coverage.py 文件源码

def get_energies(self, y: tf.Tensor, weights_in_time: tf.TensorArray):
        weight_sum = tf.cond(
            tf.greater(weights_in_time.size(), 0),
            lambda: tf.reduce_sum(weights_in_time.stack(), axis=0),
            lambda: 0.0)

        coverage = weight_sum / self.fertility * self.attention_mask
        logits = tf.reduce_sum(
            self.similarity_bias_vector * tf.tanh(
                self.hidden_features + y + self.coverage_weights *
                tf.expand_dims(tf.expand_dims(coverage, -1), -1)),
            [2, 3])

        return logits