mindspore.nn.Loss
- class mindspore.nn.Loss[source]
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
>>> x = Tensor(np.array(0.2), mindspore.float32) >>> loss = nn.Loss() >>> loss.clear() >>> loss.update(x) >>> result = loss.eval()
- eval()[source]
Calculates the average of the loss.
- Returns
Float, the average of the loss.
- Raises
RuntimeError – If the total number is 0.
- update(*inputs)[source]
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.