CRAFT

Release Note v1.3.0

New Features

CRAFT or Concept Recursive Activation FacTorization for Explainability

Introduction of the CRAFT method (see the Paper) for both frameworks: PyTorch and Tensorflow. CRAFT is a method for automatically extracting human-interpretable concepts from deep networks.

from xplique.concepts import CraftTf as Craft

# Cut the model in two parts (as explained in the paper)
# first part is g(.) our 'input_to_latent' model returning positive activations,
# second part is h(.) our 'latent_to_logit' model

g = tf.keras.Model(model.input, model.layers[-3].output)
h = tf.keras.Model(model.layers[-2].input, model.layers[-1].output)

# Create a Craft concept extractor from these 2 models
craft = Craft(input_to_latent_model = g,
			  latent_to_logit_model = h,
			  number_of_concepts = 10,
			  patch_size = 80,
			  batch_size = 64)

# Use Craft to get the crops (crops), the embedding of the crops (crops_u),
# and the concept bank (w)
crops, crops_u, w = craft.fit(images_preprocessed, class_id=rabbit_class_id)

# Compute Sobol indices to understand which concept matters
importances = craft.estimate_importance()

# Display those concepts by showing the 10 best crops for each concept
craft.plot_concepts_crops(nb_crops=10)

See related documentation, Tensorflow tutorials and PyTorch tutorial