mindspore.ops.SigmoidCrossEntropyWithLogits

class mindspore.ops.SigmoidCrossEntropyWithLogits(*args, **kwargs)[source]

Uses the given logits to compute sigmoid cross entropy between the target and the output.

Measures the distribution error in discrete classification tasks where each class is independent and not mutually exclusive using cross entropy loss.

Sets input logits as X, input label as Y, output as loss. Then,

\[p_{ij} = sigmoid(X_{ij}) = \frac{1}{1 + e^{-X_{ij}}}\]

\[loss_{ij} = -[Y_{ij} * ln(p_{ij}) + (1 - Y_{ij})ln(1 - p_{ij})]\]

Inputs:

logits (Tensor) - Input logits.
label (Tensor) - Ground truth label. With the same shape and type as logits.

Outputs:

Tensor, with the same shape and type as input logits.

Raises: TypeError – If logits or label is not a Tensor.

Supported Platforms:: Ascend GPU CPU

Examples

>>> logits = Tensor(np.array([[-0.8, 1.2, 0.7], [-0.1, -0.4, 0.7]]).astype(np.float32))
>>> labels = Tensor(np.array([[0.3, 0.8, 1.2], [-0.6, 0.1, 2.2]]).astype(np.float32))
>>> sigmoid = ops.SigmoidCrossEntropyWithLogits()
>>> output = sigmoid(logits, labels)
>>> print(output)
[[ 0.6111007   0.5032824   0.26318604]
 [ 0.58439666  0.5530153  -0.4368139 ]]