image 绘图

本笔记本旨在演示（并记录）如何使用 shap.plots.image 函数。

import json

from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input

import shap

# 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,  6.10s/it]

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