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October 4, 2019 00:25
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Elixir and Reinforcement Learning - IV
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| defmodule AutonomousCar.NeuralNetwork.Network do | |
| alias AutonomousCar.NeuralNetwork.{Layer, Network, Neuron} | |
| alias AutonomousCar.Math.Activation | |
| defstruct pid: nil, input_layer: nil, hidden_layers: [], output_layer: nil, error: 0 | |
| def start_link(layer_sizes \\ []) do | |
| {:ok, pid} = Agent.start_link(fn -> %Network{} end) | |
| layers = | |
| map_layers( | |
| input_neurons(layer_sizes), | |
| hidden_neurons(layer_sizes), | |
| output_neurons(layer_sizes) | |
| ) | |
| pid |> update_layers(layers) | |
| pid |> connect_layers | |
| {:ok, pid} | |
| end | |
| def get(pid), do: Agent.get(pid, & &1) | |
| def update_layers(pid, layers) do | |
| layers = Map.merge(layers, %{pid: pid}) | |
| Agent.update(pid, &Map.merge(&1, layers)) | |
| end | |
| def predict(network, input_values) do | |
| network.input_layer | |
| |> Layer.activate(:relu, input_values) | |
| Enum.each(network.hidden_layers, fn hidden_layer -> | |
| hidden_layer | |
| |> Layer.activate(:relu) | |
| end) | |
| network.output_layer | |
| |> Layer.activate(:sigmoid) | |
| prob_actions = | |
| network.output_layer | |
| |> Layer.get() | |
| |> Layer.neurons_output() | |
| |> Activation.calculate_output(:softmax) | |
| action = | |
| prob_actions | |
| |> Enum.find_index(fn value -> Enum.max(prob_actions) == value end) | |
| end | |
| defp input_neurons(layer_sizes) do | |
| size = layer_sizes |> List.first() | |
| {:ok, pid} = Layer.start_link(%{neuron_size: size}) | |
| pid | |
| end | |
| defp hidden_neurons(layer_sizes) do | |
| layer_sizes | |
| |> hidden_layer_slice | |
| |> Enum.map(fn size -> | |
| {:ok, pid} = Layer.start_link(%{neuron_size: size}) | |
| pid | |
| end) | |
| end | |
| defp output_neurons(layer_sizes) do | |
| size = layer_sizes |> List.last() | |
| {:ok, pid} = Layer.start_link(%{neuron_size: size}) | |
| pid | |
| end | |
| defp hidden_layer_slice(layer_sizes) do | |
| layer_sizes | |
| |> Enum.slice(1..(length(layer_sizes) - 2)) | |
| end | |
| defp connect_layers(pid) do | |
| layers = | |
| pid | |
| |> Network.get() | |
| |> flatten_layers | |
| layers | |
| |> Stream.with_index() | |
| |> Enum.each(fn tuple -> | |
| {layer, index} = tuple | |
| next_index = index + 1 | |
| if Enum.at(layers, next_index) do | |
| Layer.connect(layer, Enum.at(layers, next_index)) | |
| end | |
| end) | |
| end | |
| defp flatten_layers(network) do | |
| [network.input_layer] ++ network.hidden_layers ++ [network.output_layer] | |
| end | |
| defp map_layers(input_layer, hidden_layers, output_layer) do | |
| %{ | |
| input_layer: input_layer, | |
| output_layer: output_layer, | |
| hidden_layers: hidden_layers | |
| } | |
| end | |
| end |
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