initiate 2 graphs and sessions in Tensorflow

session.py

import numpy as np
import tensorflow as tf

class Feed_forward:
    def __init__(self, input_dimension):
        self.X = tf.placeholder(tf.float32, [None, input_dimension])
        self.Y = tf.placeholder(tf.float32, [None, 1])
        hidden_layer = tf.Variable(tf.random_uniform([input_dimension, 1], -1.0, 1.0))
        logits = tf.matmul(self.X, hidden_layer)
        self.cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = logits, labels = self.Y))
        self.optimizer = tf.train.GradientDescentOptimizer(learning_rate = 0.1).minimize(self.cost)
        
X = np.random.normal(size=(100, 10))
Y = np.random.randint(0, 2, size=(100,1))
epoch = 10

first_graph = tf.Graph()
with first_graph.as_default():
    first_model = Feed_forward(X.shape[1])
    first_sess = tf.InteractiveSession()
    first_sess.run(tf.global_variables_initializer())
    
second_graph = tf.Graph()
with second_graph.as_default():
    second_model = Feed_forward(X.shape[1])
    second_sess = tf.InteractiveSession()
    second_sess.run(tf.global_variables_initializer())
        
for i in range(epoch):
    loss, _ = first_sess.run([first_model.cost, first_model.optimizer], feed_dict={first_model.X:X, first_model.Y:Y})
    print('epoch:', i, ',loss from first model:', loss)
    loss, _ = second_sess.run([second_model.cost, second_model.optimizer], feed_dict={second_model.X:X, second_model.Y:Y})
    print('epoch:', i, ',loss from second model:', loss)