<read files>
image = tf.image.decode_png(file_contents)
label = tf.string_to_number(label_str, out_type=tf.int32)
# Batch examples here into two queues.
image_batch, label_batch = tf.train.shuffle_batch(
    [image, label], ... )
model = build_model(image_batch)
loss = build_loss(model, label_batch)

(model_output, ) = sess.run([model])  # Uses the image queue as input
(model_output, true_labels) = sess.run([model, label_batch])  # Are image/labels pairs valid?

train_predict = model(train_input)
do something with the label here...
alternate:
use_predict = model(use_input, reuse=true)