lib/ai4r/neural_network/backpropagation.rb in ai4r-1.9 vs lib/ai4r/neural_network/backpropagation.rb in ai4r-1.11

@@ -98,11 +98,11 @@
             "lambda { |y| y*(1-y) }, where y=propagation_function(x)",
         :learning_rate => "By default 0.25",        
         :momentum => "By default 0.1. Set this parameter to 0 to disable "+
             "momentum."
           
-      attr_accessor :structure, :weights, :activation_nodes
+      attr_accessor :structure, :weights, :activation_nodes, :last_changes
       
       # Creates a new network specifying the its architecture.
       # E.g.
       #    
       #   net = Backpropagation.new([4, 3, 2])  # 4 inputs
@@ -156,11 +156,44 @@
         init_activation_nodes
         init_weights
         init_last_changes
         return self
       end
-      
+
       protected
+
+      # Custom serialization. It used to fail trying to serialize because
+      # it uses lambda functions internally, and they cannot be serialized.
+      # Now it does not fail, but if you customize the values of
+      # * initial_weight_function
+      # * propagation_function
+      # * derivative_propagation_function
+      # you must restore their values manually after loading the instance.
+      def marshal_dump
+        [
+          @structure,
+          @disable_bias,
+          @learning_rate,
+          @momentum,
+          @weights,
+          @last_changes,
+          @activation_nodes
+        ]
+     end
+
+     def marshal_load(ary)
+       @structure,
+          @disable_bias,
+          @learning_rate,
+          @momentum,
+          @weights,
+          @last_changes,
+          @activation_nodes = ary
+       @initial_weight_function = lambda { |n, i, j| ((rand 2000)/1000.0) - 1}
+       @propagation_function = lambda { |x| 1/(1+Math.exp(-1*(x))) } #lambda { |x| Math.tanh(x) }
+       @derivative_propagation_function = lambda { |y| y*(1-y) } #lambda { |y| 1.0 - y**2 }
+     end
+
 
       # Propagate error backwards
       def backpropagate(expected_output_values)
         check_output_dimension(expected_output_values.length)
         calculate_output_deltas(expected_output_values)