基于对评论进行分类的持续运行模型

由于工作的原因，在运行tensorflow时，有时候必须中断训练，而为了下次再训练的时候可以接着上次的模型继续运行，做了如下整理：
代码如下：

import osimport randomimport tensorflow as tfimport pickleimport numpy as npfrom nltk.tokenize import word_tokenizefrom nltk.stem import WordNetLemmatizerf = open('lexcion.pickle', 'rb')lex = pickle.load(f)f.close()# print(np.size(lex))def get_random_line(file, point):    file.seek(point)    file.readline()    return file.readline()# 从文件中随机选择n条记录def get_n_random_line(file_name, n=150):    lines = []    file = open(file_name, encoding='latin-1')    total_bytes = os.stat(file_name).st_size     for i in range(n):        random_point = random.randint(0, total_bytes)        lines.append(get_random_line(file, random_point))    file.close()    return linesdef get_test_dataset(test_file):    with open(test_file, encoding='latin-1') as f:        test_x = []        test_y = []        lemmatizer = WordNetLemmatizer()        for line in f:            label = line.split(':%:%:%:')[0]            tweet = line.split(':%:%:%:')[1]            words = word_tokenize(tweet.lower())            words = [lemmatizer.lemmatize(word) for word in words]            features = np.zeros(len(lex))            for word in words:                if word in lex:                    features[lex.index(word)] = 1            test_x.append(list(features))            test_y.append(eval(label))    return test_x, test_ytest_x, test_y = get_test_dataset('tesing.csv')##############################################################################input_size = len(lex)print(input_size)num_classes = 3X = tf.placeholder(tf.int32, [None, input_size])Y = tf.placeholder(tf.float32, [None, num_classes])dropout_keep_prob = tf.placeholder(tf.float32)batch_size = 10def neural_network():    # embedding layer    with tf.device('/cpu:0'), tf.name_scope("embedding"):        embedding_size = 128        W = tf.Variable(tf.random_uniform([input_size, embedding_size], -1.0, 1.0))        embedded_chars = tf.nn.embedding_lookup(W, X)        embedded_chars_expanded = tf.expand_dims(embedded_chars, -1)    # convolution + maxpool layer    num_filters = 128    filter_sizes = [3,4,5]    pooled_outputs = []    for i, filter_size in enumerate(filter_sizes):        with tf.name_scope("conv-maxpool-%s" % filter_size):            filter_shape = [filter_size, embedding_size, 1, num_filters]            W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1))            b = tf.Variable(tf.constant(0.1, shape=[num_filters]))            conv = tf.nn.conv2d(embedded_chars_expanded, W, strides=[1, 1, 1, 1], padding="VALID")            h = tf.nn.relu(tf.nn.bias_add(conv, b))            pooled = tf.nn.max_pool(h, ksize=[1, input_size - filter_size + 1, 1, 1], strides=[1, 1, 1, 1], padding='VALID')            pooled_outputs.append(pooled)    num_filters_total = num_filters * len(filter_sizes)    h_pool = tf.concat(3, pooled_outputs)    h_pool_flat = tf.reshape(h_pool, [-1, num_filters_total])    # dropout    with tf.name_scope("dropout"):        h_drop = tf.nn.dropout(h_pool_flat, dropout_keep_prob)    # output    with tf.name_scope("output"):        W = tf.get_variable("W", shape=[num_filters_total, num_classes], initializer=tf.contrib.layers.xavier_initializer())        b = tf.Variable(tf.constant(0.1, shape=[num_classes]))        output = tf.nn.xw_plus_b(h_drop, W, b)    return outputdef train_neural_network():    output = neural_network()    optimizer = tf.train.AdamOptimizer(1e-3)    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(output, Y))    grads_and_vars = optimizer.compute_gradients(loss)    train_op = optimizer.apply_gradients(grads_and_vars)    saver = tf.train.Saver(tf.global_variables())    with tf.Session() as sess:        sess.run(tf.global_variables_initializer())        lemmatizer = WordNetLemmatizer()        i = 0        pre_accuracy = 0        while True:            batch_x = []            batch_y = []            #每次循环时，先判断            if os.path.exists('checkpoint'):                saver.restore(sess, tf.train.latest_checkpoint('.'))  #恢复保存的session                print("-----------")            try:                lines = get_n_random_line('training.csv', batch_size)                for line in lines:                    label = line.split(':%:%:%:')[0]                    tweet = line.split(':%:%:%:')[1]                    words = word_tokenize(tweet.lower())                    words = [lemmatizer.lemmatize(word) for word in words]                    features = np.zeros(len(lex))                    for word in words:                        if word in lex:                            features[lex.index(word)] = 1  # 一个句子中某个词可能出现两次,可以用+=1，其实区别不大                    batch_x.append(list(features))                    batch_y.append(eval(label))                _, loss_ = sess.run([train_op, loss], feed_dict={X:batch_x, Y:batch_y, dropout_keep_prob:0.5})                print(loss_)            except Exception as e:                print(e)            if i % 10 == 0:                predictions = tf.argmax(output, 1)                correct_predictions = tf.equal(predictions, tf.argmax(Y, 1))                accuracy = tf.reduce_mean(tf.cast(correct_predictions, "float"))                accur = sess.run(accuracy, feed_dict={X:test_x[0:50], Y:test_y[0:50], dropout_keep_prob:1.0})                print('第%s步的准确率： %.3f' % (i, accur))                saver.save(sess, './model.ckpt')   #  每隔10次保存一下模型            i += 1train_neural_network()