Continuing from the previous post(170_Image Classification using Torchvision(3)). def show_confusion_matrix(confusion_matrix, class_names): cm = confusion_matrix.copy() cell_counts = cm.flatten() cm_row_norm = cm / cm.sum(axis=1)[:, np.newaxis] row_percentages = ["{0:.2f}".format(value) for value in cm_row_norm.flatten()] cell_labels = [f"{cnt}\n{per}" for cnt, per in zip(cell_cou..
