Class: Torch::Optim::Rprop

Inherits:
Optimizer show all
Defined in:
lib/torch/optim/rprop.rb

Instance Attribute Summary

Attributes inherited from Optimizer

#param_groups

Instance Method Summary collapse

Methods inherited from Optimizer

#add_param_group, #load_state_dict, #state_dict, #zero_grad

Constructor Details

#initialize(params, lr: 1e-2, etas: [0.5, 1.2], step_sizes: [1e-6, 50]) ⇒ Rprop

Returns a new instance of Rprop.

Raises:

  • (ArgumentError) 


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# File 'lib/torch/optim/rprop.rb', line 5

def initialize(params, lr: 1e-2, etas: [0.5, 1.2], step_sizes: [1e-6, 50])
  raise ArgumentError, "Invalid learning rate: #{lr}" if lr < 0
  raise ArgumentError, "Invalid eta values: #{etas[0]}, #{etas[1]}" if etas[0] < 0 || etas[0] >= 1 || etas[1] < 1

  defaults = {lr: lr, etas: etas, step_sizes: step_sizes}
  super(params, defaults)
end

Instance Method Details

#step(closure = nil) ⇒ Object 



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# File 'lib/torch/optim/rprop.rb', line 13

def step(closure = nil)
  loss = nil
  if closure
    loss = closure.call
  end

  @param_groups.each do |group|
    group[:params].each do |p|
      next unless p.grad
      grad = p.grad.data
      if grad.sparse?
        raise Error, "Rprop does not support sparse gradients"
      end
      state = @state[p]

      # State initialization
      if state.size == 0
        state[:step] = 0
        state[:prev] = Torch.zeros_like(p.data)
        state[:step_size] = grad.new.resize_as!(grad).fill!(group[:lr])
      end

      etaminus, etaplus = group[:etas]
      step_size_min, step_size_max = group[:step_sizes]
      step_size = state[:step_size]

      state[:step] += 1

      sign = grad.mul(state[:prev]).sign
      sign[sign.gt(0)] = etaplus
      sign[sign.lt(0)] = etaminus
      sign[sign.eq(0)] = 1

      # update stepsizes with step size updates
      step_size.mul!(sign).clamp!(step_size_min, step_size_max)

      # for dir<0, dfdx=0
      # for dir>=0 dfdx=dfdx
      grad = grad.clone
      grad[sign.eq(etaminus)] = 0

      # update parameters
      p.data.addcmul!(-1, grad.sign, step_size)

      state[:prev].copy!(grad)
    end
  end

  loss
end