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 = {
'alpha_initializer':
initializers.serialize(self.alpha_initializer),
'alpha_regularizer':
regularizers.serialize(self.alpha_regularizer),
'alpha_constraint': constraints.serialize(self.alpha_constraint),
'shared_axes': self.shared_axes
}
base_config = super(FReLU, 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,
'mode': self.mode,
'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(PositionEmbedding, 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(DepthConv2D, 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(AttentionLSTM, self).get_config()
del base_config['cell']
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(AttGRUCell, self).get_data_info()
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':tf.Session().run(self.fc),
'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),
# 'gammatone': self.impulse_gammatone()
}
base_config = super(Conv1D_gammatone, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
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 = {
'input_dim':
self.input_dim,
'output_dim':
self.output_dim,
'embeddings_initializer':
initializers.serialize(self.embeddings_initializer),
'embeddings_regularizer':
regularizers.serialize(self.embeddings_regularizer),
'embeddings_constraint':
constraints.serialize(self.embeddings_constraint),
}
base_config = super(Embedding2D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
