I have good understanding of applying Transfer learning for image classification. However I wanted to apply the SHAP (SHapley Additive exPlanation) for TL based image classification. I found that SHAP works for image classification without Transfer learning but I am receiving this error when I apply SHAP on TL based image classification . below is the code followed by the error:
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
base_model = VGG16(weights='imagenet', include_top=False, input_shape=(32, 32, 3))
x_train = preprocess_input(x_train)
x_test = preprocess_input(x_test)
x_train_features = base_model.predict(x_train)
x_test_features = base_model.predict(x_test)
model = Sequential()
model.add(GlobalAveragePooling2D(input_shape=x_train_features.shape[1:]))
model.add(Dense(256, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train_features, y_train, batch_size=64, epochs=1, validation_split=0.1)
sample_index = 0
sample_image = x_test_features[sample_index]
sample_label = y_test[sample_index]
masker = shap.maskers.Image("inpaint_telea", x_train_features[0].shape)
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
explainer = shap.Explainer(model, masker, output_names=class_names)
shap_values = explainer(sample_image[np.newaxis, ...])
However I received this error on running this code:
--> 127 return cv2.inpaint(
128 x.reshape(self.input_shape).astype(np.uint8),
129 reshaped_mask,
error: OpenCV(4.7.0) /io/opencv/modules/photo/src/inpaint.cpp:767: error: (-210:Unsupported format or combination of formats) 8-bit, 16-bit unsigned or 32-bit float 1-channel and 8-bit 3-channel input/output images are supported in function 'icvInpaint'
Any suggestion/comments is highly appreciated. i spent lot of time on it without any success even CHATGPT was not very helpful