import torch
from torch import nn
 
# Automatically use GPU if available, otherwise fall back to CPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using {device} device")
 
# Define model
class NeuralNetwork(nn.Module):
    def __init__(self):
        super().__init__()
        self.flatten = nn.Flatten()
        self.linear_relu_stack = nn.Sequential(
            nn.Linear(28*28, 512),
            nn.ReLU(),
            nn.Linear(512, 512),
            nn.ReLU(),
            nn.Linear(512, 10)
        )
 
    def forward(self, x):
        x = self.flatten(x)
        logits = self.linear_relu_stack(x)
        return logits
 
model = NeuralNetwork().to(device)        # Move data to GPU
print(model)
 
# Save the model
torch.save(model.state_dict(), "model.pth")
print("Saved PyTorch model state to model.pth")