ltfn_9_2.py

saver = tf.train.Saver()                                                        #Create saver

num_steps = 1000
batch_size = 100
for step in range(num_steps):
    offset = (step * batch_size) % (train_labels.shape[0] - batch_size)
    batch_images = train_images[offset:(offset + batch_size), :]
    batch_labels = train_labels[offset:(offset + batch_size), :]
    feed_dict = {input: batch_images, labels: batch_labels}

    _, c, acc = session.run([optimizer, cost, accuracy], feed_dict=feed_dict)

    if step % 100 == 0: 
        print("Cost: ", c)
        print("Accuracy: ", acc * 100.0, "%")
        saver.save(session, "/tmp/vggnet/vgg_net.ckpt", global_step=step)       #Save session every 100 mini-batches