Multi-class ResNet50 on ImageNet (TensorFlow)

[1]:
from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
import json
import shap
import tensorflow as tf

# load pre-trained model and choose two images to explain
model = ResNet50(weights='imagenet')
def f(X):
    tmp = X.copy()
    preprocess_input(tmp)
    return model(tmp)
X, y = shap.datasets.imagenet50()

# load the ImageNet class names as a vectorized mapping function from ids to names
url = "https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json"
with open(shap.datasets.cache(url)) as file:
    class_names = [v[1] for v in json.load(file).values()]

# define a masker that is used to mask out partitions of the input image, this one uses a blurred background
masker = shap.maskers.Image("inpaint_telea", X[0].shape)

# By default the Partition explainer is used for all  partition explainer
explainer = shap.Explainer(f, masker, output_names=class_names)

# here we use 500 evaluations of the underlying model to estimate the SHAP values
shap_values = explainer(X[1:3], max_evals=500, batch_size=50, outputs=shap.Explanation.argsort.flip[:1])
shap.image_plot(shap_values)
explainers.Partition is still in an alpha state, so use with caution...
Partition explainer: 3it [00:18,  6.13s/it]
../../../_images/example_notebooks_image_examples_image_classification_Multi-class_ResNet50_on_ImageNet_(TensorFlow)-checkpoint_1_3.png

Have an idea for more helpful examples? Pull requests that add to this documentation notebook are encouraged!