Class: Torch::Optim::RMSprop

Inherits:
Optimizer show all
Defined in:
lib/torch/optim/rmsprop.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, alpha: 0.99, eps: 1e-8, weight_decay: 0, momentum: 0, centered: false) ⇒ RMSprop

Returns a new instance of RMSprop.

Raises:

  • (ArgumentError) 


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

def initialize(params, lr: 1e-2, alpha: 0.99, eps: 1e-8, weight_decay: 0, momentum: 0, centered: false)
  raise ArgumentError, "Invalid learning rate: #{lr}" if lr < 0
  raise ArgumentError, "Invalid epsilon value: #{eps}" if eps < 0
  raise ArgumentError, "Invalid momentum value: #{momentum}" if momentum < 0
  raise ArgumentError, "Invalid weight_decay value: #{weight_decay}" if weight_decay < 0
  raise ArgumentError, "Invalid momentum alpha: #{alpha}" if alpha < 0

  defaults = {lr: lr, momentum: momentum, alpha: alpha, eps: eps, centered: centered, weight_decay: weight_decay}
  super(params, defaults)
end

Instance Method Details

#step(closure = nil) ⇒ Object 



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# File 'lib/torch/optim/rmsprop.rb', line 16

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, "RMSprop does not support sparse gradients"
      end
      state = @state[p]

      # State initialization
      if state.size == 0
        state[:step] = 0
        state[:square_avg] = Torch.zeros_like(p.data)
        if group[:momentum] > 0
          state[:momentum_buffer] = Torch.zeros_like(p.data)
        end
        if group[:centered]
          state[:grad_avg] = Torch.zeros_like(p.data)
        end
      end

      square_avg = state[:square_avg]
      alpha = group[:alpha]

      state[:step] += 1

      if group[:weight_decay] != 0
        grad = grad.add(group[:weight_decay], p.data)
      end

      square_avg.mul!(alpha).addcmul!(1 - alpha, grad, grad)

      if group[:centered]
        grad_avg = state[:grad_avg]
        grad_avg.mul!(alpha).add!(1 - alpha, grad)
        avg = square_avg.addcmul(-1, grad_avg, grad_avg).sqrt!.add!(group[:eps])
      else
        avg = square_avg.sqrt.add!(group[:eps])
      end

      if group[:momentum] > 0
        buf = state[:momentum_buffer]
        buf.mul!(group[:momentum]).addcdiv!(grad, avg)
        p.data.add!(-group[:lr], buf)
      else
        p.data.addcdiv!(-group[:lr], grad, avg)
      end
    end
  end

  loss
end