nn.rb in nn-2.3.0

- old
+ new

@@ -1,10 +1,10 @@
 require "numo/narray"
 require "json"
 
 class NN
-  VERSION = "2.2"
+  VERSION = "2.3"
 
   include Numo
 
   attr_accessor :weights
   attr_accessor :biases
@@ -122,21 +122,21 @@
     end
     correct.to_f / num_test_data
   end
 
   def learn(x_train, y_train, &block)
-    x = SFloat.zeros(@batch_size, @num_nodes.first)
-    y = SFloat.zeros(@batch_size, @num_nodes.last)
-    @batch_size.times do |i|
-      if x_train.is_a?(SFloat)
-        r = rand(x_train.shape[0])
-        x[i, true] = x_train[r, true]
-        y[i, true] = y_train[r, true]
-      else
-        r = rand(x_train.length)
-        x[i, true] = SFloat.cast(x_train[r])
-        y[i, true] = SFloat.cast(y_train[r])
+    if x_train.is_a?(SFloat)
+      indexes = (0...x_train.shape[0]).to_a.sample(@batch_size)
+      x = x_train[indexes, true]
+      y = y_train[indexes, true]
+    else
+      indexes = (0...x_train.length).to_a.sample(@batch_size)
+      x = SFloat.zeros(@batch_size, @num_nodes.first)
+      y = SFloat.zeros(@batch_size, @num_nodes.last)
+      @batch_size.times do |i|
+        x[i, true] = SFloat.cast(x_train[indexes[i]])
+        y[i, true] = SFloat.cast(y_train[indexes[i]])
       end
     end
     x, y = block.call(x, y) if block
     forward(x)
     backward(y)
@@ -413,18 +413,16 @@
     @nn = nn
     @index = index
   end
 
   def forward(x)
-    @x = x
     @mean = x.mean(0)
     @xc = x - @mean
     @var = (@xc ** 2).mean(0)
     @std = NMath.sqrt(@var + 1e-7)
     @xn = @xc / @std
-    out = @nn.gammas[@index] * @xn + @nn.betas[@index]
-    out.reshape(*@x.shape)
+    @nn.gammas[@index] * @xn + @nn.betas[@index]
   end
 
   def backward(dout)
     @d_beta = dout.sum(0).mean
     @d_gamma = (@xn * dout).sum(0).mean
@@ -432,9 +430,8 @@
     dxc = dxn / @std
     dstd = -((dxn * @xc) / (@std ** 2)).sum(0)
     dvar = 0.5 * dstd / @std
     dxc += (2.0 / @nn.batch_size) * @xc * dvar
     dmean = dxc.sum(0)
-    dx = dxc - dmean / @nn.batch_size
-    dx.reshape(*@x.shape)
+    dxc - dmean / @nn.batch_size
   end
 end