mindspore.nn.Loss¶
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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()
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eval
()[source]¶ Calculates the average of the loss.
- Returns
Float, the average of the loss.
- Raises
RuntimeError – If the total number is 0.
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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.
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