Metric wrappers and reduction metrics

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MeanMetricWrapper class

keras.metrics.MeanMetricWrapper(fn, name=None, dtype=None, **kwargs)

Wrap a stateless metric function with the Mean metric.

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 mse(y_true, y_pred):
    return (y_true - y_pred) ** 2

mse_metric = MeanMetricWrapper(fn=mse)

Arguments

• 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.

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Mean class

keras.metrics.Mean(name="mean", dtype=None)

Compute the (weighted) mean of the given values.

For example, if values is [1, 3, 5, 7] then the mean is 4. If sample_weight was specified as [1, 1, 0, 0] then the mean would be 2.

This metric creates two variables, total and count. The mean value returned is simply total divided by count.

Arguments

• name: (Optional) string name of the metric instance.
• dtype: (Optional) data type of the metric result.

Example

>>> m = Mean()
>>> m.update_state([1, 3, 5, 7])
>>> m.result()
4.0

>>> m.reset_state()
>>> m.update_state([1, 3, 5, 7], sample_weight=[1, 1, 0, 0])
>>> m.result()
2.0

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Sum class

keras.metrics.Sum(name="sum", dtype=None)

Compute the (weighted) sum of the given values.

For example, if values is [1, 3, 5, 7] then their sum is 16. If sample_weight was specified as [1, 1, 0, 0] then the sum would be 4.

This metric creates one variable, total. This is ultimately returned as the sum value.

Arguments

• name: (Optional) string name of the metric instance.
• dtype: (Optional) data type of the metric result.

Example

>>> m = metrics.Sum()
>>> m.update_state([1, 3, 5, 7])
>>> m.result()
16.0

>>> m = metrics.Sum()
>>> m.update_state([1, 3, 5, 7], sample_weight=[1, 1, 0, 0])
>>> m.result()
4.0