import torch
import torch.nn as nn
import torch.optim as optim

# Synthetic input and target data
inputs = torch.randn(100, 10)          # 100 samples, 10 features each
targets = torch.randn(100, 1)          # 100 target values

class TinyModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.layer1 = nn.Linear(10, 5)
        self.relu = nn.ReLU()
        self.layer2 = nn.Linear(5, 1)

    def forward(self, x):
        x = self.layer1(x)
        x = self.relu(x)
        x = self.layer2(x)
        return x

    model = TinyModel()
loss_fn = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

for epoch in range(200):
    outputs = model(inputs)
    loss = loss_fn(outputs, targets)

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

    if epoch % 20 == 0:
        print(f"Epoch {epoch}: Loss = {loss.item():.4f}")