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"""
Utility functions for the CIFAR-10 CNN project
"""
import os
import torch
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import confusion_matrix, classification_report
import seaborn as sns
import config
def save_checkpoint(model, optimizer, epoch, accuracy, filepath):
"""
Save model checkpoint
Args:
model: PyTorch model
optimizer: Optimizer
epoch: Current epoch
accuracy: Current accuracy
filepath: Path to save checkpoint
"""
checkpoint = {
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'accuracy': accuracy
}
torch.save(checkpoint, filepath)
def load_checkpoint(model, optimizer, filepath):
"""
Load model checkpoint
Args:
model: PyTorch model
optimizer: Optimizer
filepath: Path to checkpoint file
Returns:
tuple: (epoch, accuracy)
"""
checkpoint = torch.load(filepath, map_location=config.DEVICE)
model.load_state_dict(checkpoint['model_state_dict'])
if optimizer:
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
epoch = checkpoint['epoch']
accuracy = checkpoint['accuracy']
return epoch, accuracy
def plot_training_history(history, save_dir):
"""
Plot training history
Args:
history: Dictionary containing training history
save_dir: Directory to save plots
"""
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))
# Plot loss
ax1.plot(history['train_loss'], label='Train Loss', linewidth=2)
ax1.plot(history['val_loss'], label='Validation Loss', linewidth=2)
ax1.set_xlabel('Epoch', fontsize=12)
ax1.set_ylabel('Loss', fontsize=12)
ax1.set_title('Training and Validation Loss', fontsize=14, fontweight='bold')
ax1.legend(fontsize=10)
ax1.grid(True, alpha=0.3)
# Plot accuracy
ax2.plot(history['train_acc'], label='Train Accuracy', linewidth=2)
ax2.plot(history['val_acc'], label='Validation Accuracy', linewidth=2)
ax2.set_xlabel('Epoch', fontsize=12)
ax2.set_ylabel('Accuracy (%)', fontsize=12)
ax2.set_title('Training and Validation Accuracy', fontsize=14, fontweight='bold')
ax2.legend(fontsize=10)
ax2.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig(os.path.join(save_dir, 'training_history.png'), dpi=300, bbox_inches='tight')
plt.close()
def plot_confusion_matrix(y_true, y_pred, save_path):
"""
Plot confusion matrix
Args:
y_true: True labels
y_pred: Predicted labels
save_path: Path to save the plot
"""
cm = confusion_matrix(y_true, y_pred)
plt.figure(figsize=(12, 10))
sns.heatmap(
cm, annot=True, fmt='d', cmap='Blues',
xticklabels=config.CLASS_NAMES,
yticklabels=config.CLASS_NAMES,
cbar_kws={'label': 'Count'}
)
plt.xlabel('Predicted Label', fontsize=12)
plt.ylabel('True Label', fontsize=12)
plt.title('Confusion Matrix', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.close()
def print_classification_report(y_true, y_pred):
"""
Print classification report
Args:
y_true: True labels
y_pred: Predicted labels
"""
report = classification_report(
y_true, y_pred,
target_names=config.CLASS_NAMES,
digits=4
)
print("\nClassification Report:")
print("=" * 80)
print(report)
print("=" * 80)
def visualize_predictions(model, test_loader, device, num_images=16):
"""
Visualize model predictions
Args:
model: PyTorch model
test_loader: Test data loader
device: Device to run on
num_images: Number of images to visualize
"""
model.eval()
# Get a batch of images
images, labels = next(iter(test_loader))
images, labels = images[:num_images], labels[:num_images]
images_device = images.to(device)
# Get predictions
with torch.no_grad():
outputs = model(images_device)
_, predicted = outputs.max(1)
# Plot
fig, axes = plt.subplots(4, 4, figsize=(12, 12))
axes = axes.ravel()
for idx in range(num_images):
# Denormalize image
img = images[idx].cpu().numpy().transpose(1, 2, 0)
mean = np.array([0.4914, 0.4822, 0.4465])
std = np.array([0.2470, 0.2435, 0.2616])
img = img * std + mean
img = np.clip(img, 0, 1)
# Plot
axes[idx].imshow(img)
axes[idx].axis('off')
true_label = config.CLASS_NAMES[labels[idx]]
pred_label = config.CLASS_NAMES[predicted[idx].cpu()]
color = 'green' if labels[idx] == predicted[idx].cpu() else 'red'
axes[idx].set_title(
f'True: {true_label}\nPred: {pred_label}',
color=color, fontsize=10
)
plt.tight_layout()
plt.savefig(os.path.join(config.PLOTS_DIR, 'predictions.png'), dpi=300, bbox_inches='tight')
plt.close()
print(f"Predictions visualization saved to {config.PLOTS_DIR}/predictions.png")
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