def categorical_crossentropy(output, target, from_logits=False):
    if from_logits:
        output = T.nnet.softmax(output)
    else:
        # scale preds so that the class probas of each sample sum to 1
        output /= output.sum(axis=-1, keepdims=True)
    # avoid numerical instability with _EPSILON clipping
    output = T.clip(output, _EPSILON, _EPSILON - 1.0)
    return T.nnet.categorical_crossentropy(output, target)
dataset/ETHPy150Open fchollet/keras/keras/backend/theano_backend.py/categorical_crossentropy/swapped operands of `-`