pytorch下使用LSTM神经网络写诗实例

Python  /  管理员 发布于 5年前   257

在pytorch下，以数万首唐诗为素材，训练双层LSTM神经网络，使其能够以唐诗的方式写诗。

代码结构分为四部分，分别为

1.model.py，定义了双层LSTM模型

2.data.py，定义了从网上得到的唐诗数据的处理方法

3.utlis.py 定义了损失可视化的函数

4.main.py定义了模型参数，以及训练、唐诗生成函数。

参考：电子工业出版社的《深度学习框架PyTorch：入门与实践》第九章

main代码及注释如下

import sys, osimport torch as tfrom data import get_datafrom model import PoetryModelfrom torch import nnfrom torch.autograd import Variablefrom utils import Visualizerimport tqdmfrom torchnet import meterimport ipdb class Config(object):data_path = 'data/'pickle_path = 'tang.npz'author = Noneconstrain = Nonecategory = 'poet.tang' #or poet.songlr = 1e-3weight_decay = 1e-4use_gpu = Trueepoch = 20batch_size = 128maxlen = 125plot_every = 20#use_env = True #是否使用visodmenv = 'poety' #visdom envmax_gen_len = 200debug_file = '/tmp/debugp'model_path = Noneprefix_words = '细雨鱼儿出，微风燕子斜。' #不是诗歌组成部分，是意境start_words = '闲云潭影日悠悠' #诗歌开始acrostic = False #是否藏头model_prefix = 'checkpoints/tang' #模型保存路径opt = Config() def generate(model, start_words, ix2word, word2ix, prefix_words=None):'''给定几个词，根据这几个词接着生成一首完整的诗歌'''results = list(start_words)start_word_len = len(start_words)# 手动设置第一个词为<START># 这个地方有问题，最后需要再看一下input = Variable(t.Tensor([word2ix['<START>']]).view(1,1).long())if opt.use_gpu:input=input.cuda()hidden = Noneif prefix_words:for word in prefix_words:output,hidden = model(input,hidden)# 下边这句话是为了把input变成1*1？input = Variable(input.data.new([word2ix[word]])).view(1,1)for i in range(opt.max_gen_len):output,hidden = model(input,hidden)if i<start_word_len:w = results[i]input = Variable(input.data.new([word2ix[w]])).view(1,1)else:top_index = output.data[0].topk(1)[1][0]w = ix2word[top_index]results.append(w)input = Variable(input.data.new([top_index])).view(1,1)if w=='<EOP>':del results[-1] #-1的意思是倒数第一个breakreturn results def gen_acrostic(model,start_words,ix2word,word2ix, prefix_words = None): ''' 生成藏头诗 start_words : u'深度学习' 生成： 深木通中岳，青苔半日脂。 度山分地险，逆浪到南巴。 学道兵犹毒，当时燕不移。 习根通古岸，开镜出清羸。 ''' results = [] start_word_len = len(start_words) input = Variable(t.Tensor([word2ix['<START>']]).view(1,1).long()) if opt.use_gpu:input=input.cuda() hidden = None  index=0 # 用来指示已经生成了多少句藏头诗 # 上一个词 pre_word='<START>'  if prefix_words:  for word in prefix_words:   output,hidden = model(input,hidden)   input = Variable(input.data.new([word2ix[word]])).view(1,1)  for i in range(opt.max_gen_len):  output,hidden = model(input,hidden)  top_index = output.data[0].topk(1)[1][0]  w = ix2word[top_index]   if (pre_word in {u'。',u'！','<START>'} ):   # 如果遇到句号，藏头的词送进去生成    if index==start_word_len:    # 如果生成的诗歌已经包含全部藏头的词，则结束    break   else:     # 把藏头的词作为输入送入模型    w = start_words[index]    index+=1    input = Variable(input.data.new([word2ix[w]])).view(1,1)   else:   # 否则的话，把上一次预测是词作为下一个词输入   input = Variable(input.data.new([word2ix[w]])).view(1,1)  results.append(w)  pre_word = w return results def train(**kwargs):for k,v in kwargs.items():setattr(opt,k,v) #设置apt里属性的值vis = Visualizer(env=opt.env)#获取数据data, word2ix, ix2word = get_data(opt) #get_data是data.py里的函数data = t.from_numpy(data)#这个地方出错了，是大写的Ldataloader = t.utils.data.DataLoader(data, batch_size = opt.batch_size,shuffle = True,num_workers = 1) #在python里，这样写程序可以吗？ #模型定义model = PoetryModel(len(word2ix), 128, 256)optimizer = t.optim.Adam(model.parameters(), lr=opt.lr)criterion = nn.CrossEntropyLoss() if opt.model_path:model.load_state_dict(t.load(opt.model_path))if opt.use_gpu:model.cuda()criterion.cuda()#The tnt.AverageValueMeter measures and returns the average value #and the standard deviation of any collection of numbers that are #added to it. It is useful, for instance, to measure the average #loss over a collection of examples.  #The add() function expects as input a Lua number value, which  #is the value that needs to be added to the list of values to  #average. It also takes as input an optional parameter n that  #assigns a weight to value in the average, in order to facilitate  #computing weighted averages (default = 1).  #The tnt.AverageValueMeter has no parameters to be set at initialization time. loss_meter = meter.AverageValueMeter()for epoch in range(opt.epoch):loss_meter.reset()for ii,data_ in tqdm.tqdm(enumerate(dataloader)):#tqdm是python中的进度条#训练data_ = data_.long().transpose(1,0).contiguous()#上边一句话，把data_变成long类型，把1维和0维转置，把内存调成连续的if opt.use_gpu: data_ = data_.cuda()optimizer.zero_grad()input_, target = Variable(data_[:-1,:]), Variable(data_[1:,:])#上边一句，将输入的诗句错开一个字，形成训练和目标output,_ = model(input_)loss = criterion(output, target.view(-1))loss.backward()optimizer.step()loss_meter.add(loss.data[0]) #为什么是data[0]?#可视化用到的是utlis.py里的函数if (1+ii)%opt.plot_every ==0:if os.path.exists(opt.debug_file):ipdb.set_trace()vis.plot('loss',loss_meter.value()[0])# 下面是对目前模型情况的测试，诗歌原文poetrys = [[ix2word[_word] for _word in data_[:,_iii]] for _iii in range(data_.size(1))][:16]#上面句子嵌套了两个循环，主要是将诗歌索引的前十六个字变成原文vis.text('</br>'.join([''.join(poetry) for poetry in poetrys]),win = u'origin_poem')gen_poetries = []#分别以以下几个字作为诗歌的第一个字，生成8首诗for word in list(u'春江花月夜凉如水'):gen_poetry = ''.join(generate(model,word,ix2word,word2ix))gen_poetries.append(gen_poetry)vis.text('</br>'.join([''.join(poetry) for poetry in gen_poetries]), win = u'gen_poem')t.save(model.state_dict(), '%s_%s.pth' %(opt.model_prefix,epoch)) def gen(**kwargs):'''提供命令行接口，用以生成相应的诗'''for k,v in kwargs.items():setattr(opt,k,v)data, word2ix, ix2word = get_data(opt)model = PoetryModel(len(word2ix), 128, 256)map_location = lambda s,l:s# 上边句子里的map_location是在load里用的，用以加载到指定的CPU或GPU，# 上边句子的意思是将模型加载到默认的GPU上state_dict = t.load(opt.model_path, map_location = map_location)model.load_state_dict(state_dict)if opt.use_gpu:model.cuda()if sys.version_info.major == 3:if opt.start_words.insprintable():start_words = opt.start_wordsprefix_words = opt.prefix_words if opt.prefix_words else Noneelse:start_words = opt.start_words.encode('ascii',\'surrogateescape').decode('utf8')prefix_words = opt.prefix_words.encode('ascii',\'surrogateescape').decode('utf8') if opt.prefix_words else Nonestart_words = start_words.replace(',',u'，')\.replace('.',u'。')\.replace('?',u'？')gen_poetry = gen_acrostic if opt.acrostic else generateresult = gen_poetry(model,start_words,ix2word,word2ix,prefix_words)print(''.join(result))if __name__ == '__main__':import firefire.Fire()

以上代码给我一些经验，

1. 了解python的编程方式，如空格、换行等；进一步了解python的各个基本模块；

2. 可能出的错误：函数名写错，大小写，变量名写错，括号不全。

3. 对cuda()的用法有了进一步认识；

4. 学会了调试程序（fire）；

5. 学会了训练结果的可视化（visdom）；

6. 进一步的了解了LSTM，对深度学习的架构、实现有了宏观把控。

这篇pytorch下使用LSTM神经网络写诗实例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。