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()))