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 = {
"filters":
self.filters,
"kernel_size":
self.kernel_size,
"strides":
self.strides,
"padding":
self.padding,
"dilation_rate":
self.dilation_rate,
"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),
"demod":
self.demod,
}
base_config = super(Conv2DMod, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {
'units':
self.units,
'use_gating':
self.use_gating,
'kernel_W_initializer':
initializers.serialize(self.kernel_W_initializer),
'kernel_M_initializer':
initializers.serialize(self.kernel_M_initializer),
'gate_initializer':
initializers.serialize(self.gate_initializer),
'kernel_W_regularizer':
regularizers.serialize(self.kernel_W_regularizer),
'kernel_M_regularizer':
regularizers.serialize(self.kernel_M_regularizer),
'gate_regularizer':
regularizers.serialize(self.gate_regularizer),
'kernel_W_constraint':
constraints.serialize(self.kernel_W_constraint),
'kernel_M_constraint':
constraints.serialize(self.kernel_M_constraint),
'gate_constraint':
constraints.serialize(self.gate_constraint),
'epsilon':
self.epsilon
}
base_config = super(NALU, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {
'norm_method':
self.norm_method,
'filter_size':
self.filter_size,
'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(ImageNormalization3D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
"""
Gets class configuration for Keras serialization
"""
config = {
"units": self.units,
"attn_heads": self.attn_heads,
"attn_heads_reduction": self.attn_heads_reduction,
"in_dropout_rate": self.in_dropout_rate,
"attn_dropout_rate": self.attn_dropout_rate,
"activation": activations.serialize(self.activation),
"use_bias": self.use_bias,
"final_layer": self.final_layer,
"saliency_map_support": self.saliency_map_support,
"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),
"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
),
}
base_config = super().get_config()
return {**base_config, **config}
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),
'spectral_normalization':
self.spectral_normalization
}
base_config = super(Conv2DTranspose_Spectral, 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,
'normalize_weight': self.normalize_weight,
'kernel_initializer': sanitizedInitSer(self.kernel_initializer),
'bias_initializer': sanitizedInitSer(self.bias_initializer),
'gamma_diag_initializer': sanitizedInitSer(self.gamma_diag_initializer),
'gamma_off_initializer': sanitizedInitSer(self.gamma_off_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'gamma_diag_regularizer': regularizers.serialize(self.gamma_diag_regularizer),
'gamma_off_regularizer': regularizers.serialize(self.gamma_off_regularizer),
'activity_regularizer': regularizers.serialize(self.activity_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint),
'gamma_diag_constraint': constraints.serialize(self.gamma_diag_constraint),
'gamma_off_constraint': constraints.serialize(self.gamma_off_constraint),
'init_criterion': self.init_criterion,
'spectral_parametrization': self.spectral_parametrization,
}
base_config = super(ComplexConv, 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_diag_initializer':
initializers.serialize(self.gamma_diag_initializer),
'gamma_off_initializer':
initializers.serialize(self.gamma_off_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(ComplexLayerNorm, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
"""
Gets class configuration for Keras serialization.
Used by keras model serialization.
Returns:
A dictionary that contains the config of the layer
"""
config = {
"units": self.units,
"use_bias": self.use_bias,
"final_layer": self.final_layer,
"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),
"bias_initializer": initializers.serialize(self.bias_initializer),
"bias_regularizer": regularizers.serialize(self.bias_regularizer),
"bias_constraint": constraints.serialize(self.bias_constraint),
}
base_config = super().get_config()
return {**base_config, **config}
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),
'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(ConvGRU2D, self).get_config()
del base_config['cell']
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = super().get_config()
config.update({
"blade_indices_kernel":
self.blade_indices_kernel.numpy(),
"blade_indices_bias":
self.blade_indices_bias.numpy(),
"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)
})
return config
def get_config(self):
config = {
'filters':
self.filters,
'kernel_size':
self.kernel_size,
'strides':
self.strides,
'padding':
self.padding,
'dilation_rate':
self.dilation_rate,
'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),
'demod':
self.demod
}
base_config = super(Conv2DMod, 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):
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 = {
'filters':
self.filters,
'kernel_size':
self.kernel_size,
'use_bias':
self.use_bias,
'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)
}
base_config = super().get_config()
return {**base_config, **config}
def get_config(self):
config = {
'units':
self.units,
'x_imputation':
self.x_imputation,
'input_decay':
serialize_keras_object(self.input_decay),
'hidden_decay':
serialize_keras_object(self.hidden_decay),
'use_decay_bias':
self.use_decay_bias,
'feed_masking':
self.feed_masking,
'masking_decay':
serialize_keras_object(self.masking_decay),
'decay_initializer':
initializers.get(self.decay_initializer),
'decay_regularizer':
regularizers.get(self.decay_regularizer),
'decay_constraint':
constraints.get(self.decay_constraint),
'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),
'dropout':
self.dropout,
'recurrent_dropout':
self.recurrent_dropout,
'implementation':
self.implementation,
'reset_after':
self.reset_after
}
base_config = super().get_config()
del base_config['cell']
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
"""
Part of keras layer interface, where the signature is converted into a dict
:return:
"""
config = {
'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().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,
"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(Conv, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
"""
Method for returning the configuration of the MMoE layer.
:return: Config dictionary
"""
config = {
'units': self.units,
'num_experts': self.num_experts,
'num_tasks': self.num_tasks,
'use_expert_bias': self.use_expert_bias,
'use_gate_bias': self.use_gate_bias,
'expert_activation': activations.serialize(self.expert_activation),
'gate_activation': activations.serialize(self.gate_activation),
'expert_bias_initializer': initializers.serialize(self.expert_bias_initializer),
'gate_bias_initializer': initializers.serialize(self.gate_bias_initializer),
'expert_bias_regularizer': regularizers.serialize(self.expert_bias_regularizer),
'gate_bias_regularizer': regularizers.serialize(self.gate_bias_regularizer),
'expert_bias_constraint': constraints.serialize(self.expert_bias_constraint),
'gate_bias_constraint': constraints.serialize(self.gate_bias_constraint),
'expert_kernel_initializer': initializers.serialize(self.expert_kernel_initializer),
'gate_kernel_initializer': initializers.serialize(self.gate_kernel_initializer),
'expert_kernel_regularizer': regularizers.serialize(self.expert_kernel_regularizer),
'gate_kernel_regularizer': regularizers.serialize(self.gate_kernel_regularizer),
'expert_kernel_constraint': constraints.serialize(self.expert_kernel_constraint),
'gate_kernel_constraint': constraints.serialize(self.gate_kernel_constraint),
'activity_regularizer': regularizers.serialize(self.activity_regularizer)
}
base_config = super(MMoE, 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,
'octave':
self.octave,
'ratio_out':
self.ratio_out,
'strides':
self.strides,
'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(OctaveConv1D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
"""
Part of keras layer interface, where the signature is converted into a dict
Returns:
configurational dictionary
"""
config = {
"T": self.T,
"n_hidden": self.n_hidden,
"activation": activations.serialize(self.activation),
"activation_lstm": activations.serialize(self.activation_lstm),
"recurrent_activation": activations.serialize(self.recurrent_activation),
"kernel_initializer": initializers.serialize(self.kernel_initializer),
"recurrent_initializer": initializers.serialize(self.recurrent_initializer),
"bias_initializer": initializers.serialize(self.bias_initializer),
"use_bias": self.use_bias,
"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),
}
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),
'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
}
# config.update(_config_for_enable_caching_device(self))
base_config = super(IndRNNCell, 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_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(BatchNormalizationF16, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
# TODO(WindQAQ): Get rid of this hacky way.
config = super(tf.keras.layers.Dense, self).get_config()
config.update({
"units":
self.units,
"sigma":
self.sigma,
"use_factorised":
self.use_factorised,
"activation":
activations.serialize(self.activation),
"use_bias":
self.use_bias,
"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),
})
return config
def get_config(self):
config = {
"units":
self.units,
"activation":
activations.serialize(self.activation),
"use_bias":
self.use_bias,
"kernel_quantizer":
constraints.serialize(self.kernel_quantizer_internal),
"bias_quantizer":
constraints.serialize(self.bias_quantizer_internal),
"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),
"kernel_range":
self.kernel_range,
"bias_range":
self.bias_range
}
base_config = super(QDense, 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)
}
base_config = super(MDense, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self) -> Dict:
"""
Part of keras layer interface, where the signature is converted into a dict
Returns:
configurational dictionary
"""
config = {
"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().get_config()
return dict(list(base_config.items()) + list(config.items())) # noqa
def get_config(self):
config = {
'units':
self.units,
'attention_width':
self.attention_width,
'attention_type':
self.attention_type,
'return_attention':
self.return_attention,
'history_only':
self.history_only,
'use_additive_bias':
self.use_additive_bias,
'use_attention_bias':
self.use_attention_bias,
'kernel_initializer':
regularizers.serialize(self.kernel_initializer),
'bias_initializer':
regularizers.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),
'attention_activation':
activations.serialize(self.attention_activation),
'attention_regularizer_weight':
self.attention_regularizer_weight,
}
base_config = super(SeqSelfAttention, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = super(FRN, self).get_config()
config.update({
'reg_epsilon': self.reg_epsilon,
'tau_regularizer': regularizers.serialize(self.tau_regularizer),
'beta_regularizer': regularizers.serialize(self.beta_regularizer),
'gamma_regularizer': regularizers.serialize(self.gamma_regularizer),
})
return config
def get_config(self):
config = {
'rank':
self.rank,
'n_filters':
self.n_filters,
'n_experts_per_filter':
self.n_experts_per_filter,
'kernel_size':
self.kernel_size,
'strides':
self.strides,
'padding':
self.padding,
'data_format':
self.data_format,
'dilation_rate':
self.dilation_rate,
'expert_activation':
activations.serialize(self.expert_activation),
'gating_activation':
activations.serialize(self.gating_activation),
'use_expert_bias':
self.use_expert_bias,
'use_gating_bias':
self.use_gating_bias,
'expert_kernel_initializer_scale':
self.expert_kernel_initializer_scale,
'gating_kernel_initializer_scale':
self.gating_kernel_initializer_scale,
'expert_bias_initializer':
initializers.serialize(self.expert_bias_initializer),
'gating_bias_initializer':
initializers.serialize(self.gating_bias_initializer),
'expert_kernel_regularizer':
regularizers.serialize(self.expert_kernel_regularizer),
'gating_kernel_regularizer':
regularizers.serialize(self.gating_kernel_regularizer),
'expert_bias_regularizer':
regularizers.serialize(self.expert_bias_regularizer),
'gating_bias_regularizer':
regularizers.serialize(self.gating_bias_regularizer),
'expert_kernel_constraint':
constraints.serialize(self.expert_kernel_constraint),
'gating_kernel_constraint':
constraints.serialize(self.gating_kernel_constraint),
'expert_bias_constraint':
constraints.serialize(self.expert_bias_constraint),
'gating_bias_constraint':
constraints.serialize(self.gating_bias_constraint),
'activity_regularizer':
regularizers.serialize(self.activity_regularizer)
}
base_config = super(_ConvMoE, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
