lstm_out.contiguous().view(-1, self.hidden_dim * 2)什么意思
时间: 2024-04-26 08:20:46 浏览: 12
这行代码是将LSTM输出的张量进行变形,其具体含义如下:
首先,`lstm_out`是LSTM的输出张量,其形状为`(seq_len, batch_size, hidden_dim * 2)`,其中`seq_len`表示序列长度,`batch_size`表示批次大小,`hidden_dim`表示LSTM每个时刻输出的隐状态的维度,由于LSTM有两个方向,所以`hidden_dim * 2`表示将正向和反向的隐状态拼接在一起。
接着,`contiguous()`方法是为了保证张量在内存中是连续的,这是一些操作的前提条件。
最后,`view(-1, self.hidden_dim * 2)`将张量变形为二维张量,其中第一维是自动计算的(-1表示自动计算),第二维是将每个时刻的正向和反向隐状态展平成一维,即每个时刻的隐状态变成一个向量,可以作为后续的输入。
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解释这段代码class LSTM(nn.Module): def __init__(self,p,input_size, output_size, hidden_layer_size,past_history_size): """ :param input_size: 输入数据的维度 :param hidden_layer_size:隐层的数目 :param output_size: 输出的个数 """ super().__init__() # self.hidden_layer_size1 = hidden_layer_size*past_history_size self.hidden_layer_size1 = hidden_layer_size self.lstm = nn.LSTM(input_size, hidden_layer_size, batch_first=True) self.linear1 = nn.Linear(self.hidden_layer_size1, output_size) self.linear2 = nn.Linear(self.hidden_layer_size1, output_size) self.linear3 = nn.Linear(self.hidden_layer_size1, output_size) self.linear4 = nn.Linear(self.hidden_layer_size1, output_size) self.linear5 = nn.Linear(self.hidden_layer_size1, output_size) self.dropout = nn.Dropout(p=p) def forward(self, input_x): lstm_out, self.hidden_cell = self.lstm(input_x) lstm_out = self.dropout(lstm_out) # lstm_out = lstm_out.reshape(len(input_x),-1) linear_out1, linear_out2,linear_out3, linear_out4, linear_out5 = self.linear1(lstm_out), self.linear2(lstm_out),self.linear3(lstm_out), self.linear4(lstm_out),self.linear5(lstm_out) # print(linear_out1.shape) linear_out1, linear_out2,linear_out3, linear_out4, linear_out5 = linear_out1[:, -1, :], linear_out2[:, -1, :],linear_out3[:, -1, :], linear_out4[:, -1, :],linear_out5[:, -1, :] linear_out = torch.stack([linear_out1, linear_out2,linear_out3, linear_out4,linear_out5], dim=-1) linear_out = torch.squeeze(linear_out) return linear_out
这段代码定义了一个名为LSTM的神经网络类,继承自PyTorch中的nn.Module类。该类包含了一个LSTM层和5个全连接层,用于将LSTM层的输出映射到5个不同的输出。其中,输入数据的维度为input_size,隐层的数目为hidden_layer_size,输出的个数为output_size。在前向传播时,输入数据input_x首先被传入LSTM层中,得到LSTM层的输出lstm_out和隐藏状态self.hidden_cell。然后,lstm_out经过一个dropout层进行正则化处理,再分别经过5个全连接层进行线性变换,得到5个输出。最后,这5个输出通过torch.stack函数沿着最后一维进行拼接,得到一个形状为[batch_size, output_size, 5]的张量,再通过torch.squeeze函数将其压缩为形状为[batch_size, output_size]的张量,并作为前向传播的输出返回。
这段代码中加一个test loss功能 class LSTM(nn.Module): def __init__(self, input_size, hidden_size, num_layers, output_size, batch_size, device): super().__init__() self.device = device self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.output_size = output_size self.num_directions = 1 # 单向LSTM self.batch_size = batch_size self.lstm = nn.LSTM(self.input_size, self.hidden_size, self.num_layers, batch_first=True) self.linear = nn.Linear(65536, self.output_size) def forward(self, input_seq): h_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device) c_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device) output, _ = self.lstm(input_seq, (h_0, c_0)) pred = self.linear(output.contiguous().view(self.batch_size, -1)) return pred if __name__ == '__main__': # 加载已保存的模型参数 saved_model_path = '/content/drive/MyDrive/危急值/model/dangerous.pth' device = 'cuda:0' lstm_model = LSTM(input_size=1, hidden_size=64, num_layers=1, output_size=3, batch_size=256, device='cuda:0').to(device) state_dict = torch.load(saved_model_path) lstm_model.load_state_dict(state_dict) dataset = ECGDataset(X_train_df.to_numpy()) dataloader = DataLoader(dataset, batch_size=256, shuffle=True, num_workers=0, drop_last=True) loss_fn = nn.CrossEntropyLoss() optimizer = optim.SGD(lstm_model.parameters(), lr=1e-4) for epoch in range(200000): print(f'epoch:{epoch}') lstm_model.train() epoch_bar = tqdm(dataloader) for x, y in epoch_bar: optimizer.zero_grad() x_out = lstm_model(x.to(device).type(torch.cuda.FloatTensor)) loss = loss_fn(x_out, y.long().to(device)) loss.backward() epoch_bar.set_description(f'loss:{loss.item():.4f}') optimizer.step() if epoch % 100 == 0 or epoch == epoch - 1: torch.save(lstm_model.state_dict(), "/content/drive/MyDrive/危急值/model/dangerous.pth") print("权重成功保存一次")
以下是加入 Test Loss 功能后的代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
class LSTM(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, output_size, batch_size, device):
super().__init__()
self.device = device
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.output_size = output_size
self.num_directions = 1 # 单向LSTM
self.batch_size = batch_size
self.lstm = nn.LSTM(self.input_size, self.hidden_size, self.num_layers, batch_first=True)
self.linear = nn.Linear(65536, self.output_size)
def forward(self, input_seq):
h_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device)
c_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device)
output, _ = self.lstm(input_seq, (h_0, c_0))
pred = self.linear(output.contiguous().view(self.batch_size, -1))
return pred
if __name__ == '__main__':
# 加载已保存的模型参数
saved_model_path = '/content/drive/MyDrive/危急值/model/dangerous.pth'
device = 'cuda:0'
lstm_model = LSTM(input_size=1, hidden_size=64, num_layers=1, output_size=3, batch_size=256, device=device).to(device)
state_dict = torch.load(saved_model_path)
lstm_model.load_state_dict(state_dict)
# 加载测试数据集
test_dataset = ECGDataset(X_test_df.to_numpy())
test_loader = DataLoader(test_dataset, batch_size=256, shuffle=False, num_workers=0, drop_last=False)
# 定义损失函数和优化器
loss_fn = nn.CrossEntropyLoss()
optimizer = optim.SGD(lstm_model.parameters(), lr=1e-4)
# 开始训练
for epoch in range(200000):
print(f'epoch:{epoch}')
lstm_model.train()
epoch_bar = tqdm(dataloader)
for x, y in epoch_bar:
optimizer.zero_grad()
x_out = lstm_model(x.to(device).type(torch.cuda.FloatTensor))
loss = loss_fn(x_out, y.long().to(device))
loss.backward()
epoch_bar.set_description(f'loss:{loss.item():.4f}')
optimizer.step()
# 计算测试集上的平均损失
test_loss = 0.0
lstm_model.eval()
with torch.no_grad():
for x, y in test_loader:
x_out = lstm_model(x.to(device).type(torch.cuda.FloatTensor))
loss = loss_fn(x_out, y.long().to(device))
test_loss += loss.item() * x.size(0)
test_loss /= len(test_dataset)
print(f'Test Loss: {test_loss:.4f}')
if epoch % 100 == 0 or epoch == epoch - 1:
torch.save(lstm_model.state_dict(), "/content/drive/MyDrive/危急值/model/dangerous.pth")
print("权重成功保存一次")
```
在这个代码中,我们首先加载了测试数据集 `test_dataset` 和测试数据加载器 `test_loader`。在每个 epoch 完成后,我们计算测试集上的平均损失值。我们使用 `lstm_model.eval()` 将模型设为评估模式,并利用 `torch.no_grad()` 避免计算梯度,以加速计算。最后,我们输出测试集上的平均损失值。