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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。

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")

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