RNN做cifar-10图像分类任务pytorch版本的代码
时间: 2023-12-25 10:05:44 浏览: 34
以下是使用PyTorch实现的RNN对CIFAR-10图像分类任务的代码示例:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import torchvision
import torchvision.transforms as transforms
# 定义超参数
input_size = 3 * 32 * 32
hidden_size = 100
num_classes = 10
num_epochs = 5
batch_size = 100
learning_rate = 0.001
# 加载CIFAR-10数据集
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size,
shuffle=True, num_workers=2)
test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size,
shuffle=False, num_workers=2)
# 定义RNN模型
class RNN(nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(RNN, self).__init__()
self.hidden_size = hidden_size
self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)
self.fc = nn.Linear(hidden_size, num_classes)
def forward(self, x):
h0 = Variable(torch.zeros(1, x.size(0), self.hidden_size))
out, _ = self.rnn(x, h0)
out = self.fc(out[:, -1, :])
return out
rnn = RNN(input_size, hidden_size, num_classes)
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(rnn.parameters(), lr=learning_rate)
# 训练模型
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
images = Variable(images.view(-1, 32, 32 * 3))
labels = Variable(labels)
# 前向传播
outputs = rnn(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 [%d/%d], Iter [%d/%d] Loss: %.4f'
% (epoch + 1, num_epochs, i + 1, len(train_dataset) // batch_size, loss.data[0]))
# 测试模型
correct = 0
total = 0
for images, labels in test_loader:
images = Variable(images.view(-1, 32, 32 * 3))
outputs = rnn(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum()
print('Accuracy of the RNN on the 10000 test images: %d %%' % (100 * correct / total))
```
这段代码使用了PyTorch的nn.RNN模块来实现RNN模型,并使用交叉熵损失函数和Adam优化器来训练模型。在训练过程中,代码使用了PyTorch的DataLoader来加载CIFAR-10数据集,并使用Variable来进行数据的自动求导。最后,代码计算了模型在测试集上的准确率。