chainer.functions.softmax_cross_entropy(x, t, normalize=True, cache_score=True, class_weight=None, ignore_label=-1, reduce='mean')[source]

Computes cross entropy loss for pre-softmax activations.

  • x (Variable) – Variable holding a multidimensional array whose element indicates unnormalized log probability: the first axis of the variable represents the number of samples, and the second axis represents the number of classes. While this function computes a usual softmax cross entropy if the number of dimensions is equal to 2, it computes a cross entropy of the replicated softmax if the number of dimensions is greater than 2.
  • t (Variable) – Variable holding an int32 vector of ground truth labels. If t[i] == ignore_label, corresponding x[i] is ignored.
  • normalize (bool) – If True, this function normalizes the cross entropy loss across all instances. If False, it only normalizes along a batch size.
  • cache_score (bool) – When it is True, the function stores result of forward computation to use it on backward computation. It reduces computational cost though consumes more memory.
  • class_weight (ndarray or ndarray) – An array that contains constant weights that will be multiplied with the loss values along with the second dimension. The shape of this array should be (x.shape[1],). If this is not None, each class weight class_weight[i] is actually multiplied to y[:, i] that is the corresponding log-softmax output of x and has the same shape as x before calculating the actual loss value.
  • ignore_label (int) – Label value you want to ignore. Its default value is -1. See description of the argument t.
  • reduce (str) – A string that determines whether to reduce the loss values. If it is 'mean', it computes the sum of the individual cross entropy and normalize it according to normalize option. If it is 'no', this function computes cross entropy for each instance and does not normalize it (normalize option is ignored). In this case, the loss value of the ignored instance, which has ignore_label as its target value, is set to 0.

A variable holding a scalar array of the cross entropy loss. If reduce is 'mean', it is a scalar array. If reduce is 'no', the shape is same as that of x.

Return type:



This function is differentiable only by x.