自学教程：Tensorflow深度学习使用CNN分类英文文本

import tensorflow as tfimport numpy as npimport osimport timeimport datetimeimport data_helpersfrom text_cnn import TextCNNfrom tensorflow.contrib import learn# Parameters# ==================================================# Data loading params# 语料文件路径定义tf.flags.DEFINE_float("dev_sample_percentage", .1, "Percentage of the training data to use for validation")tf.flags.DEFINE_string("positive_data_file", "./data/rt-polaritydata/rt-polarity.pos", "Data source for the positive data.")tf.flags.DEFINE_string("negative_data_file", "./data/rt-polaritydata/rt-polarity.neg", "Data source for the negative data.")# Model Hyperparameters# 定义网络超参数tf.flags.DEFINE_integer("embedding_dim", 128, "Dimensionality of character embedding (default: 128)")tf.flags.DEFINE_string("filter_sizes", "3,4,5", "Comma-separated filter sizes (default: '3,4,5')")tf.flags.DEFINE_integer("num_filters", 128, "Number of filters per filter size (default: 128)")tf.flags.DEFINE_float("dropout_keep_prob", 0.5, "Dropout keep probability (default: 0.5)")tf.flags.DEFINE_float("l2_reg_lambda", 0.0, "L2 regularization lambda (default: 0.0)")# Training parameters# 训练参数tf.flags.DEFINE_integer("batch_size", 32, "Batch Size (default: 32)")tf.flags.DEFINE_integer("num_epochs", 200, "Number of training epochs (default: 200)") # 总训练次数tf.flags.DEFINE_integer("evaluate_every", 100, "Evaluate model on dev set after this many steps (default: 100)") # 每训练100次测试一下tf.flags.DEFINE_integer("checkpoint_every", 100, "Save model after this many steps (default: 100)") # 保存一次模型tf.flags.DEFINE_integer("num_checkpoints", 5, "Number of checkpoints to store (default: 5)")# Misc Parameterstf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement") # 加上一个布尔类型的参数，要不要自动分配tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices") # 加上一个布尔类型的参数，要不要打印日志# 打印一下相关初始参数FLAGS = tf.flags.FLAGSFLAGS._parse_flags()print("/nParameters:")for attr, value in sorted(FLAGS.__flags.items()):    print("{}={}".format(attr.upper(), value))print("")# Data Preparation# ==================================================# Load dataprint("Loading data...")x_text, y = data_helpers.load_data_and_labels(FLAGS.positive_data_file, FLAGS.negative_data_file)# Build vocabularymax_document_length = max([len(x.split(" ")) for x in x_text]) # 计算最长邮件vocab_processor = learn.preprocessing.VocabularyProcessor(max_document_length) # tensorflow提供的工具，将数据填充为最大长度，默认0填充x = np.array(list(vocab_processor.fit_transform(x_text)))# Randomly shuffle data# 数据洗牌np.random.seed(10)# np.arange生成随机序列shuffle_indices = np.random.permutation(np.arange(len(y)))x_shuffled = x[shuffle_indices]y_shuffled = y[shuffle_indices]# 将数据按训练train和测试dev分块# Split train/test set# TODO: This is very crude, should use cross-validationdev_sample_index = -1 * int(FLAGS.dev_sample_percentage * float(len(y)))x_train, x_dev = x_shuffled[:dev_sample_index], x_shuffled[dev_sample_index:]y_train, y_dev = y_shuffled[:dev_sample_index], y_shuffled[dev_sample_index:]print("Vocabulary Size: {:d}".format(len(vocab_processor.vocabulary_)))print("Train/Dev split: {:d}/{:d}".format(len(y_train), len(y_dev))) # 打印切分的比例# Training# ==================================================with tf.Graph().as_default():    session_conf = tf.ConfigProto(        allow_soft_placement=FLAGS.allow_soft_placement,        log_device_placement=FLAGS.log_device_placement)    sess = tf.Session(config=session_conf)    with sess.as_default():        # 卷积池化网络导入        cnn = TextCNN(            sequence_length=x_train.shape[1],            num_classes=y_train.shape[1], # 分几类            vocab_size=len(vocab_processor.vocabulary_),            embedding_size=FLAGS.embedding_dim,            filter_sizes=list(map(int, FLAGS.filter_sizes.split(","))), # 上面定义的filter_sizes拿过来，"3,4,5"按","分割            num_filters=FLAGS.num_filters, # 一共有几个filter            l2_reg_lambda=FLAGS.l2_reg_lambda) # l2正则化项        # Define Training procedure        global_step = tf.Variable(0, name="global_step", trainable=False)        optimizer = tf.train.AdamOptimizer(1e-3) # 定义优化器        grads_and_vars = optimizer.compute_gradients(cnn.loss)        train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step)        # Keep track of gradient values and sparsity (optional)        grad_summaries = []        for g, v in grads_and_vars:            if g is not None:                grad_hist_summary = tf.summary.histogram("{}/grad/hist".format(v.name), g)                sparsity_summary = tf.summary.scalar("{}/grad/sparsity".format(v.name), tf.nn.zero_fraction(g))                grad_summaries.append(grad_hist_summary)                grad_summaries.append(sparsity_summary)        grad_summaries_merged = tf.summary.merge(grad_summaries)        # Output directory for models and summaries        timestamp = str(int(time.time()))        out_dir = os.path.abspath(os.path.join(os.path.curdir, "runs", timestamp))        print("Writing to {}/n".format(out_dir))        # Summaries for loss and accuracy        # 损失函数和准确率的参数保存        loss_summary = tf.summary.scalar("loss", cnn.loss)        acc_summary = tf.summary.scalar("accuracy", cnn.accuracy)        # Train Summaries        # 训练数据保存        train_summary_op = tf.summary.merge([loss_summary, acc_summary, grad_summaries_merged])        train_summary_dir = os.path.join(out_dir, "summaries", "train")        train_summary_writer = tf.summary.FileWriter(train_summary_dir, sess.graph)        # Dev summaries        # 测试数据保存        dev_summary_op = tf.summary.merge([loss_summary, acc_summary])        dev_summary_dir = os.path.join(out_dir, "summaries", "dev")        dev_summary_writer = tf.summary.FileWriter(dev_summary_dir, sess.graph)        # Checkpoint directory. Tensorflow assumes this directory already exists so we need to create it        checkpoint_dir = os.path.abspath(os.path.join(out_dir, "checkpoints"))        checkpoint_prefix = os.path.join(checkpoint_dir, "model")        if not os.path.exists(checkpoint_dir):            os.makedirs(checkpoint_dir)        saver = tf.train.Saver(tf.global_variables(), max_to_keep=FLAGS.num_checkpoints) # 前面定义好参数num_checkpoints        # Write vocabulary        vocab_processor.save(os.path.join(out_dir, "vocab"))        # Initialize all variables        sess.run(tf.global_variables_initializer()) # 初始化所有变量        # 定义训练函数        def train_step(x_batch, y_batch):            """            A single training step            """            feed_dict = {              cnn.input_x: x_batch,              cnn.input_y: y_batch,              cnn.dropout_keep_prob: FLAGS.dropout_keep_prob # 参数在前面有定义            }            _, step, summaries, loss, accuracy = sess.run(                [train_op, global_step, train_summary_op, cnn.loss, cnn.accuracy], feed_dict)            time_str = datetime.datetime.now().isoformat() # 取当前时间，python的函数            print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))            train_summary_writer.add_summary(summaries, step)        # 定义测试函数        def dev_step(x_batch, y_batch, writer=None):            """            Evaluates model on a dev set            """            feed_dict = {              cnn.input_x: x_batch,              cnn.input_y: y_batch,              cnn.dropout_keep_prob: 1.0 # 神经元全部保留            }            step, summaries, loss, accuracy = sess.run(                [global_step, dev_summary_op, cnn.loss, cnn.accuracy], feed_dict)            time_str = datetime.datetime.now().isoformat()            print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))            if writer:                writer.add_summary(summaries, step)        # Generate batches        batches = data_helpers.batch_iter(list(zip(x_train, y_train)), FLAGS.batch_size, FLAGS.num_epochs)        # Training loop. For each batch...        # 训练部分        for batch in batches:            x_batch, y_batch = zip(*batch) # 按batch把数据拿进来            train_step(x_batch, y_batch)            current_step = tf.train.global_step(sess, global_step) # 将Session和global_step值传进来            if current_step % FLAGS.evaluate_every == 0: # 每FLAGS.evaluate_every次每100执行一次测试                print("/nEvaluation:")                dev_step(x_dev, y_dev, writer=dev_summary_writer)                print("")            if current_step % FLAGS.checkpoint_every == 0: # 每checkpoint_every次执行一次保存模型                path = saver.save(sess, './', global_step=current_step) # 定义模型保存路径                print("Saved model checkpoint to {}/n".format(path))

import numpy as npimport reimport itertoolsfrom collections import Counterdef clean_str(string):    """    Tokenization/string cleaning for all datasets except for SST.    Original taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py    """    # 清理数据替换掉无词义的符号    string = re.sub(r"[^A-Za-z0-9(),!?/'/`]", " ", string)    string = re.sub(r"/'s", " /'s", string)    string = re.sub(r"/'ve", " /'ve", string)    string = re.sub(r"n/'t", " n/'t", string)    string = re.sub(r"/'re", " /'re", string)    string = re.sub(r"/'d", " /'d", string)    string = re.sub(r"/'ll", " /'ll", string)    string = re.sub(r",", " , ", string)    string = re.sub(r"!", " ! ", string)    string = re.sub(r"/(", " /( ", string)    string = re.sub(r"/)", " /) ", string)    string = re.sub(r"/?", " /? ", string)    string = re.sub(r"/s{2,}", " ", string)    return string.strip().lower()def load_data_and_labels(positive_data_file, negative_data_file):    """    Loads MR polarity data from files, splits the data into words and generates labels.    Returns split sentences and labels.    """    # Load data from files    positive = open(positive_data_file, "rb").read().decode('utf-8')    negative = open(negative_data_file, "rb").read().decode('utf-8')    # 按回车分割样本    positive_examples = positive.split('/n')[:-1]    negative_examples = negative.split('/n')[:-1]    # 去空格    positive_examples = [s.strip() for s in positive_examples]    negative_examples = [s.strip() for s in negative_examples]    #positive_examples = list(open(positive_data_file, "rb").read().decode('utf-8'))    #positive_examples = [s.strip() for s in positive_examples]    #negative_examples = list(open(negative_data_file, "rb").read().decode('utf-8'))    #negative_examples = [s.strip() for s in negative_examples]    # Split by words    x_text = positive_examples + negative_examples    x_text = [clean_str(sent) for sent in x_text] # 字符过滤，实现函数见clean_str()    # Generate labels    positive_labels = [[0, 1] for _ in positive_examples]    negative_labels = [[1, 0] for _ in negative_examples]    y = np.concatenate([positive_labels, negative_labels], 0) # 将两种label连在一起    return [x_text, y]# 创建batch迭代模块def batch_iter(data, batch_size, num_epochs, shuffle=True): # shuffle=True洗牌    """    Generates a batch iterator for a dataset.    """    # 每次只输出shuffled_data[start_index:end_index]这么多    data = np.array(data)    data_size = len(data)    num_batches_per_epoch = int((len(data)-1)/batch_size) + 1 # 每一个epoch有多少个batch_size    for epoch in range(num_epochs):        # Shuffle the data at each epoch        if shuffle:            shuffle_indices = np.random.permutation(np.arange(data_size)) # 洗牌            shuffled_data = data[shuffle_indices]        else:            shuffled_data = data        for batch_num in range(num_batches_per_epoch):            start_index = batch_num * batch_size # 当前batch的索引开始            end_index = min((batch_num + 1) * batch_size, data_size) # 判断下一个batch是不是超过最后一个数据了            yield shuffled_data[start_index:end_index]

import tensorflow as tfimport numpy as np# 定义CNN网络实现的类class TextCNN(object):    """    A CNN for text classification.    Uses an embedding layer, followed by a convolutional, max-pooling and softmax layer.    """    def __init__(self, sequence_length, num_classes, vocab_size,                 embedding_size, filter_sizes, num_filters, l2_reg_lambda=0.0): # 把train.py中TextCNN里定义的参数传进来        # Placeholders for input, output and dropout        self.input_x = tf.placeholder(tf.int32, [None, sequence_length], name="input_x") # input_x输入语料,待训练的内容,维度是sequence_length,"N个词构成的N维向量"        self.input_y = tf.placeholder(tf.float32, [None, num_classes], name="input_y") # input_y输入语料,待训练的内容标签,维度是num_classes,"正面 || 负面"        self.dropout_keep_prob = tf.placeholder(tf.float32, name="dropout_keep_prob") # dropout_keep_prob dropout参数,防止过拟合,训练时用        # Keeping track of l2 regularization loss (optional)        l2_loss = tf.constant(0.0) # 先不用，写0        # Embedding layer        # 指定运算结构的运行位置在cpu非gpu,因为"embedding"无法运行在gpu        # 通过tf.name_scope指定"embedding"        with tf.device('/cpu:0'), tf.name_scope("embedding"): # 指定cpu            self.W = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0), name="W") # 定义W并初始化            self.embedded_chars = tf.nn.embedding_lookup(self.W, self.input_x)            self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1) # 加一个维度，转换为4维的格式        # Create a convolution + maxpool layer for each filter size        pooled_outputs = []        # filter_sizes卷积核尺寸,枚举后遍历        for i, filter_size in enumerate(filter_sizes):            with tf.name_scope("conv-maxpool-%s" % filter_size):                # Convolution Layer                filter_shape = [filter_size, embedding_size, 1, num_filters] # 4个参数分别为filter_size高h，embedding_size宽w，channel为1，filter个数                W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name="W") # W进行高斯初始化                b = tf.Variable(tf.constant(0.1, shape=[num_filters]), name="b") # b给初始化为一个常量                conv = tf.nn.conv2d(                    self.embedded_chars_expanded,                    W,                    strides=[1, 1, 1, 1],                    padding="VALID", # 这里不需要padding                    name="conv")                # Apply nonlinearity 激活函数                # 可以理解为,正面或者负面评价有一些标志词汇,这些词汇概率被增强，即一旦出现这些词汇,倾向性分类进正或负面评价,                # 该激励函数可加快学习进度，增加稀疏性,因为让确定的事情更确定,噪声的影响就降到了最低。                h = tf.nn.relu(tf.nn.bias_add(conv, b), name="relu")                # Maxpooling over the outputs                # 池化                pooled = tf.nn.max_pool(                    h,                    ksize=[1, sequence_length - filter_size + 1, 1, 1], # (h-filter+2padding)/strides+1=h-f+1                    strides=[1, 1, 1, 1],                    padding='VALID', # 这里不需要padding                    name="pool")                pooled_outputs.append(pooled)        # Combine all the pooled features        num_filters_total = num_filters * len(filter_sizes)        self.h_pool = tf.concat(3, pooled_outputs)        self.h_pool_flat = tf.reshape(self.h_pool, [-1, num_filters_total]) # 扁平化数据，跟全连接层相连        # Add dropout        # drop层,防止过拟合,参数为dropout_keep_prob        # 过拟合的本质是采样失真,噪声权重影响了判断，如果采样足够多,足够充分,噪声的影响可以被量化到趋近事实,也就无从过拟合。        # 即数据越大,drop和正则化就越不需要。        with tf.name_scope("dropout"):            self.h_drop = tf.nn.dropout(self.h_pool_flat, self.dropout_keep_prob)        # Final (unnormalized) scores and predictions        # 输出层        with tf.name_scope("output"):            W = tf.get_variable(                "W",                shape=[num_filters_total, num_classes], #前面连扁平化后的池化操作                initializer=tf.contrib.layers.xavier_initializer()) # 定义初始化方式            b = tf.Variable(tf.constant(0.1, shape=[num_classes]), name="b")            # 损失函数导入            l2_loss += tf.nn.l2_loss(W)            l2_loss += tf.nn.l2_loss(b)            # xw+b            self.scores = tf.nn.xw_plus_b(self.h_drop, W, b, name="scores") # 得分函数            self.predictions = tf.argmax(self.scores, 1, name="predictions") # 预测结果        # CalculateMean cross-entropy loss        with tf.name_scope("loss"):            # loss，交叉熵损失函数            losses = tf.nn.softmax_cross_entropy_with_logits(logits=self.scores, labels=self.input_y)            self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss        # Accuracy        with tf.name_scope("accuracy"):            # 准确率，求和计算算数平均值            correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))            self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, "float"), name="accuracy")