Fixed some tests and removed MultipleInput/MultipleOutput test since the...

Fixed some tests and removed MultipleInput/MultipleOutput test since the functionality is already covered in MultipleStreams test

src/test/java/de/monticore/lang/monticar/emadl/GenerationTest.java

@@ -235,7 +235,6 @@ public class GenerationTest extends AbstractSymtabTest {
         }
     }
 
-    @Ignore
     @Test
     public void gluonDdpgTest() {
         Log.getFindings().clear();

src/test/java/de/monticore/lang/monticar/emadl/IntegrationGluonTest.java

@@ -42,30 +42,6 @@ public class IntegrationGluonTest extends IntegrationTest {
         super("GLUON", "39253EC049D4A4E5FA0536AD34874B9D#1DBAEE1B1BD83FB7CB5F70AE91B29638#C4C23549E737A759721D6694C75D9771#5AF0CE68E408E8C1F000E49D72AC214A");
     }
 
-    @Test
-    public void testMultipleInputs() {
-        Log.getFindings().clear();
-
-        deleteHashFile(multipleInputsHashFile);
-
-        String[] args = {"-m", "src/test/resources/models/", "-r", "MultipleInputs", "-b", "GLUON"};
-        EMADLGeneratorCli.main(args);
-
-        assertTrue(Log.getFindings().isEmpty());
-    }
-
-    @Test
-    public void testMultipleOutputs() {
-        Log.getFindings().clear();
-
-        deleteHashFile(multipleOutputsHashFile);
-
-        String[] args = {"-m", "src/test/resources/models/", "-r", "MultipleOutputs", "-b", "GLUON"};
-        EMADLGeneratorCli.main(args);
-
-        assertTrue(Log.getFindings().isEmpty());
-    }
-
     @Test
     public void testMultipleStreams() {
         Log.getFindings().clear();

src/test/resources/target_code/gluon/CNNSupervisedTrainer_mnist_mnistClassifier_net.py

@@ -94,7 +94,8 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
                 with autograd.record():
                     predictions_output = self._networks[0](image_data)
 
-                    loss = loss_functions['predictions'](predictions_output, predictions_label)
+                    loss = \
+                        loss_functions['predictions'](predictions_output, predictions_label)
 
                 loss.backward()
 
@@ -125,7 +126,8 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
                     batch.label[0].as_in_context(mx_context)
                 ]
 
-                predictions_output = self._networks[0](image_data)
+                if True: # Fix indentation
+                    predictions_output = self._networks[0](image_data)
 
                 predictions = [
                     mx.nd.argmax(predictions_output, axis=1)
@@ -143,7 +145,9 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
                     batch.label[0].as_in_context(mx_context)
                 ]
 
-                predictions_output = self._networks[0](image_data)
+                if True: # Fix indentation
+                    predictions_output = self._networks[0](image_data)
+
                 predictions = [
                     mx.nd.argmax(predictions_output, axis=1)
                 ]

src/test/resources/target_code/gluon/reinforcementModel/cartpole/CNNTrainer_cartpole_master_dqn.py

@@ -96,7 +96,7 @@ if __name__ == "__main__":
         resume_agent_params = {
             'session_dir': resume_directory,
             'environment': env,
-            'net': qnet_creator.net,
+            'net': qnet_creator.networks[0],
         }
         agent = DqnAgent.resume_from_session(**resume_agent_params)
     else:
@@ -108,4 +108,4 @@ if __name__ == "__main__":
     train_successful = agent.train()
 
     if train_successful:
-        agent.save_best_network(qnet_creator._model_dir_ + qnet_creator._model_prefix_ + '_newest', epoch=0)
+        agent.save_best_network(qnet_creator._model_dir_ + qnet_creator._model_prefix_ + '_0_newest', epoch=0)