lib/dnn/core/activations.rb in ruby-dnn-0.5.9 vs lib/dnn/core/activations.rb in ruby-dnn-0.5.10

@@ -2,29 +2,27 @@
   module Activations
     Layer = Layers::Layer
 
     class Sigmoid < Layer
       def forward(x)
-        @out = 1 / (1 + NMath.exp(-x))
+        @out = 1 / (1 + Xumo::NMath.exp(-x))
       end
     
       def backward(dout)
         dout * (1 - @out) * @out
       end
     end
 
 
     class Tanh < Layer
-      include Xumo
-
       def forward(x)
         @x = x
-        NMath.tanh(x)
+        Xumo::NMath.tanh(x)
       end
       
       def backward(dout)
-        dout * (1.0 / NMath.cosh(@x)**2)
+        dout * (1.0 / Xumo::NMath.cosh(@x)**2)
       end
     end
     
     
     class ReLU < Layer
@@ -41,12 +39,10 @@
       end
     end
 
 
     class LeakyReLU < Layer
-      include Xumo
-
       attr_reader :alpha
 
       def initialize(alpha = 0.3)
         @alpha = alpha
       end
@@ -55,11 +51,11 @@
         self.new(hash[:alpha])
       end
 
       def forward(x)
         @x = x.clone
-        a = SFloat.ones(x.shape)
+        a = Xumo::SFloat.ones(x.shape)
         a[x <= 0] = @alpha
         x * a
       end
 
       def backward(dout)
@@ -142,27 +138,25 @@
     end
     
     
     class SoftmaxWithLoss < Layers::OutputLayer
       def forward(x)
-        @out = NMath.exp(x) / NMath.exp(x).sum(1).reshape(x.shape[0], 1)
+        @out = Xumo::NMath.exp(x) / Xumo::NMath.exp(x).sum(1).reshape(x.shape[0], 1)
       end
     
       def backward(y)
         @out - y
       end
     
       def loss(y)
         batch_size = y.shape[0]
-        -(y * NMath.log(@out + 1e-7)).sum / batch_size + ridge
+        -(y * Xumo::NMath.log(@out + 1e-7)).sum / batch_size + ridge
       end
     end
 
 
     class SigmoidWithLoss < Layers::OutputLayer
-      include Xumo
-
       def initialize
         @sigmoid = Sigmoid.new
       end
 
       def forward(x)
@@ -173,10 +167,10 @@
         @out - y
       end
 
       def loss(y)
         batch_size = y.shape[0]
-        -(y * NMath.log(@out + 1e-7) + (1 - y) * NMath.log(1 - @out + 1e-7)).sum / batch_size + ridge
+        -(y * Xumo::NMath.log(@out + 1e-7) + (1 - y) * Xumo::NMath.log(1 - @out + 1e-7)).sum / batch_size + ridge
       end
     end
 
   end
 end