import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import TensorDataset, DataLoader

# Toy data
x = torch.randn(100, 10)  # 100 samples, 10 features each
y = torch.randn(100, 1)  # 100 targets

# Wrap in a Dataset and DataLoader for minibatch training
dataset = TensorDataset(x, y)
data_loader = DataLoader(dataset, batch_size=20)  # Minibatch size of 20

# Define a simple model
model = nn.Sequential(
    nn.Linear(10, 5),
    nn.ReLU(),
    nn.Linear(5, 1)
)

# Define loss function and optimizer
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# Training loop
for epoch in range(50):
    for batch_x, batch_y in data_loader:
        optimizer.zero_grad()
        outputs = model(batch_x)
        loss = criterion(outputs, batch_y)
        loss.backward()
        optimizer.step()

    print(f'Epoch {epoch+1}, Loss: {loss.item()}')