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.

In [1]:
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('')
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.
In [2]:
# segment the image so with don't have to explain every pixel
segments_slic = slic(img, n_segments=50, compactness=30, sigma=3)
In [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)))
In [4]:
# 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
In [6]:
# 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)
In [7]:
# make a color map
from matplotlib.colors import LinearSegmentedColormap
colors = []
for l in np.linspace(1,0,100):
for l in np.linspace(0,1,100):
cm = LinearSegmentedColormap.from_list("shap", colors)
In [9]:
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]
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].imshow(img.convert('LA'), alpha=0.15)
    im = axes[i+1].imshow(m, cmap=cm, vmin=-max_val, vmax=max_val)
cb = fig.colorbar(im, ax=axes.ravel().tolist(), label="SHAP value", orientation="horizontal", aspect=60)