# train with base lr in the first 100 epochs # and half the lr in the last 100 epochs

To train with a base learning rate for the first 100 epochs and half the learning rate for the last 100 epochs, you can use a learning rate scheduler in PyTorch. Here's an example of how you can modify the training loop in your code:

import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim.lr_scheduler import MultiStepLR

# Define your model, criterion, and optimizer
model = YourModel()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# Define the number of epochs and the milestone epochs
num_epochs = 200
milestones = [100]

# Create a learning rate scheduler
scheduler = MultiStepLR(optimizer, milestones=milestones, gamma=0.5)

# Train the model
for epoch in range(num_epochs):
    # Train with base lr for the first 100 epochs, and half the lr for the last 100 epochs
    if epoch >= milestones[0]:
        scheduler.step()

    for inputs, labels in train_loader:
        # Forward pass
        outputs = model(inputs)
        loss = criterion(outputs, labels)

        # Backward pass and optimization
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    # Perform validation or testing after each epoch
    with torch.no_grad():
        # Validation or testing code

    # Print training information
    print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}, LR: {scheduler.get_last_lr()[0]}")

# Save the model or perform other operations after training

In this code snippet, we create a `MultiStepLR` scheduler and specify the `milestones` as `[100]` and `gamma` as `0.5`. The learning rate is halved at the specified milestone epochs. Inside the training loop, we check if the current epoch is greater than or equal to the milestone epoch, and if so, we call `scheduler.step()` to update the learning rate.

Remember to adjust the `num_epochs` and other hyperparameters according to your specific requirements.