Outputs now can be used as inputs

src/main/java/de/monticore/lang/monticar/cnnarch/gluongenerator/CNNArch2GluonArchitectureSupportChecker.java

@@ -37,4 +37,9 @@ public class CNNArch2GluonArchitectureSupportChecker extends ArchitectureSupport
         return true;
     }
 
+    @Override
+    protected boolean checkOutputAsInput(ArchitectureSymbol architecture) {
+        return true;
+    }
+
 }

src/main/java/de/monticore/lang/monticar/cnnarch/gluongenerator/CNNArch2GluonTemplateController.java

@@ -173,7 +173,7 @@ public class CNNArch2GluonTemplateController extends CNNArchTemplateController {
         Map<String, List<String>> inputs = new LinkedHashMap<>();
 
         for (ArchitectureElementSymbol element : stream.getFirstAtomicElements()) {
-            if (element.isInput()) {
+            if (element.isInput() || element.isOutput()) {
                 List<Integer> intDimensions = element.getOutputTypes().get(0).getDimensions();
 
                 List<String> dimensions = new ArrayList<>();

src/main/resources/templates/gluon/elements/Output.ftl

+<#if element.inputs?size gte 1>
 <#assign input = element.inputs[0]>
 <#if mode == "FORWARD_FUNCTION">
         ${element.name} = ${input}
@@ -6,3 +7,4 @@
 <#elseif mode == "CPP_INLINE">
     ${element.name} = ${input};
 </#if>
+</#if>
\ No newline at end of file

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

 <#list tc.getLayerVariableMembers("batch_size")?keys as member>
                     ${member} = mx.nd.zeroes((${tc.join(tc.getLayerVariableMembers("batch_size")[member], ", ")},), ctx=mx_context)
 </#list>
+<#list tc.architecture.outputs as output>
+                    ${tc.getName(output)} = mx.nd.zeroes(((${tc.join(output.ioDeclaration.type.dimensions, ", ")},), ctx=mx_context)
+</#list>
 
 <#list tc.architecture.streams as stream>
 <#if stream.isTrainable()>

src/test/resources/target_code/CNNSupervisedTrainer_Alexnet.py

@@ -136,6 +136,7 @@ class CNNSupervisedTrainer_Alexnet:
                 predictions_label = batch.label[0].as_in_context(mx_context)
 
                 with autograd.record():
+                    predictions_ = mx.nd.zeroes(((10,), ctx=mx_context)
 
                     predictions_ = self._networks[0](data_)
 
@@ -172,6 +173,7 @@ class CNNSupervisedTrainer_Alexnet:
                 ]
 
                 if True:
+                    predictions_ = mx.nd.zeroes(((10,), ctx=mx_context)
 
                     predictions_ = self._networks[0](data_)
 
@@ -192,6 +194,7 @@ class CNNSupervisedTrainer_Alexnet:
                 ]
 
                 if True:
+                    predictions_ = mx.nd.zeroes(((10,), ctx=mx_context)
 
                     predictions_ = self._networks[0](data_)
 

src/test/resources/target_code/CNNSupervisedTrainer_CifarClassifierNetwork.py

@@ -136,6 +136,7 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
                 softmax_label = batch.label[0].as_in_context(mx_context)
 
                 with autograd.record():
+                    softmax_ = mx.nd.zeroes(((10,), ctx=mx_context)
 
                     softmax_ = self._networks[0](data_)
 
@@ -172,6 +173,7 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
                 ]
 
                 if True:
+                    softmax_ = mx.nd.zeroes(((10,), ctx=mx_context)
 
                     softmax_ = self._networks[0](data_)
 
@@ -192,6 +194,7 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
                 ]
 
                 if True:
+                    softmax_ = mx.nd.zeroes(((10,), ctx=mx_context)
 
                     softmax_ = self._networks[0](data_)
 

src/test/resources/target_code/CNNSupervisedTrainer_VGG16.py

@@ -136,6 +136,7 @@ class CNNSupervisedTrainer_VGG16:
                 predictions_label = batch.label[0].as_in_context(mx_context)
 
                 with autograd.record():
+                    predictions_ = mx.nd.zeroes(((1000,), ctx=mx_context)
 
                     predictions_ = self._networks[0](data_)
 
@@ -172,6 +173,7 @@ class CNNSupervisedTrainer_VGG16:
                 ]
 
                 if True:
+                    predictions_ = mx.nd.zeroes(((1000,), ctx=mx_context)
 
                     predictions_ = self._networks[0](data_)
 
@@ -192,6 +194,7 @@ class CNNSupervisedTrainer_VGG16:
                 ]
 
                 if True:
+                    predictions_ = mx.nd.zeroes(((1000,), ctx=mx_context)
 
                     predictions_ = self._networks[0](data_)