layers.py 文件源码

def apply(self, is_train, x, mask=None):
        if self.map_layer is not None:
            x = self.map_layer.apply(is_train, x, mask)

        rank = len(x.shape) - 2
        if mask is not None:
            shape = tf.shape(x)
            mask = tf.sequence_mask(tf.reshape(mask, (-1,)), shape[-2])
            mask = tf.cast(tf.reshape(mask, (shape[0], shape[1], shape[2], 1)), tf.float32)
            # this min_val thing is kind of a hack, really we should do something like compute the
            # min val over the entire batch, or maybe just pick a very negative values, or maybe
            # do something a bit more finicky with tf.bool_mask
            # In practice it doesn't seem to be problem, and some of the earlier models used these
            # scheme so I have been sticking with it.
            if self.min_val == 0:
                x *= mask
            else:
                x = x * mask + self.min_val * (1 - mask)
            return tf.maximum(tf.reduce_max(x, axis=rank), tf.fill([1] * (len(x.shape)-1),
                                                                   float(self.min_val)))
        else:
            return tf.reduce_max(x, axis=rank)