tf_session_batch.py

with tf.Session() as sess:
	sess.run(tf.global_variables_initializer())
	for epoch in range(n_epochs):
		train_data_batch_gen = get_train_data_batch(batch_size)
		epoch_loss = 0
		i=0
		while i < total_lines:
			train_x, train_y = next(train_data_batch_gen)
			
			batch_x = np.array(train_x)
			batch_y = np.array(train_y)
			
			_, c = sess.run([optimizer, cost], feed_dict={x: batch_x,y: batch_y})
			
			epoch_loss += c
			i+=batch_size