--- a
+++ b/modules/NeuralNet/management/execManager.py
@@ -0,0 +1,60 @@
+# Essentially the main for NeuralNet
+from trainingManager import trainingManager as tr
+from testingManager import testingManager as te
+from predictManager import predictManager as pr
+import argparse
+import tensorflow as tf
+import sys
+from Status.Status import Status
+sys.path.append('/home/skjena/cnnCancerTherapy/modules/NeuralNet/core/classifiers/dnnClassifier')
+sys.path.append('/home/skjena/cnnCancerTherapy/modules/NeuralNet/core/regressors/dnnRegressor')
+from NeuralNet.core import dataProcessor
+from NeuralNet.core.classifiers.dnnClassifier.DNNClassifierModel import DNNClassifierModel
+#from NeuralNet.core.regressors.dnnRegressor import DNNRegressorModel
+
+class execManager():
+
+    def __init__(self, trainpath, testpath, network, problem):
+        (self.train_x, self.train_y) = dataProcessor.load_train_data(trainpath)
+        (self.test_x, self.test_y) = dataProcessor.load_test_data(testpath)
+        self.network = network
+        self.problemType = problem
+        if(self.problemType == "0"):
+            self.classifier = DNNClassifierModel(self.network)
+            self.classifier.build(self.network.state.networkShape, self.train_x)
+        else:
+            self.regressor = DNNRegressorModel(self.network)
+            #regressor has no build function. Look into this later.
+            self.regressor.build(self.network.state.networkShape, self.train_x)
+        self.predict_x = ""
+        self.expected = []
+        self.status = Status("execManager")
+
+    def train(self):
+        self.status.message(1, "train(self)")
+        if(self.problemType == "0"):
+            trainer = tr(self.train_x, self.train_y, self.network, self.classifier)
+            self.classifier = trainer.run()
+        else:
+            trainer = tr(self.train_x, self.train_y, self.network, self.regressor)
+            self.regressor = trainer.run()
+        self.status.message(0, "train(self)")
+
+    def test(self):
+        self.status.message(1, "test(self)")
+        if(self.problemType == "0"):
+            tester = te(self.test_x, self.test_y, self.network, self.classifier)
+            self.result = tester.run(self.classifier)
+        else:
+           tester = te(self.test_x, self.test_y, self.network, self.regressor)
+           self.result = tester.run(self.regressor)
+        self.status.message(0, "test(self)")
+
+    def predict(self):
+        self.status.message(1, "predict(self)")
+        predictor = pr(self.predict_x, self.expected)
+        if(self.problemType == "0"):
+            predictor.run(self.classifier, self.problemType)
+        else:
+            predictor.run(self.regressor, self.problemType)
+        self.status.message(0, "predict(self)")