| import matplotlib.pyplot as plt | |
| from sklearn.metrics import mean_squared_error, structural_similarity as ssim | |
| def evaluate_model(model, x_test, y_test): | |
| predictions = model.predict(x_test) | |
| mse = mean_squared_error(y_test.flatten(), predictions.flatten()) | |
| ssim_score = ssim(y_test[0, -1, :, :, 0], predictions[0, -1, :, :, 0], data_range=1.0) | |
| print(f"Mean Squared Error: {mse}") | |
| print(f"Structural Similarity Index: {ssim_score}") | |
| return predictions | |
| def visualize_predictions(x_test, y_test, predictions, idx=0): | |
| fig, axes = plt.subplots(1, 3, figsize=(15, 5)) | |
| axes[0].imshow(x_test[idx, -1, :, :, 0], cmap='gray') | |
| axes[0].set_title("Input Frame") | |
| axes[1].imshow(y_test[idx, -1, :, :, 0], cmap='gray') | |
| axes[1].set_title("True Frame") | |
| axes[2].imshow(predictions[idx, -1, :, :, 0], cmap='gray') | |
| axes[2].set_title("Predicted Frame") | |
| plt.show() | |