fashionmnist训练函数 pytorch
时间: 2023-08-30 20:09:53 浏览: 91
以下是使用PyTorch进行FashionMNIST训练的示例代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torchvision.datasets import FashionMNIST
# Define the neural network model
class FashionMNISTNet(nn.Module):
def __init__(self):
super(FashionMNISTNet, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=5, padding=2)
self.conv2 = nn.Conv2d(32, 64, kernel_size=5, padding=2)
self.fc1 = nn.Linear(64*7*7, 1024)
self.fc2 = nn.Linear(1024, 10)
def forward(self, x):
x = nn.functional.relu(self.conv1(x))
x = nn.functional.max_pool2d(x, 2)
x = nn.functional.relu(self.conv2(x))
x = nn.functional.max_pool2d(x, 2)
x = x.view(-1, 64*7*7)
x = nn.functional.relu(self.fc1(x))
x = self.fc2(x)
return x
# Set device to GPU if available, otherwise use CPU
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Load FashionMNIST dataset
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
train_dataset = FashionMNIST(root='./data', train=True, transform=transform, download=True)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
# Initialize the neural network model and optimizer
model = FashionMNISTNet().to(device)
optimizer = optim.Adam(model.parameters())
# Train the model
criterion = nn.CrossEntropyLoss()
num_epochs = 10
for epoch in range(num_epochs):
running_loss = 0.0
for i, data in enumerate(train_loader, 0):
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 100 == 99:
print('[Epoch %d, Batch %5d] Loss: %.3f' %
(epoch + 1, i + 1, running_loss / 100))
running_loss = 0.0
print('Finished Training')
```