unv_model.py

import torch  
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import scipy

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")# assign the device to the model

#hyper-parameters
learning_rate = 0.005
batch_size = 128
hidden_size = 300
num_classes = 10
num_epochs = 550

#load data 

transform = transforms.Compose(
    [transforms.Resize(64), transforms.CenterCrop(64), transforms.ToTensor()]
)

train_dataset = torchvision.datasets.STL10(root='./dataSTL10', split="train", transform=transform, download=True)
test_dataset = torchvision.datasets.STL10(root='./dataSTL10', split="test", transform=transform, download=True)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)

# CNN 

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, 5)
        self.conv2 = nn.Conv2d(32, 64, 5)
        #full layer
        self.fc1 = nn.Linear(64 * 13 * 13, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, num_classes)

    def forward(self, x):
        x = F.max_pool2d(F.relu(self.conv1(x)), (2,2))
        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
        x = x.view(-1, self.num_flat_features(x))
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x
    
    def num_flat_features(self, x):
        size = x.size()[1:]  # all dimensions except the batch dimension
        num_features = 1
        for s in size:
            num_features *= s
        return num_features
    
cnn = CNN().to(device)

criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(cnn.parameters(), lr=learning_rate)

# training loop

for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        images = images.to(device)
        labels = labels.to(device)

        out = cnn(images)
        loss = criterion(out, labels)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    if(i+1) % 1 == 0:
        print(f'epoch: {epoch+1}/{num_epochs} step: {i+1}, loss: loss: {loss.item():.4f}')


with torch.no_grad():
    n_correct = 0
    n_samples = 0
    for images, labels in test_loader:
        images = images.to(device)
        labels = labels.to(device)
        outputs = cnn(images)
        # max returns (value ,index)
        _, predicted = torch.max(outputs.data, 1)
        n_samples += labels.size(0)
        n_correct += (predicted == labels).sum().item()

    acc = 100.0 * n_correct / n_samples
    print(f'Accuracy of the network on the {n_samples} test images: {acc} %')


# Save the model
torch.save(cnn.state_dict(), "cnn_model.pth")