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import matplotlib.pyplot as plt
import numpy as np
from datetime import datetime
import os
class TrainingMetrics:
def __init__(self):
self.train_losses = []
self.val_losses = []
self.epochs = []
self.sample_predictions = []
self.sample_targets = []
def add_epoch(self, epoch, train_loss, val_loss):
self.epochs.append(epoch)
self.train_losses.append(train_loss)
self.val_losses.append(val_loss)
def add_predictions(self, predictions, targets):
self.sample_predictions.extend(predictions)
self.sample_targets.extend(targets)
def plot_losses(self, save_path="Metrics/training_losses.png"):
plt.figure(figsize=(10, 6))
plt.plot(self.epochs, self.train_losses, 'b-', label='Training Loss', linewidth=2)
plt.plot(self.epochs, self.val_losses, 'r-', label='Validation Loss', linewidth=2)
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.title('Training and Validation Loss Over Time')
plt.legend()
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.close()
print(f"Loss plot saved to: {save_path}")
def plot_loss_comparison(self, save_path="Metrics/loss_comparison.png"):
plt.figure(figsize=(12, 8))
# Main loss plot
plt.subplot(2, 2, 1)
plt.plot(self.epochs, self.train_losses, 'b-', label='Training Loss')
plt.plot(self.epochs, self.val_losses, 'r-', label='Validation Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.title('Training vs Validation Loss')
plt.legend()
plt.grid(True, alpha=0.3)
# Loss difference plot
plt.subplot(2, 2, 2)
loss_diff = [t - v for t, v in zip(self.train_losses, self.val_losses)]
plt.plot(self.epochs, loss_diff, 'g-', label='Train - Val Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss Difference')
plt.title('Overfitting Indicator')
plt.legend()
plt.grid(True, alpha=0.3)
# Loss ratio plot
plt.subplot(2, 2, 3)
loss_ratio = [v/t if t > 0 else 0 for t, v in zip(self.train_losses, self.val_losses)]
plt.plot(self.epochs, loss_ratio, 'm-', label='Val/Train Loss Ratio')
plt.xlabel('Epoch')
plt.ylabel('Ratio')
plt.title('Validation/Training Loss Ratio')
plt.legend()
plt.grid(True, alpha=0.3)
# Loss improvement plot
plt.subplot(2, 2, 4)
train_improvement = [self.train_losses[0] - t for t in self.train_losses]
val_improvement = [self.val_losses[0] - v for v in self.val_losses]
plt.plot(self.epochs, train_improvement, 'b-', label='Training Improvement')
plt.plot(self.epochs, val_improvement, 'r-', label='Validation Improvement')
plt.xlabel('Epoch')
plt.ylabel('Loss Improvement')
plt.title('Loss Improvement from Start')
plt.legend()
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.close()
print(f"Loss comparison plot saved to: {save_path}")
def save_metrics(self, save_path="Metrics/training_metrics.txt"):
with open(save_path, 'w') as f:
f.write("CAPTCHA OCR Training Metrics\n")
f.write("=" * 50 + "\n\n")
f.write(f"Training completed at: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n")
f.write(f"Total epochs: {len(self.epochs)}\n\n")
f.write("Loss Summary:\n")
f.write("-" * 20 + "\n")
f.write(f"Final training loss: {self.train_losses[-1]:.4f}\n")
f.write(f"Final validation loss: {self.val_losses[-1]:.4f}\n")
f.write(f"Best training loss: {min(self.train_losses):.4f}\n")
f.write(f"Best validation loss: {min(self.val_losses):.4f}\n")
f.write(f"Training loss improvement: {self.train_losses[0] - self.train_losses[-1]:.4f}\n")
f.write(f"Validation loss improvement: {self.val_losses[0] - self.val_losses[-1]:.4f}\n\n")
f.write("Sample Predictions:\n")
f.write("-" * 20 + "\n")
for i, (pred, target) in enumerate(zip(self.sample_predictions[:10], self.sample_targets[:10])):
f.write(f"Sample {i+1}: Predicted='{pred}', Target='{target}'\n") |