eval_output_embeddings.py 文件源码

def bag_of_tokens(config, labels, label_lengths):
    if config.train_output_embeddings:
        with tf.variable_scope('embed', reuse=True):
            output_embeddings = tf.get_variable('output_embedding')
    else:
        output_embeddings = tf.constant(config.output_embedding_matrix)

    #everything_label_placeholder = tf.placeholder(shape=(None, config.max_length,), dtype=tf.int32)
    #everything_label_length_placeholder = tf.placeholder(shape=(None,), dtype=tf.int32)

    labels = tf.constant(np.array(labels))
    embedded_output = tf.gather(output_embeddings, labels)
    print('embedded_output before', embedded_output)
    #mask = tf.sequence_mask(label_lengths, maxlen=config.max_length, dtype=tf.float32)
    # note: this multiplication will broadcast the mask along all elements of the depth dimension
    # (which is why we run the expand_dims to choose how to broadcast)
    #embedded_output = embedded_output * tf.expand_dims(mask, axis=2)
    #print('embedded_output after', embedded_output)

    return tf.reduce_sum(embedded_output, axis=1)