memory.py 文件源码

def get_content_adressing(self, memory_matrix, keys, strengths):
        """
        retrives a content-based addressing weighting given the keys

        Parameters:
        ----------
        memory_matrix: Tensor (batch_size, memory_locations, word_size)
            the memory matrix to lookup in
        keys: Tensor (batch_size, word_size, number_of_keys)
            the keys to query the memory with
        strengths: Tensor (batch_size, number_of_keys)
            the list of strengths for each lookup key

        Returns: Tensor (batch_size, memory_locations, number_of_keys)
            The list of lookup weightings for each provided key
        """

        normalized_memory = tf.nn.l2_normalize(memory_matrix, 2)
        normalized_keys = tf.nn.l2_normalize(keys, 1)

        similiarity = tf.batch_matmul(normalized_memory, normalized_keys)
        strengths = tf.expand_dims(strengths, 1)

        return tf.nn.softmax(similiarity * strengths, 1)