7. Saliency maps

from vis.visualization import visualize_saliency

# This corresponds to the Dense linear layer.
for class_idx in np.arange(10):    
    indices = np.where(y_test[:, class_idx] == 1.)[0]
    idx = indices[0]
    layer_idx = -1
    f, ax = plt.subplots(1, 4)
    ax[0].imshow(X_test[idx][..., 0])
    
    for i, modifier in enumerate([None, 'guided', 'relu']):
        grads = visualize_saliency(model, layer_idx, filter_indices=class_idx, 
                                   seed_input=X_test[idx], backprop_modifier=modifier)
        if modifier is None:
            modifier = 'vanilla'
        ax[i+1].set_title(modifier)    
        ax[i+1].imshow(grads, cmap='jet')

from vis.visualization import visualize_cam

# This corresponds to the Dense linear layer.
for class_idx in np.arange(10):    
    indices = np.where(y_test[:, class_idx] == 1.)[0]
    idx = indices[0]
    layer_idx = -1
    f, ax = plt.subplots(1, 4)
    ax[0].imshow(X_test[idx][..., 0])
    
    for i, modifier in enumerate([None, 'guided', 'relu']):
        grads = visualize_cam(model, layer_idx, filter_indices=class_idx, 
                              seed_input=X_test[idx], backprop_modifier=modifier)        
        if modifier is None:
            modifier = 'vanilla'
        ax[i+1].set_title(modifier)    
        ax[i+1].imshow(grads, cmap='jet')