Tensorflow深度学习使用CNN分类英文文本
程序员文章站
2022-06-26 16:02:34
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...
前言
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。
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
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="accuracy")
以上就是tensorflow深度学习cnn实现英文文本分类的详细内容,更多关于tensorflow实现cnn分类英文文本的资料请关注其它相关文章!