In this step by step, guide we will walk you through the core concept of interpretable image classification with hands-on approach to have a basic glance of interpretable machine learning and explainable artificial intelligence (XAI).
Lets Dive In !
Step 1 — Importing Required Libraries
Firstly, we importing the required libraries for our project.
- TensorFlow: It is used for making large machine learning and deep learning models.
- Matplotlib: It is used to creating engaging and interactive visuals.
- skimage: It is used for image processing.
- LIME: It is used for generating local interpretable model with agnostic explanations.
Step 2 — Loading and Preprocessing an Image
Now we load an image and preprocess it to meet the requirements of the InceptionV3 model. The image is resized to 299x299 pixels and converted to a NumPy array and once you done with that, then you expanded to a 4D tensor with a batch dimension.
Step 3 – Loading a Pre-trained InceptionV3 Model
We load the InceptionV3 model pre-trained on ImageNet. This model is a powerful convolutional neural network (CNN) for image classification. The last layer is responsible for classification and it is removed to obtain a feature extractor.
Step 4 – Making Predictions
We use the loaded model to make predictions on the preprocessed image and decode the predictions into human readable form. This provides insights into what the model sees in the image.
Step 5 – Generating LIME Explanations
LIME is used to generate local interpretable explanations for the image classification. It perturbs the input image and observes the model’s predictions to understand which parts of the image contribute most to the classification.
Step 6 – Visualizing LIME Explanation
Now we visualize the original image alongside the LIME explanation. LIME highlights the regions of the image that heavily influenced the model’s decision. The mark_boundaries function helps visualize these regions.
Conclusion
In this entire journey of image classification, we demonstrates a comprehensive workflow for interpretable image classification. It loads a pre-trained model, makes predictions and employs LIME to provide local explanations helping to interpret and understand the model’s decision making process for a given image.
