import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
# Define a neural network architecture
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(torch.relu(self.conv1(x)))
x = self.pool(torch.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = self.fc3(x)
return x
# Define the training dataset and data loader
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = datasets.CIFAR10(
root='./data', train=True, download=True, transform=transform)
trainloader = DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)
# Move the model to the GPU
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net = Net().to(device)
# Define the optimization algorithms
optimizers = [optim.SGD(net.parameters('fc3'), lr=0.001, momentum=0.9),
optim.Adagrad(net.parameters('fc2'), lr=0.001),
optim.Adam(net.parameters('fc1'), lr=0.001)]
# Train the neural network using different optimization algorithms
for epoch in range(10):
running_loss = 0.0
correct = 0
total = 0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
# move data and target to the GPU
inputs, labels = inputs.to(device), labels.to(device)
for optimizer in optimizers:
optimizer.zero_grad()
outputs = net(inputs)
EntropyLoss = nn.CrossEntropyLoss()(outputs, labels)
fc1_loss = nn.L1Loss()(net.fc1.weight, torch.zeros_like(net.fc1.weight))
fc2_loss = nn.L1Loss()(net.fc2.weight, torch.zeros_like(net.fc2.weight))
total_loss = EntropyLoss + fc1_loss + fc2_loss
total_loss.backward()
for optimizer in optimizers:
optimizer.step()
running_loss += total_loss.item()
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Epoch: %d | Loss: %.3f | Accuracy: %.3f %%' %
(epoch + 1, running_loss / len(trainloader), 100 * correct / total))