c shape: (num_layers * num_directions, batch_size, hidden_size) _, (h, c) = self.lstm(x, (h, c))是什么意思
时间: 2023-11-17 10:06:55 浏览: 36
这段代码是在进行LSTM(长短时记忆)计算时使用的。c shape表示输出的形状,其中num_layers表示LSTM的层数,num_directions表示LSTM的方向性(单向或双向),batch_size表示输入数据的批次数,hidden_size表示隐藏状态的维度。代码中的x表示输入,(h, c)是LSTM的初始隐藏状态和细胞状态,通过LSTM计算后得到的(h, c)就是更新后的隐藏状态和细胞状态。
相关问题
class Net(nn.Module): def __init__(self,input_size,hidden_size,num_layers,output_size,batch_size,seq_length) -> None: super(Net,self).__init__() self.input_size=input_size self.hidden_size=hidden_size self.num_layers=num_layers self.output_size=output_size self.batch_size=batch_size self.seq_length=seq_length self.num_directions=1 # 单向LSTM self.lstm=nn.LSTM(input_size=input_size,hidden_size=hidden_size,num_layers=num_layers,batch_first=True) # LSTM层 self.fc=nn.Linear(hidden_size,output_size) # 全连接层 def forward(self,x): # e.g. x(10,3,100) 三个句子,十个单词,一百维的向量,nn.LSTM(input_size=100,hidden_size=20,num_layers=4) # out.shape=(10,3,20) h/c.shape=(4,b,20) batch_size, seq_len = x.size()[0], x.size()[1] # x.shape=(604,3,3) h_0 = torch.randn(self.num_directions * self.num_layers, x.size(0), self.hidden_size) c_0 = torch.randn(self.num_directions * self.num_layers, x.size(0), self.hidden_size) # output(batch_size, seq_len, num_directions * hidden_size) output, _ = self.lstm(x, (h_0, c_0)) # output(5, 30, 64) pred = self.fc(output) # (5, 30, 1) pred = pred[:, -1, :] # (5, 1) return pred改成python代码
这段代码是一个简单的使用 PyTorch 实现的 LSTM 模型。下面是将其转化为 Python 代码的版本:
```python
import torch
import torch.nn as nn
class Net(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, output_size, batch_size, seq_length):
super(Net, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.output_size = output_size
self.batch_size = batch_size
self.seq_length = seq_length
self.num_directions = 1 # 单向LSTM
# LSTM层
self.lstm = nn.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True)
# 全连接层
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, x):
# x.shape=(10,3,100),三个句子,十个单词,一百维的向量
# out.shape=(10,3,20),h/c.shape=(4,b,20)
batch_size, seq_len = x.size()[0], x.size()[1]
h_0 = torch.randn(self.num_directions * self.num_layers, x.size(0), self.hidden_size)
c_0 = torch.randn(self.num_directions * self.num_layers, x.size(0), self.hidden_size)
output, _ = self.lstm(x, (h_0, c_0)) # output(batch_size, seq_len, num_directions * hidden_size)
pred = self.fc(output) # (5, 30, 1)
pred = pred[:, -1, :] # (5, 1)
return pred
```
补充以下代码: def __init__(self, embedding_dim, hidden_dim, vocab_size, label_size, batch_size): super(LSTMClassifier, self).__init__() self.hidden_dim = hidden_dim self.batch_size = batch_size # 实验三(扩展):更换为 glove 词向量 self.word_embeddings = nn.Embedding(vocab_size, embedding_dim) # 实验一:定义 LSTM 层,并替换为 BiLSTM,RNN,比较其不同 self.lstm = nn.LSTM(embedding_dim,hidden_dim) # 使用lstm层 lstm_out, self.hidden = self.lstm( , self.hidden) self.hidden2label = nn.Linear(hidden_dim, label_size) self.hidden = self.init_hidden()
lstm_out, self.hidden = self.lstm(input, self.hidden),其中 input 是通过词向量层获得的词嵌入向量,维度为 (batch_size, sequence_length, embedding_dim)。同时,需要在类中定义 init_hidden 方法来初始化 LSTM 的隐藏状态和记忆状态,如下所示:
def init_hidden(self):
# 两个状态的shape:(num_layers * num_directions, batch_size, hidden_dim)
return (torch.zeros(1, self.batch_size, self.hidden_dim),
torch.zeros(1, self.batch_size, self.hidden_dim))
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