saliency_map.py

# pip install scipy==1.1.0
# pip install keras-vis
from vis.visualization import visualize_saliency

def plot_saliency(img_idx=None):
    img_idx = plot_features_map(img_idx)
    grads = visualize_saliency(cnn_saliency, -1, filter_indices=ytest[img_idx][0], 
                               seed_input=x_test[img_idx], backprop_modifier=None,
                               grad_modifier="absolute")
    predicted_label = labels[np.argmax(cnn.predict(x_test[img_idx].reshape(1,32,32,3)),1)[0]]
    fig, ax = plt.subplots(1,2, figsize=(10,5))
    ax[0].imshow(x_test[img_idx])
    ax[0].set_title('original img id {} - {}'.format(img_idx, labels[ytest[img_idx][0]]))
    ax[1].imshow(grads, cmap='jet')
    ax[1].set_title('saliency - predicted {}'.format(predicted_label))

plot_saliency()