lib/dnn/core/optimizers.rb in ruby-dnn-0.8.6 vs lib/dnn/core/optimizers.rb in ruby-dnn-0.8.7

@@ -35,11 +35,11 @@
         @momentum = momentum
         @v = {}
       end
     
       def update(params)
-        params.select { |key, param| param.is_a?(LearningParam) }.each_value do |param|
+        params.select { |key, param| param.grad }.each_value do |param|
           amount = param.grad * @learning_rate
           if @momentum > 0
             @v[param] ||= 0
             amount += @momentum * @v[param]
             @v[param] = amount
@@ -62,11 +62,11 @@
       def initialize(learning_rate = 0.01, momentum: 0.9)
         super(learning_rate, momentum: momentum)
       end
     
       def update(params)
-        params.select { |key, param| param.is_a?(LearningParam) }.each_value do |param|
+        params.select { |key, param| param.grad }.each_value do |param|
           @v[param] ||= 0
           amount = param.grad * @learning_rate
           @v[param] = @v[param] * @momentum - amount
           param.data = (param.data + @momentum**2 * @v[param]) - (1 + @momentum) * amount
         end
@@ -83,11 +83,11 @@
       def self.load_hash(hash)
         self.new(hash[:learning_rate])
       end
     
       def update(params)
-        params.select { |key, param| param.is_a?(LearningParam) }.each_value do |param|
+        params.select { |key, param| param.grad }.each_value do |param|
           @g[param] ||= 0
           @g[param] += param.grad**2
           param.data -= (@learning_rate / Xumo::NMath.sqrt(@g[param] + 1e-7)) * param.grad
         end
       end
@@ -106,11 +106,11 @@
         @alpha = alpha
         @g = {}
       end
     
       def update(params)
-        params.select { |key, param| param.is_a?(LearningParam) }.each_value do |param|
+        params.select { |key, param| param.grad }.each_value do |param|
           @g[param] ||= 0
           @g[param] = @alpha * @g[param] + (1 - @alpha) * param.grad**2
           param.data -= (@learning_rate / Xumo::NMath.sqrt(@g[param] + 1e-7)) * param.grad
         end
       end
@@ -134,11 +134,11 @@
         @h = {}
         @s = {}
       end
 
       def update(params)
-        params.select { |key, param| param.is_a?(LearningParam) }.each_value do |param|
+        params.select { |key, param| param.grad }.each_value do |param|
           @h[param] ||= Xumo::SFloat.zeros(*param.data.shape)
           @s[param] ||= Xumo::SFloat.zeros(*param.data.shape)
           @h[param] = @rho * @h[param] + (1 - @rho) * param.grad**2
           v = (Xumo::NMath.sqrt(@s[param] + 1e-6) / Xumo::NMath.sqrt(@h[param] + 1e-6)) * param.grad
           @s[param] = @rho * @s[param] + (1 - @rho) * v**2
@@ -170,10 +170,10 @@
       end
 
       def update(params)
         @iter += 1
         lr = @learning_rate * Math.sqrt(1 - @beta2**@iter) / (1 - @beta1**@iter) 
-        params.select { |key, param| param.is_a?(LearningParam) }.each_value do |param|
+        params.select { |key, param| param.grad }.each_value do |param|
           @m[param] ||= 0
           @v[param] ||= 0
           @m[param] += (1 - @beta1) * (param.grad - @m[param])
           @v[param] += (1 - @beta2) * (param.grad**2 - @v[param])
           param.data -= lr * @m[param] / Xumo::NMath.sqrt(@v[param] + 1e-7)