Neural network visualization toolkit for tf.keras
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.
- SmoothGrad: removing noise by adding noise (DONE)
- Deep Dream
- Style transfer
- Python 3.5, 3.6 or 3.7
$ pip install tf-keras-vis tensorflow
$ docker pull keisen/tf-keras-vis:0.2.0
You can find other images (that's nvidia-docker images) at dockerhub.
- 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
- 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.
channels-firstmodels and datas.
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