metrics.py 文件源码

def crossentropy(y_pred, y_true, void_labels, one_hot=False):
    # Clip predictions
    y_pred = T.clip(y_pred, _EPSILON, 1.0 - _EPSILON)

    if one_hot:
        y_true = T.argmax(y_true, axis=1)

    # Create mask
    mask = T.ones_like(y_true, dtype=_FLOATX)
    for el in void_labels:
        mask = T.set_subtensor(mask[T.eq(y_true, el).nonzero()], 0.)

    # Modify y_true temporarily
    y_true_tmp = y_true * mask
    y_true_tmp = y_true_tmp.astype('int32')

    # Compute cross-entropy
    loss = T.nnet.categorical_crossentropy(y_pred, y_true_tmp)

    # Compute masked mean loss
    loss *= mask
    loss = T.sum(loss) / T.sum(mask)

    return loss