Fixed a bug

src/main/resources/templates/gluon/CNNSupervisedTrainer.ftl

@@ -266,7 +266,7 @@ class ${tc.fileNameWithoutEnding}:
             if not os.path.isdir(self._net_creator._model_dir_):
                 raise
 
-        trainers = [mx.gluon.Trainer(network.collect_params(), optimizer, optimizer_params) if len(net.collect_params().values()) != 0 for network in self._networks.values()]
+        trainers = [mx.gluon.Trainer(network.collect_params(), optimizer, optimizer_params) for network in self._networks.values() if len(network.collect_params().values()) != 0]
 
         margin = loss_params['margin'] if 'margin' in loss_params else 1.0
         sparseLabel = loss_params['sparse_label'] if 'sparse_label' in loss_params else True

src/main/resources/templates/gluon/pythonExecuteWithLoss.ftl

@@ -23,7 +23,7 @@
 <#else>
 <#if networkInstruction.body.isTrainable()>
                     ${tc.join(tc.getStreamOutputNames(networkInstruction.body), ", ")} = self._networks[${networkInstruction?index}](${tc.join(tc.getStreamInputNames(networkInstruction.body), ", ")})
-                    <#if !(tc.getStreamOutputNames(networkInstruction.body)[0]?ends_with("_output_"))>
+                    <#if !(tc.getStreamOutputNames(networkInstruction.body)[0]?ends_with("_output_")) && !(tc.getStreamOutputNames(networkInstruction.body)[0]?ends_with("_state_"))>
                     lossList.append(loss_function(${tc.getStreamOutputNames(networkInstruction.body)[0]}, ${tc.getStreamOutputNames(networkInstruction.body)[0]}label))
                     </#if>
                     <#list networkInstruction.body.elements as element>

src/test/resources/target_code/CNNSupervisedTrainer_Alexnet.py

@@ -266,7 +266,7 @@ class CNNSupervisedTrainer_Alexnet:
             if not os.path.isdir(self._net_creator._model_dir_):
                 raise
 
-        trainers = [mx.gluon.Trainer(network.collect_params(), optimizer, optimizer_params) if len(net.collect_params().values()) != 0 for network in self._networks.values()]
+        trainers = [mx.gluon.Trainer(network.collect_params(), optimizer, optimizer_params) for network in self._networks.values() if len(network.collect_params().values()) != 0]
 
         margin = loss_params['margin'] if 'margin' in loss_params else 1.0
         sparseLabel = loss_params['sparse_label'] if 'sparse_label' in loss_params else True

src/test/resources/target_code/CNNSupervisedTrainer_CifarClassifierNetwork.py

@@ -266,7 +266,7 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
             if not os.path.isdir(self._net_creator._model_dir_):
                 raise
 
-        trainers = [mx.gluon.Trainer(network.collect_params(), optimizer, optimizer_params) if len(net.collect_params().values()) != 0 for network in self._networks.values()]
+        trainers = [mx.gluon.Trainer(network.collect_params(), optimizer, optimizer_params) for network in self._networks.values() if len(network.collect_params().values()) != 0]
 
         margin = loss_params['margin'] if 'margin' in loss_params else 1.0
         sparseLabel = loss_params['sparse_label'] if 'sparse_label' in loss_params else True

src/test/resources/target_code/CNNSupervisedTrainer_VGG16.py

@@ -266,7 +266,7 @@ class CNNSupervisedTrainer_VGG16:
             if not os.path.isdir(self._net_creator._model_dir_):
                 raise
 
-        trainers = [mx.gluon.Trainer(network.collect_params(), optimizer, optimizer_params) if len(net.collect_params().values()) != 0 for network in self._networks.values()]
+        trainers = [mx.gluon.Trainer(network.collect_params(), optimizer, optimizer_params) for network in self._networks.values() if len(network.collect_params().values()) != 0]
 
         margin = loss_params['margin'] if 'margin' in loss_params else 1.0
         sparseLabel = loss_params['sparse_label'] if 'sparse_label' in loss_params else True