modeci_mdf.functions.onnx.softmaxcrossentropyloss(*args, **kwargs)

Loss function that measures the softmax cross entropy between ‘scores’ and ‘labels’. This operator first computes a loss tensor whose shape is identical to the labels input. If the input is 2-D with shape (N, C), the loss tensor may be a N-element vector L = (l_1, l_2, …, l_N). If the input is N-D tensor with shape (N, C, D1, D2, …, Dk), the loss tensor L may have (N, D1, D2, …, Dk) as its shape and L[i,][j_1][j_2]…[j_k] denotes a scalar element in L. After L is available, this operator can optionally do a reduction operator.

shape(scores): (N, C) where C is the number of classes, or (N, C, D1, D2,…, Dk),

with K >= 1 in case of K-dimensional loss.

shape(labels): (N) where each value is 0 <= labels[i] <= C-1, or (N, D1, D2,…, Dk),

with K >= 1 in case of K-dimensional loss.

The loss for one sample, l_i, can caculated as follows:

l[i][d1][d2]…[dk] = -y[i][c][d1][d2]..[dk], where i is the index of classes.


l[i][d1][d2]…[dk] = -y[i][c][d1][d2]..[dk] * weights[c], if ‘weights’ is provided.

loss is zero for the case when label-value equals ignore_index.

l[i][d1][d2]…[dk] = 0, when labels[n][d1][d2]…[dk] = ignore_index


p = Softmax(scores) y = Log(p) c = labels[i][d1][d2]…[dk]

Finally, L is optionally reduced: If reduction = ‘none’, the output is L with shape (N, D1, D2, …, Dk). If reduction = ‘sum’, the output is scalar: Sum(L). If reduction = ‘mean’, the output is scalar: ReduceMean(L), or if weight is provided: ReduceSum(L) / ReduceSum(W), where tensor W is of shape (N, D1, D2, …, Dk) and W[n][d1][d2]…[dk] = weights[labels[i][d1][d2]…[dk]].