.. _l-onnx-doccom.microsoft-SoftmaxCrossEntropyLossInternalGrad: =================================================== com.microsoft - SoftmaxCrossEntropyLossInternalGrad =================================================== .. contents:: :local: .. _l-onnx-opcom-microsoft-softmaxcrossentropylossinternalgrad-1: SoftmaxCrossEntropyLossInternalGrad - 1 (com.microsoft) ======================================================= **Version** * **name**: `SoftmaxCrossEntropyLossInternalGrad (GitHub) `_ * **domain**: **com.microsoft** * **since_version**: **1** * **function**: * **support_level**: * **shape inference**: This version of the operator has been available **since version 1 of domain com.microsoft**. **Summary** SoftmaxCrossEntropyLossInternalGrad **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 5 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 1-D tensor. * **ignore_index** (optional, heterogeneous) - **I**: Scalar tensor to specify a target value that is ignored and does not contribute to the input gradient. **Outputs** * **d_logits** (heterogeneous) - **T**: gradient of logits **Examples**