How to interpret models in Keras?

In Keras, there are tools that can be used to interpret models, as shown below:

1. Utilizing Grad-CAM (Gradient-weighted Class Activation Mapping): Grad-CAM can assist us in understanding the basis of a neural network’s prediction results for input images. We can implement Grad-CAM in Keras using libraries such as keras-vis.
2. Utilize LIME (Local Interpretable Model-agnostic Explanations): LIME is a tool used to explain model prediction results, assisting in understanding the decision-making process of the model on specific samples. The lime package can be used to implement LIME.
3. Utilize SHAP (SHapley Additive exPlanations): SHAP can aid in understanding how the predictions of a model are determined by the importance of input features. We can implement SHAP using the shap package.
4. Utilizing Integrated Gradients: Integrated Gradients can assist in understanding the contributions of input features to the model. It can be implemented using layers such as keras.layers.Softmax, keras.layers.Input, and keras.layers.Lambda.

By utilizing the tools mentioned above, we can gain a better understanding of the decision-making process of neural network models, and explain the basis of the model’s predictions on input data.