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Programming language: Elixir
License: MIT License

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README

EXNN travis

Evolutive Neural Networks framework à la G.Sher written in Elixir

Preliminary Notice

The work presented here is strongly influenced by the seminal book Handbook of Neuroevolution Through Erlang by Gene I.Sher, which is a vast source of inspiring concepts and thoughts on the topic.

On the other hand I tried to give the whole system a modular and configurable DSL-like approach exploiting Elixir macros and protocols, as well as OTP application components.

:construction: All this is quite still in progress :construction:

This library is in a very early stage, and it can at the moment only train a trivial xor function. With some love and time, it will see some improvements. Contributions are welcome, of course.

Docs

I am slowly building an ex_doc Documentation out of the code.

Usage Instructions

To use EXNN you will first mix EXNN as dependency and then push EXNN.Supervisor module into the stack of your application's supevised children.

Your main application file will also serve as configuration for customizing initial conditions of the system:

Configuration


defmodule MyApp do
  use EXNN.Application

  sensor :temp, MyApp.TempSensor, dim: 1
  sensor :wind, MyApp.WindSensor, dim: 2
  actuator :servo, MyApp.WindowServo

  fitness MyApp.Fitness

  initial_pattern [
    sensor: [:temp, :wind],
    neuron: {3, 2, 3},
    actuator: [:servo]
  ]

  def start(_, _) do
    import Supervisor.Spec
    children = [
      supervisor(MyApp.MainSupervisor),
      # ... all these beautiful supervised boys
      supervisor(EXNN.Supervisor, [[config: __MODULE__]])
    ]

    Supervisor.start_link children, [strategy: :one_for_one]
  end
end

Note EXNN.Application is just a wrapper around Elixir/OTP Application module, exposing the configuration DSL.

With sensor and actuator macros, you can register sensor and actuator modules by name. For sensors you have to specify the dimension of the signal vector.

With the fitness macro you register a fitness module wich will compute in real time how much the present system is close to one (of the possible) optimal configuration and topology.

initial_pattern decides the initial topology for your network, it's a Keyword accepting the 3 keys above. You can only mention previously registered sensors and actuators. The value for neuron key has to be a tuple denoting the size of each neural layer you want your system to start with. In the above example, the initial_pattern given would correspond to the digraph:

[digraph](digraph.png)

where all elements in a layer are connected to all vertices of the following.

Sensors

Your sensor module will use EXNN.Sensor. At its heart is an OTP genserver. You have to implement the function sense/2 which is called when EXNN.Trainer synchronizes all sensors in the system.

On the other hand, since it's a genserver registered with its module name, you can reach it from any external service providing sample data:

An optional state given to use accepts a keyword list to feed initial conditions to the genserver state. The state itself is a struct, named by your module's name.

defmodule MyApp.TempSensor do
  use EXNN.Sensor, state: [outer_temp: 0]

  def sense(state, _metadata) do
    {state.outer_temp}
  end

  def handle_cast({:update_temp, value}, state) do
    {:noreply, %{state | outer_temp: value}}
  end
end

defmodule MyApp.WindSensor do
  use EXNN.Sensor, state: [speed: 0, direction: 0 * :math.pi]

  def sense(state, _meta) do
    {state.speed, state.direction}
  end

  # ...some callbacks to update state...
end

sense/2 forwards the desired signal to the front neuronal layer. It takes the current state of the sensor and a tuple of scalar values of the same length as the configured dimension. Read more about sensors in the docs

Actuators

Using EXNN.Actuator in your modules you can setup a genserver which reacts to signals coming from the terminal neural layer.

You have to implement an act/3 method which takes the current state, a signal arrived from the neuronal layer and some metadata (more on metadata later). act/3 can have the side-effects you desire and must return the modified state. The message passed to act/3 is a keyword list [n_id: v] with a key n_id identifying the firing neuron, and a scalar float value v.

defmodule MyApp.WindowServo do
  use EXNN.Actuator, state: %{current: 0}

  def act(state, message, _metadata) do
    {:ok, new_val} = MyApp.WindowServo.Command.turn(message)
    %{state | current: new_val}
  end

end

Fitness

A Fitness module evaluates how the system is performing in real-time, it's your responsibility to implement an eval/3 function taking the same message passed to the actuator, the usual metadata and it's state.

The trainer will call eval in your module right after your actuators have changed the environment so that it can measure the effects actuators have introduced.

defmodule MyApp.Fitness do
  use EXNN.Fitness, state: %{inner_temp: 0.0, outer_temp: 40.0}

  def eval(state, _message, _metadata) do
    diff = state.outer_temp - get_inner_temp
    fitness = 1/(1 + :math.pow(diff, 2))
    emit(fitness)
    state
  end

  def get_inner_temp do
    # ask inner temperature...
  end

end

Examples

You can -- at present -- train a basic XOR problem by running:

mix test test/examples/xor_test.exs

Core Concepts

  • Sensors, Neuron and Actuators all live as GenServer processes registered with an identifier present in the underlying genome. They specialise an abstract EXNN.NodeServer and implement a EXNN.Connection protocol to forward signals between nodes.

  • Fitness modules intercept signals from the final neural layer and evaluates and emit a fitness rank to the EXNN.Trainer.Sync which decides to mutate or revert the system to previous configuration.

  • A mutation semigroup takes care of altering the genomes and injecting live (and concurrently) new weights/bias specifications into neuronal genserver processes. Each mutation has an inverse mutation, which restores a single node to its previous state. The trainer keeps track of the changes in a mutation history.

Future Plans

  • Instrumentation for learning times and logging.

  • Explorative search along mutation paths (see EXNN.Trainer.Mutations) to split an individuum into two or more (mutation + mitosis).

  • Handle sync among a population of nets through routing with fixed sensors and actuators.

  • Rewrite EXNN.Trainer.Sync as a :gen_fsm with states 'learning'/'production'. Switching to 'production' whenever fitness stably enters a tolerance neighborhood of 1.

References

  1. Gene.I.Sher, Handbook of Neuroevolution Through Erlang, 2013, Springer