pytorch实现用CNN和LSTM对文本进行分类方式

model.py:

#!/usr/bin/python

# -*- coding: utf-8 -*-

import torch

from torch import nn

import numpy as np

from torch.autograd import Variable

import torch.nn.functional as F

class TextRNN(nn.Module):

"""文本分类,RNN模型"""

def __init__(self):

super(TextRNN, self).__init__()

# 三个待输入的数据

self.embedding = nn.Embedding(5000, 64) # 进行词嵌入

# self.rnn = nn.LSTM(input_size=64, hidden_size=128, num_layers=2, bidirectional=True)

self.rnn = nn.GRU(input_size=64, hidden_size=128, num_layers=2, bidirectional=True)

self.f1 = nn.Sequential(nn.Linear(256,128),

nn.Dropout(0.8),

nn.ReLU())

self.f2 = nn.Sequential(nn.Linear(128,10),

nn.Softmax())

def forward(self, x):

x = self.embedding(x)

x,_ = self.rnn(x)

x = F.dropout(x,p=0.8)

x = self.f1(x[:,-1,:])

return self.f2(x)

class TextCNN(nn.Module):

def __init__(self):

super(TextCNN, self).__init__()

self.embedding = nn.Embedding(5000,64)

self.conv = nn.Conv1d(64,256,5)

self.f1 = nn.Sequential(nn.Linear(256*596, 128),

nn.ReLU())

self.f2 = nn.Sequential(nn.Linear(128, 10),

nn.Softmax())

def forward(self, x):

x = self.embedding(x)

x = x.detach().numpy()

x = np.transpose(x,[0,2,1])

x = torch.Tensor(x)

x = Variable(x)

x = self.conv(x)

x = x.view(-1,256*596)

x = self.f1(x)

return self.f2(x)

train.py:

# coding: utf-8

from __future__ import print_function

import torch

from torch import nn

from torch import optim

from torch.autograd import Variable

import os

import numpy as np

from model import TextRNN,TextCNN

from cnews_loader import read_vocab, read_category, batch_iter, process_file, build_vocab

base_dir = 'cnews'

train_dir = os.path.join(base_dir, 'cnews.train.txt')

test_dir = os.path.join(base_dir, 'cnews.test.txt')

val_dir = os.path.join(base_dir, 'cnews.val.txt')

vocab_dir = os.path.join(base_dir, 'cnews.vocab.txt')

def train():

x_train, y_train = process_file(train_dir, word_to_id, cat_to_id,600)#获取训练数据每个字的id和对应标签的oe-hot形式

x_val, y_val = process_file(val_dir, word_to_id, cat_to_id,600)

#使用LSTM或者CNN

model = TextRNN()

# model = TextCNN()

#选择损失函数

Loss = nn.MultiLabelSoftMarginLoss()

# Loss = nn.BCELoss()

# Loss = nn.MSELoss()

optimizer = optim.Adam(model.parameters(),lr=0.001)

best_val_acc = 0

for epoch in range(1000):

batch_train = batch_iter(x_train, y_train,100)

for x_batch, y_batch in batch_train:

x = np.array(x_batch)

y = np.array(y_batch)

x = torch.LongTensor(x)

y = torch.Tensor(y)

# y = torch.LongTensor(y)

x = Variable(x)

y = Variable(y)

out = model(x)

loss = Loss(out,y)

optimizer.zero_grad()

loss.backward()

optimizer.step()

accracy = np.mean((torch.argmax(out,1)==torch.argmax(y,1)).numpy())

#对模型进行验证

if (epoch+1)%20 == 0:

batch_val = batch_iter(x_val, y_val, 100)

for x_batch, y_batch in batch_train:

x = np.array(x_batch)

y = np.array(y_batch)

x = torch.LongTensor(x)

y = torch.Tensor(y)

# y = torch.LongTensor(y)

x = Variable(x)

y = Variable(y)

out = model(x)

loss = Loss(out, y)

optimizer.zero_grad()

loss.backward()

optimizer.step()

accracy = np.mean((torch.argmax(out, 1) == torch.argmax(y, 1)).numpy())

if accracy > best_val_acc:

torch.save(model.state_dict(),'model_params.pkl')

best_val_acc = accracy

print(accracy)

if __name__ == '__main__':

#获取文本的类别及其对应id的字典

categories, cat_to_id = read_category()

#获取训练文本中所有出现过的字及其所对应的id

words, word_to_id = read_vocab(vocab_dir)

#获取字数

vocab_size = len(words)

train()

test.py:

# coding: utf-8

from __future__ import print_function

import os

import tensorflow.contrib.keras as kr

import torch

from torch import nn

from cnews_loader import read_category, read_vocab

from model import TextRNN

from torch.autograd import Variable

import numpy as np

try:

bool(type(unicode))

except NameError:

unicode = str

base_dir = 'cnews'

vocab_dir = os.path.join(base_dir, 'cnews.vocab.txt')

class TextCNN(nn.Module):

def __init__(self):

super(TextCNN, self).__init__()

self.embedding = nn.Embedding(5000,64)

self.conv = nn.Conv1d(64,256,5)

self.f1 = nn.Sequential(nn.Linear(152576, 128),

nn.ReLU())

self.f2 = nn.Sequential(nn.Linear(128, 10),

nn.Softmax())

def forward(self, x):

x = self.embedding(x)

x = x.detach().numpy()

x = np.transpose(x,[0,2,1])

x = torch.Tensor(x)

x = Variable(x)

x = self.conv(x)

x = x.view(-1,152576)

x = self.f1(x)

return self.f2(x)

class CnnModel:

def __init__(self):

self.categories, self.cat_to_id = read_category()

self.words, self.word_to_id = read_vocab(vocab_dir)

self.model = TextCNN()

self.model.load_state_dict(torch.load('model_params.pkl'))

def predict(self, message):

# 支持不论在python2还是python3下训练的模型都可以在2或者3的环境下运行

content = unicode(message)

data = [self.word_to_id[x] for x in content if x in self.word_to_id]

data = kr.preprocessing.sequence.pad_sequences([data],600)

data = torch.LongTensor(data)

y_pred_cls = self.model(data)

class_index = torch.argmax(y_pred_cls[0]).item()

return self.categories[class_index]

class RnnModel:

def __init__(self):

self.categories, self.cat_to_id = read_category()

self.words, self.word_to_id = read_vocab(vocab_dir)

self.model = TextRNN()

self.model.load_state_dict(torch.load('model_rnn_params.pkl'))

def predict(self, message):

# 支持不论在python2还是python3下训练的模型都可以在2或者3的环境下运行

content = unicode(message)

data = [self.word_to_id[x] for x in content if x in self.word_to_id]

data = kr.preprocessing.sequence.pad_sequences([data], 600)

data = torch.LongTensor(data)

y_pred_cls = self.model(data)

class_index = torch.argmax(y_pred_cls[0]).item()

return self.categories[class_index]

if __name__ == '__main__':

model = CnnModel()

# model = RnnModel()

test_demo = ['湖人助教力助科比恢复手感 他也是阿泰的精神导师新浪体育讯记者戴高乐报道 上赛季,科比的右手食指遭遇重创,他的投篮手感也因此大受影响。不过很快科比就调整了自己的投篮手型,并通过这一方式让自己的投篮命中率回升。而在这科比背后,有一位特别助教对科比帮助很大,他就是查克·珀森。珀森上赛季担任湖人的特别助教,除了帮助科比调整投篮手型之外,他的另一个重要任务就是担任阿泰的精神导师。来到湖人队之后,阿泰收敛起了暴躁的脾气,成为湖人夺冠路上不可或缺的一员,珀森的“心灵按摩”功不可没。经历了上赛季的成功之后,珀森本赛季被“升职”成为湖人队的全职助教,每场比赛,他都会坐在球场边,帮助禅师杰克逊一起指挥湖人球员在场上拼杀。对于珀森的工作,禅师非常欣赏,“查克非常善于分析问题,”菲尔·杰克逊说,“他总是在寻找问题的答案,同时也在找造成这一问题的原因,这是我们都非常乐于看到的。我会在平时把防守中出现的一些问题交给他,然后他会通过组织球员练习找到解决的办法。他在球员时代曾是一名很好的外线投手,不过现在他与内线球员的配合也相当不错。',

'弗老大被裁美国媒体看热闹“特权”在中国像蠢蛋弗老大要走了。虽然他只在首钢男篮效力了13天,而且表现毫无亮点,大大地让球迷和俱乐部失望了,但就像中国人常说的“好聚好散”,队友还是友好地与他告别,俱乐部与他和平分手,球迷还请他留下了在北京的最后一次签名。相比之下,弗老大的同胞美国人却没那么“宽容”。他们嘲讽这位NBA前巨星的英雄迟暮,批评他在CBA的业余表现,还惊讶于中国人的“大方”。今天,北京首钢俱乐部将与弗朗西斯继续商讨解约一事。从昨日的进展来看,双方可以做到“买卖不成人意在”,但回到美国后,恐怕等待弗朗西斯的就没有这么轻松的环境了。进展@北京昨日与队友告别 最后一次为球迷签名弗朗西斯在13天里为首钢队打了4场比赛,3场的得分为0,只有一场得了2分。昨天是他来到北京的第14天,虽然他与首钢还未正式解约,但双方都明白“缘分已尽”。下午,弗朗西斯来到首钢俱乐部与队友们告别。弗朗西斯走到队友身边,依次与他们握手拥抱。“你们都对我很好,安排的条件也很好,我很喜欢这支球队,想融入你们,但我现在真的很不适应。希望你们']

for i in test_demo:

print(i,":",model.predict(i))

cnews_loader.py:

# coding: utf-8

import sys

from collections import Counter

import numpy as np

import tensorflow.contrib.keras as kr

if sys.version_info[0] > 2:

is_py3 = True

else:

reload(sys)

sys.setdefaultencoding("utf-8")

is_py3 = False

def native_word(word, encoding='utf-8'):

"""如果在python2下面使用python3训练的模型,可考虑调用此函数转化一下字符编码"""

if not is_py3:

return word.encode(encoding)

else:

return word

def native_content(content):

if not is_py3:

return content.decode('utf-8')

else:

return content

def open_file(filename, mode='r'):

"""

常用文件操作,可在python2和python3间切换.

mode: 'r' or 'w' for read or write

"""

if is_py3:

return open(filename, mode, encoding='utf-8', errors='ignore')

else:

return open(filename, mode)

def read_file(filename):

"""读取文件数据"""

contents, labels = [], []

with open_file(filename) as f:

for line in f:

try:

label, content = line.strip().split('\t')

if content:

contents.append(list(native_content(content)))

labels.append(native_content(label))

except:

pass

return contents, labels

def build_vocab(train_dir, vocab_dir, vocab_size=5000):

"""根据训练集构建词汇表,存储"""

data_train, _ = read_file(train_dir)

all_data = []

for content in data_train:

all_data.extend(content)

counter = Counter(all_data)

count_pairs = counter.most_common(vocab_size - 1)

words, _ = list(zip(*count_pairs))

# 添加一个 <PAD> 来将所有文本pad为同一长度

words = ['<PAD>'] + list(words)

open_file(vocab_dir, mode='w').write('\n'.join(words) + '\n')

def read_vocab(vocab_dir):

"""读取词汇表"""

# words = open_file(vocab_dir).read().strip().split('\n')

with open_file(vocab_dir) as fp:

# 如果是py2 则每个值都转化为unicode

words = [native_content(_.strip()) for _ in fp.readlines()]

word_to_id = dict(zip(words, range(len(words))))

return words, word_to_id

def read_category():

"""读取分类目录,固定"""

categories = ['体育', '财经', '房产', '家居', '教育', '科技', '时尚', '时政', '游戏', '娱乐']

categories = [native_content(x) for x in categories]

cat_to_id = dict(zip(categories, range(len(categories))))

return categories, cat_to_id

def to_words(content, words):

"""将id表示的内容转换为文字"""

return ''.join(words[x] for x in content)

def process_file(filename, word_to_id, cat_to_id, max_length=600):

"""将文件转换为id表示"""

contents, labels = read_file(filename)#读取训练数据的每一句话及其所对应的类别

data_id, label_id = [], []

for i in range(len(contents)):

data_id.append([word_to_id[x] for x in contents[i] if x in word_to_id])#将每句话id化

label_id.append(cat_to_id[labels[i]])#每句话对应的类别的id

#

# # 使用keras提供的pad_sequences来将文本pad为固定长度

x_pad = kr.preprocessing.sequence.pad_sequences(data_id, max_length)

y_pad = kr.utils.to_categorical(label_id, num_classes=len(cat_to_id)) # 将标签转换为one-hot表示

#

return x_pad, y_pad

def batch_iter(x, y, batch_size=64):

"""生成批次数据"""

data_len = len(x)

num_batch = int((data_len - 1) / batch_size) + 1

indices = np.random.permutation(np.arange(data_len))

x_shuffle = x[indices]

y_shuffle = y[indices]

for i in range(num_batch):

start_id = i * batch_size

end_id = min((i + 1) * batch_size, data_len)

yield x_shuffle[start_id:end_id], y_shuffle[start_id:end_id]

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