lib/dnn/core/layers.rb in ruby-dnn-0.8.0 vs lib/dnn/core/layers.rb in ruby-dnn-0.8.1

@@ -18,15 +18,19 @@
         @built
       end
 
       # Forward propagation.
       # Classes that inherit from this class must implement this method.
-      # def forward() end
+      def forward
+        raise NotImplementedError.new("Class '#{self.class.name}' has implement method 'forward'")
+      end
 
       # Backward propagation.
       # Classes that inherit from this class must implement this method.
-      # def backward() end
+      def backward
+        raise NotImplementedError.new("Class '#{self.class.name}' has implement method 'update'")
+      end
     
       # Get the shape of the layer.
       def shape
         prev_layer.shape
       end
@@ -71,11 +75,13 @@
     
       private
       
       # Initialize of the parameters.
       # Classes that inherit from this class must implement this method.
-      def init_params() end
+      def init_params
+        raise NotImplementedError.new("Class '#{self.class.name}' has implement method 'init_params'")
+      end
     end
     
     
     class InputLayer < Layer
       attr_reader :shape
@@ -117,12 +123,12 @@
         super()
         @weight_initializer = (weight_initializer || RandomNormal.new)
         @bias_initializer = (bias_initializer || Zeros.new)
         @l1_lambda = l1_lambda
         @l2_lambda = l2_lambda
-        @params[:weight] = @weight = LearningParam.new(self)
-        @params[:bias] = @bias = LearningParam.new(self)
+        @params[:weight] = @weight = LearningParam.new
+        @params[:bias] = @bias = LearningParam.new
       end
 
       def lasso
         if @l1_lambda > 0
           @l1_lambda * @weight.data.abs.sum
@@ -157,12 +163,12 @@
       end
 
       private
 
       def init_params
-        @weight_initializer.init_param(@weight)
-        @bias_initializer.init_param(@bias)
+        @weight_initializer.init_param(self, @weight)
+        @bias_initializer.init_param(self, @bias)
       end
     end
     
     
     class Dense < Connection
@@ -322,11 +328,11 @@
       end
 
       def initialize(momentum: 0.9)
         super()
         @momentum = momentum
-        @params[:gamma] = @gamma = LearningParam.new(self)
-        @params[:beta] = @beta = LearningParam.new(self)
+        @params[:gamma] = @gamma = LearningParam.new
+        @params[:beta] = @beta = LearningParam.new
         @params[:running_mean] = nil
         @params[:running_var] = nil
       end
 
       def build(model)