| #test the model | |
| test_loss, test_acc = model.evaluate(test_x, test_y) | |
| print('Test accuracy:', test_acc) | |
| #print the confusion matrix | |
| from sklearn.metrics import confusion_matrix | |
| y_pred = model.predict(test_x) | |
| y_pred = (y_pred > 0.5) | |
| cf_matrix = confusion_matrix(test_y, y_pred) | |
| print(cf_matrix) | |
| #lets try beautify the confusion metrix | |
| #I had saved this code snippet some time ago, and lost the refrence. | |
| group_names = ["True Neg","False Pos","False Neg","True Pos"] | |
| group_counts = ["{0:0.0f}".format(value) for value in | |
| cf_matrix.flatten()] | |
| group_percentages = [f"{value:.1%}" for value in | |
| cf_matrix.flatten()/np.sum(cf_matrix)] | |
| labels = [f"{v1}\n{v2}\n{v3}" for v1, v2, v3 in | |
| zip(group_names,group_counts,group_percentages)] | |
| labels = np.asarray(labels).reshape(2,2) | |
| sns.heatmap(cf_matrix, annot=labels, fmt='', cmap='Reds') |