tensorflow_probability/python/layers/conv_variational.py
def get_config(self):
    """Returns the config of the layer.

    A layer config is a Python dictionary (serializable) containing the
    configuration of a layer. The same layer can be reinstantiated later
    (without its trained weights) from this configuration.

    Returns:
      config: A Python dictionary of class keyword arguments and their
        serialized values.
    """
    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': (tf.keras.activations.serialize(self.activation)
                       if self.activation else None),
        'activity_regularizer':
            tf.keras.initializers.serialize(self.activity_regularizer),
    }
    function_keys = [
        'kernel_posterior_fn',
        'kernel_posterior_tensor_fn',
        'kernel_prior_fn',
        'kernel_divergence_fn',
        'bias_posterior_fn',
        'bias_posterior_tensor_fn',
        'bias_prior_fn',
        'bias_divergence_fn',
    ]
    for function_key in function_keys:
      function = getattr(self, function_key)
      if function is None:
        function_name = None
        function_type = None
      else:
        function_name, function_type = tfp_layers_util.serialize_function(
            function)
      config[function_key] = function_name
      config[function_key + '_type'] = function_type
    base_config = super(_ConvVariational, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
    """Returns the config of the layer.

    A layer config is a Python dictionary (serializable) containing the
    configuration of a layer. The same layer can be reinstantiated later
    (without its trained weights) from this configuration.

    Returns:
      config: A Python dictionary of class keyword arguments and their
        serialized values.
    """
    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': (tf.keras.activations.serialize(self.activation)
                       if self.activation else None),
        'activity_regularizer':
            tf.keras.initializers.serialize(self.activity_regularizer),
    }
    function_keys = [
        'kernel_posterior_fn',
        'kernel_posterior_tensor_fn',
        'kernel_prior_fn',
        'kernel_divergence_fn',
        'bias_posterior_fn',
        'bias_posterior_tensor_fn',
        'bias_prior_fn',
        'bias_divergence_fn',
    ]
    for function_key in function_keys:
      function = getattr(self, function_key)
      if function is None:
        function_name = None
        function_type = None
      else:
        function_name, function_type = tfp_layers_util.serialize_function(
            function)
      config[function_key] = function_name
      config[function_key + '_type'] = function_type
    base_config = super(_ConvVariational, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))
Returns the config of the layer.

    A layer config is a Python dictionary (serializable) containing the
    configuration of a layer. The same layer can be reinstantiated later
    (without its trained weights) from this configuration.

    Returns:
      config: A Python dictionary of class keyword arguments and their
        serialized values.