Interactive convnet features visualization for Keras

Overview

Quiver

Gitter chat

Interactive convnet features visualization for Keras

gzqll3

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, classes=['cat','dog'], input_folder='./imgs')
  3. Explore layer activations on all the different images in your input folder.

Quickstart

Installation

    pip install quiver_engine

If you want the latest version from the repo

    pip install git+git://github.com/keplr-io/quiver.git

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

        classes, # 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 stored
        input_folder='./',

        # the localhost port the dashboard is to be served on
        port=5000,
        # custom data mean
        mean=[123.568, 124.89, 111.56],
        # custom data standard deviation
        std=[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.
  • The dashboard makes use of react-redux-starter-kit

Citing Quiver

misc{bianquiver,
  title={Quiver},
  author={Bian, Jake},
  year={2016},
  publisher={GitHub},
  howpublished={\url{https://github.com/keplr-io/quiver}},
}
Lime: Explaining the predictions of any machine learning classifier

lime This project is about explaining what machine learning classifiers (or models) are doing. At the moment, we support explaining individual predict

Marco Tulio Correia Ribeiro 10.3k Jan 01, 2023
Visualizer for neural network, deep learning, and machine learning models

Netron is a viewer for neural network, deep learning and machine learning models. Netron supports ONNX (.onnx, .pb, .pbtxt), Keras (.h5, .keras), Tens

Lutz Roeder 20.9k Dec 28, 2022
Portal is the fastest way to load and visualize your deep neural networks on images and videos ๐Ÿ”ฎ

Portal is the fastest way to load and visualize your deep neural networks on images and videos ๐Ÿ”ฎ

Datature 243 Jan 05, 2023
Contrastive Explanation (Foil Trees), developed at TNO/Utrecht University

Contrastive Explanation (Foil Trees) Contrastive and counterfactual explanations for machine learning (ML) Marcel Robeer (2018-2020), TNO/Utrecht Univ

M.J. Robeer 41 Aug 29, 2022
A game theoretic approach to explain the output of any machine learning model.

SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allo

Scott Lundberg 18.3k Jan 08, 2023
A Practical Debugging Tool for Training Deep Neural Networks

Cockpit is a visual and statistical debugger specifically designed for deep learning!

31 Aug 14, 2022
Model analysis tools for TensorFlow

TensorFlow Model Analysis TensorFlow Model Analysis (TFMA) is a library for evaluating TensorFlow models. It allows users to evaluate their models on

1.2k Dec 26, 2022
Bias and Fairness Audit Toolkit

The Bias and Fairness Audit Toolkit Aequitas is an open-source bias audit toolkit for data scientists, machine learning researchers, and policymakers

Data Science for Social Good 513 Jan 06, 2023
Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet

Neural-Backed Decision Trees ยท Site ยท Paper ยท Blog ยท Video Alvin Wan, *Lisa Dunlap, *Daniel Ho, Jihan Yin, Scott Lee, Henry Jin, Suzanne Petryk, Sarah

Alvin Wan 556 Dec 20, 2022
Algorithms for monitoring and explaining machine learning models

Alibi is an open source Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-qual

Seldon 1.9k Dec 30, 2022
Quickly and easily create / train a custom DeepDream model

Dream-Creator This project aims to simplify the process of creating a custom DeepDream model by using pretrained GoogleNet models and custom image dat

56 Jan 03, 2023
PyTorch implementation of DeepDream algorithm

neural-dream This is a PyTorch implementation of DeepDream. The code is based on neural-style-pt. Here we DeepDream a photograph of the Golden Gate Br

121 Nov 05, 2022
An Empirical Review of Optimization Techniques for Quantum Variational Circuits

QVC Optimizer Review Code for the paper "An Empirical Review of Optimization Techniques for Quantum Variational Circuits". Each of the python files ca

Owen Lockwood 5 Jun 28, 2022
Lucid library adapted for PyTorch

Lucent PyTorch + Lucid = Lucent The wonderful Lucid library adapted for the wonderful PyTorch! Lucent is not affiliated with Lucid or OpenAI's Clarity

Lim Swee Kiat 520 Dec 26, 2022
Visual Computing Group (Ulm University) 99 Nov 30, 2022
Visualization Toolbox for Long Short Term Memory networks (LSTMs)

Visualization Toolbox for Long Short Term Memory networks (LSTMs)

Hendrik Strobelt 1.1k Jan 04, 2023
pytorch implementation of "Distilling a Neural Network Into a Soft Decision Tree"

Soft-Decision-Tree Soft-Decision-Tree is the pytorch implementation of Distilling a Neural Network Into a Soft Decision Tree, paper recently published

Kim Heecheol 262 Dec 04, 2022
๐Ÿ‘‹๐ŸฆŠ Xplique is a Python toolkit dedicated to explainability, currently based on Tensorflow.

๐Ÿ‘‹๐ŸฆŠ Xplique is a Python toolkit dedicated to explainability, currently based on Tensorflow.

DEEL 343 Jan 02, 2023
A data-driven approach to quantify the value of classifiers in a machine learning ensemble.

Documentation | External Resources | Research Paper Shapley is a Python library for evaluating binary classifiers in a machine learning ensemble. The

Benedek Rozemberczki 187 Dec 27, 2022
Python Library for Model Interpretation/Explanations

Skater Skater is a unified framework to enable Model Interpretation for all forms of model to help one build an Interpretable machine learning system

Oracle 1k Dec 27, 2022