Used in https://towardsdatascience.com/variational-autoencoders-explained-in-detail-d585327c660a

vae training.py

samples = []
losses_auto_encode = []
losses_digit_classifier = []
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for epoch in xrange(params['epochs']):
        for _ in xrange(mnist.train.num_examples / params['batch_size']):
            batch_images, batch_digits = mnist.train.next_batch(params['batch_size'])
            sess.run(train_op, feed_dict={images: batch_images, digits: batch_digits})
        
        train_loss_auto_encode, train_loss_digit_classifier = sess.run(
            [loss_auto_encode, loss_digit_classifier],
            {images: mnist.train.images, digits: mnist.train.labels})
        
        losses_auto_encode.append(train_loss_auto_encode)
        losses_digit_classifier.append(train_loss_digit_classifier)
        
        sample_z = np.tile(np.random.randn(1, params['z_dim']), reps=[num_digits, 1])
        gen_samples = sess.run(decoded_images,
                               feed_dict={z: sample_z, digit_prob: np.eye(num_digits)})
        samples.append(gen_samples)