Deep Learning with PyTorch

python
Run Inline
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
# Generate synthetic data
X = torch.randn(1000, 10)
y = torch.randn(1000, 1)
# Define model
model = nn.Sequential(
    nn.Linear(10, 64),
    nn.ReLU(),
    nn.Linear(64, 32),
    nn.ReLU(),
    nn.Linear(32, 1)
)
# Training
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, shuffle=True)
for epoch in range(100):
    for batch_X, batch_y in loader:
        pred = model(batch_X)
        loss = criterion(pred, batch_y)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    if epoch % 20 == 0:
        print(f"Epoch {epoch}, Loss: {loss.item():.4f}")