Quiver

Interactive convnet features visualization for Keras

The quiver workflow

Video Demo

1. Build your model in keras

   model = Model(...)

2. Launch the visualization dashboard with 1 line of code

   quiver_engine.server.launch(model, input_folder='./imgs')

3. Explore layer activations on all the different images in your input folder.

Quickstart

Installation

    pip install quiver_engine

Usage

Take your keras model, launching Quiver is a one-liner.

    from quiver_engine import server
    server.launch(model)

This will launch the visualization at localhost:5000

Options

    server.launch(
        model, # a Keras Model

        # where to store temporary files generatedby quiver (e.g. image files of layers)
        temp_folder='./tmp',

        # a folder where input images are stored
        input_folder='./',

        # the localhost port the dashboard is to be served on
        port=5000
    )

Development

Building from master

Check out this repository and run

python setup.py develop

Building the Client

    export QUIVER_URL=localhost:5000 # or whatever you set your port to be
    cd quiverboard
    npm start

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.