Added Tensorflow generator

pom.xml

@@ -21,6 +21,7 @@
     <cnnarch-mxnet-generator.version>0.2.17-SNAPSHOT</cnnarch-mxnet-generator.version>
     <cnnarch-caffe2-generator.version>0.2.13-SNAPSHOT</cnnarch-caffe2-generator.version>
     <cnnarch-gluon-generator.version>0.2.8-SNAPSHOT</cnnarch-gluon-generator.version>
+    <cnnarch-tensorflow-generator.version>0.2.14-SNAPSHOT</cnnarch-tensorflow-generator.version>
     <embedded-montiarc-math-opt-generator>0.1.4</embedded-montiarc-math-opt-generator>
 
         <!-- .. Libraries .................................................. -->
@@ -86,6 +87,12 @@
             <version>${cnnarch-gluon-generator.version}</version>
         </dependency>
 
+        <dependency>
+            <groupId>de.monticore.lang.monticar</groupId>
+            <artifactId>cnnarch-tensorflow-generator</artifactId>
+            <version>${cnnarch-tensorflow-generator.version}</version>
+        </dependency>
+
         <dependency>
             <groupId>de.monticore.lang.monticar</groupId>
             <artifactId>cnnarch-caffe2-generator</artifactId>

src/main/java/de/monticore/lang/monticar/emadl/generator/Backend.java

@@ -6,10 +6,12 @@ import de.monticore.lang.monticar.cnnarch.generator.CNNTrainGenerator;
 import de.monticore.lang.monticar.cnnarch.gluongenerator.CNNArch2Gluon;
 import de.monticore.lang.monticar.cnnarch.gluongenerator.CNNTrain2Gluon;
 import de.monticore.lang.monticar.cnnarch.mxnetgenerator.CNNArch2MxNet;
-import de.monticore.lang.monticar.cnnarch.caffe2generator.CNNArch2Caffe2;
 import de.monticore.lang.monticar.cnnarch.mxnetgenerator.CNNTrain2MxNet;
+import de.monticore.lang.monticar.cnnarch.caffe2generator.CNNArch2Caffe2;
 import de.monticore.lang.monticar.cnnarch.caffe2generator.CNNTrain2Caffe2;
 import de.monticore.lang.monticar.emadl.generator.reinforcementlearning.RewardFunctionCppGenerator;
+import de.monticore.lang.monticar.cnnarch.tensorflowgenerator.CNNArch2Tensorflow;
+import de.monticore.lang.monticar.cnnarch.tensorflowgenerator.CNNTrain2Tensorflow;
 
 import java.util.Optional;
 
@@ -44,7 +46,16 @@ public enum Backend {
             return new CNNTrain2Gluon(new RewardFunctionCppGenerator());
         }
     };
-
+    TENSORFLOW{
+        @Override
+        public CNNArchGenerator getCNNArchGenerator() {
+            return new CNNArch2Tensorflow();
+        }
+        @Override
+        public CNNTrainGenerator getCNNTrainGenerator() {
+            return new CNNTrain2Tensorflow();
+        }
+    };
     public abstract CNNArchGenerator getCNNArchGenerator();
     public abstract CNNTrainGenerator getCNNTrainGenerator();
 
@@ -55,9 +66,12 @@ public enum Backend {
 
             case "CAFFE2":
                 return Optional.of(CAFFE2);
-
+                
             case "GLUON":
                 return Optional.of(GLUON);
+                
+            case "TENSORFLOW":
+                return Optional.of(TENSORFLOW);
 
             default:
                 return Optional.empty();
@@ -70,6 +84,8 @@ public enum Backend {
                 return "CAFFE2";
             case GLUON:
                 return "GLUON";
+            case TENSORFLOW:
+                return "TENSORFLOW";
             default:
                 return "MXNET";
         }

src/main/java/de/monticore/lang/monticar/emadl/generator/EMADLGenerator.java

@@ -239,6 +239,7 @@ public class EMADLGenerator {
             String b = backend.getBackendString(backend);
             String trainingDataHash = "";
             String testDataHash = "";
+            
             if (architecture.get().getDataPath() != null) {
                 if (b.equals("CAFFE2")) {
                     trainingDataHash = getChecksumForFile(architecture.get().getDataPath() + "/train_lmdb/data.mdb");

src/main/java/de/monticore/lang/monticar/emadl/generator/EMADLGeneratorCli.java

@@ -52,7 +52,7 @@ public class EMADLGeneratorCli {
             .build();
     public static final Option OPTION_BACKEND = Option.builder("b")
             .longOpt("backend")
-            .desc("deep-learning-framework backend. Options: MXNET, CAFFE2, GLUON")
+            .desc("deep-learning-framework backend. Options: MXNET, CAFFE2, GLUON, TENSORFLOW")
             .hasArg(true)
             .required(false)
             .build();

train.log

+Loading checkpoint: model-0200.params
+Epoch[200] Train-accuracy=0.100000
+Epoch[200] Time cost=0.025
+Epoch[200] Validation-accuracy=0.300000
+Epoch[201] Train-accuracy=0.400000
+Epoch[201] Time cost=0.016
+Epoch[201] Validation-accuracy=0.200000
+Epoch[202] Train-accuracy=0.200000
+Epoch[202] Time cost=0.016
+Epoch[202] Validation-accuracy=0.100000
+Epoch[203] Train-accuracy=0.700000
+Epoch[203] Time cost=0.016
+Epoch[203] Validation-accuracy=0.300000
+Epoch[204] Train-accuracy=1.000000
+Epoch[204] Time cost=0.017
+Saved checkpoint to "model/instanceTestCifar.CifarNetwork/model-0205.params"
+Epoch[204] Validation-accuracy=0.200000
+Epoch[205] Train-accuracy=0.300000
+Epoch[205] Time cost=0.018
+Epoch[205] Validation-accuracy=0.300000
+Epoch[206] Train-accuracy=0.900000
+Epoch[206] Time cost=0.017
+Epoch[206] Validation-accuracy=0.300000
+Epoch[207] Train-accuracy=0.900000
+Epoch[207] Time cost=0.017
+Epoch[207] Validation-accuracy=0.300000
+Epoch[208] Train-accuracy=0.800000
+Epoch[208] Time cost=0.016
+Epoch[208] Validation-accuracy=0.200000
+Epoch[209] Train-accuracy=0.800000
+Epoch[209] Time cost=0.024
+Saved checkpoint to "model/instanceTestCifar.CifarNetwork/model-0210.params"
+Epoch[209] Validation-accuracy=0.300000
+Saved checkpoint to "model/instanceTestCifar.CifarNetwork/model-0210.params"
+Saved checkpoint to "model/instanceTestCifar.CifarNetwork/model_newest-0000.params"