mindspore.nn.Accuracy

class mindspore.nn.Accuracy(eval_type='classification')

Calculates the accuracy for classification and multilabel data.

The accuracy class creates two local variables, the correct number and the total number that are used to compute the frequency with which predictions matches labels. This frequency is ultimately returned as the accuracy: an idempotent operation that simply divides the correct number by the total number.

\[\text{accuracy} =\frac{\text{true_positive} + \text{true_negative}} {\text{true_positive} + \text{true_negative} + \text{false_positive} + \text{false_negative}}\]

Parameters

eval_type (str) – Metric to calculate the accuracy over a dataset, for classification (single-label), and multilabel (multilabel classification). Default: ‘classification’.

Examples

>>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]), mindspore.float32)
>>> y = Tensor(np.array([1, 0, 1]), mindspore.float32)
>>> metric = nn.Accuracy('classification')
>>> metric.clear()
>>> metric.update(x, y)
>>> accuracy = metric.eval()
>>> print(accuracy)
0.6666666666666666

clear()

Clears the internal evaluation result.

eval()

Computes the accuracy.

Returns

Float, the computed result.

Raises

RuntimeError – If the sample size is 0.

update(*inputs)

Updates the internal evaluation result \(y_{pred}\) and \(y\).

Parameters

inputs – Input y_pred and y. y_pred and y are a Tensor, a list or an array. For the ‘classification’ evaluation type, y_pred is in most cases (not strictly) a list of floating numbers in range \([0, 1]\) and the shape is \((N, C)\), where \(N\) is the number of cases and \(C\) is the number of categories. Shape of y can be \((N, C)\) with values 0 and 1 if one-hot encoding is used or the shape is \((N,)\) with integer values if index of category is used. For ‘multilabel’ evaluation type, y_pred and y can only be one-hot encoding with values 0 or 1. Indices with 1 indicate the positive category. The shape of y_pred and y are both \((N, C)\).

Raises

ValueError – If the number of the inputs is not 2.