lib/dnn/core/optimizers.rb in ruby-dnn-0.1.5 vs lib/dnn/core/optimizers.rb in ruby-dnn-0.1.6

@@ -9,10 +9,14 @@
         @learning_rate = learning_rate
       end
 
       #Update layer has params.
       def update(layer) end
+
+      def to_hash
+        {name: self.class.name, learning_rate: @learning_rate}
+      end
     end
 
 
     class SGD < Optimizer
       attr_accessor :momentum
@@ -20,10 +24,14 @@
       def initialize(learning_rate = 0.01, momentum: 0)
         super(learning_rate)
         @momentum = momentum
         @amounts = {}
       end
+
+      def self.load_hash(hash)
+        self.new(hash[:learning_rate], hash[:momentum])
+      end
     
       def update(layer)
         amount = if @amounts[layer]
           @amounts[layer]
         else
@@ -37,18 +45,30 @@
             @amounts[layer] = amount
           end
           layer.params[key] -= amount[key]
         end
       end
+
+      def to_hash
+        {
+          name: self.class.name,
+          learning_rate: @learning_rate,
+          momentum: @momentum,
+        }
+      end
     end
     
     
     class AdaGrad    
       def initialize(learning_rate = 0.01)
         super(learning_rate)
         @g = {}
       end
+
+      def self.load_hash(hash)
+        @learning_rate = hash[:learning_rate]
+      end
     
       def update(layer)
         @g[layer] ||= {}
         layer.params.each_key do |key|
           @g[layer][key] ||= 0
@@ -65,19 +85,31 @@
       def initialize(learning_rate = 0.001, muse = 0.9)
         super(learning_rate)
         @muse = muse
         @g = {}
       end
+
+      def self.load_hash(hash)
+        self.new(hash[:learning_rate], hash[:muse])
+      end
     
       def update(layer)
         @g[layer] ||= {}
         layer.params.each_key do |key|
           @g[layer][key] ||= 0
           @g[layer][key] = @muse * @g[layer][key] + (1 - @muse) * layer.grads[key]**2
           layer.params[key] -= (@learning_rate / NMath.sqrt(@g[layer][key] + 1e-7)) * layer.grads[key]
         end
       end
+
+      def to_hash
+        {
+          name: self.class.name,
+          learning_rate: @learning_rate,
+          muse: @muse,
+        }
+      end
     end
 
 
     class Adam < Optimizer
       include Numo
@@ -92,10 +124,14 @@
         @iter = 0
         @m = {}
         @v = {}
       end
 
+      def self.load_hash(hash)
+        self.new(hash[:learning_rate], hash[:beta1], hash[:beta2])
+      end
+
       def update(layer)
         @iter += 1
         @m[layer] ||= {}
         @v[layer] ||= {}
         lr = @learning_rate * Math.sqrt(1 - @beta2**@iter) / (1 - @beta1**@iter) 
@@ -104,9 +140,18 @@
           @v[layer][key] ||= 0
           @m[layer][key] += (1 - @beta1) * (layer.grads[key] - @m[layer][key])
           @v[layer][key] += (1 - @beta2) * (layer.grads[key]**2 - @v[layer][key])
           layer.params[key] -= lr * @m[layer][key] / NMath.sqrt(@v[layer][key] + 1e-7)
         end
+      end
+
+      def to_hash
+        {
+          name: self.class.name,
+          learning_rate: @learning_rate,
+          beta1: @beta1,
+          beta2: @beta2,
+        }
       end
     end
 
   end
 end