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Take your keras model, launching Quiver is a one-liner.
fromquiver_engineimportserverserver.launch(model)
This will launch the visualization at localhost:5000
Options
server.launch(
model, # a Keras Modelclasses, # list of output classes from the model to present (if not specified 1000 ImageNet classes will be used)top, # number of top predictions to show in the gui (default 5)# where to store temporary files generatedby quiver (e.g. image files of layers)temp_folder='./tmp',
# a folder where input images are storedinput_folder='./',
# the localhost port the dashboard is to be served onport=5000,
# custom data meanmean=[123.568, 124.89, 111.56],
# custom data standard deviationstd=[52.85, 48.65, 51.56]
)
Development
Building from master
Check out this repository and run
cd quiver_engine
python setup.py develop
Building the Client
cd quiverboard
npm install
export QUIVER_URL=localhost:5000 # or whatever you set your port to be
npm start
Note this will run your web application with webpack and hot reloading. If you don't care about that, or are only in this section because pip install somehow failed for you, you should tell it to simply build the javascript files instead
npm run deploy:prod
Credits
This is essentially an implementation of some ideas of deepvis and related works.
A lot of the pre/pos/de processing code was taken from here and other writings of fchollet.