Method add_metric

tensorflow/python/keras/engine/base_layer.py:1173–1241

Adds metric tensor to the layer. Args: value: Metric tensor. aggregation: Sample-wise metric reduction function. If `aggregation=None`, it indicates that the metric tensor provided has been aggregated already. eg, `bin_acc = BinaryAccuracy(name='acc')` followed by

(self, value, aggregation=None, name=None)

  @doc_controls.for_subclass_implementers
  def add_metric(self, value, aggregation=None, name=None):
    """Adds metric tensor to the layer.

    Args:
      value: Metric tensor.
      aggregation: Sample-wise metric reduction function. If `aggregation=None`,
        it indicates that the metric tensor provided has been aggregated
        already. eg, `bin_acc = BinaryAccuracy(name='acc')` followed by
        `model.add_metric(bin_acc(y_true, y_pred))`. If aggregation='mean', the
        given metric tensor will be sample-wise reduced using `mean` function.
        eg, `model.add_metric(tf.reduce_sum(outputs), name='output_mean',
        aggregation='mean')`.
      name: String metric name.

    Raises:
      ValueError: If `aggregation` is anything other than None or `mean`.
    """
    if aggregation is not None and aggregation != 'mean':
      raise ValueError(
          'We currently support only `mean` sample-wise metric aggregation. '
          'You provided aggregation=`%s`' % aggregation)

    from_metric_obj = hasattr(value, '_metric_obj')
    is_symbolic = tf_utils.is_symbolic_tensor(value)
    in_call_context = base_layer_utils.call_context().in_call

    if name is None and not from_metric_obj:
      # Eg. `self.add_metric(math_ops.reduce_sum(x), aggregation='mean')`
      # In eager mode, we use metric name to lookup a metric. Without a name,
      # a new Mean metric wrapper will be created on every model/layer call.
      # So, we raise an error when no name is provided.
      # We will do the same for symbolic mode for consistency although a name
      # will be generated if no name is provided.

      # We will not raise this error in the foll use case for the sake of
      # consistency as name in provided in the metric constructor.
      # mean = metrics.Mean(name='my_metric')
      # model.add_metric(mean(outputs))
      raise ValueError('Please provide a name for your metric like '
                       '`self.add_metric(tf.reduce_sum(inputs), '
                       'name=\'mean_activation\', aggregation=\'mean\')`')
    elif from_metric_obj:
      name = value._metric_obj.name

    if in_call_context:
      # TF Function path should take the eager path.
      if is_symbolic and not base_layer_utils.is_in_tf_function():
        self._symbolic_add_metric(value, aggregation, name)
      else:
        self._eager_add_metric(value, aggregation, name)
    else:
      if not is_symbolic:
        raise ValueError('Expected a symbolic Tensor for the metric value, '
                         'received: ' + str(value))

      # Possible a metric was added in a Layer's `build`.
      if not getattr(self, '_is_graph_network', False):
        with backend.get_graph().as_default():