mindspore.train.Metric

class mindspore.train.Metric

Base class of metric, which is used to evaluate metrics.

The clear, update, and eval should be called when evaluating metric, and they should be overridden by subclasse. update will accumulate intermediate results in the evaluation process, eval will evaluate the final result, and clear will reinitialize the intermediate results.

Never use this class directly, but instantiate one of its subclasses instead, for examples, mindspore.train.MAE, mindspore.train.Recall etc.

Supported Platforms:

Ascend GPU CPU

abstract clear()

An interface describes the behavior of clearing the internal evaluation result.

Note

All subclasses must override this interface.

Tutorial Examples:

• Evaluation Metrics - Customized Metrics

abstract eval()

An interface describes the behavior of computing the evaluation result.

Note

All subclasses must override this interface.

Tutorial Examples:

• Evaluation Metrics - Customized Metrics

property indexes

Get the current indexes value. The default value is None and can be changed by set_indexes.

set_indexes(indexes)

This interface is to rearrange the inputs of update.

Given (label0, label1, logits), set the indexes to [2, 1] then the (logits, label1) will be the actually inputs of update.

Note

When customize a metric, decorate the update function with the decorator mindspore.train.rearrange_inputs() for the indexes to take effect.

Parameters

indexes (List(int)) – The order of logits and labels to be rearranged.

Outputs:

Metric, its original Class instance.

Raises

ValueError – If the type of input ‘indexes’ is not a list or its elements are not all int.

Examples

>>> import numpy as np
>>> from mindspore import Tensor
>>> from mindspore.train import Accuracy
>>>
>>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
>>> y = Tensor(np.array([1, 0, 1]))
>>> y2 = Tensor(np.array([0, 0, 1]))
>>> metric = Accuracy('classification').set_indexes([0, 2])
>>> metric.clear()
>>> # indexes is [0, 2], using x as logits, y2 as label.
>>> metric.update(x, y, y2)
>>> accuracy = metric.eval()
>>> print(accuracy)
0.3333333333333333

abstract update(*inputs)

An interface describes the behavior of updating the internal evaluation result.

Note

All subclasses must override this interface.

Parameters

inputs – A variable-length input argument list, usually are the logits and the corresponding labels.

Tutorial Examples:

• Evaluation Metrics - Customized Metrics