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import torch
import matplotlib.pyplot as plt
from torchvision.utils import make_grid
def save_checkpoint(model, optimizer, epoch, loss, path):
torch.save({
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': loss,
}, path)
print(f"Checkpoint saved at epoch {epoch}")
def load_checkpoint(model, optimizer, path):
checkpoint = torch.load(path, weights_only=True)
model.load_state_dict(checkpoint['model_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
epoch = checkpoint['epoch']
loss = checkpoint['loss']
print(f"Checkpoint loaded, resuming from epoch {epoch}")
return model, optimizer, epoch, loss
def plot_training_curves(epochs, train_acc1, test_acc1, train_acc5, test_acc5, train_losses, test_losses, learning_rates):
plt.figure(figsize=(12, 8))
plt.subplot(2, 2, 1)
plt.plot(epochs, train_acc1, label='Train Top-1 Acc')
plt.plot(epochs, test_acc1, label='Test Top-1 Acc')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.title('Top-1 Accuracy')
plt.subplot(2, 2, 2)
plt.plot(epochs, train_acc5, label='Train Top-5 Acc')
plt.plot(epochs, test_acc5, label='Test Top-5 Acc')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.title('Top-5 Accuracy')
plt.subplot(2, 2, 3)
plt.plot(epochs, train_losses, label='Train Loss')
plt.plot(epochs, test_losses, label='Test Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.title('Loss')
plt.subplot(2, 2, 4)
plt.plot(epochs, learning_rates, label='Learning Rate')
plt.xlabel('Epoch')
plt.ylabel('Learning Rate')
plt.legend()
plt.title('Learning Rate')
plt.tight_layout()
plt.show()
def plot_misclassified_samples(misclassified_images, misclassified_labels, misclassified_preds, classes):
if misclassified_images:
print("\nDisplaying some misclassified samples:")
misclassified_grid = make_grid(misclassified_images[:16], nrow=4, normalize=True, scale_each=True)
plt.figure(figsize=(8, 8))
plt.imshow(misclassified_grid.permute(1, 2, 0))
plt.title("Misclassified Samples")
plt.axis('off')
plt.show()
def find_lr(model, criterion, optimizer, train_loader, num_epochs=1, start_lr=1e-7, end_lr=10, lr_multiplier=1.1):
"""
Find the optimal learning rate using LR Finder.
Args:
- model: The model to train
- criterion: Loss function (e.g., CrossEntropyLoss)
- optimizer: Optimizer (e.g., SGD)
- train_loader: DataLoader for training data
- num_epochs: Number of epochs to run the LR Finder (typically 1-2)
- start_lr: Starting learning rate for the experiment
- end_lr: Maximum learning rate (used for scaling)
- lr_multiplier: Factor by which the learning rate is increased every batch
Returns:
- A plot of loss vs learning rate
"""
lrs = []
losses = []
avg_loss = 0.0
batch_count = 0
lr = start_lr
for epoch in range(num_epochs):
model.train()
for inputs, labels in train_loader:
inputs, labels = inputs.to(device), labels.to(device)
optimizer.param_groups[0]['lr'] = lr # Set the learning rate
# Forward pass
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
avg_loss += loss.item()
batch_count += 1
lrs.append(lr)
losses.append(loss.item())
# Increase the learning rate for next batch
lr *= lr_multiplier
avg_loss /= batch_count
print(f"Epoch [{epoch+1}/{num_epochs}], Avg Loss: {avg_loss:.4f}")
# Plot the loss vs learning rate
plt.plot(lrs, losses)
plt.xscale('log')
plt.xlabel('Learning Rate')
plt.ylabel('Loss')
plt.title('Learning Rate Finder')
plt.show()
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