pytorch手写数字识别代码
时间: 2023-09-23 12:12:29 浏览: 32
可以的,下面是一个简单的 PyTorch 手写数字识别代码:
```
import torch
import torch.nn as nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
# 定义超参数
input_size = 784 # 输入大小为 28x28
hidden_size = 500 # 隐藏层大小
num_classes = 10 # 10 个数字
num_epochs = 5 # 训练次数
batch_size = 100 # 每个批次的大小
learning_rate = 0.001 # 学习率
# 下载 MNIST 数据
train_dataset = dsets.MNIST(root='./data',
train=True,
transform=transforms.ToTensor(),
download=True)
test_dataset = dsets.MNIST(root='./data',
train=False,
transform=transforms.ToTensor())
# 加载数据
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)
# 定义神经网络
class Net(nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(Net, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.relu = nn.ReLU()
self.fc2 = nn.Linear(hidden_size, num_classes)
def forward(self, x):
out = self.fc1(x)
out = self.relu(out)
out = self.fc2(out)
return out
net = Net(input_size, hidden_size, num_classes)
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate)
# 训练模型
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
images = images.reshape(-1, 28 * 28)
# 前向传播和计算损失
outputs = net(images)
loss = criterion(outputs, labels)
# 后向传播和优化
optimizer.zero_grad()
loss.backward()
optimizer.step()
# 每 100 步输出一次信息
if (i+1) % 100 == 0:
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
.format(epoch+1, num_epochs, i+1, len(train_loader), loss.item()))
# 测试模型
correct = 0
total = 0
for images, labels in test_loader:
images = images.reshape(-1, 28 * 28)
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum()
print('Accuracy of the network on the 10000 test images: {} %'.format(100 * correct / total))
```
希望对你有所帮助!
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