双隐藏层的循环神经网络 python代码
时间: 2023-07-07 18:33:36 浏览: 79
以下是一个使用PyTorch实现双隐藏层循环神经网络的示例代码:
```python
import torch
import torch.nn as nn
# 定义循环神经网络模型
class RNNModel(nn.Module):
def __init__(self, input_size, hidden_size, output_size, num_layers):
super(RNNModel, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.rnn = nn.RNN(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True)
self.fc1 = nn.Linear(hidden_size, hidden_size)
self.fc2 = nn.Linear(hidden_size, output_size)
def forward(self, x):
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
out, _ = self.rnn(x, h0)
out = self.fc1(out[:, -1, :])
out = torch.relu(out)
out = self.fc2(out)
return out
# 定义数据集和训练过程
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
input_size = 28
hidden_size = 128
output_size = 10
num_layers = 2
batch_size = 64
learning_rate = 0.001
num_epochs = 10
train_dataset = datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor(), download=True)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
model = RNNModel(input_size, hidden_size, output_size, num_layers).to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
total_loss = 0.0
total_correct = 0
total_samples = 0
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
images = images.view(-1, 28, 28).to(device)
labels = labels.to(device)
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
total_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
total_correct += (predicted == labels).sum().item()
total_samples += labels.size(0)
print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {total_loss/len(train_loader):.4f}, Accuracy: {total_correct/total_samples:.4f}')
```
在上面的代码中,我们首先定义了一个循环神经网络模型,它由两个隐藏层的RNN层和两个全连接层组成。在模型的forward方法中,我们依次通过两个隐藏层的RNN层处理输入序列,最后使用两个全连接层生成输出。然后,我们加载了MNIST数据集,并使用上述模型对数据进行训练。在每个epoch中,我们计算模型在训练集上的损失和准确率,并输出结果。