pytorch-ranger

import torch import torch.nn as nn from pytorch_ranger import Ranger # 1. Define a simple PyTorch model class SimpleModel(nn.Module): def __init__(self): super().__init__() self.linear = nn.Linear(10, 1) def forward(self, x): return self.linear(x) model = SimpleModel() # 2. Define dummy data and target inputs = torch.randn(32, 10) # Example batch of 32 samples, 10 features targets = torch.randn(32, 1) # Example batch of 32 targets # 3. Instantiate the Ranger optimizer # Pass model.parameters() to the optimizer optimizer = Ranger(model.parameters(), lr=0.001) # 4. Define a loss function criterion = nn.MSELoss() # 5. Perform a single training step (in a real scenario, this would be in a loop) optimizer.zero_grad() # Zero the gradients outputs = model(inputs) # Forward pass loss = criterion(outputs, targets) # Compute loss loss.backward() # Backward pass (compute gradients) optimizer.step() # Update model parameters print(f"Loss after one step: {loss.item():.4f}")