Metric wrappers and reduction metrics
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.
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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<span style="float:right;">[[source]](https://github.com/keras-team/keras/tree/v3.3.2//keras/src/metrics/reduction_metrics.py#L45)</span>
### `Sum` class
```python
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