scoring.py 文件源码

def multilinear_grad(emb: tf.Tensor, tuples: tf.Tensor, score=False) -> tf.Tensor:
    tuple_shape = [d.value for d in tuples.get_shape()]
    # if len(tuple_shape) > 2:
    #     n = np.prod(tuple_shape[:-1])
    #     tuples = tf.reshape(tuples, (n, -1))
    # n = tuples.get_shape()[0].value
    order = tuples.get_shape()[2].value
    rank = emb.get_shape()[-1].value
    if order == 2:
        if score:
            emb_sel = tf.gather(emb, tuples)
            grad_score = tf.reshape(tf.reverse(emb_sel, [False, False, True, False]), tuple_shape[:-1] + [2, rank])
            prod = tf.reduce_prod(emb_sel, 2)
            preds = tf.reshape(tf.reduce_sum(prod, 2), tuple_shape[:-1])
            return grad_score, preds
    raise NotImplementedError('Todo')
                # grad_score0 = tf.reverse(emb_sel, [False, True, False])  # reverse the row and column embeddings
    #         prod = tf.reduce_prod(emb_sel, 1)
    #         preds = tf.reshape(tf.reduce_sum(prod, 1), tuple_shape[:-1])
    #
    #     preds = tf.reshape(tf.reduce_sum(prod, 1), tuple_shape[:-1])
    # else:  # derivative of a product
    #     prod = tf.reduce_prod(emb_sel, 1)
    #     grad_score0 = tf.tile(tf.reshape(prod, (n, 1, rank)), (1, order, 1)) / emb_sel
    # grad_score = tf.reshape(grad_score0, tuple_shape + [rank])
    # if score:
    #         prod = tf.reduce_prod(emb_sel, 1)
    #     preds = tf.reshape(tf.reduce_sum(prod, 1), tuple_shape[:-1])
    #     return grad_score, preds
    # else:
    #     return grad_score