Some fixes: correct CMakeLists.txt generation for tensorflow dependencies and...

Some fixes: correct CMakeLists.txt generation for tensorflow dependencies and the gloun part in backend.java and pom.xml added

.gitlab-ci.yml

@@ -37,6 +37,12 @@ integrationGluonJobLinux:
   script:
   - mvn -Dorg.slf4j.simpleLogger.log.org.apache.maven.cli.transfer.Slf4jMavenTransferListener=warn -B  clean install --settings settings.xml -Dtest=IntegrationGluonTest
 
+integrationTensorflowJobLinux:
+  stage: linux
+  image: registry.git.rwth-aachen.de/monticore/embeddedmontiarc/generators/emadl2cpp/integrationtests/tensorflow
+  script:
+  - mvn -Dorg.slf4j.simpleLogger.log.org.apache.maven.cli.transfer.Slf4jMavenTransferListener=warn -B  clean install --settings settings.xml -Dtest=IntegrationTensorflowTest
+
 
 integrationPythonWrapperTest:
   stage: linux

README.md

 <!-- (c) https://github.com/MontiCore/monticore -->
 ![pipeline](https://git.rwth-aachen.de/monticore/EmbeddedMontiArc/generators/EMADL2CPP/badges/master/build.svg)
 ![coverage](https://git.rwth-aachen.de/monticore/EmbeddedMontiArc/generators/EMADL2CPP/badges/master/coverage.svg)
+
 # EMADL2CPP
 Generates CPP/Python code for EmbeddedMontiArcDL.
 See example project [EMADL-Demo](https://git.rwth-aachen.de/thomas.timmermanns/EMADL-Demo) for more information on how the generated code can be used.
@@ -20,7 +21,25 @@ See example project [EMADL-Demo](https://git.rwth-aachen.de/thomas.timmermanns/E
      
      * Caffe2
         * training - generated is Python code. Follow [ official instructions on Caffe2 site ](https://caffe2.ai/docs/getting-started.html?platform=ubuntu&configuration=prebuilt)
-     * Gluon
+        * See the scripts under Installation for better instructions, as an old caffe vversion is used that needs special considerations.
+        
+	 * Gluon
+	 
+	 * Tensorflow
+	 	* training - generated is Python code.
+		* prediction - generated code is C++.
+
+## Installation
+The two bash scripts found under [installation scripts](https://git.rwth-aachen.de/monticore/EmbeddedMontiArc/generators/EMADL2CPP/tree/tensorflow_group/src/main/resources/installation_scripts)
+should build and install all prerequisits for all backends as of 26.09.2019.
+Note that the installation may take some time (hours) and you will need some disk space (> 60GB) for all backends. Also enough RAM or a big
+enough swapspace is advisable (>10GB) for the installation of the cpp part of tensorflow. This scripts were tested with a completly clean Ubuntu 16.04,
+without system updates installed. Using another Ubuntu version or installing other stuff, system updates included might/ have caused problems.
+If you want to install the backends with CUDA GPU support(only MXNet/Gluon and Tensorflow, the used caffe2 version does not work with GPU support anymore), 
+you have to install CUDA 10.0(!!), CUDNN and NCCL (Obtainable from the nvidai webpage. You can follow their instructions.) inbetween the two scripts. 
+Furthermore you will have to change the pip commands for mxnet and tensorflow to the respective commented out parts. 
+Also docker images for the cpu version of each backend are provided at [Docker images](https://git.rwth-aachen.de/monticore/EmbeddedMontiArc/generators/EMADL2CPP/tree/tensorflow_group/src/test/resources/docker), 
+though some of these might be outdated.
 
 ### HowTo
 1. Define a EMADL component containing architecture of a neural network and save it in a `.emadl` file. For more information on architecture language please refer to [CNNArchLang project](https://git.rwth-aachen.de/monticore/EmbeddedMontiArc/languages/CNNArchLang). An example of NN architecture:

pom.xml

@@ -22,6 +22,7 @@
     <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>0.1.0-SNAPSHOT</cnnarch-tensorflow-generator.version>
     <embedded-montiarc-math-opt-generator>0.1.4</embedded-montiarc-math-opt-generator>
 
         <!-- .. Libraries .................................................. -->
@@ -81,11 +82,17 @@
             <version>${cnnarch-mxnet-generator.version}</version>
         </dependency>
 
-        <dependency>
+		<dependency>
             <groupId>de.monticore.lang.monticar</groupId>
             <artifactId>cnnarch-gluon-generator</artifactId>
             <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>

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

 /* (c) https://github.com/MontiCore/monticore */
 package de.monticore.lang.monticar.emadl.generator;
 
-
 import de.monticore.lang.monticar.cnnarch.generator.CNNArchGenerator;
 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;
 
@@ -43,9 +44,18 @@ public enum Backend {
         @Override
         public CNNTrainGenerator getCNNTrainGenerator() {
             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();
 
@@ -56,9 +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();
@@ -69,8 +82,13 @@ public enum Backend {
         switch (backend){
             case CAFFE2:
                 return "CAFFE2";
+                
             case GLUON:
                 return "GLUON";
+                
+            case TENSORFLOW:
+                return "TENSORFLOW";
+                
             default:
                 return "MXNET";
         }

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

@@ -44,6 +44,8 @@ import java.security.MessageDigest;
 import java.security.NoSuchAlgorithmException;
 import java.util.*;
 
+import java.io.File;
+
 public class EMADLGenerator {
 
     private GeneratorEMAMOpt2CPP emamGen;
@@ -181,6 +183,15 @@ public class EMADLGenerator {
         }
     }
 
+    public String getChecksumForLargerFile(String filePath) throws IOException {
+        try {
+            return (new File(filePath)).lastModified() + "";
+        } catch (Exception e) {
+            e.printStackTrace();
+            return "Exception_calculating_hash_large_file";
+        }
+    }
+
     public void generateFiles(TaggingResolver taggingResolver, EMAComponentInstanceSymbol EMAComponentSymbol, Scope symtab, String pythonPath, String forced) throws IOException {
         Set<EMAComponentInstanceSymbol> allInstances = new HashSet<>();
         List<FileContent> fileContents = generateStrings(taggingResolver, EMAComponentSymbol, symtab, allInstances, forced);
@@ -220,15 +231,16 @@ 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");
-                    testDataHash = getChecksumForFile(architecture.get().getDataPath() + "/test_lmdb/data.mdb");
-                } else {
-                    trainingDataHash = getChecksumForFile(architecture.get().getDataPath() + "/train.h5");
-                    testDataHash = getChecksumForFile(architecture.get().getDataPath() + "/test.h5");
-                }
-            }
+                    trainingDataHash = getChecksumForLargerFile(architecture.get().getDataPath() + "/train_lmdb/data.mdb");
+                    testDataHash = getChecksumForLargerFile(architecture.get().getDataPath() + "/test_lmdb/data.mdb");
+            	}else{
+                    trainingDataHash = getChecksumForLargerFile(architecture.get().getDataPath() + "/train.h5");
+                    testDataHash = getChecksumForLargerFile(architecture.get().getDataPath() + "/test.h5");
+            	}
+			}
             String trainingHash = emadlHash + "#" + cnntHash + "#" + trainingDataHash + "#" + testDataHash;
 
             boolean alreadyTrained = newHashes.contains(trainingHash) || isAlreadyTrained(trainingHash, componentInstance);

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

@@ -31,9 +31,10 @@ public class EMADLGeneratorCli {
             .hasArg(true)
             .required(false)
             .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();

src/main/resources/installation_scripts/install_after_cuda

+#!/bin/bash
+############################ Part 2/2 ######################################
+# This script should help you to get ready to do machine learning          #
+# at SE at RWTH, installing all backends (mxnet/gluon, tensorflow, caffe2. #
+#         - Jonas Eckhardt & Julian Steinsberger-Dührßen -                 #
+############################################################################
+# It is based both on instrcutions and docker files from the git
+# repos as well as our own work
+
+############################################################################
+# Requirements:                                                            #
+############################################################################
+# -Git set up to work with RWTH Gitlab
+# -Virgin Ubuntu 16.04.(No touching, no other version) and not isntalling the system updates
+# -Time: Installation may need more than one hour. It may prompt you
+#        for your passowrd multiple times
+# -Disable Screensaver: It just saves you some time
+# - > 10GB RAM or a swapfile so you have ram+swap > 10GB
+# - > 60GB disk space
+
+###########################################################################
+# Usage:                                                                  #
+###########################################################################
+# chmod u+x installer_bomb.sh
+# ./installer_bomb.sh 
+# Dont use twice. It will fill your ~/.bashrc with garbage#!/bin/bash
+
+sudo apt install -y --no-install-recommends cuda-command-line-tools-10-0
+
+#Install MXNet
+pip install mxnet --user
+#for cuda support: use mxnet-cu100 --user instead
+
+git clone https://github.com/apache/incubator-mxnet.git mxnet-source
+cd mxnet-source
+git checkout tags/1.4.0
+cd ..
+sudo cp -r mxnet-source/include/mxnet /usr/include/mxnet
+
+#Test mxnet
+git clone git@git.rwth-aachen.de:monticore/EmbeddedMontiArc/applications/mnistcalculator.git
+cd mnistcalculator
+cd mxnet
+./build.sh
+cd ..
+cd ..
+
+
+#Install OpenCV
+git clone https://github.com/opencv/opencv.git
+cd opencv 
+git checkout 3.3.1 
+cd ..
+git clone https://github.com/opencv/opencv_contrib.git
+cd opencv_contrib
+git checkout 3.3.1
+cd ..
+cd opencv
+mkdir build
+cd build
+cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local \
+     		      -D INSTALL_C_EXAMPLES=ON \
+      		      -D INSTALL_PYTHON_EXAMPLES=ON \
+      		      -D WITH_TBB=ON \
+      		      -D WITH_V4L=ON \
+      		      -D WITH_QT=ON \
+      		      -D WITH_OPENGL=ON \
+      		      -D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules \
+      		      -D BUILD_EXAMPLES=ON ..
+make -j$(nproc)
+sudo make install
+sudo sh -c 'echo "/usr/local/lib" >> /etc/ld.so.conf.d/opencv.conf'
+sudo ldconfig
+
+
+#Install Tensorflow
+pip install --upgrade tensorflow --user
+#for cuda support: pip install --upgrade tensorflow-gpu --user
+ 
+git clone https://github.com/FloopCZ/tensorflow_cc.git
+
+#Install bazel
+sudo wget -P /tmp https://github.com/bazelbuild/bazel/releases/download/0.21.0/bazel-0.21.0-installer-linux-x86_64.sh
+
+sudo chmod +x /tmp/bazel-0.21.0-installer-linux-x86_64.sh
+sudo /tmp/bazel-0.21.0-installer-linux-x86_64.sh
+sudo rm /tmp/bazel-0.21.0-installer-linux-x86_64.sh
+
+sudo apt-get -y clean
+sudo updatedb
+
+cd tensorflow_cc/tensorflow_cc/
+mkdir build
+cd build
+cmake -DTENSORFLOW_STATIC=OFF -DTENSORFLOW_SHARED=ON ..
+make
+sudo rm -rf /home/tensorflow_cc/.cache
+make install
+cd ..
+sudo rm -rf build
\ No newline at end of file

src/main/resources/installation_scripts/install_before_cuda

+#!/bin/bash
+############################ Part 1/2 ######################################
+# This script should help you to get ready to do machine learning          #
+# at SE at RWTH, installing all backends (mxnet/gluon, tensorflow, caffe2. #
+#         - Jonas Eckhardt & Julian Steinsberger-Dührßen -                 #
+############################################################################
+# It is based both on instrcutions and docker files from the git
+# repos as well as our own work
+
+############################################################################
+# Requirements:                                                            #
+############################################################################
+# -Git set up to work with RWTH Gitlab
+# -Virgin Ubuntu 16.04.(No touching, no other version) and not isntalling the system updates
+# -Time: Installation may need more than one hour. It may prompt you
+#        for your passowrd multiple times
+# -Disable Screensaver: It just saves you some time
+# - > 10GB RAM or a swapfile so you have ram+swap > 10GB
+# - > 60GB disk space
+
+###########################################################################
+# Usage:                                                                  #
+###########################################################################
+# chmod u+x installer_bomb.sh
+# ./installer_bomb.sh 
+# Dont use twice. It will fill your ~/.bashrc with garbage
+
+sudo apt update
+sudo apt upgrade -y
+sudo apt install -y --no-install-recommends maven libopencv-dev\
+ python-pip openjdk-8-jdk git wget python python3-numpy gcc build-essential cmake \
+ liblapack-dev libblas-dev libopenblas-dev libboost-dev libarmadillo-dev build-essential \
+ libgoogle-glog-dev libgtest-dev libiomp-dev libleveldb-dev liblmdb-dev \
+ libopenmpi-dev libsnappy-dev libprotobuf-dev openmpi-bin openmpi-doc python-numpy swig
+
+sudo apt install -y --no-install-recommends protobuf-compiler python-dev python-setuptools  \
+ libgflags-dev checkinstall pkg-config yasm gfortran libjpeg8-dev libjasper-dev libpng12-dev \
+ libtiff5-dev libavcodec-dev libavformat-dev libswscale-dev libdc1394-22-dev libxine2-dev \
+ libgstreamer-plugins-base0.10-dev qt5-default libgtk2.0-dev libtbb-dev libatlas-base-dev \
+ libfaac-dev libmp3lame-dev libtheora-dev libvorbis-dev libxvidcore-dev \
+ libopencore-amrnb-dev libopencore-amrwb-dev x264 v4l-utils libgphoto2-dev libeigen3-dev \
+ libhdf5-dev doxygen curl unzip autoconf autogen libtool mlocate zlib1g-dev g++ sudo zip automake
+
+
+#Fix JDK version
+sudo update-alternatives --config java
+
+pip install --upgrade pip --user
+pip install numpy opencv-python six lmdb graphviz pyyaml future typing --user
+pip install --no-deps h5py --user
+
+#prepare path variables
+echo "export PATH=\$PATH:\"/usr/local/lib\"" >> ~/.bashrc
+echo "export PYTHON_PATH=\"/usr/local:$(pwd)/pytorch/build:/usr/bin/python:/usr/local/lib\"">> ~/.bashrc
+echo "export LD_LIBRARY_PATH=\"/usr/local/lib\"">> ~/.bashrc
+source ~/.bashrc
+
+
+#Install caffee2
+git clone --recursive https://github.com/pytorch/pytorch.git
+cd pytorch
+git checkout v0.4.0
+git -c submodule.'third_party/nervanagpu'.update=none submodule update --init
+sudo rm -r build
+mkdir build
+cd build
+cmake -DUSE_MPI=OFF ..
+sudo make clean install
+cd ..
+sudo FULL_CAFFE2=1 python setup.py clean install
+sudo cp -r /usr/local/lib/libcaffe2.so /usr/lib/
+cd ..
+
+#Fix protocbuf
+pip2 install protobuf==3.5.1 --user
+
+
+sudo snap install --classic notepadqq
+sudo snap install pycharm-community --classic
\ No newline at end of file

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

@@ -139,6 +139,29 @@ public class GenerationTest extends AbstractSymtabTest {
                         "CNNTrainer_mnist_mnistClassifier_net.py"));
     }
 
+    @Test
+    public void testMnistClassifierForTensorflow() throws IOException, TemplateException {
+        Log.getFindings().clear();
+        String[] args = {"-m", "src/test/resources/models/", "-r", "mnist.MnistClassifier", "-b", "TENSORFLOW", "-f", "n", "-c", "n"};
+        EMADLGeneratorCli.main(args);
+        assertTrue(Log.getFindings().isEmpty());
+
+        checkFilesAreEqual(
+                Paths.get("./target/generated-sources-emadl"),
+                Paths.get("./src/test/resources/target_code/tensorflow"),
+                Arrays.asList(
+                        "mnist_mnistClassifier.cpp",
+                        "mnist_mnistClassifier.h",
+                        "CNNCreator_mnist_mnistClassifier_net.py",
+                        "CNNPredictor_mnist_mnistClassifier_net.h",
+                        "CNNDataLoader_mnist_mnistClassifier_net.py",
+                        "mnist_mnistClassifier_net.h",
+                        "HelperA.h",
+                        "CNNTranslator.h",
+                        "mnist_mnistClassifier_calculateClass.h",
+                        "CNNTrainer_mnist_mnistClassifier_net.py"));
+    }
+ 
     @Test
     public void testMnistClassifierForGluon() throws IOException, TemplateException {
         Log.getFindings().clear();
@@ -181,7 +204,7 @@ public class GenerationTest extends AbstractSymtabTest {
         EMADLGeneratorCli.main(args);
         assertTrue(Log.getFindings().size() == 0);
     }
-
+    
     @Test
     public void testGluonReinforcementModelGymEnvironment() {
         Log.getFindings().clear();
@@ -215,7 +238,7 @@ public class GenerationTest extends AbstractSymtabTest {
                 )
         );
     }
-
+    
     @Test
     public void testHashFunction() {
         EMADLGenerator tester = new EMADLGenerator(Backend.MXNET);

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

+/**
+ *
+ *  ******************************************************************************
+ *  MontiCAR Modeling Family, www.se-rwth.de
+ *  Copyright (c) 2017, Software Engineering Group at RWTH Aachen,
+ *  All rights reserved.
+ *
+ *  This project is free software; you can redistribute it and/or
+ *  modify it under the terms of the GNU Lesser General Public
+ *  License as published by the Free Software Foundation; either
+ *  version 3.0 of the License, or (at your option) any later version.
+ *  This library is distributed in the hope that it will be useful,
+ *  but WITHOUT ANY WARRANTY; without even the implied warranty of
+ *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
+ *  Lesser General Public License for more details.
+ *
+ *  You should have received a copy of the GNU Lesser General Public
+ *  License along with this project. If not, see <http://www.gnu.org/licenses/>.
+ * *******************************************************************************
+ */
+package de.monticore.lang.monticar.emadl;
+
+import de.monticore.lang.monticar.emadl.generator.EMADLGeneratorCli;
+import de.se_rwth.commons.logging.Log;
+import org.junit.Ignore;
+import org.junit.Test;
+
+import java.nio.file.Files;
+import java.nio.file.NoSuchFileException;
+import java.nio.file.Path;
+import java.nio.file.Paths;
+
+import static junit.framework.TestCase.assertTrue;
+import static org.junit.Assert.assertFalse;
+
+public class IntegrationTensorflowTest extends IntegrationTest {
+
+    private Path multipleStreamsHashFile = Paths.get("./target/generated-sources-emadl/MultipleStreams.training_hash");
+
+    public IntegrationTensorflowTest() {
+        super("TENSORFLOW", "39253EC049D4A4E5FA0536AD34874B9D#1DBAEE1B1BD83FB7CB5F70AE91B29638#C4C23549E737A759721D6694C75D9771#5AF0CE68E408E8C1F000E49D72AC214A");
+    }
+
+    @Test
+    public void testMultipleStreams() {
+        Log.getFindings().clear();
+
+        deleteHashFile(multipleStreamsHashFile);
+
+        String[] args = {"-m", "src/test/resources/models/", "-r", "MultipleStreams", "-b", "TENSORFLOW"};
+        EMADLGeneratorCli.main(args);
+
+        assertTrue(Log.getFindings().isEmpty());
+    }
+
+    private void deleteHashFile(Path hashFile) {
+        try {
+            Files.delete(hashFile);
+        }
+        catch (NoSuchFileException e) {
+
+        }
+        catch(Exception e) {
+            assertFalse("Could not delete hash file", true);
+        }
+    }
+}

src/test/resources/docker/tensorflow/Dockerfile

+FROM floopcz/tensorflow_cc:ubuntu-shared
+
+#Install everything needed with apt
+RUN apt-get update
+ARG DEBIAN_FRONTEND=noninteractive
+RUN apt-get install -y --no-install-recommends \
+        tzdata \
+        git \
+        wget python gcc \
+        build-essential cmake \
+        maven unzip\
+        python3 python3-pip python-dev python-setuptools python python-pip\
+        libopencv-dev libarmadillo-dev
+
+#Fix Java version
+RUN apt remove -y *openjdk*
+RUN apt-get install -y --no-install-recommends openjdk-8-jdk
+
+#Install stuff for python and python3
+RUN pip install tensorflow==1.13.1
+RUN pip3 install opencv-python h5py numpy matplotlib scipy Pillow

src/test/resources/models/MultipleStreams.emadl

@@ -5,8 +5,8 @@ component MultipleStreams{
 
     implementation CNN {
         data[0] ->
-        Convolution(kernel=(5,5), channels=8, padding="valid") ->
-        Convolution(kernel=(5,5), channels=8, padding="valid") ->
+        Convolution(kernel=(5,5), channels=8) ->
+        Convolution(kernel=(5,5), channels=8) ->
         FullyConnected(units=128) ->
         Dropout()->
         FullyConnected(units=10) ->
@@ -14,8 +14,8 @@ component MultipleStreams{
         softmax[0];
 
         data[1] ->
-        Convolution(kernel=(5,5), channels=8, padding="valid") ->
-        Convolution(kernel=(5,5), channels=8, padding="valid") ->
+        Convolution(kernel=(5,5), channels=8) ->
+        Convolution(kernel=(5,5), channels=8) ->
         FullyConnected(units=128) ->
         Dropout()->
         FullyConnected(units=10) ->

src/test/resources/models/cNNCalculator/Network.emadl

@@ -7,10 +7,10 @@ component Network{
 
     implementation CNN {
         image ->
-        Convolution(kernel=(5,5), channels=20, padding="valid") ->
-        Pooling(pool_type="max", kernel=(2,2), stride=(2,2), padding="valid") ->
-        Convolution(kernel=(5,5), channels=50, padding="valid") ->
-        Pooling(pool_type="max", kernel=(2,2), stride=(2,2), padding="valid") ->
+        Convolution(kernel=(5,5), channels=20) ->
+        Pooling(pool_type="max", kernel=(2,2), stride=(2,2)) ->
+        Convolution(kernel=(5,5), channels=50) ->
+        Pooling(pool_type="max", kernel=(2,2), stride=(2,2)) ->
         FullyConnected(units=500) ->
         Relu() ->
         FullyConnected(units=10) ->