Machine Learning in Elixir - Chapter 2

Mix.install([
  {:nx, "~> 0.5"},
  {:exla, "~> 0.5"},
  {:benchee, github: "bencheeorg/benchee", override: true}
])

Section

Nx.tensor([1, 2, 3])

a = Nx.tensor([[1, 2, 3], [4, 5, 6 ]])
b = Nx.tensor(1.0)
c = Nx.tensor([[[[[[1.0, 2]]]]]])
dbg(a)
dbg(b)
dbg(c)

a = Nx.tensor([1, 2, 3])
b = Nx.tensor([1.0, 2.0, 3.0])
dbg(a)
dbg(b)

Nx.tensor(0.0000000000000000000000000000000000000000000001)

Nx.tensor(1.0e-45, type: {:f, 64})

Nx.tensor(128, type: {:s, 8})

a = Nx.tensor([[1, 2, 3], [4, 5, 6]])

dbg(a)

Nx.to_binary(a)

<<1::64-signed-native, 2::64-signed-native, 3::64-signed-native>>
|> Nx.from_binary({:s, 64})

<<1::64-signed-native, 2::64-signed-native, 3::64-signed-native>>
|> Nx.from_binary({:s, 64})
|> Nx.reshape({1, 3})

a = Nx.tensor([1, 2, 3])

a
|> Nx.as_type(:f32)
|> Nx.reshape({1, 3, 1})

Nx.bitcast(a, {:f, 32})

a = Nx.tensor([[[-1, -2, -3], [-4, -5, -6]], [[1, 2, 3], [4, 5, 6]]])

Nx.abs(a)

a = Nx.tensor([[1, 2, 3], [4, 5, 6]])
b = Nx.tensor([[6, 7, 8], [9, 10, 11]])

Nx.add(a, b)
|> dbg()

Nx.multiply(a, b)

Nx.multiply(Nx.tensor(3), Nx.tensor([[1, 2, 3], [4, 5, 6]]))

Nx.multiply(Nx.tensor([1, 2, 3]), Nx.tensor([[1, 2, 3], [4, 5, 6]]))

revs = Nx.tensor([85, 76, 42, 34, 46, 23, 52, 99, 22, 32, 85, 51])

Nx.sum(revs)

revs = Nx.tensor(
  [
    [21, 64, 86, 26, 74, 81, 38, 79, 70, 48, 85, 33],
    [64, 82, 48, 39, 70, 71, 81, 53, 50, 67, 36, 50],
    [68, 74, 39, 78, 95, 62, 53, 21, 43, 59, 51, 88],
    [47, 74, 97, 51, 98, 47, 61, 36, 83, 55, 74, 43]
  ], 
  names: [:year, :month]
)

Nx.sum(revs, axes: [:year])
|> dbg()

Nx.sum(revs, axes: [:month])

defmodule MyModule do
  import Nx.Defn

  defn adds_one(x) do
    Nx.add(x, 1) |> print_expr()
  end
end

MyModule.adds_one(Nx.tensor([1, 2, 3]))

defmodule Softmax do
  import Nx.Defn

  defn softmax(n), do: Nx.exp(n) / Nx.sum(Nx.exp(n))
end

key = Nx.Random.key(42)
{tensor, _key} = Nx.Random.uniform(key, shape: {1_000_000})

Benchee.run(
  %{
    "JIT with EXLA" => fn ->
      apply(EXLA.jit(&Softmax.softmax/1), [tensor])
    end,
    "Regular Elixir" => fn ->
      Softmax.softmax(tensor)  
    end
  }
)

Nx.Defn.global_default_options(compiler: EXLA)

key = Nx.Random.key(42)
{tensor, _key} = Nx.Random.uniform(key, shape: {1_000_000})

Benchee.run(
  %{
    "JIT with EXLA" => fn ->
      apply(EXLA.jit(&Softmax.softmax/1), [tensor])
    end,
    "Regular Elixir" => fn ->
      Softmax.softmax(tensor)  
    end
  }
)