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)
}
base_config = super(LocallyConnected2D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {
'units':
self.units,
'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)
}
base_config = super(CuDNNLSTM, 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(Dense, 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(BatchNormalization, 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,
'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),
'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),
'dropout': self.dropout,
'recurrent_dropout': self.recurrent_dropout}
base_config = super(ConvLSTM2DCell, 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),
'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(GRU, 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)
}
# Only add TensorFlow-specific parameters if they are set, so as to preserve
# model compatibility with external Keras.
if self.renorm:
config['renorm'] = True
config['renorm_clipping'] = self.renorm_clipping
config['renorm_momentum'] = self.renorm_momentum
if self.virtual_batch_size is not None:
config['virtual_batch_size'] = self.virtual_batch_size
# Note: adjustment is not serializable.
if self.adjustment is not None:
logging.warning('The `adjustment` function of this `BatchNormalization` '
'layer cannot be serialized and has been omitted from '
'the layer config. It will not be included when '
're-creating the layer from the saved config.')
base_config = super(BatchNormalization, 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,
'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(LocallyConnected2D, 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,
'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),
'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
}
base_config = super(ConvLSTM2D, self).get_config()
del base_config['cell']
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(PReLU, 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(PReLU, 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(BatchNormalization, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {
'units': self.units,
'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(CuDNNGRU, 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,
'data_format': self.data_format,
'dilation_rate': self.dilation_rate,
'activation': activations.serialize(activations.get(self.activation)),
'use_bias': self.use_bias,
'kernel_initializer': initializers.serialize(initializers.get(self.kernel_initializer)),
'bias_initializer': initializers.serialize(initializers.get(self.bias_initializer)),
'kernel_regularizer': regularizers.serialize(regularizers.get(self.kernel_regularizer)),
'bias_regularizer': regularizers.serialize(regularizers.get(self.bias_regularizer)),
'activity_regularizer': regularizers.serialize(regularizers.get(self.activity_regularizer)),
'kernel_constraint': constraints.serialize(constraints.get(self.kernel_constraint)),
'bias_constraint': constraints.serialize(constraints.get(self.bias_constraint))
}
base_config = {"name": self.layername}
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),
'activity_regularizer':
regularizers.serialize(self.activity_regularizer),
'embeddings_constraint':
constraints.serialize(self.embeddings_constraint),
'mask_zero':
self.mask_zero,
'input_length':
self.input_length
}
base_config = super(Embedding, 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),
'activity_regularizer':
regularizers.serialize(self.activity_regularizer),
'embeddings_constraint':
constraints.serialize(self.embeddings_constraint),
'mask_zero':
self.mask_zero,
'input_length':
self.input_length
}
base_config = super(Embedding, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = super(DepthwiseConv2D, self).get_config()
config.pop('filters')
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 = super(DepthwiseConv2D, self).get_config()
config.pop('filters')
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 = {
'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
}
base_config = super(ConvLSTM2D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))