fixed merge request

.gitlab-ci.yml

@@ -10,7 +10,7 @@ git masterJobLinux:
   stage: deploy
   image: maven:3-jdk-8
   script: 
-  - mvn -Dorg.slf4j.simpleLogger.log.org.apache.maven.cli.transfer.Slf4jMavenTransferListener=warn -B  clean deploy --settings settings.xml -DskipTests
+  - mvn -Dorg.slf4j.simpleLogger.log.org.apache.maven.cli.transfer.Slf4jMavenTransferListener=warn -B  -U clean deploy --settings settings.xml -DskipTests
 #  - cat target/site/jacoco/index.html
 #  - mvn package sonar:sonar -s settings.xml
   only:
@@ -21,34 +21,34 @@ integrationMXNetJobLinux:
   stage: linux
   image: registry.git.rwth-aachen.de/monticore/embeddedmontiarc/generators/emadl2cpp/integrationtests/mxnet:v0.0.4
   script:
-  - mvn -Dorg.slf4j.simpleLogger.log.org.apache.maven.cli.transfer.Slf4jMavenTransferListener=warn -B  clean install --settings settings.xml -Dtest=IntegrationMXNetTest
+  - mvn -Dorg.slf4j.simpleLogger.log.org.apache.maven.cli.transfer.Slf4jMavenTransferListener=warn -B  -U clean install --settings settings.xml -Dtest=IntegrationMXNetTest
 
 
 integrationCaffe2JobLinux:
   stage: linux
   image: registry.git.rwth-aachen.de/monticore/embeddedmontiarc/generators/emadl2cpp/integrationtests/caffe2:v0.0.5
   script:
-  - mvn -Dorg.slf4j.simpleLogger.log.org.apache.maven.cli.transfer.Slf4jMavenTransferListener=warn -B  clean install --settings settings.xml -Dtest=IntegrationCaffe2Test
+  - mvn -Dorg.slf4j.simpleLogger.log.org.apache.maven.cli.transfer.Slf4jMavenTransferListener=warn -B  -U clean install --settings settings.xml -Dtest=IntegrationCaffe2Test
 
 
 integrationGluonJobLinux:
   stage: linux
   image: registry.git.rwth-aachen.de/monticore/embeddedmontiarc/generators/emadl2cpp/integrationtests/mxnet:v0.0.4
   script:
-  - mvn -Dorg.slf4j.simpleLogger.log.org.apache.maven.cli.transfer.Slf4jMavenTransferListener=warn -B  clean install --settings settings.xml -Dtest=IntegrationGluonTest
+  - mvn -Dorg.slf4j.simpleLogger.log.org.apache.maven.cli.transfer.Slf4jMavenTransferListener=warn -B  -U 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
+  - mvn -Dorg.slf4j.simpleLogger.log.org.apache.maven.cli.transfer.Slf4jMavenTransferListener=warn -B -U clean install --settings settings.xml -Dtest=IntegrationTensorflowTest
 
 
 integrationPythonWrapperTest:
   stage: linux
   image: registry.git.rwth-aachen.de/monticore/embeddedmontiarc/generators/emadl2pythonwrapper/tests/mvn-swig:latest
   script:
-  - mvn -Dorg.slf4j.simpleLogger.log.org.apache.maven.cli.transfer.Slf4jMavenTransferListener=warn -B  clean install --settings settings.xml -Dtest=IntegrationPythonWrapperTest
+  - mvn -Dorg.slf4j.simpleLogger.log.org.apache.maven.cli.transfer.Slf4jMavenTransferListener=warn -B -U  clean install --settings settings.xml -Dtest=IntegrationPythonWrapperTest
 
 
 masterJobWindows:
@@ -63,6 +63,6 @@ UnitTestJobLinux:
   stage: linux
   image: maven:3-jdk-8
   script: 
-  - mvn -Dorg.slf4j.simpleLogger.log.org.apache.maven.cli.transfer.Slf4jMavenTransferListener=warn -B  clean install sonar:sonar --settings settings.xml -Dtest="GenerationTest,SymtabTest*"
+  - mvn -Dorg.slf4j.simpleLogger.log.org.apache.maven.cli.transfer.Slf4jMavenTransferListener=warn -B  -U clean install sonar:sonar --settings settings.xml -Dtest="GenerationTest,SymtabTest*"
 #  - cat target/site/jacoco/index.html
   

pom.xml

@@ -23,7 +23,7 @@
     <cnnarch-caffe2-generator.version>0.2.14-SNAPSHOT</cnnarch-caffe2-generator.version>
     <cnnarch-gluon-generator.version>0.2.10-SNAPSHOT</cnnarch-gluon-generator.version>
     <cnnarch-tensorflow-generator.version>0.1.0-SNAPSHOT</cnnarch-tensorflow-generator.version>
-    <Common-MontiCar.version>0.0.14-20180704.113055-2</Common-MontiCar.version>
+    <Common-MontiCar.version>0.0.19-SNAPSHOT</Common-MontiCar.version>
     <embedded-montiarc-math-opt-generator>0.1.6</embedded-montiarc-math-opt-generator>
 
         <!-- .. Libraries .................................................. -->

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

@@ -62,14 +62,12 @@ public class IntegrationPythonWrapperTest extends AbstractSymtabTest {
                         "reinforcement_learning/cnnarch_logger.py"
                 )
         );
-        /*
         assertTrue(Paths.get(
                 "./target/generated-sources-emadl/reinforcement_learning/_torcs_agent_dqn_reward_executor.so")
                 .toFile().exists());
         assertTrue(Paths.get(
                 "./target/generated-sources-emadl/reinforcement_learning/torcs_agent_dqn_reward_executor.py")
                 .toFile().exists());
-        */
     }
 
     @Test

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

@@ -15,7 +15,7 @@ class CNNDataLoader_mnist_mnistClassifier_net:
     def __init__(self):
         self._data_dir = "data/mnist.LeNetNetwork/"
 
-    def load_data(self, batch_size):
+    def load_data(self, batch_size, shuffle=False):
         train_h5, test_h5 = self.load_h5_files()
 
         train_data = {}
@@ -39,7 +39,8 @@ class CNNDataLoader_mnist_mnistClassifier_net:
 
         train_iter = mx.io.NDArrayIter(data=train_data,
                                        label=train_label,
-                                       batch_size=batch_size)
+                                       batch_size=batch_size,
+                                       shuffle=shuffle)
 
         test_iter = None
 
@@ -64,47 +65,108 @@ class CNNDataLoader_mnist_mnistClassifier_net:
 
         return train_iter, test_iter, data_mean, data_std, train_images, test_images
 
-    def load_data_img(self, batch_size, img_size):
+    def load_preprocessed_data(self, batch_size, preproc_lib, shuffle=False):
         train_h5, test_h5 = self.load_h5_files()
-        width = img_size[0]
-        height = img_size[1]
 
-        comb_data = {}
+        wrapper = importlib.import_module(preproc_lib)
+        instance = getattr(wrapper, preproc_lib)()
+        instance.init()
+        lib_head, _sep, tail = preproc_lib.rpartition('_')
+        inp = getattr(wrapper, lib_head + "_input")()
+
+        train_data = {}
+        train_label = {}
         data_mean = {}
         data_std = {}
 
+        shape_output = self.preprocess_data(instance, inp, 0, train_h5)
+        train_len = len(train_h5[self._input_names_[0]])
+
         for input_name in self._input_names_:
-            train_data = train_h5[input_name][:]
-            test_data = test_h5[input_name][:]
+            if type(getattr(shape_output, input_name + "_out")) == np.ndarray:
+                cur_shape = (train_len,) + getattr(shape_output, input_name + "_out").shape
+            else:
+                cur_shape = (train_len, 1)
+            train_data[input_name] = mx.nd.zeros(cur_shape)
+        for output_name in self._output_names_:
+            if type(getattr(shape_output, output_name + "_out")) == nd.array:
+                cur_shape = (train_len,) + getattr(shape_output, output_name + "_out").shape
+            else:
+                cur_shape = (train_len, 1)
+            train_label[output_name] = mx.nd.zeros(cur_shape)
+
+        for i in range(train_len):
+            output = self.preprocess_data(instance, inp, i, train_h5)
+            for input_name in self._input_names_:
+                train_data[input_name][i] = getattr(output, input_name + "_out")
+            for output_name in self._output_names_:
+                train_label[output_name][i] = getattr(shape_output, output_name + "_out")
+
+        for input_name in self._input_names_:
+            data_mean[input_name + '_'] = nd.array(train_data[input_name][:].mean(axis=0))
+            data_std[input_name + '_'] = nd.array(train_data[input_name][:].asnumpy().std(axis=0) + 1e-5)
 
-            train_shape = train_data.shape
-            test_shape = test_data.shape
+        if 'images' in train_h5:
+            train_images = train_h5['images']
+
+        train_iter = mx.io.NDArrayIter(data=train_data,
+                                       label=train_label,
+                                       batch_size=batch_size,
+                                       shuffle=shuffle)
 
-            comb_data[input_name] = mx.nd.zeros((train_shape[0]+test_shape[0], train_shape[1], width, height))
-            for i, img in enumerate(train_data):
-                img = img.transpose(1,2,0)
-                comb_data[input_name][i] = cv2.resize(img, (width, height)).reshape((train_shape[1],width,height))
-            for i, img in enumerate(test_data):
-                img = img.transpose(1, 2, 0)
-                comb_data[input_name][i+train_shape[0]] = cv2.resize(img, (width, height)).reshape((train_shape[1], width, height))
+        test_data = {}
+        test_label = {}
 
-            data_mean[input_name + '_'] = nd.array(comb_data[input_name][:].mean(axis=0))
-            data_std[input_name + '_'] = nd.array(comb_data[input_name][:].asnumpy().std(axis=0) + 1e-5)
+        shape_output = self.preprocess_data(instance, inp, 0, test_h5)
+        test_len = len(test_h5[self._input_names_[0]])
 
-        comb_label = {}
+        for input_name in self._input_names_:
+            if type(getattr(shape_output, input_name + "_out")) == np.ndarray:
+                cur_shape = (test_len,) + getattr(shape_output, input_name + "_out").shape
+            else:
+                cur_shape = (test_len, 1)
+            test_data[input_name] = mx.nd.zeros(cur_shape)
         for output_name in self._output_names_:
-            train_labels = train_h5[output_name][:]
-            test_labels = test_h5[output_name][:]
-            comb_label[output_name] = np.append(train_labels, test_labels, axis=0)
+            if type(getattr(shape_output, output_name + "_out")) == nd.array:
+                cur_shape = (test_len,) + getattr(shape_output, output_name + "_out").shape
+            else:
+                cur_shape = (test_len, 1)
+            test_label[output_name] = mx.nd.zeros(cur_shape)
 
+        for i in range(test_len):
+            output = self.preprocess_data(instance, inp, i, test_h5)
+            for input_name in self._input_names_:
+                test_data[input_name][i] = getattr(output, input_name + "_out")
+            for output_name in self._output_names_:
+                test_label[output_name][i] = getattr(shape_output, output_name + "_out")
+
+        if 'images' in test_h5:
+            test_images = test_h5['images']
 
-        train_iter = mx.io.NDArrayIter(data=comb_data,
-                                       label=comb_label,
+        test_iter = mx.io.NDArrayIter(data=test_data,
+                                       label=test_label,
                                        batch_size=batch_size)
 
-        test_iter = None
+        return train_iter, test_iter, data_mean, data_std, train_images, test_images
 
-        return train_iter, test_iter, data_mean, data_std
+    def preprocess_data(self, instance_wrapper, input_wrapper, index, data_h5):
+        for input_name in self._input_names_:
+            data = data_h5[input_name][0]
+            attr = getattr(input_wrapper, input_name)
+            if (type(data)) == np.ndarray:
+                data = np.asfortranarray(data).astype(attr.dtype)
+            else:
+                data = type(attr)(data)
+            setattr(input_wrapper, input_name, data)
+        for output_name in self._output_names_:
+            data = data_h5[output_name][0]
+            attr = getattr(input_wrapper, output_name)
+            if (type(data)) == np.ndarray:
+                data = np.asfortranarray(data).astype(attr.dtype)
+            else:
+                data = type(attr)(data)
+            setattr(input_wrapper, output_name, data)
+        return instance_wrapper.execute(input_wrapper)
 
     def load_preprocessed_data(self, batch_size, preproc_lib):
         train_h5, test_h5 = self.load_h5_files()

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

@@ -52,10 +52,10 @@ class Reshape(gluon.HybridBlock):
 
 
 class CustomRNN(gluon.HybridBlock):
-    def __init__(self, hidden_size, num_layers, bidirectional, **kwargs):
+    def __init__(self, hidden_size, num_layers, dropout, bidirectional, **kwargs):
         super(CustomRNN, self).__init__(**kwargs)
         with self.name_scope():
-            self.rnn = gluon.rnn.RNN(hidden_size=hidden_size, num_layers=num_layers,
+            self.rnn = gluon.rnn.RNN(hidden_size=hidden_size, num_layers=num_layers, dropout=dropout,
                                      bidirectional=bidirectional, activation='tanh', layout='NTC')
 
     def hybrid_forward(self, F, data, state0):
@@ -64,10 +64,10 @@ class CustomRNN(gluon.HybridBlock):
 
 
 class CustomLSTM(gluon.HybridBlock):
-    def __init__(self, hidden_size, num_layers, bidirectional, **kwargs):
+    def __init__(self, hidden_size, num_layers, dropout, bidirectional, **kwargs):
         super(CustomLSTM, self).__init__(**kwargs)
         with self.name_scope():
-            self.lstm = gluon.rnn.LSTM(hidden_size=hidden_size, num_layers=num_layers,
+            self.lstm = gluon.rnn.LSTM(hidden_size=hidden_size, num_layers=num_layers, dropout=dropout,
                                        bidirectional=bidirectional, layout='NTC')
 
     def hybrid_forward(self, F, data, state0, state1):
@@ -76,10 +76,10 @@ class CustomLSTM(gluon.HybridBlock):
 
 
 class CustomGRU(gluon.HybridBlock):
-    def __init__(self, hidden_size, num_layers, bidirectional, **kwargs):
+    def __init__(self, hidden_size, num_layers, dropout, bidirectional, **kwargs):
         super(CustomGRU, self).__init__(**kwargs)
         with self.name_scope():
-            self.gru = gluon.rnn.GRU(hidden_size=hidden_size, num_layers=num_layers,
+            self.gru = gluon.rnn.GRU(hidden_size=hidden_size, num_layers=num_layers, dropout=dropout,
                                      bidirectional=bidirectional, layout='NTC')
 
     def hybrid_forward(self, F, data, state0):

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

@@ -143,7 +143,12 @@ class BLEU(mx.metric.EvalMetric):
         if self._size_hyp >= self._size_ref:
             return 1
         else:
-            return math.exp(1 - (self._size_ref / self._size_hyp))
+            if self._size_hyp > 0:
+                size_hyp = self._size_hyp
+            else:
+                size_hyp = 1
+
+            return math.exp(1 - (self._size_ref / size_hyp))
 
     @staticmethod
     def _get_ngrams(sentence, n):
@@ -191,7 +196,10 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
               context='gpu',
               save_attention_image=False,
               use_teacher_forcing=False,
-              normalize=True):
+              normalize=True,
+              shuffle_data=False,
+              clip_global_grad_norm=None,
+              preprocessing = False):
         if context == 'gpu':
             mx_context = mx.gpu()
         elif context == 'cpu':
@@ -199,6 +207,12 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
         else:
             logging.error("Context argument is '" + context + "'. Only 'cpu' and 'gpu are valid arguments'.")
 
+        if preprocessing:
+            preproc_lib = "CNNPreprocessor_mnist_mnistClassifier_net_executor"
+            train_iter, test_iter, data_mean, data_std, train_images, test_images = self._data_loader.load_preprocessed_data(batch_size, preproc_lib, shuffle_data)
+        else:
+            train_iter, test_iter, data_mean, data_std, train_images, test_images = self._data_loader.load_data(batch_size, shuffle_data)
+
         if 'weight_decay' in optimizer_params:
             optimizer_params['wd'] = optimizer_params['weight_decay']
             del optimizer_params['weight_decay']
@@ -214,8 +228,6 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
             del optimizer_params['step_size']
             del optimizer_params['learning_rate_decay']
 
-        train_iter, test_iter, data_mean, data_std, train_images, test_images = self._data_loader.load_data(batch_size)
-
         if normalize:
             self._net_creator.construct(context=mx_context, data_mean=data_mean, data_std=data_std)
         else:
@@ -276,6 +288,15 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
         tic = None
 
         for epoch in range(begin_epoch, begin_epoch + num_epoch):
+            if shuffle_data:
+                if preprocessing:
+                    preproc_lib = "CNNPreprocessor_mnist_mnistClassifier_net_executor"
+                    train_iter, test_iter, data_mean, data_std, train_images, test_images = self._data_loader.load_preprocessed_data(batch_size, preproc_lib, shuffle_data)
+                else:
+                    train_iter, test_iter, data_mean, data_std, train_images, test_images = self._data_loader.load_data(batch_size, shuffle_data)
+
+            global_loss_train = 0.0
+            train_batches = 0
 
             loss_total = 0
             train_iter.reset()
@@ -304,6 +325,17 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
 
                 loss_total += loss.sum().asscalar()
 
+                global_loss_train += loss.sum().asscalar()
+                train_batches += 1
+
+                if clip_global_grad_norm:
+                    grads = []
+
+                    for network in self._networks.values():
+                        grads.extend([param.grad(mx_context) for param in network.collect_params().values()])
+
+                    gluon.utils.clip_global_norm(grads, clip_global_grad_norm)
+
                 for trainer in trainers:
                     trainer.step(batch_size)
 
@@ -323,6 +355,8 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
 
                         tic = time.time()
 
+            global_loss_train /= (train_batches * batch_size)
+
             tic = None
 
 
@@ -340,10 +374,12 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
                     nd.waitall()
 
                     outputs = []
-                    attentionList=[]
+                    lossList = []
+                    attentionList = []
                     predictions_ = self._networks[0](image_)
 
                     outputs.append(predictions_)
+                    lossList.append(loss_function(predictions_, labels[0]))
 
 
                     if save_attention_image == "True":
@@ -399,6 +435,9 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
             else:
                 train_metric_score = 0
 
+            global_loss_test = 0.0
+            test_batches = 0
+
             test_iter.reset()
             metric = mx.metric.create(eval_metric, **eval_metric_params)
             for batch_i, batch in enumerate(test_iter):
@@ -413,10 +452,12 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
                     nd.waitall()
 
                     outputs = []
-                    attentionList=[]
+                    lossList = []
+                    attentionList = []
                     predictions_ = self._networks[0](image_)
 
                     outputs.append(predictions_)
+                    lossList.append(loss_function(predictions_, labels[0]))
 
 
                     if save_attention_image == "True":
@@ -460,6 +501,12 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
                             os.makedirs(target_dir)
                         plt.savefig(target_dir + '/attention_test.png')
                         plt.close()
+                loss = 0
+                for element in lossList:
+                    loss = loss + element
+
+                global_loss_test += loss.sum().asscalar()
+                test_batches += 1
 
                 predictions = []
                 for output_name in outputs:
@@ -472,15 +519,16 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
                 metric.update(preds=predictions, labels=labels)
             test_metric_score = metric.get()[1]
 
-            logging.info("Epoch[%d] Train: %f, Test: %f" % (epoch, train_metric_score, test_metric_score))
+            global_loss_test /= (test_batches * batch_size)
 
+            logging.info("Epoch[%d] Train metric: %f, Test metric: %f, Train loss: %f, Test loss: %f" % (epoch, train_metric_score, test_metric_score, global_loss_train, global_loss_test))
 
             if (epoch - begin_epoch) % checkpoint_period == 0:
                 for i, network in self._networks.items():
                     network.save_parameters(self.parameter_path(i) + '-' + str(epoch).zfill(4) + '.params')
 
         for i, network in self._networks.items():
-            network.save_parameters(self.parameter_path(i) + '-' + str(num_epoch + begin_epoch).zfill(4) + '.params')
+            network.save_parameters(self.parameter_path(i) + '-' + str(num_epoch + begin_epoch + 1).zfill(4) + '.params')
             network.export(self.parameter_path(i) + '_newest', epoch=0)
 
     def parameter_path(self, index):

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

@@ -52,10 +52,10 @@ class Reshape(gluon.HybridBlock):
 
 
 class CustomRNN(gluon.HybridBlock):
-    def __init__(self, hidden_size, num_layers, bidirectional, **kwargs):
+    def __init__(self, hidden_size, num_layers, dropout, bidirectional, **kwargs):
         super(CustomRNN, self).__init__(**kwargs)
         with self.name_scope():
-            self.rnn = gluon.rnn.RNN(hidden_size=hidden_size, num_layers=num_layers,
+            self.rnn = gluon.rnn.RNN(hidden_size=hidden_size, num_layers=num_layers, dropout=dropout,
                                      bidirectional=bidirectional, activation='tanh', layout='NTC')
 
     def hybrid_forward(self, F, data, state0):
@@ -64,10 +64,10 @@ class CustomRNN(gluon.HybridBlock):
 
 
 class CustomLSTM(gluon.HybridBlock):
-    def __init__(self, hidden_size, num_layers, bidirectional, **kwargs):
+    def __init__(self, hidden_size, num_layers, dropout, bidirectional, **kwargs):
         super(CustomLSTM, self).__init__(**kwargs)
         with self.name_scope():
-            self.lstm = gluon.rnn.LSTM(hidden_size=hidden_size, num_layers=num_layers,
+            self.lstm = gluon.rnn.LSTM(hidden_size=hidden_size, num_layers=num_layers, dropout=dropout,
                                        bidirectional=bidirectional, layout='NTC')
 
     def hybrid_forward(self, F, data, state0, state1):
@@ -76,10 +76,10 @@ class CustomLSTM(gluon.HybridBlock):
 
 
 class CustomGRU(gluon.HybridBlock):
-    def __init__(self, hidden_size, num_layers, bidirectional, **kwargs):
+    def __init__(self, hidden_size, num_layers, dropout, bidirectional, **kwargs):
         super(CustomGRU, self).__init__(**kwargs)
         with self.name_scope():
-            self.gru = gluon.rnn.GRU(hidden_size=hidden_size, num_layers=num_layers,
+            self.gru = gluon.rnn.GRU(hidden_size=hidden_size, num_layers=num_layers, dropout=dropout,
                                      bidirectional=bidirectional, layout='NTC')
 
     def hybrid_forward(self, F, data, state0):
@@ -127,6 +127,7 @@ class Net_0(gluon.HybridBlock):
         inputs = {}
         input_dimensions = (4)
         input_domains = (float,float('-inf'),float('inf'))
+        input_domains = (float,0,1)
         inputs["state_"] = input_domains + (input_dimensions,)
         return inputs
 

src/test/resources/target_code/gluon/reinforcementModel/mountaincar/CNNNet_mountaincar_master_actor.py

@@ -52,10 +52,10 @@ class Reshape(gluon.HybridBlock):
 
 
 class CustomRNN(gluon.HybridBlock):
-    def __init__(self, hidden_size, num_layers, bidirectional, **kwargs):
+    def __init__(self, hidden_size, num_layers, dropout, bidirectional, **kwargs):
         super(CustomRNN, self).__init__(**kwargs)
         with self.name_scope():
-            self.rnn = gluon.rnn.RNN(hidden_size=hidden_size, num_layers=num_layers,
+            self.rnn = gluon.rnn.RNN(hidden_size=hidden_size, num_layers=num_layers, dropout=dropout,
                                      bidirectional=bidirectional, activation='tanh', layout='NTC')
 
     def hybrid_forward(self, F, data, state0):
@@ -64,10 +64,10 @@ class CustomRNN(gluon.HybridBlock):
 
 
 class CustomLSTM(gluon.HybridBlock):
-    def __init__(self, hidden_size, num_layers, bidirectional, **kwargs):
+    def __init__(self, hidden_size, num_layers, dropout, bidirectional, **kwargs):
         super(CustomLSTM, self).__init__(**kwargs)
         with self.name_scope():
-            self.lstm = gluon.rnn.LSTM(hidden_size=hidden_size, num_layers=num_layers,
+            self.lstm = gluon.rnn.LSTM(hidden_size=hidden_size, num_layers=num_layers, dropout=dropout,
                                        bidirectional=bidirectional, layout='NTC')
 
     def hybrid_forward(self, F, data, state0, state1):
@@ -76,10 +76,10 @@ class CustomLSTM(gluon.HybridBlock):
 
 
 class CustomGRU(gluon.HybridBlock):
-    def __init__(self, hidden_size, num_layers, bidirectional, **kwargs):
+    def __init__(self, hidden_size, num_layers, dropout, bidirectional, **kwargs):
         super(CustomGRU, self).__init__(**kwargs)
         with self.name_scope():
-            self.gru = gluon.rnn.GRU(hidden_size=hidden_size, num_layers=num_layers,
+            self.gru = gluon.rnn.GRU(hidden_size=hidden_size, num_layers=num_layers, dropout=dropout,
                                      bidirectional=bidirectional, layout='NTC')
 
     def hybrid_forward(self, F, data, state0):
@@ -128,7 +128,7 @@ class Net_0(gluon.HybridBlock):
     def getInputs(self):
         inputs = {}
         input_dimensions = (2)
-        input_domains = (float,float('-inf'),float('inf'))
+        input_domains = (float,0,1)
         inputs["state_"] = input_domains + (input_dimensions,)
         return inputs
 

src/test/resources/target_code/gluon/reinforcementModel/mountaincar/reinforcement_learning/CNNNet_mountaincar_agent_mountaincarCritic.py

@@ -52,10 +52,10 @@ class Reshape(gluon.HybridBlock):
 
 
 class CustomRNN(gluon.HybridBlock):
-    def __init__(self, hidden_size, num_layers, bidirectional, **kwargs):
+    def __init__(self, hidden_size, num_layers, dropout, bidirectional, **kwargs):
         super(CustomRNN, self).__init__(**kwargs)
         with self.name_scope():
-            self.rnn = gluon.rnn.RNN(hidden_size=hidden_size, num_layers=num_layers,
+            self.rnn = gluon.rnn.RNN(hidden_size=hidden_size, num_layers=num_layers, dropout=dropout,
                                      bidirectional=bidirectional, activation='tanh', layout='NTC')
 
     def hybrid_forward(self, F, data, state0):
@@ -64,10 +64,10 @@ class CustomRNN(gluon.HybridBlock):
 
 
 class CustomLSTM(gluon.HybridBlock):
-    def __init__(self, hidden_size, num_layers, bidirectional, **kwargs):
+    def __init__(self, hidden_size, num_layers, dropout, bidirectional, **kwargs):
         super(CustomLSTM, self).__init__(**kwargs)
         with self.name_scope():
-            self.lstm = gluon.rnn.LSTM(hidden_size=hidden_size, num_layers=num_layers,
+            self.lstm = gluon.rnn.LSTM(hidden_size=hidden_size, num_layers=num_layers, dropout=dropout,
                                        bidirectional=bidirectional, layout='NTC')
 
     def hybrid_forward(self, F, data, state0, state1):
@@ -76,10 +76,10 @@ class CustomLSTM(gluon.HybridBlock):