使用python训练一个全连接神经网络
时间: 2023-12-19 10:05:26 浏览: 41
好的,以下是一个使用Python和PyTorch库训练全连接神经网络的示例代码:
```
import torch
import torch.nn as nn
import torch.optim as optim
# 定义全连接神经网络模型
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(784, 256)
self.fc2 = nn.Linear(256, 128)
self.fc3 = nn.Linear(128, 10)
def forward(self, x):
x = x.view(-1, 784)
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = self.fc3(x)
return x
# 加载MNIST数据集
train_data = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=torchvision.transforms.ToTensor())
test_data = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=torchvision.transforms.ToTensor())
# 设置训练参数
batch_size = 64
num_epochs = 10
learning_rate = 0.001
# 初始化模型、损失函数和优化器
model = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# 开始训练
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
# 向前传递
outputs = model(images)
loss = criterion(outputs, labels)
# 反向传播和优化
optimizer.zero_grad()
loss.backward()
optimizer.step()
# 输出训练进度
if (i+1) % 100 == 0:
print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, len(train_loader), loss.item()))
# 在测试集上进行测试
with torch.no_grad():
correct = 0
total = 0
for images, labels in test_loader:
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: {} %'.format(100 * correct / total))
```
该代码使用PyTorch库定义了一个包含3个全连接层的神经网络模型,并使用MNIST数据集进行训练和测试。在训练过程中,使用交叉熵损失函数和Adam优化器来更新模型权重。在测试过程中,使用模型输出的类别标签与真实标签进行比较以计算准确率。