Class: GRX::NN::Linear

Inherits:
Module
  • Object
show all
Defined in:
lib/grx/nn.rb

Overview

Linear — Capa densa (fully connected) y = x @ W^T + b

Instance Attribute Summary collapse

Instance Method Summary collapse

Methods inherited from Module

#call, #parameters, #zero_grad

Constructor Details

#initialize(in_features, out_features, bias: true) ⇒ Linear

Returns a new instance of Linear.



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# File 'lib/grx/nn.rb', line 45

def initialize(in_features, out_features, bias: true)
  @in_features  = in_features
  @out_features = out_features
  @use_bias     = bias

  # Pesos: Xavier uniform (bueno para tanh/sigmoid)
  @weight = Tensor.xavier_uniform([out_features, in_features], requires_grad: true)

  # Bias: ceros
  @bias = bias ? Tensor.zeros([out_features], requires_grad: true) : nil
end

Instance Attribute Details

#bias ⇒ Object (readonly)

Returns the value of attribute bias.



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# File 'lib/grx/nn.rb', line 43

def bias
  @bias
end

#weight ⇒ Object (readonly)

Returns the value of attribute weight.



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# File 'lib/grx/nn.rb', line 43

def weight
  @weight
end

Instance Method Details

#forward(x) ⇒ Object 



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# File 'lib/grx/nn.rb', line 57

def forward(x)
  # x: [batch, in_features]  →  out: [batch, out_features]
  # out = x @ W^T
  out = x.matmul(@weight.transpose)

  if @use_bias
    # Sumamos bias fila por fila.
    # Repetimos @bias batch_size veces para crear un tensor [batch, out_features]
    # que comparte el grafo con @bias original.
    batch_size = x.shape[0]
    # Tile del bias: concatenamos el mismo tensor bias_size veces
    # usando operaciones que mantienen el grafo conectado
    bias_tiled = _tile_bias(@bias, batch_size, @out_features)
    out + bias_tiled
  else
    out
  end
end

#to_s ⇒ Object 



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# File 'lib/grx/nn.rb', line 108

def to_s
  "Linear(#{@in_features} → #{@out_features}, bias: #{@use_bias})"
end