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Neural network visualization toolkit for tf.keras

Project description


Downloads PyPI version Build Status License: MIT

tf-keras-vis is a visualization toolkit for debugging Keras models with Tensorflow2, but not original Keras.

The features of tf-keras-vis are based on keras-vis, but tf-keras-vis's APIs doesn't have compatibility with keras-vis's because, instead of getting it, we prioritized to get following features.

  • Support processing multipul images at a time as a batch
  • Support tf.keras.Model that has multipul inputs (and, of course, multipul outpus too)
  • Allow use optimizers that embeded in tf.keras

And then we will add some algorisms such as below.


  • Python 3.5, 3.6 or 3.7
  • tensorflow>=2.0.0


  • PyPI
$ pip install tf-keras-vis tensorflow
  • Docker
$ docker pull keisen/tf-keras-vis:0.2.0

You can find other images (that's nvidia-docker images) at dockerhub.


Please see examples/attentions.ipynb, examples/visualize_dense_layer.ipynb and examples/visualize_conv_filters.ipynb.

  • Run Jupyter notebooks on Docker
$ docker run -it -v /PATH/TO/tf-keras-vis:/tf-keras-vis -p 8888:8888 keisen/tf-keras-vis:0.2.0 jupyter lab

Or, if you have GPU processors,

$ docker run -it --runtime=nvidia -v /PATH/TO/tf-keras-vis:/tf-keras-vis -p 8888:8888 keisen/tf-keras-vis:0.2.0-gpu jupyter lab

API Documentation


Known Issues

  • With InceptionV3, ActivationMaximization doesn't work well, that's, it might generate meanninglessly bulr image.
  • With cascading model, Gradcam doesn't work well, that's, it might occur some error.
  • Unsupported channels-first models and datas.

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