def get_config(self):
config = {'units': self.units,
'rank': self.rank,
'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(CLSTM, self).get_config()
del base_config['cell']
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 = {"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(Set2Set, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {
'channel': self.channel,
'normalize': self.normalize,
'init_diag': self.init_diag,
'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)
}
base_config = super(SpatialGRU, 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),
'attention_activation':
activations.serialize(self.attention_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),
'attention_initializer':
initializers.serialize(self.attention_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),
'attention_regularizer':
regularizers.serialize(self.attention_regularizer),
'kernel_constraint':
constraints.serialize(self.kernel_constraint),
'recurrent_constraint':
constraints.serialize(self.recurrent_constraint),
'bias_constraint':
constraints.serialize(self.bias_constraint),
'attention_constraint':
constraints.serialize(self.attention_constraint),
'dropout':
self.dropout,
'recurrent_dropout':
self.recurrent_dropout,
'return_attention':
self.return_attention
}
base_config = super(AttentionLSTM, 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):
"""
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 = {
'activation': activations.serialize(self.activation),
'use_bias': self.use_bias,
'bias_initializer': initializers.serialize(self.bias_initializer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
}
base_config = super(OutputLayer, self).get_config()
return dict(**base_config, **config)
def get_config(self):
config = {
'units':
self.units,
'steps':
self.steps,
'output_dim':
self.output_dim,
'atten_units':
self.atten_units,
'activation':
activations.serialize(self.activation),
'gmax':
self.gmax,
'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),
'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,
'return_probabilities':
self.return_probabilities
}
base_config = super(attention_LSTM, 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
}
base_config = super(mlpGRUCell, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {
'channel':
self.channel,
'normalize':
self.normalize,
'init_diag':
self.init_diag,
'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)
}
base_config = super(SpatialGRU, 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,
'fsHz': self.fsHz,
'fc_initializer': initializers.serialize(self.fc_initializer),
'n_order_initializer': initializers.serialize(self.n_order_initializer),
'amp_initializer': initializers.serialize(self.amp_initializer),
'beta_initializer': initializers.serialize(self.beta_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
}
base_config = super(Conv1D_gammatone, 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(Conv1D_linearphaseType, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = super().get_config()
config['activation'] = activations.serialize(self.activation)
config['size_multiplier'] = self.size_multiplier
return config
def get_config(self):
config = {"activation": activations.serialize(self.activation)}
base_config = super(TiedEmbeddingsTransposed, self).get_config()
return dict(list(base_config.items()) + list(config.items()))