import tensorflow as tf

        self.train_data['X'], self.train_data['E'], \
            self.train_data['T'], self.train_data['failures'], \
            self.train_data['atrisk'], self.train_data['ties'] = utils.parse_data(X, label)
        # New Graph
        G = tf.Graph()
        with G.as_default():
            # Data input
            X = tf.placeholder(tf.float32, [None, input_node], name = 'x-Input')
            y_ = tf.placeholder(tf.float32, [None, output_node], name = 'label-Input')
            # hidden layers
            self.nnweights = [] # collect weights of network
            prev_node = input_node
            prev_x = X
            for i in range(len(hidden_layers_node)):
                layer_name = 'layer' + str(i+1)
                with tf.variable_scope(layer_name, reuse=tf.AUTO_REUSE):
