Nx Deep Learning

Mix.install([
  {:nx, "~> 0.5"}
])

Main

# Implementing a Neural Network
defmodule NeuralNetwork do
  import Nx.Defn

  defn dense(input, weight, bias) do
    input
    |> Nx.dot(weight)
    |> Nx.ad(bias)
  end

  defn activation(input) do
    Nx.sigmoid(input)
  end

  defn hidden(input, weight, bias) do
    input
    |> dense(weight, bias)
    |> activation()
  end

  defn output(input, weight, bias) do
    input
    |> dense(weight, bias)
    |> activation()
  end

  defn predict(input, w1, b1, w2, b2) do
    input
    |> hidden(w1, b1)
    |> output(w2, b2)
  end
  
end

# Generate Intermediate Layers
key = Nx.Random.key(42)
{w1, new_key} = Nx.Random.uniform(key)
{b1, new_key} = Nx.Random.uniform(new_key)
{w2, new_key} = Nx.Random.uniform(new_key)
{b2, new_key} = Nx.Random.uniform(new_key)

# Generate Predictions
# Nx.Random.uniform_split(new_key, shape: {}) # ???
# |> NeuralNetwork.predict(w1, b1, w2, b2)