Class: DNN::Optimizers::Optimizer

Inherits:
Object
  • Object
show all
Defined in:
lib/dnn/core/optimizers.rb

Overview

Super class of all optimizer classes.

Direct Known Subclasses

AdaDelta, AdaGrad, Adam, RMSProp, RMSPropGraves, SGD

Instance Attribute Summary collapse

Class Method Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(clip_norm: nil) ⇒ Optimizer

Returns a new instance of Optimizer.

Parameters:

  • clip_norm (Float | NilClass) (defaults to: nil) —

    Gradient clip norm.



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# File 'lib/dnn/core/optimizers.rb', line 29

def initialize(clip_norm: nil)
  @clip_norm = clip_norm
end

Instance Attribute Details

#clip_norm ⇒ Object

Returns the value of attribute clip_norm.



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# File 'lib/dnn/core/optimizers.rb', line 7

def clip_norm
  @clip_norm
end

#status ⇒ Object (readonly)

Returns the value of attribute status.



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# File 'lib/dnn/core/optimizers.rb', line 6

def status
  @status
end

Class Method Details

.from_hash(hash) ⇒ Object

Raises:



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# File 'lib/dnn/core/optimizers.rb', line 9

def self.from_hash(hash)
  return nil unless hash
  optimizer_class = DNN.const_get(hash[:class])
  optimizer = optimizer_class.allocate
  raise DNN_Error.new("#{optimizer.class} is not an instance of #{self} class.") unless optimizer.is_a?(self)
  optimizer.load_hash(hash)
  optimizer
end

.load(dumped) ⇒ Object 



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# File 'lib/dnn/core/optimizers.rb', line 18

def self.load(dumped)
  opt = from_hash(dumped[:hash])
  dumped[:status].each do |key, state|
    state = state.clone
    opt.status[key] = state
    opt.instance_variable_set("@#{key}", state)
  end
  opt
end

Instance Method Details

#dump ⇒ Object 



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# File 'lib/dnn/core/optimizers.rb', line 45

def dump
  { hash: to_hash, status: @status }
end

#load_hash(hash) ⇒ Object 



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# File 'lib/dnn/core/optimizers.rb', line 69

def load_hash(hash)
  initialize(clip_norm: hash[:clip_norm])
end

#to_hash(merge_hash = nil) ⇒ Object 



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# File 'lib/dnn/core/optimizers.rb', line 49

def to_hash(merge_hash = nil)
  hash = { class: self.class.name, clip_norm: @clip_norm }
  hash.merge!(merge_hash) if merge_hash
  hash
end

#update(layers) ⇒ Object

Update layers has params.



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# File 'lib/dnn/core/optimizers.rb', line 34

def update(layers)
  target_params = layers.select { |layer| layer.is_a?(Layers::HasParamLayer) && layer.trainable }
                        .map { |layer| layer.get_params.values }.flatten.compact
                        .select { |param| param.grad }
  clip_grads(target_params) if @clip_norm
  update_params(target_params)
  target_params.each do |param|
    param.grad = Xumo::SFloat[0]
  end
end