--- a
+++ b/BraTs18Challege/train_Brats.py
@@ -0,0 +1,33 @@
+import os
+
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+from tensorflow.python.client import device_lib
+
+print(device_lib.list_local_devices())
+
+from Vnet.model_vnet3d import Vnet3dModule
+import numpy as np
+import pandas as pd
+
+
+def train():
+    '''
+    Vnet network segmentation kidney fine segmatation
+    Preprocessing for dataset
+    '''
+    # Read  data set (Train data from CSV file)
+    csvdata = pd.read_csv('dataprocess\\data/train.csv')
+    maskdata = csvdata.iloc[:, 1].values
+    imagedata = csvdata.iloc[:, 0].values
+    # shuffle imagedata and maskdata together
+    perm = np.arange(len(imagedata))
+    np.random.shuffle(perm)
+    imagedata = imagedata[perm]
+    maskdata = maskdata[perm]
+
+    Vnet3d = Vnet3dModule(128, 128, 64, channels=4, numclass=3, costname=("dice coefficient",))
+    Vnet3d.train(imagedata, maskdata, "Vnet3d.pd", "log\\segmeation\\VNet\\", 0.001, 0.5, 20, 1, [8, 8])
+
+
+train()