softmax_cross_entropy.py 文件源码

def softmax_cross_entropy(
        x, t, use_cudnn=True, normalize=True, cache_score=True,
        class_weight=None):
    """Computes cross entropy loss for pre-softmax activations.

    Args:
        x (~chainer.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 (~chainer.Variable): Variable holding an int32 vector of ground truth
            labels. If ``t[i] == -1``, 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 (~numpy.ndarray or ~cupy.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],)``.

    Returns:
        Variable: A variable holding a scalar array of the cross entropy loss.

    .. note::

       This function is differentiable only by ``x``.

    """
    return SoftmaxCrossEntropy(
        use_cudnn, normalize, cache_score, class_weight)(x, t)