models生成与加载

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TensorFlow练习2: 对评论进行分类

本帖是前一贴的补充：

使用大数据，了解怎么处理数据不能一次全部加载到内存的情况。如果你内存充足，当我没说
训练好的模型的保存和使用
使用的模型没变，还是简单的feedforward神经网络（update：添加CNN模型）
如果你要运行本帖代码，推荐使用GPU版本或强大的VPS，我使用小笔记本差点等吐血
后续有关于中文的练习《TensorFlow练习13: 制作一个简单的聊天机器人》《TensorFlow练习7: 基于RNN生成古诗词》《TensorFlow练习18: 根据姓名判断性别》

在正文开始之前，我画了一个机器学习模型的基本开发流程图：

TensorFlow练习2: 对评论进行分类

使用的数据集

使用的数据集：http://help.sentiment140.com/for-students/ (情绪分析)

数据集包含1百60万条推特，包含消极、中性和积极tweet。不知道有没有现成的微博数据集。

数据格式：移除表情符号的CSV文件，字段如下：

0 – the polarity of the tweet (0 = negative, 2 = neutral, 4 = positive)
1 – the id of the tweet (2087)
2 – the date of the tweet (Sat May 16 23:58:44 UTC 2009)
3 – the query (lyx). If there is no query, then this value is NO_QUERY.
4 – the user that tweeted (robotickilldozr)
5 – the text of the tweet (Lyx is cool)

training.1600000.processed.noemoticon.csv（238M）
testdata.manual.2009.06.14.csv（74K）

数据预处理

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import nltk
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
 
import pickle
import numpy as np
import pandas as pd
from collections import OrderedDict
 
org_train_file = 'training.1600000.processed.noemoticon.csv'
org_test_file = 'testdata.manual.2009.06.14.csv'
 
# 提取文件中有用的字段
def usefull_filed(org_file, output_file):
output = open(output_file, 'w')
with open(org_file, buffering=10000, encoding='latin-1') as f:
try:
for line in f:                # "4","2193601966","Tue Jun 16 08:40:49 PDT 2009","NO_QUERY","AmandaMarie1028","Just woke up. Having no school is the best feeling ever "
line = line.replace('"', '')
clf = line.split(',')[0]   # 4
if clf == '0':
clf = [0, 0, 1]  # 消极评论
elif clf == '2':
clf = [0, 1, 0]  # 中性评论
elif clf == '4':
clf = [1, 0, 0]  # 积极评论
 
tweet = line.split(',')[-1]
outputline = str(clf) + ':%:%:%:' + tweet
output.write(outputline)  # [0, 0, 1]:%:%:%: that's a bummer.  You shoulda got David Carr of Third Day to do it. ;D
except Exception as e:
print(e)
output.close()  # 处理完成，处理后文件大小127.5M
 
usefull_filed(org_train_file, 'training.csv')
usefull_filed(org_test_file, 'tesing.csv')
 
# 创建词汇表
def create_lexicon(train_file):
lex = []
lemmatizer = WordNetLemmatizer()
with open(train_file, buffering=10000, encoding='latin-1') as f:
try:
count_word = {}  # 统计单词出现次数
for line in f:
tweet = line.split(':%:%:%:')[1]
words = word_tokenize(line.lower())
for word in words:
word = lemmatizer.lemmatize(word)
if word not in count_word:
count_word[word] = 1
else:
count_word[word] += 1
 
count_word = OrderedDict(sorted(count_word.items(), key=lambda t: t[1]))
for word in count_word:
if count_word[word] < 100000 and count_word[word] > 100:  # 过滤掉一些词
lex.append(word)
except Exception as e:
print(e)
return lex
 
lex = create_lexicon('training.csv')
 
with open('lexcion.pickle', 'wb') as f:
pickle.dump(lex, f)
 
 
"""
# 把字符串转为向量
def string_to_vector(input_file, output_file, lex):
output_f = open(output_file, 'w')
lemmatizer = WordNetLemmatizer()
with open(input_file, buffering=10000, encoding='latin-1') as f:
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  # 一个句子中某个词可能出现两次,可以用+=1，其实区别不大
features = list(features)
output_f.write(str(label) + ":" + str(features) + '\n')
output_f.close()
 
 
f = open('lexcion.pickle', 'rb')
lex = pickle.load(f)
f.close()
 
# lexcion词汇表大小112k,training.vec大约112k*1600000  170G  太大，只能边转边训练了
# string_to_vector('training.csv', 'training.vec', lex)
# string_to_vector('tesing.csv', 'tesing.vec', lex)
"""

上面代码把原始数据转为training.csv、和tesing.csv，里面只包含label和tweet。lexcion.pickle文件保存了词汇表。

如果数据文件太大，不能一次加载到内存，可以把数据导入数据库
Dask可处理大csv文件

开始漫长的训练

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importos
importrandom
importtensorflowastf
importpickle
importnumpyasnp
fromnltk.tokenizeimportword_tokenize
fromnltk.stemimportWordNetLemmatizer
 
f=open('lexcion.pickle','rb')
lex=pickle.load(f)
f.close()
 
 
defget_random_line(file,point):
file.seek(point)
file.readline()
returnfile.readline()
# 从文件中随机选择n条记录
defget_n_random_line(file_name,n=150):
lines=[]
file=open(file_name,encoding='latin-1')
total_bytes=os.stat(file_name).st_size
foriinrange(n):
random_point=random.randint(0,total_bytes)
lines.append(get_random_line(file,random_point))
file.close()
returnlines
 
 
defget_test_dataset(test_file):
withopen(test_file,encoding='latin-1')asf:
test_x=[]
test_y=[]
lemmatizer=WordNetLemmatizer()
forlineinf:
label=line.split(':%:%:%:')[0]
tweet=line.split(':%:%:%:')[1]
words=word_tokenize(tweet.lower())
words=[lemmatizer.lemmatize(word)forwordinwords]
features=np.zeros(len(lex))
forwordinwords:
ifwordinlex:
features[lex.index(word)]=1
test_x.append(list(features))
test_y.append(eval(label))
returntest_x,test_y
 
test_x,test_y=get_test_dataset('tesing.csv')
 
 
#######################################################################
 
n_input_layer=len(lex)  # 输入层
 
n_layer_1=2000    # hide layer
n_layer_2=2000    # hide layer(隐藏层)听着很神秘，其实就是除输入输出层外的中间层
 
n_output_layer=3      # 输出层
 
 
defneural_network(data):
# 定义第一层"神经元"的权重和biases
layer_1_w_b={'w_':tf.Variable(tf.random_normal([n_input_layer,n_layer_1])),'b_':tf.Variable(tf.random_normal([n_layer_1]))}
# 定义第二层"神经元"的权重和biases
layer_2_w_b={'w_':tf.Variable(tf.random_normal([n_layer_1,n_layer_2])),'b_':tf.Variable(tf.random_normal([n_layer_2]))}
# 定义输出层"神经元"的权重和biases
layer_output_w_b={'w_':tf.Variable(tf.random_normal([n_layer_2,n_output_layer])),'b_':tf.Variable(tf.random_normal([n_output_layer]))}
 
# w·x+b
layer_1=tf.add(tf.matmul(data,layer_1_w_b['w_']),layer_1_w_b['b_'])
layer_1=tf.nn.relu(layer_1)  # 激活函数
layer_2=tf.add(tf.matmul(layer_1,layer_2_w_b['w_']),layer_2_w_b['b_'])
layer_2=tf.nn.relu(layer_2)# 激活函数
layer_output=tf.add(tf.matmul(layer_2,layer_output_w_b['w_']),layer_output_w_b['b_'])
 
returnlayer_output
 
 
X=tf.placeholder('float')
Y=tf.placeholder('float')
batch_size=90
 
deftrain_neural_network(X,Y):
predict=neural_network(X)
cost_func=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(predict,Y))
optimizer=tf.train.AdamOptimizer().minimize(cost_func)
 
withtf.Session()assession:
session.run(tf.initialize_all_variables())
 
lemmatizer=WordNetLemmatizer()
saver=tf.train.Saver()
i=0
pre_accuracy=0
whileTrue:  # 一直训练
batch_x=[]
batch_y=[]
 
#if model.ckpt文件已存在:
# saver.restore(session, 'model.ckpt')  恢复保存的session
 
try:
lines=get_n_random_line('training.csv',batch_size)
forlineinlines:
label=line.split(':%:%:%:')[0]
tweet=line.split(':%:%:%:')[1]
words=word_tokenize(tweet.lower())
words=[lemmatizer.lemmatize(word)forwordinwords]
 
features=np.zeros(len(lex))
forwordinwords:
ifwordinlex:
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))
 
session.run([optimizer,cost_func],feed_dict={X:batch_x,Y:batch_y})
exceptExceptionase:
print(e)
 
# 准确率
ifi>100:
correct=tf.equal(tf.argmax(predict,1),tf.argmax(Y,1))
accuracy=tf.reduce_mean(tf.cast(correct,'float'))
accuracy=accuracy.eval({X:test_x,Y:test_y})
ifaccuracy>pre_accuracy:  # 保存准确率最高的训练模型
print('准确率: ',accuracy)
pre_accuracy=accuracy
saver.save(session,'model.ckpt')  # 保存session
i=0
i+=1
 
 
train_neural_network(X,Y)

上面程序占用内存600M，峰值1G。

运行：

TensorFlow练习2: 对评论进行分类

训练模型保存为model.ckpt。

使用训练好的模型

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import tensorflow as tf
import pickle
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
import numpy as np
 
f = open('lexcion.pickle', 'rb')
lex = pickle.load(f)
f.close()
 
n_input_layer = len(lex)  # 输入层
 
n_layer_1 = 2000     # hide layer
n_layer_2 = 2000    # hide layer(隐藏层)听着很神秘，其实就是除输入输出层外的中间层
 
n_output_layer = 3       # 输出层
def neural_network(data):
# 定义第一层"神经元"的权重和biases
layer_1_w_b = {'w_':tf.Variable(tf.random_normal([n_input_layer, n_layer_1])), 'b_':tf.Variable(tf.random_normal([n_layer_1]))}
# 定义第二层"神经元"的权重和biases
layer_2_w_b = {'w_':tf.Variable(tf.random_normal([n_layer_1, n_layer_2])), 'b_':tf.Variable(tf.random_normal([n_layer_2]))}
# 定义输出层"神经元"的权重和biases
layer_output_w_b = {'w_':tf.Variable(tf.random_normal([n_layer_2, n_output_layer])), 'b_':tf.Variable(tf.random_normal([n_output_layer]))}
 
# w·x+b
layer_1 = tf.add(tf.matmul(data, layer_1_w_b['w_']), layer_1_w_b['b_'])
layer_1 = tf.nn.relu(layer_1)  # 激活函数
layer_2 = tf.add(tf.matmul(layer_1, layer_2_w_b['w_']), layer_2_w_b['b_'])
layer_2 = tf.nn.relu(layer_2 ) # 激活函数
layer_output = tf.add(tf.matmul(layer_2, layer_output_w_b['w_']), layer_output_w_b['b_'])
 
return layer_output
 
X = tf.placeholder('float')
def prediction(tweet_text):
predict = neural_network(X)
 
with tf.Session() as session:
session.run(tf.initialize_all_variables())
saver = tf.train.Saver()
saver.restore(session, 'model.ckpt')
 
lemmatizer = WordNetLemmatizer()
words = word_tokenize(tweet_text.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
#print(predict.eval(feed_dict={X:[features]})) [[val1,val2,val3]]
res = session.run(tf.argmax(predict.eval(feed_dict={X:[features]}),1 ))
return res
 
 
prediction("I am very happe")

上面使用简单的feedfroward模型，下面使用CNN模型

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# https://github.com/Lab41/sunny-side-up
importos
importrandom
importtensorflowastf
importpickle
importnumpyasnp
fromnltk.tokenizeimportword_tokenize
fromnltk.stemimportWordNetLemmatizer
 
f=open('lexcion.pickle','rb')
lex=pickle.load(f)
f.close()
 
defget_random_line(file,point):
file.seek(point)
file.readline()
returnfile.readline()
# 从文件中随机选择n条记录
defget_n_random_line(file_name,n=150):
lines=[]
file=open(file_name,encoding='latin-1')
total_bytes=os.stat(file_name).st_size
foriinrange(n):
random_point=random.randint(0,total_bytes)
lines.append(get_random_line(file,random_point))
file.close()
returnlines
 
defget_test_dataset(test_file):
withopen(test_file,encoding='latin-1')asf:
test_x=[]
test_y=[]
lemmatizer=WordNetLemmatizer()
forlineinf:
label=line.split(':%:%:%:')[0]
tweet=line.split(':%:%:%:')[1]
words=word_tokenize(tweet.lower())
words=[lemmatizer.lemmatize(word)forwordinwords]
features=np.zeros(len(lex))
forwordinwords:
ifwordinlex:
features[lex.index(word)]=1
test_x.append(list(features))
test_y.append(eval(label))
returntest_x,test_y
 
test_x,test_y=get_test_dataset('tesing.csv')
##############################################################################
input_size=len(lex)
num_classes=3
 
X=tf.placeholder(tf.int32,[None,input_size])
Y=tf.placeholder(tf.float32,[None,num_classes])
 
dropout_keep_prob=tf.placeholder(tf.float32)
 
batch_size=90
 
defneural_network():
# embedding layer
withtf.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=[]
fori,filter_sizeinenumerate(filter_sizes):
withtf.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
withtf.name_scope("dropout"):
h_drop=tf.nn.dropout(h_pool_flat,dropout_keep_prob)
# output
withtf.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)
returnoutput
 
deftrain_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())
withtf.Session()assess:
sess.run(tf.global_variables_initializer())
 
lemmatizer=WordNetLemmatizer()
i=0
whileTrue:
batch_x=[]
batch_y=[]
 
#if model.ckpt文件已存在:
# saver.restore(session, 'model.ckpt')  恢复保存的session
try:
lines=get_n_random_line('training.csv',batch_size)
forlineinlines:
label=line.split(':%:%:%:')[0]
tweet=line.split(':%:%:%:')[1]
words=word_tokenize(tweet.lower())
words=[lemmatizer.lemmatize(word)forwordinwords]
 
features=np.zeros(len(lex))
forwordinwords:
ifwordinlex:
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_)
exceptExceptionase:
print(e)
 
ifi%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('准确率:',accur)
 
i+=1
 
train_neural_network()

使用了CNN模型之后，准确率有了显著提升。

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