test.py

# -*- coding: utf-8 -*-
"""
Created on Tue May 29 10:45:25 2018

@author: Peter
"""

import tensorflow as tf
from bidaf import BiDAF
from prepro import get_max_length, load_embedding, load_data, next_batch


def main():
    # testing_file = "D:/DataMining/QASystem/new_data/test.ann.json"
    testing_file = "D:/DataMining/QASystem/new_data/validation.ann.json"
    # testing_file = "D:/DataMining/QASystem/new_data/training.json"
    trained_model = "checkpoints/model.ckpt"
    embedding_file = "D:/DataMining/QASystem/wiki/wiki.zh.text.vector"
    embedding_size = 60  # word embedding维度
    hidden_size = 100  # 隐藏层神经元数量
    keep_prob = 1  # 0.8
    batch_size = 60  # 分批数据大小

    max_quelen, max_evilen = get_max_length(testing_file)
    embeddings, word2idx = load_embedding(embedding_file)
    questions, evidences, y1, y2 = load_data(
        testing_file, word2idx, max_quelen, max_evilen)
    with tf.Graph().as_default():
        with tf.variable_scope('Model'):
            model = BiDAF(embeddings, max_quelen, max_evilen,
                          embedding_size, hidden_size, keep_prob)
            with tf.Session().as_default() as sess:
                saver = tf.train.Saver()
                print("开始加载模型")
                saver.restore(sess, trained_model)
                print("加载模型完毕")
                # sess.run(tf.global_variables_initializer()) 前面已经使用restore恢复变量了，如果再使用global_variables_initializer，会导致所有学习到的东西清零
                for batch_questions, batch_evidences, batch_y1, batch_y2 in next_batch(questions, evidences, y1, y2, batch_size):
                    feed_dict = {
                        model.x: batch_evidences,
                        model.q: batch_questions,
                        model.y1: batch_y1,
                        model.y2: batch_y2
                    }
                    acc_s, acc_e = sess.run(
                        [model.acc_s, model.acc_e], feed_dict)
                    print('ACC_S: %s\t\tACC_E: %s' % (acc_s, acc_e))


if __name__ == "__main__":
    main()