linear.cr

# Copyright (c) 2020 Crystal Data Contributors
#
# MIT License
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class Num::NN::LinearLayer(T) < Num::NN::Layer(T)
  getter weights : Num::Grad::Variable(T)
  getter bias : Num::Grad::Variable(T)

  def initialize(context : Num::Grad::Context(T), inp_dim : Int, outp_dim : Int)
    w = Num::NN.kaiming_normal(outp_dim, inp_dim, dtype: T)
    b = T.zeros([1, outp_dim])
    @weights = context.variable(w)
    @bias = context.variable(b)
  end

  def forward(input : Num::Grad::Variable(T)) : Num::Grad::Variable(T)
    output = input.value.matmul(@weights.value.transpose) + bias.value
    result = input.context.variable(output)

    if input.is_grad_needed || @weights.is_grad_needed || @bias.is_grad_needed
      gate = Num::NN::LinearGate.new(input, @weights, @bias)
      gate.cache(result, input, @weights, @bias)
    end
    result
  end

  def variables : Array(Num::Grad::Variable(T))
    [weights, bias]
  end

  def output_shape : Array(Int32)
    [@weights.value.shape[0]]
  end
end