Layer
responsible for the implementation of the algorithm's decays, extends the class Layer. currently overrides the radius and learning rate decay methods of Layer. Has two phases, phase one has a decay in both the learning rate and the radius. The number of epochs for both phases can be passed and the total number of epochs is the sum of epoch for phase one and phase two. In the scond phase, the learning and radius decay is steady, normally set to a small number (ie. 0.01)
nodes => number of nodes in the SOM (nodes x nodes). Has to be the same number
you pass to the SOM. Has to be an integer
radius => the initial radius for the neighborhood
phase_one => number of epochs for phase one, has to be an integer. By default it is set to 150
phase_two => number of epochs for phase two, has to be an integer. By default it is set to 100
learning_rate => sets the initial learning rate
phase_one_learning_rate => sets the learning rate for phase one
phase_two_learning_rate => sets the learning rate for phase two
# File lib/ai4r/som/two_phase_layer.rb, line 36 def initialize(nodes, learning_rate = 0.9, phase_one = 150, phase_two = 100, phase_one_learning_rate = 0.1, phase_two_learning_rate = 0) super nodes, nodes, phase_one + phase_two, learning_rate @phase_one = phase_one @phase_two = phase_two @lr = @initial_learning_rate @phase_one_learning_rate = phase_one_learning_rate @phase_two_learning_rate = phase_two_learning_rate @radius_reduction = @phase_one / (nodes/2.0 - 1) + 1 @delta_lr = (@lr - @phase_one_learning_rate)/ @phase_one @radius = (nodes / 2.0).to_i end
two different values will be returned, depending on the phase in phase one, the rate will incrementially reduced everytime this method is called on the switch of phases, the learning rate will be reset and the delta_lr (which signals the decay value of the learning rate) is reset as well in phase two, the newly reset delta_lr rate will be used to incrementially reduce the learning rate
# File lib/ai4r/som/two_phase_layer.rb, line 72 def learning_rate_decay(epoch) if epoch < @phase_one @lr -= @delta_lr return @lr elsif epoch == @phase_one @lr = @phase_one_learning_rate @delta_lr = (@phase_one_learning_rate - @phase_two_learning_rate)/@phase_two return @lr else @lr -= @delta_lr end end
two different values will be returned, depending on the phase in phase one, the radius will incrementially reduced by 1 every @radius_reduction time in phase two, the radius is fixed to 1
# File lib/ai4r/som/two_phase_layer.rb, line 54 def radius_decay(epoch) if epoch > @phase_one return 1 else if (epoch % @radius_reduction) == 0 @radius -= 1 end @radius end end
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