mindspore.ops.SparseSoftmaxCrossEntropyWithLogits
- class mindspore.ops.SparseSoftmaxCrossEntropyWithLogits(is_grad=False)[source]
Computes the softmax cross-entropy value between logits and sparse encoding labels.
Sets input logits as X, input label as Y, output as loss. Then,
\[\begin{split}\begin{array}{ll} \\ p_{ij} = softmax(X_{ij}) = \frac{\exp(x_i)}{\sum_{j = 0}^{N-1}\exp(x_j)} \\ loss_{ij} = \begin{cases} -ln(p_{ij}), &j = y_i \cr -ln(1 - p_{ij}), & j \neq y_i \end{cases} \\ loss = \sum_{ij} loss_{ij} \end{array}\end{split}\]- Parameters
is_grad (bool) – If true, this operation returns the computed gradient. Default: False.
- Inputs:
logits (Tensor) - Input logits, with shape \((N, C)\). Data type must be float16 or float32.
labels (Tensor) - Ground truth labels, with shape \((N)\). Data type must be int32 or int64.
- Outputs:
Tensor, if is_grad is False, the output tensor is the value of loss which is a scalar tensor; if is_grad is True, the output tensor is the gradient of input with the same shape as logits.
- Raises
TypeError – If is_grad is not a bool.
TypeError – If dtype of logits is neither float16 nor float32.
TypeError – If dtype of labels is neither int32 nor int64.
ValueError – If logits.shape[0] != labels.shape[0].
- Supported Platforms:
GPU
CPU
Examples
>>> logits = Tensor([[2, 3, 1, 4, 5], [2, 1, 2, 4, 3]], mindspore.float32) >>> labels = Tensor([0, 1], mindspore.int32) >>> sparse_softmax_cross = ops.SparseSoftmaxCrossEntropyWithLogits() >>> loss = sparse_softmax_cross(logits, labels) >>> print(loss) 3.4878292 >>> sparse_softmax_cross_grad = ops.SparseSoftmaxCrossEntropyWithLogits(is_grad=True) >>> loss_grad = sparse_softmax_cross_grad(logits, labels) >>> print(loss_grad) [[-0.48415753 0.04306427 0.00582811 0.11706084 0.3182043 ] [ 0.04007946 -0.4852556 0.04007946 0.2961494 0.10894729]]