Class: Torch::Optim::Adadelta

Inherits:
Optimizer show all
Defined in:
lib/torch/optim/adadelta.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: 1.0, rho: 0.9, eps: 1e-6, weight_decay: 0) ⇒ Adadelta

Returns a new instance of Adadelta.

Raises:

  • (ArgumentError) 


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

def initialize(params, lr: 1.0, rho: 0.9, eps: 1e-6, weight_decay: 0)
  raise ArgumentError, "Invalid learning rate: #{lr}" if lr < 0
  raise ArgumentError, "Invalid rho value: #{rho}" if rho < 0 || rho > 1
  raise ArgumentError, "Invalid epsilon value: #{eps}" if eps < 0
  raise ArgumentError, "Invalid weight_decay value: #{weight_decay}" if weight_decay < 0

  defaults = {lr: lr, rho: rho, eps: eps, weight_decay: weight_decay}
  super(params, defaults)
end

Instance Method Details

#step(closure = nil) ⇒ Object 



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# File 'lib/torch/optim/adadelta.rb', line 15

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

      if state.size == 0
        state[:step] = 0
        state[:square_avg] = Torch.zeros_like(p.data)
        state[:acc_delta] = Torch.zeros_like(p.data)
      end

      square_avg, acc_delta = state[:square_avg], state[:acc_delta]
      rho, eps = group[:rho], group[:eps]

      state[:step] += 1

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

      square_avg.mul!(rho).addcmul!(grad, grad, value: 1 - rho)
      std = square_avg.add(eps).sqrt!
      delta = acc_delta.add(eps).sqrt!.div!(std).mul!(grad)
      p.data.add!(delta, alpha: -group[:lr])
      acc_delta.mul!(rho).addcmul!(delta, delta, value: 1 - rho)
    end
  end

  loss
end