def config(self): return { "channels": self.channels, "attn_heads": self.attn_heads, "concat_heads": self.concat_heads, "dropout_rate": self.dropout_rate, "return_attn_coef": self.return_attn_coef, "attn_kernel_initializer": initializers.serialize(self.attn_kernel_initializer), "attn_kernel_regularizer": regularizers.serialize(self.attn_kernel_regularizer), "attn_kernel_constraint": constraints.serialize(self.attn_kernel_constraint), }
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), "kernel_quantizer": constraints.serialize(self.kernel_quantizer_internal), "recurrent_quantizer": constraints.serialize(self.recurrent_quantizer_internal), "bias_quantizer": constraints.serialize(self.bias_quantizer_internal), "state_quantizer": constraints.serialize(self.state_quantizer_internal), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout } base_config = super(QSimpleRNN, self).get_config() del base_config['cell'] return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { 'input_dims': self.input_dims, 'output_dims': self.output_dims, 'dropout_rate': self.dropout_rate, '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, } base_config = super(MultiColumnEmbedding, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self) -> dict: """ Obtain a key-value representation of the layer config. Returns: A dict holding the configuration of the layer. """ config = dict( num_sums=self.num_sums, accumulator_initializer=initializers.serialize( self.accumulator_initializer ), logspace_accumulators=self.logspace_accumulators, accumulator_regularizer=regularizers.serialize( self.accumulator_regularizer ), linear_accumulator_constraint=constraints.serialize( self.linear_accumulator_constraint ), ) base_config = super(DenseSum, 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_quantizer': constraints.serialize(self.beta_quantizer_internal), 'gamma_quantizer': constraints.serialize(self.gamma_quantizer_internal), 'mean_quantizer': constraints.serialize(self.mean_quantizer_internal), 'variance_quantizer': constraints.serialize(self.variance_quantizer_internal), '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), 'inverse_quantizer': initializers.serialize(self.inverse_quantizer_internal), '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), 'beta_range': self.beta_range, 'gamma_range': self.gamma_range, } base_config = super(QBatchNormalization, self).get_config() return dict(list(base_config.items()) + list(config.items()))