gru和mlp组合代码
时间: 2023-10-14 13:06:28 浏览: 44
下面是一个简单的 GRU 和 MLP 模型的组合代码示例,用于对 MNIST 数据集进行分类:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class GRUMLP(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, num_classes):
super(GRUMLP, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.gru = nn.GRU(input_size, hidden_size, num_layers, batch_first=True)
self.fc1 = nn.Linear(hidden_size, 128)
self.fc2 = nn.Linear(128, num_classes)
def forward(self, x):
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
out, _ = self.gru(x, h0.detach())
out = F.relu(self.fc1(out[:, -1, :]))
out = self.fc2(out)
return out
# 定义模型参数
input_size = 28
hidden_size = 128
num_layers = 2
num_classes = 10
learning_rate = 0.001
batch_size = 100
# 加载数据集
train_dataset = torchvision.datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = torchvision.datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor())
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)
# 初始化模型和优化器
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = GRUMLP(input_size, hidden_size, num_layers, num_classes).to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# 训练模型
total_step = len(train_loader)
num_epochs = 5
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
images = images.reshape(-1, 28, 28).to(device)
labels = labels.to(device)
outputs = model(images)
loss = criterion(outputs, labels)
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, 28, 28).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))
```
在这个示例中,我们首先定义了一个名为 `GRUMLP` 的类,它组合了一个 GRU 层和两个全连接层(MLP)。在正向传递期间,我们将输入通过 GRU 层传递,然后使用最后一个时间步的隐藏状态作为输入,通过两个全连接层进行分类。然后我们对模型进行训练和测试,以对 MNIST 数据集进行分类。
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