多层感知机分类python代码
时间: 2023-09-11 20:08:15 浏览: 38
下面是一个简单的多层感知机分类的Python代码实现,使用了PyTorch框架:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
class MLP(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(MLP, self).__init__()
self.hidden = nn.Linear(input_size, hidden_size)
self.relu = nn.ReLU()
self.output = nn.Linear(hidden_size, output_size)
def forward(self, x):
x = self.hidden(x)
x = self.relu(x)
x = self.output(x)
return x
# 定义模型参数
input_size = 784 # 输入大小为28*28=784
hidden_size = 256 # 隐藏层大小
output_size = 10 # 输出大小为10(10个类别)
# 构建模型
model = MLP(input_size, hidden_size, output_size)
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)
# 加载数据集
train_dataset = torchvision.datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)
# 训练模型
num_epochs = 10
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
images = Variable(images.view(-1, 28*28))
labels = Variable(labels)
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print('Epoch [%d/%d], Step [%d/%d], Loss: %.4f' % (epoch+1, num_epochs, i+1, len(train_dataset)//32, loss.data.item()))
# 测试模型
test_dataset = torchvision.datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor())
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=32, shuffle=False)
correct = 0
total = 0
for images, labels in test_loader:
images = Variable(images.view(-1, 28*28))
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum()
print('Accuracy of the model on the 10000 test images: %d %%' % (100 * correct / total))
```
这个代码使用了MNIST数据集进行训练和测试,其中模型只有一个隐藏层,使用ReLU作为激活函数,损失函数为交叉熵,优化器为随机梯度下降(SGD)。