--- a
+++ b/modules/NeuralNet/management/trainingManager.py
@@ -0,0 +1,27 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+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/regressor/dnnRegressor')
+
+from NeuralNet.core.classifiers.dnnClassifier.DNNClassifierModel import DNNClassifierModel
+# from NeuralNet.core.regressor.dnnRegressor.DNNRegressorModel import DNNRegressorModel
+from NeuralNet.core.classifiers.dnnClassifier import dataProcessor
+
+
+class trainingManager():
+  def __init__(self, train_x, train_y, network, dnnModel):
+    self.train_x = train_x
+    self.train_y = train_y
+    self.network = network
+    self.dnnModel = dnnModel
+    self.status = Status("trainingManager")
+
+  def run(self):
+    self.status.message(1, "run()")
+    self.dnnModel.model.train(input_fn=lambda:dataProcessor.train_input_fn(self.train_x, self.train_y, self.network.state.batchSize,steps=self.network.arguments.learningRate))
+    self.status.message(3, self.network.state.networkShape)
+    self.status.message(0, "run()")
+    return True