It takes the following. img: local path of img to be explained class_names: the classes available as predictions for the given model img_shape: shape of the image accepts by the neural network model: the model to be explained get from tf.keras.models.load_model("your model path") img1: local path background data point for produce explanations with SHAP img2: local path background data point for produce explanations with SHAP scale: for manual image scaling if scaling layer absent in the model to be explained filter_radius: the pixel value of the radius of the High pass filter
pip install LISA_CNN_ExplainerV3
Open terminal and type python/python3 according to your OS.
import LISA_CNN_ExplainerV3 as e \n
e.ExplainLISA(img,class_names,img_shape,model,img1,img2,scale,filter_radius) \n
e.displayImages() \n
© 2021 Sudil H.P Abeyagunasekera
This repository is licensed under the MIT license. See LICENSE for details.