ImageNet VGG16 Model with Keras

This notebook demonstrates how to use the model agnostic Kernel SHAP algorithm to explain predictions from the VGG16 network in Keras.

import keras
from keras.applications.vgg16 import VGG16, preprocess_input, decode_predictions
from keras.preprocessing import image
import requests
from skimage.segmentation import slic
import matplotlib.pylab as pl
import numpy as np
import shap

# load model data
r = requests.get('https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json')
feature_names = r.json()
model = VGG16()

# load an image
file = "data/apple_strawberry.jpg"
img = image.load_img(file, target_size=(224, 224))
img_orig = image.img_to_array(img)

# segment the image so we don't have to explain every pixel
segments_slic = slic(img, n_segments=50, compactness=30, sigma=3)

Using TensorFlow backend.

# segment the image so with don't have to explain every pixel
segments_slic = slic(img, n_segments=50, compactness=30, sigma=3)

# define a function that depends on a binary mask representing if an image region is hidden
def mask_image(zs, segmentation, image, background=None):
    if background is None:
        background = image.mean((0,1))
    out = np.zeros((zs.shape[0], image.shape[0], image.shape[1], image.shape[2]))
    for i in range(zs.shape[0]):
        out[i,:,:,:] = image
        for j in range(zs.shape[1]):
            if zs[i,j] == 0:
                out[i][segmentation == j,:] = background
    return out
def f(z):
    return model.predict(preprocess_input(mask_image(z, segments_slic, img_orig, 255)))

# use Kernel SHAP to explain the network's predictions
explainer = shap.KernelExplainer(f, np.zeros((1,50)))
shap_values = explainer.shap_values(np.ones((1,50)), nsamples=1000) # runs VGG16 1000 times

# get the top predictions from the model
preds = model.predict(preprocess_input(np.expand_dims(img_orig.copy(), axis=0)))
top_preds = np.argsort(-preds)

# make a color map
from matplotlib.colors import LinearSegmentedColormap
colors = []
for l in np.linspace(1,0,100):
    colors.append((245/255,39/255,87/255,l))
for l in np.linspace(0,1,100):
    colors.append((24/255,196/255,93/255,l))
cm = LinearSegmentedColormap.from_list("shap", colors)

def fill_segmentation(values, segmentation):
    out = np.zeros(segmentation.shape)
    for i in range(len(values)):
        out[segmentation == i] = values[i]
    return out

# plot our explanations
fig, axes = pl.subplots(nrows=1, ncols=4, figsize=(12,4))
inds = top_preds[0]
axes[0].imshow(img)
axes[0].axis('off')
max_val = np.max([np.max(np.abs(shap_values[i][:,:-1])) for i in range(len(shap_values))])
for i in range(3):
    m = fill_segmentation(shap_values[inds[i]][0], segments_slic)
    axes[i+1].set_title(feature_names[str(inds[i])][1])
    axes[i+1].imshow(img.convert('LA'), alpha=0.15)
    im = axes[i+1].imshow(m, cmap=cm, vmin=-max_val, vmax=max_val)
    axes[i+1].axis('off')
cb = fig.colorbar(im, ax=axes.ravel().tolist(), label="SHAP value", orientation="horizontal", aspect=60)
cb.outline.set_visible(False)
pl.show()