| import json | |
| import matplotlib.pyplot as plt | |
| with open("performance.json", "r") as f: | |
| performance = json.load(f) | |
| # Extract values from the performance list | |
| epochs = range(1, len(performance) + 1) | |
| train_losses = [epoch["avg_train_loss"] for epoch in performance] | |
| val_losses = [epoch["avg_val_loss"] for epoch in performance] | |
| train_accuracies = [epoch["train_accuracy"] for epoch in performance] | |
| val_accuracies = [epoch["val_accuracy"] for epoch in performance] | |
| # Plot Training and Validation Loss | |
| plt.figure(figsize=(14, 6)) | |
| # Subplot for Loss | |
| plt.subplot(1, 2, 1) | |
| plt.plot(epochs, train_losses, label="Training Loss") | |
| plt.plot(epochs, val_losses, label="Validation Loss") | |
| plt.xlabel("Epochs") | |
| plt.ylabel("Loss") | |
| plt.title("Training and Validation Loss") | |
| plt.legend() | |
| plt.xticks(epochs) | |
| # Subplot for Accuracy | |
| plt.subplot(1, 2, 2) | |
| plt.plot(epochs, train_accuracies, label="Training Accuracy") | |
| plt.plot(epochs, val_accuracies, label="Validation Accuracy") | |
| plt.xlabel("Epochs") | |
| plt.ylabel("Accuracy") | |
| plt.title("Training and Validation Accuracy") | |
| plt.legend() | |
| plt.xticks(epochs) | |
| plt.tight_layout() | |
| # Save the plot as an image file | |
| plt.savefig("performance_plot.png", dpi=300) | |
| plt.show() | |