add main scipts

+import numpy as np
+import tensorflow as tf
+
+FLAGS = tf.app.flags.FLAGS
+
+class BaseModel():
+
+    def __init__(self,X,n_input,n_classes,n_hidden_1,n_hidden_2,n_hidden_3,n_hidden_4,n_hidden_5,n_hidden_6,is_training,keep_prob):
+        self.X = X
+        self.n_input = n_input
+        self.is_training = is_training
+
+        #Hyperparameters
+        self.keep_prob = keep_prob # Dropout
+
+        self.n_classes=n_classes
+        self.n_hidden_1=n_hidden_1
+        self.n_hidden_2=n_hidden_2
+        self.n_hidden_3=n_hidden_3
+        self.n_hidden_4=n_hidden_4
+        self.n_hidden_5=n_hidden_5
+        self.n_hidden_6=n_hidden_6
+
+    def store_layer_weights_and_bias(self):
+        self.weights = {
+            'h1_w': tf.get_variable('W1', shape=(self.n_input, self.n_hidden_1), initializer=tf.contrib.layers.variance_scaling_initializer()),
+            'h2_w': tf.get_variable('W2', shape=(self.n_hidden_1, self.n_hidden_2), initializer=tf.contrib.layers.variance_scaling_initializer()),
+            'h3_w': tf.get_variable('W3', shape=(self.n_hidden_2, self.n_hidden_3), initializer=tf.contrib.layers.variance_scaling_initializer()),
+            'h4_w': tf.get_variable('W4', shape=(self.n_hidden_3, self.n_hidden_4), initializer=tf.contrib.layers.variance_scaling_initializer()),
+            'h5_w': tf.get_variable('W5', shape=(self.n_hidden_4, self.n_hidden_5), initializer=tf.contrib.layers.variance_scaling_initializer()),
+            'h6_w': tf.get_variable('W6', shape=(self.n_hidden_5, self.n_hidden_6), initializer=tf.contrib.layers.variance_scaling_initializer()),
+            'out_w': tf.get_variable('W_out',shape=(self.n_hidden_6, self.n_classes), initializer=tf.contrib.layers.variance_scaling_initializer())
+        }
+        self.biases = {
+            'h1_b': tf.get_variable('B1',shape=(self.n_hidden_1),initializer=tf.contrib.layers.variance_scaling_initializer()),
+            'h2_b': tf.get_variable('B2',shape=(self.n_hidden_2),initializer=tf.contrib.layers.variance_scaling_initializer()),
+            'h3_b': tf.get_variable('B3',shape=(self.n_hidden_3),initializer=tf.contrib.layers.variance_scaling_initializer()),
+            'h4_b': tf.get_variable('B4',shape=(self.n_hidden_4),initializer=tf.contrib.layers.variance_scaling_initializer()),
+            'h5_b': tf.get_variable('B5',shape=(self.n_hidden_5),initializer=tf.contrib.layers.variance_scaling_initializer()),
+            'h6_b': tf.get_variable('B6',shape=(self.n_hidden_6),initializer=tf.contrib.layers.variance_scaling_initializer()),
+            'out_b': tf.get_variable('B_out',shape=(self.n_classes),initializer=tf.contrib.layers.variance_scaling_initializer())
+        }
+
+    def fc(self,input,weights,biases,name,dim):
+        h = tf.add(tf.matmul(input, weights), biases)
+        if FLAGS.bn:
+            h = tf.layers.batch_normalization(h,training=self.is_training,name='bn_'+name)
+        h = tf.nn.relu(h, name=name)
+        h = tf.nn.dropout(h, self.keep_prob)
+        return h
+
+    def net(self):  
+        self.h1 = self.fc(self.X,self.weights['h1_w'],self.biases['h1_b'],name='layer1',dim=self.n_hidden_1)
+        self.h2 = self.fc(self.h1,self.weights['h2_w'],self.biases['h2_b'],name='layer2',dim=self.n_hidden_2)
+        self.h3 = self.fc(self.h2, self.weights['h3_w'], self.biases['h3_b'],name='layer3',dim=self.n_hidden_3)
+        self.h4 = self.fc(self.h3,self.weights['h4_w'],self.biases['h4_b'],name='layer4',dim=self.n_hidden_4)
+        self.h5 = self.fc(self.h4,self.weights['h5_w'],self.biases['h5_b'],name='layer5',dim=self.n_hidden_5)
+        self.h6 = self.fc(self.h5, self.weights['h6_w'], self.biases['h6_b'],name='layer6',dim=self.n_hidden_6)
+        output_layer = tf.add(tf.matmul(self.h6, self.weights['out_w']), self.biases['out_b'],name='output')     
+        return output_layer
+
+    def __call__(self):
+        self.store_layer_weights_and_bias()
+        return self.net()
+