models.rb in ruby-dnn-0.14.2

- old
+ new

@@ -143,11 +143,11 @@
       # @param [Numo::SFloat] y Output training data.
       # @param [Integer] batch_size Batch size used for one test.
       # @return [Hash] Hash of contents to be output to log.
       private def test(x, y, batch_size: 100)
         acc, test_loss = accuracy(x, y, batch_size: batch_size)
-        { accuracy: acc, test_loss: test_loss.mean }
+        { accuracy: acc, test_loss: test_loss }
       end
 
       # Training once.
       # Setup the model before use this method.
       # @param [Numo::SFloat] x Input training data.
@@ -178,11 +178,11 @@
         check_xy_type(x, y)
         num_test_datas = x.is_a?(Array) ? x[0].shape[0] : x.shape[0]
         batch_size = batch_size >= num_test_datas[0] ? num_test_datas : batch_size
         iter = Iterator.new(x, y, random: false)
         total_correct = 0
-        sum_loss = Xumo::SFloat[0]
+        sum_loss = 0
         max_steps = (num_test_datas.to_f / batch_size).ceil
         iter.foreach(batch_size) do |x_batch, y_batch|
           correct, loss_value = test_on_batch(x_batch, y_batch)
           total_correct += correct
           sum_loss += loss_value
@@ -200,10 +200,10 @@
       # @return [Array] Returns the test data accuracy and mean loss in the form [accuracy, mean_loss].
       def test_on_batch(x, y)
         call_callbacks(:before_test_on_batch)
         x = forward(x, false)
         correct = evaluate(x, y)
-        loss_value = @loss_func.loss(x, y, layers)
+        loss_value = @loss_func.loss(x, y)
         call_callbacks(:after_test_on_batch)
         [correct, loss_value]
       end
 
       # Implement the process to evaluate this model.