pytorch实战rnn
时间: 2023-10-19 10:09:20 浏览: 114
用pytorch实现dnn
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好的,我明白了。关于pytorch实战rnn,你可以参考以下步骤:
1. 导入必要的库和数据集
2. 定义超参数
3. 定义RNN模型
4. 定义损失函数和优化器
5. 训练模型
6. 测试模型
以下是一个简单的例子:
```python
import torch
import torch.nn as nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
# 导入数据集
train_dataset = dsets.MNIST(root='./data',
train=True,
transform=transforms.ToTensor(),
download=True)
test_dataset = dsets.MNIST(root='./data',
train=False,
transform=transforms.ToTensor())
# 定义超参数
input_size = 28
sequence_length = 28
hidden_size = 128
num_layers = 2
num_classes = 10
batch_size = 100
num_epochs = 2
learning_rate = 0.01
# 定义RNN模型
class RNN(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, num_classes):
super(RNN, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.rnn = nn.RNN(input_size, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, num_classes)
def forward(self, x):
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
out, _ = self.rnn(x, h0)
out = out[:, -1, :]
out = self.fc(out)
return out
# 实例化模型
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = RNN(input_size, hidden_size, num_layers, num_classes).to(device)
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# 训练模型
total_step = len(train_loader)
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
images = images.reshape(-1, sequence_length, input_size).to(device)
labels = labels.to(device)
# 前向传播
outputs = model(images)
loss = criterion(outputs, labels)
# 反向传播和优化
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
.format(epoch+1, num_epochs, i+1, total_step, loss.item()))
# 测试模型
with torch.no_grad():
correct = 0
total = 0
for images, labels in test_loader:
images = images.reshape(-1, sequence_length, input_size).to(device)
labels = labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))
```
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