Module: GRX::Utils

Defined in:
lib/grx/utils.rb

Class Method Summary collapse

Class Method Details

.clip_grad_norm!(parameters, max_norm) ⇒ Object

================================================================

clip_grad_norm! — Clips gradients of parameter collection so their combined L2 norm does not exceed max_norm. Prevents exploding gradient problems during deep network training.



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# File 'lib/grx/utils.rb', line 10

def self.clip_grad_norm!(parameters, max_norm)
  max_norm = max_norm.to_f
  total_norm_sq = 0.0
  parameters.each do |p|
    next unless p.grad
    total_norm_sq += p.grad.square.to_a.sum
  end
  total_norm = Math.sqrt(total_norm_sq)
  clip_coef = max_norm / (total_norm + 1e-6)
  if clip_coef < 1.0
    parameters.each do |p|
      next unless p.grad
      p.grad = p.grad.scale(clip_coef)
    end
  end
  total_norm
end

.one_hot(indices, num_classes: nil, requires_grad: false) ⇒ Object

================================================================

one_hot — Generates a 2D One-Hot encoded Tensor from class indices



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# File 'lib/grx/utils.rb', line 31

def self.one_hot(indices, num_classes: nil, requires_grad: false)
  ids = indices.is_a?(Tensor) ? indices.to_a.map(&:to_i) : Array(indices).map(&:to_i)
  c = num_classes || (ids.empty? ? 0 : ids.max + 1)
  n = ids.size
  matrix_data = Array.new(n * c, 0.0)
  ids.each_with_index do |class_id, row|
    raise IndexError, "Class index #{class_id} out of bounds [0, #{c})" if class_id < 0 || class_id >= c
    matrix_data[row * c + class_id] = 1.0
  end
  Tensor.create(matrix_data, [n, c], requires_grad: requires_grad)
end