Welcome to Netlens!

Library containing attribution and interpretation methods for deep nets.

Installation

These installation instructions assume that you have conda installed and added to your path.

0. Create a virtual environment (or modify an existing one).

conda create -n "<my_name>" python=3.7  # Skip if using existing environment.
conda activate <my_name>

1. Install dependencies.

conda install tensorflow-gpu=1  # Or whatever backend you're using.
conda install keras             # Or whatever backend you're using.
conda install matplotlib        # For visualizations.
conda install scikit-learn      # To get LFW dataset.

1. Install the netlens repo

pip install netlens

Overview

Backends

The netlens library supports several common machine learning libraries, including Keras, Pytorch, and TensorFlow.

In order to set the backend to the backend of your choice, use the NETLENS_BACKEND flag, e.g., to use the Keras backend, the following code could be used before NetLens imports:

import os
os.environ['NETLENS_BACKEND'] = 'keras'

Attributions

Model Wrappers

In order to support a wide variety of backends with different interfaces for their respective models, NetLens uses its own Model class which provides a general model interface to simplify the implementation of the API functions. A model wrapper class exists for backend's model that converts a model in the respective backend's format to the general NetLens Model interface. The wrappers are found in the models module, and any model defined using Keras, Pytorch, or Tensorflow should be wrapped with the appropriate wrapper before being used with the other API functions that require a model -- all other NetLens functionalities expect models to be an instance of netlens.models.Model.

For example,

wrapped_model = KerasModel(model_defined_via_keras)

Attribution Methods

Attribution methods, in the most general sense, allow us to quantify the contribution of particular variables in a model towards a particular behavior of the model. In many cases, for example, this may simply measure the effect each input variable has on the output of the network.

Attribution methods extend the AttributionMethod class, and many concrete instances are found in the attribution module.

Once an attribution method has been instantiated, its main function is its attributions method, which takes an np.Array of batched instances, where each instance matches the shape of the input to the model the attribution method was instantiated with.

See the method comparison demo for further information on the different types of attribution methods, their uses, and their relationships with one another.

Slices, Quantities, and Distributions

In order to obtain a high degree of flexibility in the types of attributions that can be produced, we implement Internal Influence, which is parameterized by a slice, quantity of interest, and distribution of interest, explained below.

The slice essentially defines a layer to use for internal attributions. The slice for the InternalInfluence method can be specified by an instance of the Slice class in the slices module. A Slice object specifies two layers: (1) the layer of the variables that we are calculating attribution for (e.g., the input layer), and (2) the layer whose output defines our quantity of interest (e.g., the output layer, see below for more on quantities of interest).

The quantity of interest (QoI) essentially defines the model behavior we would like to explain using attributions. The QoI is a function of the model's output at some layer. For example, it may select the confidence score for a particular class. In its most general form, the QoI can be pecified by an implementation of the QoI class in the quantities module. Several common default implementations are provided in this module as well.

The distribution of interest (DoI) essentially specifies for which points surrounding each instance the calculated attribution should be valid. The distribution can be specified via an implementation of the DoI class in the distributions module, which is a function taking an input instance and producing a list of input points to aggregate attribution over. A few common default distributions implementing the DoI class can be found in the distributions module.

See the parameterization demo for further explanations of the purpose of these parameters and examples of their usage.

Visualizations

In order to interpret the attributions produced by an AttributionMethod, a few useful visualizers are provided in the visualizations module. While the interface of each visualizer varies slightly, in general, the visualizers are a function taking an np.Array representing the attributions returned from an AttributionMethod and producing an image that can be used to interpret the attributions.