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 = {'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 = {'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), 'group': self.group} base_config = super(GroupNormalization, 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 = { '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 = { '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 = {'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 = { '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 = { '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 = { '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 = 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 = { 'num_units': self.num_units, 'num_heads': self.num_heads, 'residual': self.residual, 'normalize': self.normalize, 'initializer': initializers.serialize(self.initializer), 'regularizer': regularizers.serialize(self.regularizer), 'constraint': constraints.serialize(self.constraint) } base_config = super(MultiHeadAttention, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): if self.kernel_initializer in {'complex'}: ki = self.kernel_initializer else: ki = initializers.serialize(self.kernel_initializer) config = { 'units': self.units, 'activation': activations.serialize(self.activation), 'use_bias': self.use_bias, 'init_criterion': self.init_criterion, 'kernel_initializer': ki, '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), 'seed': self.seed, } base_config = super(ComplexDense, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'groups': self.groups, '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(GroupNormalization, 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().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, 'sc_dropout': self.sc_dropout} base_config = super(SC_LSTM, 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), 'unit_forget_bias': self.unit_forget_bias, 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint) } base_config = super(CuDNNLSTM, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { '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().get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'ch_j': self.ch_j, 'n_j': self.n_j, 'r_num': self.r_num, 'b_alphas': self.b_alphas, '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(DenseCaps, 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 = {'num_capsule': self.num_capsule, 'dim_capsule': self.dim_capsule, 'routings': self.routings, 'share_weights': self.share_weights, 'activation': activations.serialize(self.activation), 'regularizer': regularizers.serialize(self.regularizer), 'initializer': initializers.serialize(self.initializer), 'constraint': constraints.serialize(self.constraint)} base_config = super(Capsule, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'rank': self.rank, 'filters': self.filters, 'kernel_size': self.kernel_size, 'strides': self.strides, 'padding': self.padding, 'data_format': self.data_format, 'dilation_rate': self.dilation_rate, 'activation': activations.serialize(self.activation), 'use_bias': self.use_bias, '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(Convolution2D_test, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'output_dim': self.output_dim, 'adjacency_matrix': self.adjacency_matrix, 'num_filters': self.num_filters, 'graph_conv_filters': self.graph_conv_filters, 'num_attention_heads': self.num_attention_heads, 'attention_combine': self.attention_combine, 'attention_dropout': self.attention_dropout, '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(GraphAttentionCNN, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'matrix_initializer': initializers.serialize(self.matrix_initializer), 'matrix_regularizer': regularizers.serialize(self.matrix_regularizer), 'matrix_constraint': constraints.serialize(self.matrix_constraint), 'matrix_scope': self.matrix_scope, 'projected_atom_shape': self.projected_atom_shape } super_config = super(PointMatrixPrototype, self).get_config() return dict(list(super_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), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout, 'implementation': self.implementation } config.update(rnn_utils.config_for_enable_caching_device(self)) base_config = super(LSTMCell, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'filters': self.filters, 'kernel_size': self.kernel_size, 'strides': self.strides, 'padding': self.padding, 'data_format': self.data_format, 'dilation_rate': self.dilation_rate, 'activation': activations.serialize(self.activation), 'use_bias': self.use_bias, 'preslice': self.preslice, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'attention_function': self.attention_function, 'mu1_regularizer': regularizers.serialize(self.mu1_regularizer), 'sig1_regularizer': regularizers.serialize(self.sig1_regularizer), 'mu2_regularizer': regularizers.serialize(self.mu2_regularizer), 'sig2_regularizer': regularizers.serialize(self.sig2_regularizer), 'mu1_constraint': constraints.serialize(self.mu1_constraint), 'sig1_constraint': constraints.serialize(self.sig1_constraint), 'mu2_constraint': constraints.serialize(self.mu2_constraint), 'sig2_constraint': constraints.serialize(self.sig2_constraint) } base_config = super(Target2D, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { '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 = { "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, } config.update(rnn_utils.config_for_enable_caching_device(self)) base_config = super().get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'tt_input_shape': self.tt_input_shape, 'tt_output_shape': self.tt_output_shape, 'tt_ranks': self.tt_ranks, 'activation': activations.serialize(self.activation), '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 = { "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, '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, 'recurrent_clip_min': self.recurrent_clip_min, 'recurrent_clip_max': self.recurrent_clip_max, 'activation': activations.serialize(self.activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout, 'implementation': self.implementation } base_config = super(IndRNNCell, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = {'units': self.units, 'weight_dim': self.weight_dim, 'interval_dim': self.interval_dim, 'activation': activations.serialize(self.activation), 'recurrent_activation': activations.serialize(self.recurrent_activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'unit_forget_bias': self.unit_forget_bias, 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout, 'implementation': self.implementation} base_config = super(BLSTMCell, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { '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), 'memory_size': self.memory_size } base_config = super(NeuralMap, self).get_config() del base_config['cell'] return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = super(DepthwiseConv2D, self).get_config() 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 = {'units': self.units, 'activation': activations.serialize(self.activation), 'recurrent_activation': activations.serialize(self.recurrent_activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'unit_forget_bias': self.unit_forget_bias, 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout, 'implementation': self.implementation} base_config = super(Adaptive_SFM, self).get_config() del base_config['cell'] return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = {'units': self.units, 'support': self.support, '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(GraphConvolution, 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_diag_initializer': initializers.serialize(self.gamma_diag_initializer) if self.gamma_diag_initializer != sqrt_init else 'sqrt_init', 'gamma_off_initializer': initializers.serialize(self.gamma_off_initializer), 'moving_mean_initializer': initializers.serialize(self.moving_mean_initializer), 'moving_variance_initializer': initializers.serialize(self.moving_variance_initializer) if self.moving_variance_initializer != sqrt_init else 'sqrt_init', 'moving_covariance_initializer': initializers.serialize(self.moving_covariance_initializer), 'beta_regularizer': regularizers.serialize(self.beta_regularizer), 'gamma_diag_regularizer': regularizers.serialize(self.gamma_diag_regularizer), 'gamma_off_regularizer': regularizers.serialize(self.gamma_off_regularizer), 'beta_constraint': constraints.serialize(self.beta_constraint), 'gamma_diag_constraint': constraints.serialize(self.gamma_diag_constraint), 'gamma_off_constraint': constraints.serialize(self.gamma_off_constraint), } base_config = super(QuaternionBatchNormalization, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = {'out_dim': self.out_dim, 'drop_rate': self.drop_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(GraphAttention, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): """Returns the config of the layer. The Keras configuration for the layer. Returns -------- dict A python dictionary containing the layer configuration """ 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), 'group': self.group } base_config = super().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, 'arma_conv_AR': self.arma_conv_AR, 'arma_conv_MA': self.arma_conv_MA, 'input_signal': self.input_signal, '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(DFNets, self).get_config() return dict(list(base_config.items()) + list(config.items()))