LSTM整体架构图如下：
遗忘门如下：
第一个遗忘门得到的结果是不是全都属于0-1的数，相当于不同的权重。
输入门（其实也可以叫更新门）如下：
输出门如下：
对于输出门，有两个分支，一个是直接变成下一层的隐藏变量，一个是表示这一层的输出。
代码来源：BiLSTM的PyTorch应用 - mathor
'''
code by Tae Hwan Jung(Jeff Jung) @graykode, modify by wmathor
'''
import torch
import numpy as np
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as Data
dtype = torch.FloatTensor准备数据
sentence = (
'GitHub Actions makes it easy to automate all your software workflows from continuous integration and delivery to issue triage and more'
)
word2idx = {w: i for i, w in enumerate(list(set(sentence.split())))}
idx2word = {i: w for i, w in enumerate(list(set(sentence.split())))}
n_class = len(word2idx) # classification problem
max_len = len(sentence.split())
n_hidden = 5#word2idx={'automate': 0,'all': 1,'and': 2,'integration': 3,'your': 4,'issue': 5,'continuous': 6,'triage': 7， 'delivery': 8,'Actions': 9,
'from': 10, 'easy': 11, 'software': 12,'makes': 13, 'it': 14, 'workflows': 15, 'GitHub': 16, 'to': 17,'more': 18}
#id2word就直接与word2idx的键值对调换就行了
#n_class=19
#max_len=21处理数据
def make_data(sentence):
input_batch = []
target_batch = []
words = sentence.split()
for i in range(max_len - 1):
input = [word2idx[n] for n in words[:(i + 1)]]
input = input + [-1] * (max_len - len(input))
target = word2idx[words[i + 1]]
input_batch.append(np.eye(n_class)[input])
target_batch.append(target)
return torch.Tensor(input_batch), torch.LongTensor(target_batch)
# input_batch: [max_len - 1, max_len, n_class]
input_batch, target_batch = make_data(sentence)
dataset = Data.TensorDataset(input_batch, target_batch)
loader = Data.DataLoader(dataset, 16, True)#16表示batch_size根据自己的电脑配置更改， 相关变量可视化
class BiLSTM(nn.Module):
def __init__(self):
super(BiLSTM, self).__init__()
self.lstm = nn.LSTM(input_size=n_class, hidden_size=n_hidden, bidirectional=True)
# fc
self.fc = nn.Linear(n_hidden * 2, n_class)
def forward(self, X):
# X: [batch_size, max_len, n_class]
batch_size = X.shape[0]
input = X.transpose(0, 1) # input : [max_len, batch_size, n_class]
hidden_state = torch.randn(1*2, batch_size, n_hidden) # [num_layers(=1) * num_directions(=2), batch_size, n_hidden]
cell_state = torch.randn(1*2, batch_size, n_hidden) # [num_layers(=1) * num_directions(=2), batch_size, n_hidden]
outputs, (_, _) = self.lstm(input, (hidden_state, cell_state))
outputs = outputs[-1] # [batch_size, n_hidden * 2]
model = self.fc(outputs) # model : [batch_size, n_class]
return model
model = BiLSTM()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001) pytorch对于LSTM的输入输出格式如下图所示：
# Training
for epoch in range(10000):
for x, y in loader:
pred = model(x)
loss = criterion(pred, y)
if (epoch + 1) % 1000 == 0:
print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Pred
predict = model(input_batch).data.max(1, keepdim=True)[1]
print(sentence)
print([idx2word[n.item()] for n in predict.squeeze()])
效果比原作者的效果要好一点，因为占位符我更改成了 -1，这样就不会影响到标签了。
注意，对于代码的模型的前项传播不懂的，可以看下面：
class BiLSTM_1(nn.Module):
def __init__(self):
super(BiLSTM_1, self).__init__()
self.lstm = nn.LSTM(input_size=10, hidden_size=5, bidirectional=False)
# fc
self.fc = nn.Linear(n_hidden * 1, n_class)
def forward(self, X):
# X: [batch_size, max_len, n_class]
batch_size = X.shape[0]
input = X.transpose(0, 1) # input : [max_len, batch_size, n_class]
#随机初试化隐藏变量和记忆细胞变量
hidden_state = torch.randn(1*1, batch_size, n_hidden) # [num_layers(=1) * num_directions(=2), batch_size, n_hidden]
cell_state = torch.randn(1*1, batch_size, n_hidden) # [num_layers(=1) * num_directions(=2), batch_size, n_hidden]
outputs, (hc, c) = self.lstm(input, (hidden_state, cell_state))
outputs = outputs # [batch_size, n_hidden * 2]
#model = self.fc(outputs) # model : [batch_size, n_class]
return outputs,hc, c
model = BiLSTM_1()a=torch.randn(2,5,10)
output,hc,c=model(a)
其实每一层的输出，都直接拼接在一起了，而hc只表示最后一层的输出，所以output[-1]==hc的。
最后祝大家学有所成！
