chainer.functions.softmax_cross_entropy¶

chainer.functions.
softmax_cross_entropy
(x, t, normalize=True, cache_score=True, class_weight=None, ignore_label=1, reduce='mean')¶ Computes cross entropy loss for presoftmax activations.
Parameters:  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
, correspondingx[i]
is ignored.  normalize (bool) – If
True
, this function normalizes the cross entropy loss across all instances. IfFalse
, 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 notNone
, each class weightclass_weight[i]
is actually multiplied toy[:, i]
that is the corresponding logsoftmax output ofx
and has the same shape asx
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 tonormalize
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 hasignore_label
as its target value, is set to0
.
Returns: A variable holding a scalar array of the cross entropy loss. If
reduce
is'mean'
, it is a scalar array. Ifreduce
is'no'
, the shape is same as that ofx
.Return type: Note
This function is differentiable only by
x
.