Interpret Tensor - Slim TF wrapper to compute LRP

The Layer-wise Relevance Propagation (LRP) algorithm explains a classifer's prediction specific to a given data point by attributing relevance scores to important components of the input by using the topology of the learned model itself.

This tensorflow wrapper provides simple and accessible stand-alone implementations of LRP for artificial neural networks.

Requirements

tensorflow >= 1.0.0
python == 2.7
matplotlib >= 1.3.1
scikit-image > 0.11.3

Features

1. Model

This TF-wrapper considers the layers in the neural network to be in the form of a Sequence. A quick way to define a network would be

    net = Sequential([Linear(input_dim=784,output_dim=1296, act ='relu', batch_size=FLAGS.batch_size),
                 Linear(1296, act ='relu'), 
                 Linear(1296, act ='relu'),
                 Linear(10, act ='relu'),
                 Softmax()])

    output = net.forward(input_data)

2. Train the network

This net can then be used to propogate and optimize using

    trainer = net.fit(output, ground_truth, loss='softmax_crossentropy', optimizer='adam', opt_params=[FLAGS.learning_rate])

3. LRP - Layer-wise relevance propagation

And compute the contributions of the input pixels towards the decision by

    relevance = net.lrp(output, 'simple', 1.0)

the different lrp variants available are:

    'simple'and 'epsilon','flat','ww' and 'alphabeta' 

4. Compute relevances every layer backwards from the output to the input

Follow steps (1) from Features mentioned above.

   relevance_layerwise = []
   R = output
   for layer in net.modules[::-1]:
       R = net.lrp_layerwise(layer, R, 'simple')
       relevance_layerwise.append(R)

Examples

To run the given mnist examples,

    cd examples
    python mnist_linear.py --relevance=True

It downloads and extract the mnist datset, runs it on a neural netowrk and plots the relevances once the network is optimized. The relvances of the images can be viewed on the tensorboard using

    tensorboard --logdir=mnist_linear_logs

LRP for a pretrained model

Follow steps (1) and (3) from Features mentioned above.

The LRP Toolbox Paper

When using (any part) of this wrapper, please cite our paper

@article{JMLR:v17:15-618,
    author  = {Sebastian Lapuschkin and Alexander Binder and Gr{{\'e}}goire Montavon and Klaus-Robert M{{{\"u}}}ller and Wojciech Samek},
    title   = {The LRP Toolbox for Artificial Neural Networks},
    journal = {Journal of Machine Learning Research},
    year    = {2016},
    volume  = {17},
    number  = {114},
    pages   = {1-5},
    url     = {http://jmlr.org/papers/v17/15-618.html}
}

Misc

For further research and projects involving LRP, visit heatmapping.org