lib/dnn/core/models.rb in ruby-dnn-0.14.0 vs lib/dnn/core/models.rb in ruby-dnn-0.14.1

@@ -133,11 +133,11 @@
       # @param [Numo::SFloat] x Input training data.
       # @param [Numo::SFloat] y Output training data.
       # @return [Hash] Hash of contents to be output to log.
       private def train_step(x, y)
         loss_value = train_on_batch(x, y)
-        { loss: loss_value.mean }
+        { loss: loss_value }
       end
 
       # Implement the test process to be performed.
       # @param [Numo::SFloat] x Input training data.
       # @param [Numo::SFloat] y Output training data.
@@ -183,11 +183,11 @@
         sum_loss = Xumo::SFloat[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.mean
+          sum_loss += loss_value
         end
         mean_loss = sum_loss / max_steps
         acc = total_correct.to_f / num_test_datas
         @last_log[:test_loss] = mean_loss
         @last_log[:test_accuracy] = acc
@@ -257,12 +257,13 @@
         @callbacks = []
       end
 
       # Save the model in marshal format.
       # @param [String] file_name Name to save model.
-      def save(file_name)
-        saver = Savers::MarshalSaver.new(self)
+      # @param [Boolean] include_optimizer Set true to save data included optimizer status.
+      def save(file_name, include_optimizer: true)
+        saver = Savers::MarshalSaver.new(self, include_optimizer: include_optimizer)
         saver.save(file_name)
       end
 
       # @return [DNN::Models::Model] Return the copy this model.
       def copy
@@ -310,10 +311,15 @@
       private
 
       def forward(x, learning_phase)
         DNN.learning_phase = learning_phase
         @layers_cache = nil
-        output_tensor = call(Tensor.new(x, nil))
+        inputs = if x.is_a?(Array)
+                   x.map { |a| Tensor.new(a, nil) }
+                 else
+                   Tensor.new(x, nil)
+                 end
+        output_tensor = call(inputs)
         @last_link = output_tensor.link
         unless @built
           @built = true
           naming
         end