularizer': regularizers.serialize(self.temporal_frequencies_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'spatial_kernel_constraint': constraints.serialize(self.spatial_kernel_constraint), 'temporal_kernel_constraint': constraints.serialize(self.temporal_kernel_constraint), 'temporal_frequencies_constraint': constraints.serialize(self.temporal_frequencies_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint) } base_config = super(Convolution2DEnergy_TemporalBasis, self).get_config() return dict(list(base_config.items()) + list(config.items())) # separate temporal freqs per channel
def get_config(self): config = {'filters_simple': self.filters_simple, 'filters_complex': self.filters_complex, 'filters_temporal': self.filters_temporal, 'spatial_kernel_size': self.spatial_kernel_size, 'temporal_frequencies': self.temporal_frequencies, 'temporal_frequencies_initial_max': self.temporal_frequencies_initial_max, 'temporal_frequencies_scaling': self.temporal_frequencies_scaling, '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, 'spatial_kernel_initializer': initializers.serialize(self.spatial_kernel_initializer), 'temporal_kernel_initializer': initializers.serialize(self.temporal_kernel_initializer), 'temporal_frequencies_initializer': initializers.serialize(self.temporal_frequencies_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'spatial_kernel_regularizer': regularizers.serialize(self.spatial_kernel_regularizer), 'temporal_kernel_regularizer': regularizers.serialize(self.temporal_kernel_regularizer), 'temporal_frequencies_regularizer': regularizers.serialize(self.temporal_frequencies_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'spatial_kernel_constraint': constraints.serialize(self.spatial_kernel_constraint), 'temporal_kernel_constraint': constraints.serialize(self.temporal_kernel_constraint), 'temporal_frequencies_constraint': constraints.serialize(self.temporal_frequencies_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint) } base_config = super(Convolution2DEnergy_TemporalBasis2, self).get_config() return dict(list(base_config.items()) + list(config.items())) # separate biases per channel
def get_config(self): config = {'filters_simple': self.filters_simple, 'filters_complex': self.filters_complex, 'filters_temporal': self.filters_temporal, 'spatial_kernel_size': self.spatial_kernel_size, 'temporal_frequencies': self.temporal_frequencies, 'temporal_frequencies_initial_max': self.temporal_frequencies_initial_max, 'temporal_frequencies_scaling': self.temporal_frequencies_scaling, '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, 'spatial_kernel_initializer': initializers.serialize(self.spatial_kernel_initializer), 'temporal_kernel_initializer': initializers.serialize(self.temporal_kernel_initializer), 'temporal_frequencies_initializer': initializers.serialize(self.temporal_frequencies_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'spatial_kernel_regularizer': regularizers.serialize(self.spatial_kernel_regularizer), 'temporal_kernel_regularizer': regularizers.serialize(self.temporal_kernel_regularizer), 'temporal_frequencies_regularizer': regularizers.serialize(self.temporal_frequencies_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'spatial_kernel_constraint': constraints.serialize(self.spatial_kernel_constraint), 'temporal_kernel_constraint': constraints.serialize(self.temporal_kernel_constraint), 'temporal_frequencies_constraint': constraints.serialize(self.temporal_frequencies_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint) } base_config = super(Convolution2DEnergy_TemporalBasis3, 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), 'k_initializer': initializers.serialize(self.k_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'k_regularizer': regularizers.serialize(self.k_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'k_constraint': constraints.serialize(self.k_constraint) } base_config = super(Conv2DSoftMinMax, 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), '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 = {'filters_simple': self.filters_simple, 'filters_complex': self.filters_complex, 'filters_temporal': self.filters_temporal, 'spatial_kernel_size': self.spatial_kernel_size, 'temporal_frequencies': self.temporal_frequencies, 'temporal_frequencies_initial_max': self.temporal_frequencies_initial_max, 'temporal_frequencies_scaling': self.temporal_frequencies_scaling, '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, 'spatial_kernel_initializer': initializers.serialize(self.spatial_kernel_initializer), 'temporal_kernel_initializer': initializers.serialize(self.temporal_kernel_initializer), 'temporal_frequencies_initializer': initializers.serialize(self.temporal_frequencies_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'spatial_kernel_regularizer': regularizers.serialize(self.spatial_kernel_regularizer), 'temporal_kernel_regularizer': regularizers.serialize(self.temporal_kernel_regularizer), 'temporal_frequencies_regularizer': regularizers.serialize(self.temporal_frequencies_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'spatial_kernel_constraint': constraints.serialize(self.spatial_kernel_constraint), 'temporal_kernel_constraint': constraints.serialize(self.temporal_kernel_constraint), 'temporal_frequencies_constraint': constraints.serialize(self.temporal_frequencies_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'neurons': self.neurons, 'gauss_scale': self.gauss_scale, 'centers_initializer': initializers.serialize(self.centers_initializer), 'stds_initializer': initializers.serialize(self.stds_initializer), 'centers_regularizer': regularizers.serialize(self.centers_regularizer), 'stds_regularizer': regularizers.serialize(self.stds_regularizer), 'centers_constraint': constraints.serialize(self.centers_constraint), 'stds_constraint': constraints.serialize(self.stds_constraint), } base_config = super(Convolution2DEnergy_TemporalBasis_GaussianRF, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = {'quadratic_filters_ex': self.quadratic_filters_ex, 'quadratic_filters_sup': self.quadratic_filters_sup, 'W_quad_ex_initializer': initializers.serialize(self.W_quad_ex_initializer), 'W_quad_ex_regularizer': regularizers.serialize(self.W_quad_ex_regularizer), 'W_quad_ex_constraint': constraints.serialize(self.W_quad_ex_constraint), 'W_quad_sup_initializer': initializers.serialize(self.W_quad_sup_initializer), 'W_quad_sup_regularizer': regularizers.serialize(self.W_quad_sup_regularizer), 'W_lin_regularizer': constraints.serialize(self.W_lin_regularizer), 'W_lin_initializer': initializers.serialize(self.W_lin_initializer), 'W_quad_sup_regularizer': regularizers.serialize(self.W_quad_sup_regularizer), 'W_lin_constraint': constraints.serialize(self.W_lin_constraint), } base_config = super(RustSTC, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'bias_initializer': initializers.serialize(self.bias_initializer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'bias_constraint': constraints.serialize(self.bias_constraint), } base_config = super(EminusS, 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), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint) } base_config = super(WeightedMean, 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 = { 'init': initializers.serialize(self.init), 'U_regularizer': regularizers.serialize(self.U_regularizer), 'b_start_regularizer': regularizers.serialize(self.b_start_regularizer), 'b_end_regularizer': regularizers.serialize(self.b_end_regularizer), 'U_constraint': constraints.serialize(self.U_constraint), 'b_start_constraint': constraints.serialize(self.b_start_constraint), 'b_end_constraint': constraints.serialize(self.b_end_constraint) } base_config = super(ChainCRF, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = {'W_regularizer': regularizers.serialize(self.W_regularizer), 'u_regularizer': regularizers.serialize(self.u_regularizer), 'b_regularizer': regularizers.serialize(self.b_regularizer), 'W_constraint': constraints.serialize(self.W_constraint), 'u_constraint': constraints.serialize(self.u_constraint), 'b_constraint': constraints.serialize(self.b_constraint), 'W_dropout': self.W_dropout, 'u_dropout': self.u_dropout, 'bias': self.bias} base_config = super(AttentionWithContext, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'size': self.size, 'initializer': initializers.serialize(self.initializer), 'regularizer': regularizers.serialize(self.regularizer) } base_config = Layer.get_config(self) return dict(list(base_config.items()) + list(config.items()))
def _serialize_state_initializer(self): si = self.state_initializer if si is None: return None elif type(si) is list: return list(map(initializers.serialize, si)) else: return initializers.serialize(si)
def get_config(self): config = {'cells': list(map(serialize, self.cells)), 'decode': self.decode, 'output_length': self.output_length, 'readout': self.readout, 'teacher_force': self.teacher_force, 'return_states': self.return_states, 'state_sync': self.state_sync, 'state_initializer': self._serialize_state_initializer(), 'readout_activation': activations.serialize(self.readout_activation)} base_config = super(RecurrentModel, self).get_config() config.update(base_config) return config
def get_config(self): config = {'filters_simple': self.filters_simple, 'filters_complex': self.filters_complex, 'filters_temporal': self.filters_temporal, 'spatial_kernel_size': self.spatial_kernel_size, 'temporal_frequencies': self.temporal_frequencies, 'temporal_frequencies_initial_max': self.temporal_frequencies_initial_max, 'temporal_frequencies_scaling': self.temporal_frequencies_scaling, '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, 'spatial_kernel_initializer': initializers.serialize( self.spatial_kernel_initializer), 'temporal_kernel_initializer': initializers.serialize( self.temporal_kernel_initializer), 'temporal_frequencies_initializer': initializers.serialize( self.temporal_frequencies_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'spatial_kernel_regularizer': regularizers.serialize( self.spatial_kernel_regularizer), 'temporal_kernel_regularizer': regularizers.serialize( self.temporal_kernel_regularizer), 'temporal_frequencies_regularizer': regularizers.serialize( self.temporal_frequencies_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'spatial_kernel_constraint': constraints.serialize(self.spatial_kernel_constraint), 'temporal_kernel_constraint': constraints.serialize(self.temporal_kernel_constraint), 'temporal_frequencies_constraint': constraints.serialize( self.temporal_frequencies_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint) } base_config = super( Convolution2DEnergy_TemporalCorrelation, self).get_config() return dict(list(base_config.items()) + list(config.items()))
