https://github.com/borjaest/nnet
Neural networks representation in erlang
https://github.com/borjaest/nnet
erlang machine-learning neural-network representation
Last synced: 6 months ago
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Neural networks representation in erlang
- Host: GitHub
- URL: https://github.com/borjaest/nnet
- Owner: BorjaEst
- License: gpl-3.0
- Created: 2020-05-25T15:28:08.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-08-04T08:22:17.000Z (over 5 years ago)
- Last Synced: 2025-03-21T08:32:52.687Z (10 months ago)
- Topics: erlang, machine-learning, neural-network, representation
- Language: Erlang
- Homepage:
- Size: 672 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
### Erlang Neural Networks (nnet) is an application to represent neural networks in erlang terms.
# Installation
Create your own project with rebar3.
```sh
$ rebar3 new app yourapp
```
Then in your project path find rebar.config file and add nnet as dependency under the deps key:
```erlang
{deps,
[
{nnet, {git, "https://github.com/BorjaEst/nnet.git", {tag, ""}}}
]}.
```
Then using compile command, rebar3 will fetch the defined dependencies and compile them as well for your application.
```sh
$ rebar3 compile
```
At the end for making a release you first need to create your release structure and then making a release with following commands.
```sh
$ rebar3 new release yourrel
$ rebar3 release
```
>You can find more information about dependencies in [rebar3 - dependencies](https://www.rebar3.org/docs/dependencies).
# Usage
Load the app using your prefered method. For example in the project folder executing rebar3 shell:
```sh
$ rebar3 shell
===> Booted nnet
```
All user functions are defined inside the module [src/nnet](https://github.com/BorjaEst/nnet/blob/master/src/nnet.erl), however here is an example:
## Start tables
As library without OTP supervision tree, the best way to start is to ensure the tables are created.
```erl
1> nnet:start_tables().
ok
```
> The function will not fail if the tables are already created and correct.
## Create a network from a model
To create a network from a model it is very easy. Just create a map where each layer is a key (inputs & outputs are mandatory) and the values are:
- "connections": Type of connection and density between layers.
- "units": The amount of neurons on each layer.
- "data": The information on each node on each layer.
```erl
2> M = #{inputs => #{connections => #{
2> layer1 => sequential,
2> layer2 => {sequential,0.5}},
2> units => 2, data=>#{}},
2> layer1 => #{connections => #{
2> layer2 => sequential,
2> outputs => {sequential,0.5}},
2> units => 4, data=>#{}},
2> layer2 => #{connections => #{
2> layer2 => recurrent,
2> outputs => sequential},
2> units => 4, data=>#{}},
2> outputs => #{connections => #{
2> inputs => {recurrent, 0.5},
2> layer2 => recurrent},
2> units => 2, data=>#{}}}.
...
3> {atomic,Id} = mnesia:transaction(fun() -> nnet:from_model(M) end)
{atomic,{network,#Ref<0.1530882211.2808086529.180929>}}
```
## Get the network nodes, inputs and outputs
Using the function `nnet:to_map/1` you can retrieve the network data.
```erl
4> mnesia:transaction(fun() -> nnet:to_map(Id) end).
{atomic,#{{network,#Ref<0.1530882211.2808086529.180929>} =>
[{{network,#Ref<0.1530882211.2808086529.180929>},
{nnode,#Ref<0.1530882211.2808086529.180893>}},
{{network,#Ref<0.1530882211.2808086529.180929>},
{nnode,#Ref<0.1530882211.2808086529.180896>}}],
{nnode,#Ref<0.1530882211.2808086529.180893>} =>
[{{nnode,#Ref<0.1530882211.2808086529.180893>},
{nnode,#Ref<0.1530882211.2808086529.180899>}},
{{nnode,#Ref<0.1530882211.2808086529.180893>},
{nnode,#Ref<0.1530882211.2808086529.180902>}},
{{nnode,#Ref<0.1530882211.2808086529.180893>},
{nnode,#Ref<0.1530882211.2808086529.180905>}},
{{nnode,#Ref<0.1530882211.2808086529.180893>},
{nnode,#Ref<0.1530882211.2808086529.180908>}},
{{nnode,#Ref<0.1530882211.2808086529.180893>},
{nnode,#Ref<0.1530882211.2808086529.180911>}},
{{nnode,#Ref<0.1530882211.2808086529.180893>},
{nnode,#Ref<0.1530882211.2808086529.180914>}}],
...
{nnode,#Ref<0.1530882211.2808086529.180926>} =>
[{{nnode,#Ref<0.1530882211.2808086529.180926>},
{network,#Ref<0.1530882211.2808086529.180929>}}]}}
```
> Note the network inputs are out(Network): Network -> NNode
> Note the network outputs are in(Network): NNode -> Network
## Get a nnode inputs and outputs
After a network is create, you can modify it using the functions:
```erl
%% NNode operations (run inside 'fun edit/1')
-export([rnode/1, wnode/2, rlink/1, wlink/2]).
-export([out/1, out_seq/1, out_rcc/1, in/1, lx/1]).
%% Connections operations (run inside 'fun edit/1')
-export([connect/1, connect_seq/1, connect_rcc/1, disconnect/1]).
-export([move/2, reset/1]).
%% Network operations (run inside 'fun edit/1')
-export([copy/2, clone/2, divide/2, split/2, delete/2, join/2]).
```
# Visualization
There is a secret (not so secret) module "umlnn" which prints/formats the neural network in UML format (components diagram).
You can use the results of that function together with [plantUML](http://www.plantuml.com/plantuml/uml) to display your nerwork.
To display the sequential connections use the output from: `umlnn:print_seq(Network_id)`
To display the recurrent connections use the output from: `umlnn:print_rcc(Network_id)`
# More examples
Inside the module **./src/nnet.erl** you will find the spec and comments for each function. Note that most of the functions are mnesia transactions therefore should run inside a `mnesia:transaction/1` context.
For examples of usage you can take a look on the test suite **./test/nnet_SUITE.erl**.
# Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.
## Improvement ideas and requests
All ideas and sugestions are welcome:
-
# License
This software is under [GPL-3.0](https://www.gnu.org/licenses/gpl-3.0.en.html) license.