https://github.com/holsee/nnex
Neural Network in Elixir
https://github.com/holsee/nnex
elixir neural-network
Last synced: 2 months ago
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Neural Network in Elixir
- Host: GitHub
- URL: https://github.com/holsee/nnex
- Owner: holsee
- Created: 2015-09-18T12:49:22.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2015-09-18T12:49:33.000Z (over 9 years ago)
- Last Synced: 2025-02-05T06:44:46.087Z (4 months ago)
- Topics: elixir, neural-network
- Language: Elixir
- Homepage:
- Size: 113 KB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
NNex
====
A Neural Network prototype in Elixir (Under development)
Used as part of my [Transcendence in Erlang Talk](https://docs.google.com/presentation/d/1AGYBEL8Ng3VWc_WiHhjs4MrMFdn3gGsuNxYShS1BxL0/edit?usp=sharing), to demonstrate how Erlang processes can represent Neurons in an elegant way.
All Neurons (nodes) at present are ```GenServers``` with ```SimpleNeuron``` struct state.
- Input Nodes: detected as they do not have any registered in connections therefore forward sensor input.
- Hidden Nodes: use hyperbolic tangent activation function + wait for 1 input from each in node before becoming activated.
- Output Nodes (not implemented): can be detected the same way as input neurons are, but will apply Softmax activation function.
## Example
Simple Feed Forward Demo:
``` elixir
# Create our neurons
{:ok, n1} = SimpleNeuron.start_link()
{:ok, n2} = SimpleNeuron.start_link()
{:ok, n3} = SimpleNeuron.start_link()
{:ok, n4} = SimpleNeuron.start_link([bias: 0.13])
# Create the synaptic connections
SimpleNeuron.connect(n1, n4, 0.01)
SimpleNeuron.connect(n2, n4, 0.05)
SimpleNeuron.connect(n3, n4, 0.09)
# Trigger input neurons
SimpleNeuron.signal(n1, 1)
SimpleNeuron.signal(n2, 2)
SimpleNeuron.signal(n3, 3)
```
``` shell
$ iex -S mix
ex(1)> Nnex.example
13:41:07.418 [debug] Creating node with bias: 0.13
13:41:07.418 [info] <0.91.0> Received signal 1 from <0.89.0> input sensor
13:41:07.421 [info] <0.92.0> Received signal 2 from <0.89.0> input sensor
13:41:07.426 [info] <0.93.0> Received signal 3 from <0.89.0> input sensor
13:41:07.426 [info] <0.91.0> sending value 1 to [<0.94.0>]
13:41:07.430 [info] <0.92.0> sending value 2 to [<0.94.0>]
13:41:07.430 [info] <0.93.0> sending value 3 to [<0.94.0>]
13:41:07.430 [info] <0.94.0> Received signal 1 from <0.91.0> with connection weight 0.01
13:41:07.430 [info] <0.94.0> Received signal 2 from <0.92.0> with connection weight 0.05
13:41:07.430 [info] <0.94.0> Received signal 3 from <0.93.0> with connection weight 0.09
13:41:07.430 [info] <0.94.0> ACTIVATED!
13:41:07.430 [info] Apply weight: 0.09 to value: 3
13:41:07.430 [info] Apply weight: 0.05 to value: 2
13:41:07.430 [info] Apply weight: 0.01 to value: 1
13:41:07.430 [info] Apply weight: 1 to value: 0.13
13:41:07.431 [info] <0.94.0> sending value 0.46994519893303766 to []
```
```0.46994519893303766``` represents the value node 4 computed from 3 input nodes.