abhinavvv / Tensorflow_Deep_Taylor_LRP Public

forked from dshieble/Tensorflow_Deep_Taylor_LRP

Notifications You must be signed in to change notification settings
Fork 0
Star 0

Layerwise Relevance Propagation with Deep Taylor Series in TensorFlow

0 stars 24 forks Branches Tags Activity

Notifications

Name		Name	Last commit message	Last commit date
Latest commit History 6 Commits
.ipynb_checkpoints		.ipynb_checkpoints
LRP_Example.ipynb		LRP_Example.ipynb
README.md		README.md
lrp.py		lrp.py
lrp.pyc		lrp.pyc
visualize.py		visualize.py

Repository files navigation

Tensorflow_Deep_Taylor_LRP

Layerwise Relevance Propagation with Deep Taylor Series in TensorFlow.

You can use LRP to visualize the relative feature importances of the input to a neural network.

How to Use

Step 1: Construct your tensorflow graph

Step 2: Make sure your prediction layer (output layer) is named "absolute_output"

Step 3: Make sure your input layer (shaped as [num_batches, height, width, num_channels]) is named "absolute_input"

Step 4: `relevance_heatmap = lrp.lrp(prediction*label, lowest_value, highest_value)`

About

Layerwise Relevance Propagation with Deep Taylor Series in TensorFlow

Report repository

Releases

No releases published

Packages

No packages published

Languages

Jupyter Notebook 99.4%
Python 0.6%