https://github.com/cloudkj/layer

Neural network inference the Unix way
https://github.com/cloudkj/layer

chicken-scheme convolutional-neural-networks deep-learning lisp machine-learning neural-networks scheme unix-command

Last synced: 3 months ago
JSON representation

Neural network inference the Unix way

• Host: GitHub
• URL: https://github.com/cloudkj/layer
• Owner: cloudkj
• License: mit
• Created: 2018-12-08T09:38:55.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2019-04-21T07:04:23.000Z (about 6 years ago)
• Last Synced: 2025-01-17T09:10:38.352Z (4 months ago)
• Topics: chicken-scheme, convolutional-neural-networks, deep-learning, lisp, machine-learning, neural-networks, scheme, unix-command
• Language: Scheme
• Homepage:
• Size: 396 KB
• Stars: 559
• Watchers: 13
• Forks: 17
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-racket - layer - Neural network inference the Unix way. (Machine Learning)
• awesome-racket-and-scheme - layer

README

        
# layer - neural network inference from the command line

`layer` is a program for doing neural network inference the Unix way. Many

modern neural network operations can be represented as sequential,

unidirectional streams of data processed by pipelines of [filters](https://en.wikipedia.org/wiki/Filter_(software)).

The computations at each layer in these neural networks are equivalent to an

invocation of the `layer` program, and multiple invocations can be chained

together to represent the entirety of such networks.

For example, performing inference on a neural network with two fully-connected

layers might look something like this:

    cat input | layer full -w w.1 --input-shape=2 -f tanh | layer full -w w.2 --input-shape=3 -f sigmoid

`layer` applies the Unix philosophy to neural network inference. Each type of

a neural network layer is a distinct subcommand. Simple text streams of

delimited numeric values serve as the interface between different layers of a

neural network. Each invocation of `layer` does one thing: it feeds the numeric

input values forward through an instantiation of a neural network layer, then

emits the resulting output numeric values.

## Usage

Example: a convolutional neural network for [CIFAR-10](https://en.wikipedia.org/wiki/CIFAR-10).

```shell

$ cat cifar10_x.csv \

    | layer convolutional -w w0.csv -b b0.csv --input-shape=32,32,3  --filter-shape=3,3 --num-filters=32 -f relu \

    | layer convolutional -w w1.csv -b b1.csv --input-shape=30,30,32 --filter-shape=3,3 --num-filters=32 -f relu \

    | layer pooling --input-shape=28,28,32 --filter-shape=2,2 --stride=2 -f max

```

Example: a multi-layer perceptron for XOR.

```shell

$ # Fully connected layer with three neurons

echo "-2.35546875,-2.38671875,3.63671875,3.521484375,-2.255859375,-2.732421875" > layer1.weights

echo "0.7958984375,0.291259765625,1.099609375" > layer1.biases

$ # Fully connected layer with one neuron

echo "-5.0625,-3.515625,-5.0625" > layer2.weights

echo "1.74609375" > layer2.biases

$ # Compute XOR for all possible binary inputs

echo -e "0,0\n0,1\n1,0\n1,1" \

    | layer full -w layer1.weights -b layer1.biases --input-shape=2 -f tanh \

    | layer full -w layer2.weights -b layer2.biases --input-shape=3 -f sigmoid

0.00129012749948779

0.99147053740106

0.991243357927591

0.0111237568184365

```

## Installation

Requirements: BLAS 3.6.0+

1. Download a [release](https://github.com/cloudkj/layer/releases)

2. Install BLAS 3.6.0+

  * On Debian-based systems: `apt-get install -y libblas3`

  * On RPM-based system: `yum install -y blas`

  * On macOS 10.3+, BLAS is pre-installed as part of the

    [Accelerate framework](https://developer.apple.com/documentation/accelerate/blas)

3. Unzip the release and run `[sudo] ./install.sh`, or manually relocate the

   binaries to the path of your choice.

## About

`layer` is currently implemented as a proof-of-concept and supports a limited

number of neural network layer types. The types of layers are currently limited

to feed-forward layers that can be modeled as sequential, unidirectional

pipelines.

Input values, weights and biases for parameterized layers, and output values

are all read and written in [row-major order](https://en.wikipedia.org/wiki/Row-_and_column-major_order),

based on the shape parameters specified for each layer.

`layer` is implemented in [CHICKEN Scheme](https://www.call-cc.org/).

## License

Copyright © 2018-2019