A Deep Neural Network explanation-by-example library for generating meaningful explanations. Used in our Explainability Study

Install the required Python packages

pip3 install -r requirements.txt

1. Import the ExMatchina class

from ExMatchina import ExMatchina

2. Load ExMatchina with a particular TensorFlow model + example prototypes (e.g. training data)

# X_train.npy: a numpy array of prototypes
# model: the model of interest
training_data = np.load('./X_train.npy')
model = load_model('./model')

# selected_layer: the layer to use in identifying examples.
# We recommend the layer immediately following the last convolution (e.g. flatten layer)
selected_layer = "Flatten_1"

exm = ExMatchina(model=model, layer=selected_layer, examples=training_data)

3. Fetch examples and corresponding indices for a given input

# X_train.npy: a numpy array of model inputs
test_data = np.load('./X_test.npy')
test_input = test_data[0]
(examples, indices) = exm.return_nearest_examples(test_input)

The Examples/ folder contains the tutorial in python notebooks on using Exmatchina for different types of input data

Here's the Google Drive Link to the preprocessed data: Link

Download each of the folders there and place them in Examples/data/

Inside the trained_models/ folder, there are the pretrained models, named as [domain].hdf5 for each of the domains: image, text, ECG

If you find this code and results useful in your research, please cite:

@article{jeyakumar2020can,
  title={How Can I Explain This to You? An Empirical Study of Deep Neural Network Explanation Methods},
  author={Jeyakumar, Jeya Vikranth and Noor, Joseph and Cheng, Yu-Hsi and Garcia, Luis and Srivastava, Mani},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}