TensorFlow练习2: 对评论进行分类

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：

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

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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import os

import random

import tensorflow as tf

import pickle

import numpy as np

from nltk.tokenize import word_tokenize

from nltk.stem import WordNetLemmatizer

f = open('lexcion.pickle', 'rb')

lex = pickle.load(f)

f.close()

def get_random_line(file, point):

file.seek(point)

file.readline()

return file.readline()

# 从文件中随机选择n条记录

def get_n_random_line(file_name, n=150):

lines = []

file = open(file_name, encoding='latin-1')

total_bytes = os.stat(file_name).st_size

for i in range(n):

random_point = random.randint(0, total_bytes)

lines.append(get_random_line(file, random_point))

file.close()

return lines

def get_test_dataset(test_file):

with open(test_file, encoding='latin-1') as f:

test_x = []

test_y = []

lemmatizer = WordNetLemmatizer()

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

test_x.append(list(features))

test_y.append(eval(label))

return test_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

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')

Y = tf.placeholder('float')

batch_size = 90

def train_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)

with tf.Session() as session:

session.run(tf.initialize_all_variables())

lemmatizer = WordNetLemmatizer()

saver = tf.train.Saver()

i = 0

pre_accuracy = 0

while True: # 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)

for line in lines:

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

batch_x.append(list(features))

batch_y.append(eval(label))

session.run([optimizer, cost_func], feed_dict={X:batch_x,Y:batch_y})

except Exception as e:

print(e)

# 准确率

if i > 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})

if accuracy > 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

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

import os

import random

import tensorflow as tf

import pickle

import numpy as np

from nltk.tokenize import word_tokenize

from nltk.stem import WordNetLemmatizer

f = open('lexcion.pickle', 'rb')

lex = pickle.load(f)

f.close()

def get_random_line(file, point):

file.seek(point)

file.readline()

return file.readline()

# 从文件中随机选择n条记录

def get_n_random_line(file_name, n=150):

lines = []

file = open(file_name, encoding='latin-1')

total_bytes = os.stat(file_name).st_size

for i in range(n):

random_point = random.randint(0, total_bytes)

lines.append(get_random_line(file, random_point))

file.close()

return lines

def get_test_dataset(test_file):

with open(test_file, encoding='latin-1') as f:

test_x = []

test_y = []

lemmatizer = WordNetLemmatizer()

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

test_x.append(list(features))

test_y.append(eval(label))

return test_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

def neural_network():

# embedding layer

with tf.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 = []

for i, filter_size in enumerate(filter_sizes):

with tf.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

with tf.name_scope("dropout"):

h_drop = tf.nn.dropout(h_pool_flat, dropout_keep_prob)

# output

with tf.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)

return output

def train_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())

with tf.Session() as sess:

sess.run(tf.global_variables_initializer())

lemmatizer = WordNetLemmatizer()

i = 0

while True:

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)

for line in lines:

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

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_)

except Exception as e:

print(e)

if i % 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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