Tensorflow深度学习应用CNN分类英文文本
发布时间：2021-11-07 18:47:08 所属栏目：教程 来源：互联网
导读：目录 前言源码与数据源码数据train.py 源码及分析data_helpers.py 源码及分析text_cnn.py 源码及分析 前言 Github源码地址 本文同时也是学习唐宇迪老师深度学习课程的一些理解与记录。 文中代码是实现在TensorFlow下使用卷积神经网络（CNN）做英文文本的分类
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目录 前言源码与数据源码数据train.py 源码及分析data_helpers.py 源码及分析text_cnn.py 源码及分析 前言 Github源码地址 本文同时也是学习唐宇迪老师深度学习课程的一些理解与记录。 文中代码是实现在TensorFlow下使用卷积神经网络（CNN）做英文文本的分类任务（本次是垃圾邮件的二分类任务），当然垃圾邮件分类是一种应用环境，模型方法也可以推广到其它应用场景，如电商商品好评差评分类、正负面新闻等。 这里写图片描述 源码与数据 源码 - data_helpers.py - train.py - text_cnn.py - eval.py（Save the evaluations to a csv, in case the user wants to inspect,analyze, or otherwise use the classifications generated by the neural net） 数据 - rt-polarity.neg - rt-polarity.pos 这里写图片描述 这里写图片描述 train.py 源码及分析 import tensorflow as tf import numpy as np import os import time import datetime import data_helpers from text_cnn import TextCNN from 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 Parameters tf.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.FLAGS FLAGS._parse_flags() print("nParameters:") for attr, value in sorted(FLAGS.__flags.items()): print("{}={}".format(attr.upper(), value)) print("") # Data Preparation # ================================================== # Load data print("Loading data...") x_text, y = data_helpers.load_data_and_labels(FLAGS.positive_data_file, FLAGS.negative_data_file) # Build vocabulary max_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-validation dev_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)) data_helpers.py 源码及分析 import numpy as np import re import itertools from collections import Counter def 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] text_cnn.py 源码及分析 import tensorflow as tf import 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 （编辑：好传媒网） 【声明】本站内容均来自网络，其相关言论仅代表作者个人观点，不代表本站立场。若无意侵犯到您的权利，请及时与联系站长删除相关内容! |
