Merge branch 'develop' into 'master'

Implemented layer variables and RNN layer

See merge request !25

pom.xml

@@ -15,12 +15,12 @@
   <properties>
 
     <!-- .. SE-Libraries .................................................. -->
-    <emadl.version>0.2.8-SNAPSHOT</emadl.version>
+    <emadl.version>0.2.9-SNAPSHOT</emadl.version>
     <CNNTrain.version>0.3.6-SNAPSHOT</CNNTrain.version>
-    <cnnarch-generator.version>0.0.2-SNAPSHOT</cnnarch-generator.version>
-    <cnnarch-mxnet-generator.version>0.2.16-SNAPSHOT</cnnarch-mxnet-generator.version>
-    <cnnarch-caffe2-generator.version>0.2.12-SNAPSHOT</cnnarch-caffe2-generator.version>
-    <cnnarch-gluon-generator.version>0.2.6-SNAPSHOT</cnnarch-gluon-generator.version>
+    <cnnarch-generator.version>0.0.3-SNAPSHOT</cnnarch-generator.version>
+    <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>0.2.7-SNAPSHOT</cnnarch-gluon-generator.version>
     <embedded-montiarc-math-opt-generator>0.1.4</embedded-montiarc-math-opt-generator>
 
         <!-- .. Libraries .................................................. -->

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

@@ -448,7 +448,7 @@ public class EMADLGenerator {
 
         int i = 0;
         for (SerialCompositeElementSymbol stream : architecture.getStreams()) {
-            if (stream.isNetwork()) {
+            if (stream.isTrainable()) {
                 networkAttributes += "\n" + predictorClassName + "_" + i + " _predictor_" + i + "_;";
             }
 

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

@@ -198,6 +198,14 @@ public class GenerationTest extends AbstractSymtabTest {
         assertTrue(Log.getFindings().size() == 0);
     }
 
+    @Test
+    public void testRNNtestForGluon() throws IOException, TemplateException {
+        Log.getFindings().clear();
+        String[] args = {"-m", "src/test/resources/models/", "-r", "RNNtest", "-b", "GLUON", "-f", "n", "-c", "n"};
+        EMADLGeneratorCli.main(args);
+        assertTrue(Log.getFindings().size() == 0);
+    }
+
     @Test
     public void testGluonReinforcementModelGymEnvironment() {
         Log.getFindings().clear();

src/test/resources/models/RNNtest.cnnt

+configuration RNNtest{
+     num_epoch:10
+     batch_size:5
+     context:cpu
+     optimizer:adam{
+         learning_rate:0.01
+         learning_rate_decay:0.8
+         step_size:1000
+         weight_decay:0.0001
+     }
+}

src/test/resources/models/RNNtest.emadl

+component RNNtest{
+    ports in Q(-oo:oo)^{50, 30001} source[2],
+         out Q(-oo:oo)^{50, 30001} target[2];
+
+    implementation CNN {
+        layer RNN(units=500, layers=2) encoder;
+        layer RNN(units=500, layers=2) decoder;
+
+        source[0] ->
+        encoder;
+
+        encoder.output ->
+        target[0];
+
+        encoder.state ->
+        decoder.state;
+
+        source[1] ->
+        decoder ->
+        target[1];
+    }
+}
\ No newline at end of file

src/test/resources/models/data_paths.txt

@@ -11,4 +11,5 @@ mnist.LeNetNetwork data/mnist.LeNetNetwork
 MultipleInputs src/test/resources/training_data/MultipleInputs
 MultipleOutputs src/test/resources/training_data/MultipleOutputs
 MultipleStreams src/test/resources/training_data/MultipleStreams
-Invariant src/test/resources/training_data/Invariant
\ No newline at end of file
+Invariant src/test/resources/training_data/Invariant
+RNNtest data/RNNtest
\ No newline at end of file

src/test/resources/target_code/CNNCreator_cifar10_cifar10Classifier_net.py

@@ -21,9 +21,12 @@ class CNNCreator_cifar10_cifar10Classifier_net:
     _data_dir_ = "src/test/resources/training_data/Cifar/"
     _model_dir_ = "model/cifar10.CifarNetwork/"
     _model_prefix_ = "model"
-    _input_names_ = ['data']
+    _input_names_ = ['data_']
     _input_shapes_ = [(3,32,32)]
-    _output_names_ = ['softmax_label']
+    _output_names_ = ['softmax__label']
+    _input_data_names_ = ['data']
+    _output_data_names_ = ['softmax_label']
+
 
 
     def load(self, context):
@@ -62,18 +65,18 @@ class CNNCreator_cifar10_cifar10Classifier_net:
     def load_data(self, batch_size):
         train_h5, test_h5 = self.load_h5_files()
 
-        data_mean = train_h5[self._input_names_[0]][:].mean(axis=0)
-        data_std = train_h5[self._input_names_[0]][:].std(axis=0) + 1e-5
+        data_mean = train_h5[self._input_data_names_[0]][:].mean(axis=0)
+        data_std = train_h5[self._input_data_names_[0]][:].std(axis=0) + 1e-5
 
-        train_iter = mx.io.NDArrayIter(train_h5[self._input_names_[0]],
-                                       train_h5[self._output_names_[0]],
+        train_iter = mx.io.NDArrayIter(train_h5[self._input_data_names_[0]],
+                                       train_h5[self._output_data_names_[0]],
                                        batch_size=batch_size,
                                        data_name=self._input_names_[0],
                                        label_name=self._output_names_[0])
         test_iter = None
         if test_h5 != None:
-            test_iter = mx.io.NDArrayIter(test_h5[self._input_names_[0]],
-                                          test_h5[self._output_names_[0]],
+            test_iter = mx.io.NDArrayIter(test_h5[self._input_data_names_[0]],
+                                          test_h5[self._output_data_names_[0]],
                                           batch_size=batch_size,
                                           data_name=self._input_names_[0],
                                           label_name=self._output_names_[0])
@@ -86,16 +89,16 @@ class CNNCreator_cifar10_cifar10Classifier_net:
         test_path = self._data_dir_ + "test.h5"
         if os.path.isfile(train_path):
             train_h5 = h5py.File(train_path, 'r')
-            if not (self._input_names_[0] in train_h5 and self._output_names_[0] in train_h5):
+            if not (self._input_data_names_[0] in train_h5 and self._output_data_names_[0] in train_h5):
                 logging.error("The HDF5 file '" + os.path.abspath(train_path) + "' has to contain the datasets: "
-                              + "'" + self._input_names_[0] + "', '" + self._output_names_[0] + "'")
+                              + "'" + self._input_data_names_[0] + "', '" + self._output_data_names_[0] + "'")
                 sys.exit(1)
             test_iter = None
             if os.path.isfile(test_path):
                 test_h5 = h5py.File(test_path, 'r')
-                if not (self._input_names_[0] in test_h5 and self._output_names_[0] in test_h5):
+                if not (self._input_data_names_[0] in test_h5 and self._output_data_names_[0] in test_h5):
                     logging.error("The HDF5 file '" + os.path.abspath(test_path) + "' has to contain the datasets: "
-                                  + "'" + self._input_names_[0] + "', '" + self._output_names_[0] + "'")
+                                  + "'" + self._input_data_names_[0] + "', '" + self._output_data_names_[0] + "'")
                     sys.exit(1)
             else:
                 logging.warning("Couldn't load test set. File '" + os.path.abspath(test_path) + "' does not exist.")
@@ -254,9 +257,9 @@ class CNNCreator_cifar10_cifar10Classifier_net:
 
 
     def construct(self, context, data_mean=None, data_std=None):
-        data = mx.sym.var("data",
+        data_ = mx.sym.var("data_",
             shape=(0,3,32,32))
-        # data, output shape: {[3,32,32]}
+        # data_, output shape: {[3,32,32]}
 
         if not data_mean is None:
             assert(not data_std is None)
@@ -264,9 +267,9 @@ class CNNCreator_cifar10_cifar10Classifier_net:
             _data_mean_ = mx.sym.BlockGrad(_data_mean_)
             _data_std_ = mx.sym.Variable("_data_std_", shape=(3,32,32), init=MyConstant(value=data_mean.tolist()))
             _data_std_ = mx.sym.BlockGrad(_data_std_)
-            data = mx.symbol.broadcast_sub(data, _data_mean_)
-            data = mx.symbol.broadcast_div(data, _data_std_)
-        conv2_1_ = mx.symbol.pad(data=data,
+            data_ = mx.symbol.broadcast_sub(data_, _data_mean_)
+            data_ = mx.symbol.broadcast_div(data_, _data_std_)
+        conv2_1_ = mx.symbol.pad(data=data_,
             mode='constant',
             pad_width=(0,0,0,0,1,1,1,1),
             constant_value=0)
@@ -300,7 +303,7 @@ class CNNCreator_cifar10_cifar10Classifier_net:
         batchnorm3_1_ = mx.symbol.BatchNorm(data=conv3_1_,
             fix_gamma=True,
             name="batchnorm3_1_")
-        conv2_2_ = mx.symbol.Convolution(data=data,
+        conv2_2_ = mx.symbol.Convolution(data=data_,
             kernel=(1,1),
             stride=(1,1),
             num_filter=8,
@@ -741,10 +744,10 @@ class CNNCreator_cifar10_cifar10Classifier_net:
         softmax32_ = mx.symbol.softmax(data=fc32_,
             axis=1,
             name="softmax32_")
-        softmax = mx.symbol.SoftmaxOutput(data=softmax32_,
-            name="softmax")
+        softmax_ = mx.symbol.SoftmaxOutput(data=softmax32_,
+            name="softmax_")
 
-        self.module = mx.mod.Module(symbol=mx.symbol.Group([softmax]),
+        self.module = mx.mod.Module(symbol=mx.symbol.Group([softmax_]),
                                          data_names=self._input_names_,
                                          label_names=self._output_names_,
                                          context=context)

src/test/resources/target_code/CNNCreator_mnist_mnistClassifier_net.py

@@ -72,9 +72,9 @@ class CNNCreator_mnist_mnistClassifier_net:
     def create_model(self, model, data, device_opts, is_test):
     	with core.DeviceScope(device_opts):
 
-    		image = data
-    		# image, output shape: {[1,28,28]}
-    		conv1_ = brew.conv(model, image, 'conv1_', dim_in=1, dim_out=20, kernel=5, stride=1)
+    		image_ = data
+    		# image_, output shape: {[1,28,28]}
+    		conv1_ = brew.conv(model, image_, 'conv1_', dim_in=1, dim_out=20, kernel=5, stride=1)
     		# conv1_, output shape: {[20,24,24]}
     		pool1_ = brew.max_pool(model, conv1_, 'pool1_', kernel=2, stride=2)
     		# pool1_, output shape: {[20,12,12]}
@@ -87,9 +87,9 @@ class CNNCreator_mnist_mnistClassifier_net:
     		relu2_ = brew.relu(model, fc2_, fc2_)
     		fc3_ = brew.fc(model, relu2_, 'fc3_', dim_in=500, dim_out=10)
     		# fc3_, output shape: {[10,1,1]}
-    		predictions = brew.softmax(model, fc3_, 'predictions')
+    		predictions_ = brew.softmax(model, fc3_, 'predictions_')
 
-    		return predictions
+    		return predictions_
 
     # this adds the loss and optimizer
     def add_training_operators(self, model, output, label, device_opts, loss, opt_type, base_learning_rate, policy, stepsize, epsilon, beta1, beta2, gamma, momentum) :
@@ -150,10 +150,10 @@ class CNNCreator_mnist_mnistClassifier_net:
     	# == Training model ==
     	train_model= model_helper.ModelHelper(name="train_net", arg_scope=arg_scope)
     	data, label, train_dataset_size = self.add_input(train_model, batch_size=batch_size, db=os.path.join(self._data_dir_, 'train_lmdb'), db_type='lmdb', device_opts=device_opts)
-    	predictions = self.create_model(train_model, data, device_opts=device_opts, is_test=False)
-    	self.add_training_operators(train_model, predictions, label, device_opts, loss, opt_type, base_learning_rate, policy, stepsize, epsilon, beta1, beta2, gamma, momentum)
+    	predictions_ = self.create_model(train_model, data, device_opts=device_opts, is_test=False)
+    	self.add_training_operators(train_model, predictions_, label, device_opts, loss, opt_type, base_learning_rate, policy, stepsize, epsilon, beta1, beta2, gamma, momentum)
     	if not loss == 'euclidean':
-    		self.add_accuracy(train_model, predictions, label, device_opts, eval_metric)
+    		self.add_accuracy(train_model, predictions_, label, device_opts, eval_metric)
     	with core.DeviceScope(device_opts):
     		brew.add_weight_decay(train_model, weight_decay)
 
@@ -185,9 +185,9 @@ class CNNCreator_mnist_mnistClassifier_net:
     	# == Testing model. ==
     	test_model= model_helper.ModelHelper(name="test_net", arg_scope=arg_scope, init_params=False)
     	data, label, test_dataset_size = self.add_input(test_model, batch_size=batch_size, db=os.path.join(self._data_dir_, 'test_lmdb'), db_type='lmdb', device_opts=device_opts)
-    	predictions = self.create_model(test_model, data, device_opts=device_opts, is_test=True)
+    	predictions_ = self.create_model(test_model, data, device_opts=device_opts, is_test=True)
     	if not loss == 'euclidean':
-    		self.add_accuracy(test_model, predictions, label, device_opts, eval_metric)
+    		self.add_accuracy(test_model, predictions_, label, device_opts, eval_metric)
     	workspace.RunNetOnce(test_model.param_init_net)
     	workspace.CreateNet(test_model.net, overwrite=True)
 

src/test/resources/target_code/CNNPredictor_cifar10_cifar10Classifier_net.h

@@ -13,8 +13,9 @@ class CNNPredictor_cifar10_cifar10Classifier_net_0{
 public:
     const std::string json_file = "model/cifar10.CifarNetwork/model_newest-symbol.json";
     const std::string param_file = "model/cifar10.CifarNetwork/model_newest-0000.params";
-    //const std::vector<std::string> input_keys = {"data"};
-    const std::vector<std::string> input_keys = {"data"};
+    const std::vector<std::string> input_keys = {
+        "data"
+    };
     const std::vector<std::vector<mx_uint>> input_shapes = {{1,3,32,32}};
     const bool use_gpu = false;
 
@@ -28,10 +29,9 @@ public:
         if(handle) MXPredFree(handle);
     }
 
-    void predict(const std::vector<float> &data,
-                 std::vector<float> &softmax){
-        MXPredSetInput(handle, "data", data.data(), data.size());
-        //MXPredSetInput(handle, "data", data.data(), data.size());
+    void predict(const std::vector<float> &data_,
+                 std::vector<float> &softmax_){
+        MXPredSetInput(handle, input_keys[0].c_str(), data_.data(), data_.size());
 
         MXPredForward(handle);
 
@@ -44,8 +44,8 @@ public:
         MXPredGetOutputShape(handle, output_index, &shape, &shape_len);
         size = 1;
         for (mx_uint i = 0; i < shape_len; ++i) size *= shape[i];
-        assert(size == softmax.size());
-        MXPredGetOutput(handle, 0, &(softmax[0]), softmax.size());
+        assert(size == softmax_.size());
+        MXPredGetOutput(handle, 0, &(softmax_[0]), softmax_.size());
 
     }
 

src/test/resources/target_code/CNNPredictor_mnist_mnistClassifier_net.h

@@ -85,9 +85,9 @@ class CNNPredictor_mnist_mnistClassifier_net_0{
             input.Resize(input_shapes);
         }
 
-        void predict(const std::vector<float> &image, std::vector<float> &predictions){
+        void predict(const std::vector<float> &image_, std::vector<float> &predictions_){
             //Note: ShareExternalPointer requires a float pointer.
-            input.ShareExternalPointer((float *) image.data());
+            input.ShareExternalPointer((float *) image_.data());
 
             // Get input blob
             #ifdef USE_GPU
@@ -104,11 +104,11 @@ class CNNPredictor_mnist_mnistClassifier_net_0{
 
             // Get output blob
             #ifdef USE_GPU
-            auto predictionsBlob = TensorCPU(workSpace.GetBlob("predictions")->Get<TensorCUDA>());
+            auto predictions_Blob = TensorCPU(workSpace.GetBlob("predictions")->Get<TensorCUDA>());
             #else
-            auto predictionsBlob = workSpace.GetBlob("predictions")->Get<TensorCPU>();
+            auto predictions_Blob = workSpace.GetBlob("predictions")->Get<TensorCPU>();
             #endif
-            predictions.assign(predictionsBlob.data<float>(),predictionsBlob.data<float>() + predictionsBlob.size());
+            predictions_.assign(predictions_Blob.data<float>(),predictions_Blob.data<float>() + predictions_Blob.size());
 
             google::protobuf::ShutdownProtobufLibrary();
         }

src/test/resources/target_code/cifar10_cifar10Classifier_net.h

@@ -19,12 +19,12 @@ data = icube(3, 32, 32);
 softmax=colvec(classes);
 }
 void execute(){
-    vector<float> CNN_softmax(10);
+    vector<float> CNN_softmax_(10);
 
     _predictor_0_.predict(CNNTranslator::translate(data),
-                CNN_softmax);
+                CNN_softmax_);
 
-    softmax = CNNTranslator::translateToCol(CNN_softmax, std::vector<size_t> {10});
+    softmax = CNNTranslator::translateToCol(CNN_softmax_, std::vector<size_t> {10});
 
 }
 

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

@@ -50,7 +50,7 @@ class CNNCreator_mnist_mnistClassifier_net:
         self.networks[0] = Net_0(data_mean=data_mean, data_std=data_std)
         self.networks[0].collect_params().initialize(self.weight_initializer, ctx=context)
         self.networks[0].hybridize()
-        self.networks[0](mx.nd.zeros((1, 1,28,28,), ctx=context))
+        self.networks[0](mx.nd.zeros((1,1,28,28,), ctx=context))
 
         if not os.path.exists(self._model_dir_):
             os.makedirs(self._model_dir_)

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

@@ -21,8 +21,8 @@ class CNNDataLoader_mnist_mnistClassifier_net:
 
         for input_name in self._input_names_:
             train_data[input_name] = train_h5[input_name]
-            data_mean[input_name] = nd.array(train_h5[input_name][:].mean(axis=0))
-            data_std[input_name] = nd.array(train_h5[input_name][:].std(axis=0) + 1e-5)
+            data_mean[input_name + '_'] = nd.array(train_h5[input_name][:].mean(axis=0))
+            data_std[input_name + '_'] = nd.array(train_h5[input_name][:].std(axis=0) + 1e-5)
 
         train_label = {}
         for output_name in self._output_names_:

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

@@ -85,10 +85,10 @@ class Net_0(gluon.HybridBlock):
         with self.name_scope():
             if data_mean:
                 assert(data_std)
-                self.input_normalization_image = ZScoreNormalization(data_mean=data_mean['image'],
-                                                                               data_std=data_std['image'])
+                self.input_normalization_image_ = ZScoreNormalization(data_mean=data_mean['image_'],
+                                                                               data_std=data_std['image_'])
             else:
-                self.input_normalization_image = NoNormalization()
+                self.input_normalization_image_ = NoNormalization()
 
             self.conv1_ = gluon.nn.Conv2D(channels=20,
                 kernel_size=(5,5),
@@ -123,10 +123,9 @@ class Net_0(gluon.HybridBlock):
             self.softmax3_ = Softmax()
 
 
-    def hybrid_forward(self, F, image):
-        outputs = []
-        image = self.input_normalization_image(image)
-        conv1_ = self.conv1_(image)
+    def hybrid_forward(self, F, image_):
+        image_ = self.input_normalization_image_(image_)
+        conv1_ = self.conv1_(image_)
         pool1_ = self.pool1_(conv1_)
         conv2_ = self.conv2_(pool1_)
         pool2_ = self.pool2_(conv2_)
@@ -135,6 +134,7 @@ class Net_0(gluon.HybridBlock):
         relu2_ = self.relu2_(fc2_)
         fc3_ = self.fc3_(relu2_)
         softmax3_ = self.softmax3_(fc3_)
-        outputs.append(softmax3_)
+        predictions_ = softmax3_
+
+        return predictions_
 
-        return outputs[0]

src/test/resources/target_code/gluon/CNNPredictor_mnist_mnistClassifier_net.h

@@ -29,9 +29,9 @@ public:
         if(handle) MXPredFree(handle);
     }
 
-    void predict(const std::vector<float> &image,
-                 std::vector<float> &predictions){
-        MXPredSetInput(handle, "data", image.data(), static_cast<mx_uint>(image.size()));
+    void predict(const std::vector<float> &in_image_,
+                 std::vector<float> &out_predictions_){
+        MXPredSetInput(handle, input_keys[0].c_str(), in_image_.data(), static_cast<mx_uint>(in_image_.size()));
 
         MXPredForward(handle);
 
@@ -44,8 +44,8 @@ public:
         MXPredGetOutputShape(handle, output_index, &shape, &shape_len);
         size = 1;
         for (mx_uint i = 0; i < shape_len; ++i) size *= shape[i];
-        assert(size == predictions.size());
-        MXPredGetOutput(handle, 0, &(predictions[0]), predictions.size());
+        assert(size == out_predictions_.size());
+        MXPredGetOutput(handle, 0, &(out_predictions_[0]), out_predictions_.size());
 
     }
 

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

@@ -132,14 +132,15 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
         for epoch in range(begin_epoch, begin_epoch + num_epoch):
             train_iter.reset()
             for batch_i, batch in enumerate(train_iter):
-                image_data = batch.data[0].as_in_context(mx_context)
+                image_ = batch.data[0].as_in_context(mx_context)
                 predictions_label = batch.label[0].as_in_context(mx_context)
 
                 with autograd.record():
-                    predictions_output = self._networks[0](image_data)
+
+                    predictions_ = self._networks[0](image_)
 
                     loss = \
-                        loss_function(predictions_output, predictions_label)
+                        loss_function(predictions_, predictions_label)
 
                 loss.backward()
 
@@ -164,17 +165,18 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
             train_iter.reset()
             metric = mx.metric.create(eval_metric)
             for batch_i, batch in enumerate(train_iter):
-                image_data = batch.data[0].as_in_context(mx_context)
+                image_ = batch.data[0].as_in_context(mx_context)
 
                 labels = [
                     batch.label[0].as_in_context(mx_context)
                 ]
 
-                if True: # Fix indentation
-                    predictions_output = self._networks[0](image_data)
+                if True:
+
+                    predictions_ = self._networks[0](image_)
 
                 predictions = [
-                    mx.nd.argmax(predictions_output, axis=1)
+                    mx.nd.argmax(predictions_, axis=1)
                 ]
 
                 metric.update(preds=predictions, labels=labels)
@@ -183,17 +185,18 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
             test_iter.reset()
             metric = mx.metric.create(eval_metric)
             for batch_i, batch in enumerate(test_iter):
-                image_data = batch.data[0].as_in_context(mx_context)
+                image_ = batch.data[0].as_in_context(mx_context)
 
                 labels = [
                     batch.label[0].as_in_context(mx_context)
                 ]
 
-                if True: # Fix indentation
-                    predictions_output = self._networks[0](image_data)
+                if True:
+
+                    predictions_ = self._networks[0](image_)
 
                 predictions = [
-                    mx.nd.argmax(predictions_output, axis=1)
+                    mx.nd.argmax(predictions_, axis=1)
                 ]
 
                 metric.update(preds=predictions, labels=labels)

src/test/resources/target_code/gluon/mnist_mnistClassifier_net.h

@@ -19,12 +19,12 @@ image = icube(1, 28, 28);
 predictions=colvec(classes);
 }
 void execute(){
-    vector<float> CNN_predictions(10);
+    vector<float> image_ = CNNTranslator::translate(image);
+    vector<float> predictions_(10);
 
-    _predictor_0_.predict(CNNTranslator::translate(image),
-                CNN_predictions);
+    _predictor_0_.predict(image_, predictions_);
 
-    predictions = CNNTranslator::translateToCol(CNN_predictions, std::vector<size_t> {10});
+    predictions = CNNTranslator::translateToCol(predictions_, std::vector<size_t> {10});
 
 }
 

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

@@ -50,7 +50,7 @@ class CNNCreator_cartpole_master_dqn:
         self.networks[0] = Net_0(data_mean=data_mean, data_std=data_std)
         self.networks[0].collect_params().initialize(self.weight_initializer, ctx=context)
         self.networks[0].hybridize()
-        self.networks[0](mx.nd.zeros((1, 4,), ctx=context))
+        self.networks[0](mx.nd.zeros((1,4,), ctx=context))
 
         if not os.path.exists(self._model_dir_):
             os.makedirs(self._model_dir_)

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

@@ -85,10 +85,10 @@ class Net_0(gluon.HybridBlock):
         with self.name_scope():
             if data_mean:
                 assert(data_std)
-                self.input_normalization_state = ZScoreNormalization(data_mean=data_mean['state'],
-                                                                               data_std=data_std['state'])
+                self.input_normalization_state_ = ZScoreNormalization(data_mean=data_mean['state_'],
+                                                                               data_std=data_std['state_'])
             else:
-                self.input_normalization_state = NoNormalization()
+                self.input_normalization_state_ = NoNormalization()
 
             self.fc1_ = gluon.nn.Dense(units=128, use_bias=True)
             # fc1_, output shape: {[128,1,1]}
@@ -103,14 +103,14 @@ class Net_0(gluon.HybridBlock):
 
 
 
-    def hybrid_forward(self, F, state):
-        outputs = []
-        state = self.input_normalization_state(state)
-        fc1_ = self.fc1_(state)
+    def hybrid_forward(self, F, state_):
+        state_ = self.input_normalization_state_(state_)
+        fc1_ = self.fc1_(state_)
         tanh1_ = self.tanh1_(fc1_)
         fc2_ = self.fc2_(tanh1_)
         tanh2_ = self.tanh2_(fc2_)
         fc3_ = self.fc3_(tanh2_)
-        outputs.append(fc3_)
+        qvalues_ = fc3_
+
+        return qvalues_
 
-        return outputs[0]