mindspore.ops.SoftmaxCrossEntropyWithLogits

class mindspore.ops.SoftmaxCrossEntropyWithLogits

Gets the softmax cross-entropy value between logits and labels with one-hot encoding.

The updating formulas of SoftmaxCrossEntropyWithLogits algorithm are as follows,

$$\begin{array}{r}\begin{array}{l}\\ p_{ij}=softmax(X_{ij})=\frac{\exp (x_i)}{\sum _{j=0}^{N-1}\exp (x_j)}\\ los{s}_{ij}=-\sum _j{Y}_{ij}\ast ln(p_{ij})\end{array}\end{array}$$

where $X$ represents logits. $Y$ represents label. $loss$ represents output.

Inputs:

• logits (Tensor) - Input logits, with shape $(N,C)$. Data type must be float16 or float32.

• labels (Tensor) - Ground truth labels, with shape $(N,C)$, has the same data type with logits.

Outputs:

Tuple of 2 tensors( loss , dlogits ), the loss shape is $(N,)$, and the dlogits with the same shape as logits.

Raises

• TypeError – If dtype of logits or labels is neither float16 nor float32.

• TypeError – If logits or labels is not a Tensor.

• ValueError – If shape of logits is not the same as labels.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> from mindspore import Tensor, ops
>>> logits = Tensor([[2, 4, 1, 4, 5], [2, 1, 2, 4, 3]], mindspore.float32)
>>> labels = Tensor([[0, 0, 0, 0, 1], [0, 0, 0, 1, 0]], mindspore.float32)
>>> softmax_cross = ops.SoftmaxCrossEntropyWithLogits()
>>> loss, dlogits = softmax_cross(logits, labels)
>>> print(loss)
[0.5899297  0.52374405]
>>> print(dlogits)
[[ 0.02760027  0.20393994  0.01015357  0.20393994 -0.44563377]
 [ 0.08015892  0.02948882  0.08015892 -0.4077012   0.21789455]]