menoh: Haskell binding for Menoh DNN inference library

[ deep-learning, library, machine-learning, mit, program ] [ Propose Tags ]

Menoh is a MKL-DNN based DNN inference library for ONNX models. See https://github.com/pfnet-research/menoh/ for details.


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Versions 0.1.0, 0.1.1, 0.2.0, 0.3.0
Dependencies aeson (>=0.8 && <1.5), base (>=4.7 && <5), bytestring (==0.10.*), containers (>=0.5 && <0.7), filepath (>=1.3 && <1.5), JuicyPixels (>=3.2.7 && <3.4), menoh, monad-control (==1.0.*), optparse-applicative (>=0.11 && <0.15), transformers (>=0.3 && <0.6), vector (>=0.10 && <0.13) [details]
License MIT
Copyright Copyright 2018 Preferred Networks, Inc.
Author Masahiro Sakai <sakai@preferred.jp>
Maintainer Masahiro Sakai <sakai@preferred.jp>
Category Machine Learning, Deep Learning
Source repo head: git clone https://github.com/pfnet-research/menoh-haskell/
Uploaded by MasahiroSakai at Thu Nov 8 10:08:17 UTC 2018
Distributions NixOS:0.3.0
Executables mnist_example, vgg16_example
Downloads 189 total (39 in the last 30 days)
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build example programs

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Readme for menoh-0.3.0

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menoh-haskell

Hackage Hackage Deps Build Status (Travis CI) Build Status (AppVeyor) License: MIT

Haskell binding for Menoh DNN inference library.

Requirements

Build

Execute below commands in root directory.

sh retrieve_data.sh
stack build

Running VGG16 example

Execute below command in root directory.

cd menoh
stack exec vgg16_example

Result is below

vgg16 example
fc6_out: -19.079105 -37.94045 -16.185831 25.51685 4.432623 ...
top 5 categories are:
8 0.958079 n01514859 hen
7 0.039541963 n01514668 cock
86 0.0018722217 n01807496 partridge
82 0.00027406064 n01797886 ruffed grouse, partridge, Bonasa umbellus
97 0.00003177848 n01847000 drake

Please give --help option for details

stack exec vgg16_example --help

Installation

stack install

Documents

License

Note: retrieve_data.sh downloads data/VGG16.onnx. data/VGG16.onnx is generated by onnx-chainer from pre-trained model which is uploaded at http://www.robots.ox.ac.uk/%7Evgg/software/very_deep/caffe/VGG_ILSVRC_16_layers.caffemodel

That pre-trained model is released under Creative Commons Attribution License.