Class: Rumale::Optimizer::Adam

Inherits:
Object
  • Object
show all
Includes:
Base::BaseEstimator
Defined in:
lib/rumale/optimizer/adam.rb

Overview

Adam is a class that implements Adam optimizer.

Reference

  • D P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” Proc. ICLR’15, 2015.

Examples:

optimizer = Rumale::Optimizer::Adam.new(learning_rate: 0.01, momentum: 0.9, decay1: 0.9, decay2: 0.999)
estimator = Rumale::LinearModel::LinearRegression.new(optimizer: optimizer, random_seed: 1)
estimator.fit(samples, values)

Instance Attribute Summary

Attributes included from Base::BaseEstimator

#params

Instance Method Summary collapse

Constructor Details

#initialize(learning_rate: 0.001, decay1: 0.9, decay2: 0.999) ⇒ Adam

Create a new optimizer with Adam

Parameters:

  • learning_rate (Float) (defaults to: 0.001) —

    The initial value of learning rate.

  • decay1 (Float) (defaults to: 0.9) —

    The smoothing parameter for the first moment.

  • decay2 (Float) (defaults to: 0.999) —

    The smoothing parameter for the second moment.



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# File 'lib/rumale/optimizer/adam.rb', line 26

def initialize(learning_rate: 0.001, decay1: 0.9, decay2: 0.999)
  check_params_float(learning_rate: learning_rate, decay1: decay1, decay2: decay2)
  check_params_positive(learning_rate: learning_rate, decay1: decay1, decay2: decay2)
  @params = {}
  @params[:learning_rate] = learning_rate
  @params[:decay1] = decay1
  @params[:decay2] = decay2
  @fst_moment = nil
  @sec_moment = nil
  @iter = 0
end

Instance Method Details

#call(weight, gradient) ⇒ Numo::DFloat

Calculate the updated weight with Nadam adaptive learning rate.

Parameters:

  • weight (Numo::DFloat) —

    (shape: [n_features]) The weight to be updated.

  • gradient (Numo::DFloat) —

    (shape: [n_features]) The gradient for updating the weight.

Returns:

  • (Numo::DFloat) —

    (shape: [n_feautres]) The updated weight.



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# File 'lib/rumale/optimizer/adam.rb', line 43

def call(weight, gradient)
  @fst_moment ||= Numo::DFloat.zeros(weight.shape[0])
  @sec_moment ||= Numo::DFloat.zeros(weight.shape[0])

  @iter += 1

  @fst_moment = @params[:decay1] * @fst_moment + (1.0 - @params[:decay1]) * gradient
  @sec_moment = @params[:decay2] * @sec_moment + (1.0 - @params[:decay2]) * gradient**2
  nm_fst_moment = @fst_moment / (1.0 - @params[:decay1]**@iter)
  nm_sec_moment = @sec_moment / (1.0 - @params[:decay2]**@iter)

  weight - @params[:learning_rate] * nm_fst_moment / (nm_sec_moment**0.5 + 1e-8)
end

#marshal_dump ⇒ Hash

Dump marshal data.

Returns:

  • (Hash) —

    The marshal data.



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# File 'lib/rumale/optimizer/adam.rb', line 59

def marshal_dump
  { params: @params,
    fst_moment: @fst_moment,
    sec_moment: @sec_moment,
    iter: @iter }
end

#marshal_load(obj) ⇒ nil

Load marshal data.

Returns:

  • (nil) 


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# File 'lib/rumale/optimizer/adam.rb', line 68

def marshal_load(obj)
  @params = obj[:params]
  @fst_moment = obj[:fst_moment]
  @sec_moment = obj[:sec_moment]
  @iter = obj[:iter]
  nil
end