self.lstm = torch.nn.LSTM( input_size = 224, hidden_size = 128, num_layer = 2,
时间: 2024-02-15 14:45:57 浏览: 19
batch_first = True)
This code initializes an LSTM module in PyTorch with the following parameters:
- `input_size`: The size of the input tensor at each time step. Here, it is 224.
- `hidden_size`: The number of features in the hidden state at each time step. Here, it is set to 128.
- `num_layers`: The number of recurrent layers in the LSTM module. Here, it is set to 2.
- `batch_first`: Whether the input and output tensors should have batch size as their first dimension. Here, it is set to `True`.
Overall, this code creates an LSTM module with two layers and a hidden state size of 128, which can process input sequences of length 224 with batch size as the first dimension.
相关问题
import torch train_data = torch.tensor([[[ 58.]], [[367.]], [[324.]], [[620.]], [[146.]], [[681.]], [[582.]], [[432.]], [[ 87.]], [[415.]], [[443.]], [[680.]], [[ 0.]], [[230.]], [[484.]], [[497.]], [[324.]], [[620.]], [[681.]], [[ 84.]], [[484.]], [[448.]], [[144.]], [[536.]], [[680.]], [[ 0.]]], dtype = torch.float32) class POEM_LSTM(torch.nn.Module): def __init__(self, input_size, hidden_size, num_layers): super(POEM_LSTM, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.LstmLayer = torch.nn.LSTM(input_size=self.input_size, hidden_size=self.hidden_size, num_layers=self.num_layers, bias= False) self.LinearLayer = torch.nn.Linear(in_features=self.hidden_size, out_features=self.input_size) def forward(self, x): LstmLayerOutput, _ = self.LstmLayer(x) # h_c = (h_t, c_t) FinalOutput = self.LinearLayer(LstmLayerOutput) #需要对输出进行圆整,因为onehot为0~681的整数 return FinalOutput poem_lstm = POEM_LSTM(1,3,2) #网络模型实例化 loss = torch.nn.MSELoss() opt = torch.optim.Adam(poem_lstm.parameters(), lr = 0.001) for i in range(200): # input = train_data[0] for j in range(0,26): opt.zero_grad() # 每个iteration梯度清0 output= poem_lstm(torch.tensor([[j]],dtype=torch.float32)) l_loss = loss(output, train_data[j]) l_loss.backward() opt.step() if i == 199: # print(poem_lstm.state_dict()) print(output) print(train_data[j]) print(l_loss) print('\n')
这段代码定义了一个名为POEM_LSTM的PyTorch模型,该模型包含一个LSTM层和一个线性层。输入数据train_data是一个三维张量,包含了28个时间步、每个时间步一个特征值。LSTM层将输入数据train_data传入,得到LstmLayerOutput作为输出。而LinearLayer将LstmLayerOutput作为输入,输出和输入相同的形状的张量。
用pytorch实现双向LSTM类,要求不能使用nn.LSTM函数,输入和输出参数和nn.LSTM一致
好的,我可以为您编写一个双向LSTM类,输入和输出参数与nn.LSTM一致,但是不使用nn.LSTM函数。代码如下:
```python
import torch
import torch.nn as nn
class BiLSTM(nn.Module):
def __init__(self, input_size, hidden_size, num_layers=1, bias=True, batch_first=False, dropout=0, bidirectional=True):
super(BiLSTM, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.bias = bias
self.batch_first = batch_first
self.dropout = dropout
self.bidirectional = bidirectional
# 前向LSTM
self.f_lstm = nn.ModuleList()
for i in range(num_layers):
if i == 0:
self.f_lstm.append(nn.LSTMCell(input_size=input_size, hidden_size=hidden_size))
else:
self.f_lstm.append(nn.LSTMCell(input_size=hidden_size, hidden_size=hidden_size))
# 后向LSTM
if bidirectional:
self.b_lstm = nn.ModuleList()
for i in range(num_layers):
if i == 0:
self.b_lstm.append(nn.LSTMCell(input_size=input_size, hidden_size=hidden_size))
else:
self.b_lstm.append(nn.LSTMCell(input_size=hidden_size, hidden_size=hidden_size))
def forward(self, input, hx=None):
# 初始化前向LSTM的隐状态和记忆状态
h_f = []
c_f = []
for i in range(self.num_layers):
h_f.append(torch.zeros(input.size(0), self.hidden_size).to(input.device))
c_f.append(torch.zeros(input.size(0), self.hidden_size).to(input.device))
# 初始化反向LSTM的隐状态和记忆状态
if self.bidirectional:
h_b = []
c_b = []
for i in range(self.num_layers):
h_b.append(torch.zeros(input.size(0), self.hidden_size).to(input.device))
c_b.append(torch.zeros(input.size(0), self.hidden_size).to(input.device))
# 前向传播
outputs = []
steps = range(input.size(1))
if self.batch_first:
steps = range(input.size(0))
for time in steps:
x = input[:, time, :]
for layer in range(self.num_layers):
h_prev_f = h_f[layer]
c_prev_f = c_f[layer]
h_f[layer], c_f[layer] = self.f_lstm[layer](x, (h_prev_f, c_prev_f))
x = h_f[layer]
if self.dropout != 0 and layer != self.num_layers - 1:
x = nn.Dropout(self.dropout)(x)
if self.bidirectional:
# 反向传播
x_b = input[:, -time-1, :]
for layer in range(self.num_layers):
h_prev_b = h_b[layer]
c_prev_b = c_b[layer]
h_b[layer], c_b[layer] = self.b_lstm[layer](x_b, (h_prev_b, c_prev_b))
x_b = h_b[layer]
if self.dropout != 0 and layer != self.num_layers - 1:
x_b = nn.Dropout(self.dropout)(x_b)
outputs.append(torch.cat([h_f[-1], h_b[-1]], dim=-1))
else:
outputs.append(h_f[-1])
output = torch.stack(outputs, dim=1)
return output, (h_f[-1], c_f[-1])
```
这个BiLSTM类实现了双向LSTM,输入和输出参数与nn.LSTM一致。我们使用了nn.LSTMCell来代替nn.LSTM,手动编写前向和反向传播,并使用nn.ModuleList来实现多层LSTM的叠加。您可以将这个类用于您的深度学习模型中,来实现双向LSTM的功能。