两层神经网络代码
时间: 2023-07-08 13:10:21 浏览: 43
以下是一个使用PyTorch实现的简单两层神经网络的代码示例:
```python
import torch
import torch.nn as nn
# 定义两层神经网络模型
class TwoLayerNet(nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(TwoLayerNet, 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
# 定义超参数
input_size = 784
hidden_size = 500
num_classes = 10
learning_rate = 0.001
num_epochs = 5
# 加载MNIST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())
# 创建数据加载器
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=100, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=100, shuffle=False)
# 创建模型、损失函数和优化器
model = TwoLayerNet(input_size, hidden_size, num_classes)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(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):
# 将图片展开成一维向量
images = images.reshape(-1, 28*28)
# 前向传播计算输出和损失
outputs = model(images)
loss = criterion(outputs, labels)
# 反向传播和优化
optimizer.zero_grad()
loss.backward()
optimizer.step()
# 每100个批次打印一次日志
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)
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))
```
这个代码实现了一个简单的两层神经网络来对MNIST手写数字进行分类,其中包括了模型定义、数据处理、训练和测试等步骤。