Help protect the Great Barrier Reef with TensorFlow on Kaggle Join Challenge


Wraps a stateless metric function with the Mean metric.

Inherits From: Mean, Metric, Layer, Module

You could use this class to quickly build a mean metric from a function. The function needs to have the signature fn(y_true, y_pred) and return a per-sample loss array. MeanMetricWrapper.result() will return the average metric value across all samples seen so far.

For example:

def accuracy(y_true, y_pred):
  return tf.cast(tf.math.equal(y_true, y_pred), tf.float32)

accuracy_metric = tf.keras.metrics.MeanMetricWrapper(fn=accuracy)

keras_model.compile(..., metrics=accuracy_metric)

fn The metric function to wrap, with signature fn(y_true, y_pred, **kwargs).
name (Optional) string name of the metric instance.
dtype (Optional) data type of the metric result.
**kwargs Keyword arguments to pass on to fn.



View source

Merges the state from one or more metrics.

This method can be used by distributed systems to merge the state computed by different metric instances. Typically the state will be stored in the form of the metric's weights. For example, a tf.keras.metrics.Mean metric contains a list of two weight values: a total and a count. If there were two instances of a tf.keras.metrics.Accuracy that each independently aggregated partial state for an overall accuracy calculation, these two metric's states could be combined as follows:

m1 = tf.keras.metrics.Accuracy()
_ = m1.update_state([[1], [2]], [[0], [2]])
m2 = tf.keras.metrics.Accuracy()
_ = m2.update_state([[3], [4]], [[3], [4]])

metrics an iterable of metrics. The metrics must have compatible state.

ValueError If the provided iterable does not contain metrics matching the metric's required specifications.


View source

Resets all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.


View source

Computes and returns the metric value tensor.

Result computation is an idempotent operation that simply calculates the metric value using the state variables.


View source

Accumulates metric statistics.

For sparse categorical metrics, the shapes of y_true and y_pred are different.

y_true Ground truth label values. shape = [batch_size, d0, .. dN-1] or shape = [batch_size, d0, .. dN-1, 1].
y_pred The predicted probability values. shape = [batch_size, d0, .. dN].
sample_weight Optional sample_weight acts as a coefficient for the metric. If a scalar is provided, then the metric is simply scaled by the given value. If sample_weight is a tensor of size [batch_size], then the metric for each sample of the batch is rescaled by the corresponding element in the sample_weight vector. If the shape of sample_weight is [batch_size, d0, .. dN-1] (or can be broadcasted to this shape), then each metric element of y_pred is scaled by the corresponding value of sample_weight. (Note on dN-1: all metric functions reduce by 1 dimension, usually the last axis (-1)).

Update op.