tf_session.py

with tf.Session() as sess:
	sess.run(tf.global_variables_initializer())

	for epoch in range(n_epochs):
		epoch_loss = 0
		i=0
		while i < len(train_x):
			start = i
			end = i+batch_size
			batch_x = np.array(train_x[start:end])
			batch_y = np.array(train_y[start:end])

			_, c = sess.run([optimizer, cost], feed_dict={x: batch_x,
								y: batch_y})
			epoch_loss += c
			i+=batch_size