tf-explain implements methods you can use at different levels:

  • either on a loaded model with the core API (which saves outputs to disk)
  • either at training time with callbacks (which integrates into Tensorboard)

This section introduces both usages.

Core API

All methods implemented in tf-explain keep the same interface:

  • a explain method which outputs the explaination (for instance, a heatmap)
  • a save method compatible with its output

Usage of the core API should be the following:

# Import explainer
from tf_explain.core.grad_cam import GradCAM

# Instantiation of the explainer
explainer = GradCAM()

# Call to explain() method
output = explainer.explain(*explainer_args)

# Save output, output_dir, output_name)

Recurrent arguments contained in explainer_args are typically the data to use for the explanation, the model to inspect. Refer to each method docstring to know which elements are needed.

All methods are kept inside tf_explain.core.


To use those methods during trainings and inspect evolutions over the epochs, each one of them has its corresponding tf.keras.Callback.

Callback usage is coherent with Keras Callbacks:

from tf_explain.callbacks.grad_cam import GradCAMCallback

model = [...]

callbacks = [
        validation_data=(x_val, y_val),
], y_train, batch_size=2, epochs=2, callbacks=callbacks)

Then, launch Tensorboard and visualize the outputs in the Images section.