.. _l-onnx-doc-SparseSoftmaxCrossEntropyGrad:

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SparseSoftmaxCrossEntropyGrad
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.. _l-onnx-op-sparsesoftmaxcrossentropygrad-9:

SparseSoftmaxCrossEntropyGrad - 9
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**Version**

* **name**: `SparseSoftmaxCrossEntropyGrad (GitHub) <https://github.com/onnx/onnx/blob/main/docs/Operators.md#SparseSoftmaxCrossEntropyGrad>`_
* **domain**: **main**
* **since_version**: **9**
* **function**:
* **support_level**:
* **shape inference**:

This version of the operator has been available
**since version 9**.

**Summary**

SparseSoftmaxCrossEntropyGrad

**Attributes**

* **reduction**:
  Type of reduction to apply to loss: none, sum, mean(default).
  'none': the output is the loss for each sample in the batch.'sum':
  the output will be summed. 'mean': the sum of the output will be
  divided by the batch_size. Default value is ``?``.

**Inputs**

Between 3 and 4 inputs.

* **dY** (heterogeneous) - **T**:
  gradient of Y
* **log_prob** (heterogeneous) - **T**:
  logsoftmax(logits), (N+1)-D input of shape (batch_size).
* **label** (heterogeneous) - **Tind**:
  label is N-D input whose shape should match that of logits. It is a
  tensor of nonnegative integers, where each element is the
  nonnegative integer label for the element of the batch.
* **weight** (optional, heterogeneous) - **T**:
  weight for each sample. The shape is the same as label's

**Outputs**

* **d_logits** (heterogeneous) - **T**:
  gradient of logits

**Examples**