基于pytorch的LSTM进行字符级文本生成实战
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基于pytorch的LSTM进行字符级文本生成实战
文章目录
前言
随着人工智能的不断发展，机器学习这门技术也越来越重要，很多人都开启了学习机器学习，本文就介绍了机器学习中深度学习的内容使用pytorch构建LSTM模型进行字符级文本生成任务
一、数据集
我已经上传到我的资源里了，0积分放心下载
https://download.csdn.net/download/qq_52785473/78428834
二、代码实现
1.导入库及LSTM模型构建
代码如下：
# coding: utf-8
import torch
import torch.nn as nn
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import OneHotEncoder
import torch.nn.functional as F
class lstm_model(nn.Module):
def __init__(self, vocab, hidden_size, num_layers, dropout=0.5):
super(lstm_model, self).__init__()
self.vocab = vocab # 字符数据集
# 索引，字符
self.int_char = i: char for i, char in enumerate(vocab)
self.char_int = char: i for i, char in self.int_char.items()
# 对字符进行one-hot encoding
self.encoder = OneHotEncoder(sparse=True).fit(vocab.reshape(-1, 1))
self.hidden_size = hidden_size
self.num_layers = num_layers
# lstm层
self.lstm = nn.LSTM(len(vocab), hidden_size, num_layers, batch_first=True, dropout=dropout)
# 全连接层
self.linear = nn.Linear(hidden_size, len(vocab))
def forward(self, sequence, hs=None):
out, hs = self.lstm(sequence, hs) # lstm的输出格式（batch_size, sequence_length, hidden_size）
out = out.reshape(-1, self.hidden_size) # 这里需要将out转换为linear的输入格式，即（batch_size * sequence_length, hidden_size）
output = self.linear(out) # linear的输出格式，(batch_size * sequence_length, vocab_size)
return output, hs
def onehot_encode(self, data): # 对数据进行编码
return self.encoder.transform(data)
def onehot_decode(self, data): # 对数据进行解码
return self.encoder.inverse_transform(data)
def label_encode(self, data): # 对标签进行编码
return np.array([self.char_int[ch] for ch in data])
def label_decode(self, data): # 对标签进行解码
return np.array([self.int_char[ch] for ch in data])
2.数据预处理函数
代码如下：
def get_batches(data, batch_size, seq_len):
'''
:param data: 源数据，输入格式(num_samples, num_features)
:param batch_size: batch的大小
:param seq_len: 序列的长度（精度）
:return: （batch_size, seq_len, num_features）
'''
num_features = data.shape[1]
num_chars = batch_size * seq_len # 一个batch_size的长度
num_batches = int(np.floor(data.shape[0] / num_chars)) # 计算出有多少个batches
need_chars = num_batches * num_chars # 计算出需要的总字符量
targets = np.vstack((data[1:].A, data[0].A)) # 可能版本问题，取成numpy比较好reshape
inputs = data[:need_chars].A.astype("int") # 从原始数据data中截取所需的字符数量need_words
targets = targets[:need_chars]
targets = targets.reshape(batch_size, -1, num_features)
inputs = inputs.reshape(batch_size, -1, num_features)
for i in range(0, inputs.shape[1], seq_len):
x = inputs[:, i: i+seq_len]
y = targets[:, i: i+seq_len]
yield x, y # 节省内存
3.训练函数
def train(model, data, batch_size, seq_len, epochs, lr=0.01, valid=None):
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
criterion = nn.CrossEntropyLoss()
if valid is not None:
data = model.onehot_encode(data.reshape(-1, 1))
valid = model.onehot_encode(valid.reshape(-1, 1))
else:
data = model.onehot_encode(data.reshape(-1, 1))
train_loss = []
val_loss = []
for epoch in range(epochs):
model.train()
hs = None # hs等于hidden_size隐藏层节点
train_ls = 0.0
val_ls = 0.0
for x, y in get_batches(data, batch_size, seq_len):
optimizer.zero_grad()
x = torch.tensor(x).float().to(device)
out, hs = model(x, hs)
hs = ([h.data for h in hs])
y = y.reshape(-1, len(model.vocab))
y = model.onehot_decode(y)
y = model.label_encode(y.squeeze())
y = torch.from_numpy(y).long().to(device)
loss = criterion(out, y.squeeze())
loss.backward()
optimizer.step()
train_ls += loss.item()
if valid is not None:
model.eval()
hs = None
with torch.no_grad():
for x, y in get_batches(valid, batch_size, seq_len):
x = torch.tensor(x).float().to(device) # x为一组测试数据，包含batch_size * seq_len个字
out, hs = model(x, hs)
# out.shape输出为tensor[batch_size * seq_len, vocab_size]
hs = ([h.data for h in hs]) # 更新参数
y = y.reshape(-1, len(model.vocab)) # y.shape为(128,100,43)，因此需要转成两维，每行就代表一个字了，43为字典大小
y = model.onehot_decode(y) # y标签即为测试数据各个字的下一个字，进行one_hot解码，即变为字符
# 但是此时y 是[[..],[..]]形式
y = model.label_encode(y.squeeze()) # 因此需要去掉一维才能成功解码
# 此时y为[12...]成为一维的数组，每个代表自己字典里对应字符的字典序
y = torch.from_numpy(y).long().to(device)
# 这里y和y.squeeze()出来的东西一样，可能这里没啥用，不太懂
loss = criterion(out, y.squeeze()) # 计算损失值
val_ls += loss.item()
val_loss.append(np.mean(val_ls))
train_loss.append(np.mean(train_ls))
print("train_loss:", train_ls)
plt.plot(train_loss, label="train_loss")
plt.plot(val_loss, label="val loss")
plt.title("loop vs epoch")
plt.legend()
plt.show()
model_name = "lstm_model.net"
with open(model_name, 'wb') as f: # 训练完了保存模型
torch.save(model.state_dict(), f)
4.预测函数
def predict(model, char, top_k=None, hidden_size=None):
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model.to(device)
model.eval() # 固定参数
with torch.no_grad():
char = np.array([char]) # 输入一个字符，预测下一个字是什么，先转成numpy
char = char.reshape(-1, 1) # 变成二维才符合编码规范
char_encoding = model.onehot_encode(char).A # 对char进行编码，取成numpy比较方便reshape
char_encoding = char_encoding.reshape(1, 1, -1) # char_encoding.shape为(1, 1, 43)变成三维才符合模型输入格式
char_tensor = torch.tensor(char_encoding, dtype=torch.float32) # 转成tensor
char_tensor = char_tensor.to(device)
out, hidden_size = model(char_tensor, hidden_size) # 放入模型进行预测，out为结果
probs = F.softmax(out, dim=1).squeeze() # 计算预测值,即所有字符的概率
if top_k is None: # 选择概率最大的top_k个
indices = np.arange(vocab_size)
else:
probs, indices = probs.topk(top_k)
indices = indices.cpu().numpy()
probs = probs.cpu().numpy()
char_index = np.random.choice(indices, p=probs/probs.sum()) # 随机选择一个字符索引作为预测值
char = model.int_char[char_index] # 通过索引找出预测字符
return char, hidden_size
5.文本生成函数
def sample(model, length, top_k=None, sentence="c"):
hidden_size = None
new_sentence = [char for char in sentence]
for i in range(length):
next_char, hidden_size = predict(model, new_sentence[-1], top_k=top_k, hidden_size=hidden_size)
new_sentence.append(next_char)
return "".join(new_sentence)
6.主函数
def main():
hidden_size = 512
num_layers = 2
batch_size = 128
seq_len = 100
epochs = 2
lr = 0.01
f = pd.read_csv("../datasets/dev.tsv", sep="\\t", header=None)
f = f[0]
text = list(f)
text = ".".join(text)
vocab = np.array(sorted(set(text))) # 建立字典
vocab_size = len(vocab)
val_len = int(np.floor(0.2 * len(text))) # 划分训练测试集
trainset = np.array(list(text[:-val_len]))
validset = np.array(list(text[-val_len:]))
model = lstm_model(vocab, hidden_size, num_layers) # 模型实例化
train(model, trainset, batch_size, seq_len, epochs, lr=lr, valid=validset) # 训练模型
model.load_state_dict(torch.load("lstm_model.net")) # 调用保存的模型
new_text = sample(model, 100, top_k=5) # 预测模型，生成100个字符,预测时选择概率最大的前5个
print(new_text) # 输出预测文本
if __name__ == "__main__":
main()
总结
这其实是b站上一个例子，感觉讲的很细致，我是照着他一行一行打下来的，然后因为版本问题，我对一些地方进行了修改，并且留了一些注释（千言万语都在注释里了），个人认为输入输出还是比较容易搞错的，以防以后忘记代码什么意思，这代码是在我的电脑上能跑，感觉流程应该没错吧，只是效果不行，因为我为了赶快理解这套流程，训练次数设的很小，而且训练数据也很小，所以看不到效果的。
这就是训练一次的结果，看得出很糟糕，但是至少出来结果了。
其实一般应该用面向对象的方法来写，用一些pytorch的一些类来写代码会显得更加简洁，奈何我的python水平限制了我对一些高级操作的理解，就先用简单而繁冗的代码好了
并且还是有很大改进空间的，例如进行词语级的文本生成，以及使用word2vec等引入词向量等，都可以是的模型获得更好的效果。
本文参考：https://www.bilibili.com/video/BV1Dv411W78n/?spm_id_from=pageDriver
完整代码
# coding: utf-8
import torch
import torch.nn as nn
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import OneHotEncoder
import torch.nn.functional as F
class lstm_model(nn.Module):
def __init__(self, vocab, hidden_size, num_layers, dropout=0.5):
super(lstm_model, self).__init__()
self.vocab = vocab # 字符数据集
# 索引，字符
self.int_char = i: char for i, char in enumerate(vocab)
self.char_int = char: i for i, char in self.int_char.items()
# 对字符进行one-hot encoding
self.encoder = OneHotEncoder(sparse=True).fit(vocab.reshape(-1, 1))
self.hidden_size = hidden_size
self.num_layers = num_layers
# lstm层
self.lstm = nn.LSTM(len(vocab), hidden_size, num_layers, batch_first=True, dropout=dropout)
# 全连接层
self.linear = nn.Linear(hidden_size, len(vocab))
def forward(self, sequence, hs=None):
out, hs = self.lstm(sequence, hs) # lstm的输出格式（batch_size, sequence_length, hidden_size）
out = out.reshape(-1, self.hidden_size) # 这里需要将out转换为linear的输入格式，即（batch_size * sequence_length, hidden_size）
output = self.linear(out) # linear的输出格式，(batch_size * sequence_length, vocab_size)
return output, hs
def onehot_encode(self, data):
return self.encoder.transform(data)
def onehot_decode(self, data):
return self.encoder.inverse_transform(data)
def label_encode(self, data):
return np.array([self.char_int[ch] for ch in data])
def label_decode(self, data):
return np.array([self.int_char[ch] for ch in data])
def get_batches(data, batch_size, seq_len):
'''
:param data: 源数据，输入格式(num_samples, num_features)
:param batch_size: batch的大小
:param seq_len: 序列的长度（精度）
:return: （batch_size, seq_len, num_features）
'''
num_features = data.shape[1]
num_chars = batch_size * seq_len # 一个batch_size的长度
num_batches = int(np.floor(data.shape[0] / num_chars)) # 计算出有多少个batches
need_chars = num_batches * num_chars # 计算出需要的总字符量
targets = np.vstack((data[1:].A, data[0].A)) # 可能版本问题，取成numpy比较好reshape
inputs = data[:need_chars].A.astype("int") # 从原始数据data中截取所需的字符数量need_words
targets = targets[:need_chars]
targets = targets.reshape(batch_size, -1, num_features)
inputs = inputs.reshape(batch_size, -1, num_features)
for i in range(0, inputs.shape[1], seq_len):
x = inputs[:, i: i+seq_len]
y = targets[:, i: i+seq_len]
yield x, y # 节省内存
def train(model, data以上是关于基于pytorch的LSTM进行字符级文本生成实战的主要内容，如果未能解决你的问题，请参考以下文章