seti25.py

fig, ax = plt.subplots(2, 4, figsize=(12, 6))

for idx, img_idx in enumerate([15, 123, 230, 340, 450, 560, 670]):
    id1 = 1 if idx > 3 else 0
    id2 = idx % 4
    predicted_label = class_label_mapping[
                            np.argmax(
                                resnet_ft_model4.predict(
                                    np.array([test_data_X[img_idx]])
                                ),axis=1
                            )[0]
    ]
    f = ax[id1, id2].imshow(test_data_X[img_idx], aspect='auto')
    t = ax[id1, id2].set_title('Actual: {}\nPredicted: {}'.format(test_data_y[img_idx],
                                                                  predicted_label), 
                               fontsize=10)
ax[1,3].set_axis_off()
fig.tight_layout()