mindspore.nn.Loss

class mindspore.nn.Loss

Calculates the average of the loss. If method ‘update’ is called every \(n\) iterations, the result of evaluation will be:

\[loss = \frac{\sum_{k=1}^{n}loss_k}{n}\]

Examples

>>> import numpy as np
>>> from mindspore import nn, Tensor
>>>
>>> x = Tensor(np.array(0.2), mindspore.float32)
>>> loss = nn.Loss()
>>> loss.clear()
>>> loss.update(x)
>>> result = loss.eval()

clear()

Clears the internal evaluation result.

eval()

Calculates the average of the loss.

Returns

Float, the average of the loss.

Raises

RuntimeError – If the total number is 0.

update(*inputs)

Updates the internal evaluation result.

Parameters

inputs – Inputs contain only one element, the element is loss. The dimension of loss must be 0 or 1.

Raises

• ValueError – If the length of inputs is not 1.

• ValueError – If the dimensions of loss is not 1.