Answer by tejas for How to use Test data on saved model with queue approach (without feed_dict) #tensorflow?

OK, So I have found the answer.

The original challenge was to to toggle between train and test data while training and validation phase when using queues. Now as queues are part of graph structure, we can't simply modify them.

I found an article to use tf.case to toggle between train and test queue but I wasn't able to use shuffle batch along with it.

The real task at hand was to save the model post training and use the saved model to predict in production.

So here is the flow:

Training

• create a method that creates your graph (will take image tensor as input).
• Build a training graph by passing training image batches
• Perform training and save the model with saver object.

Evaluation

• Now reconstruct the same graph with test image batches.
• In the session use saver object to restore the weights (Note you dont need to pass which variables to restore, by default it restores all restore able variables) Dont run gloabl variable initializer at this time
• Run your predict op (generated from the newly constructed graph)

Also make sure you switch off the drop out functionality in the eval as it would keep varying the output for the same input

Below is the pseudocode

train_op, y_predict, accuracy = create_graph(train_input, train_label)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)
    model_saver = tf.train.Saver()

    for i in range(2000):
        if i%100 == 0:
           train_accuracy = sess.run(accuracy)
           print("step %d, training accuracy %f" %(i, train_accuracy))
        sess.run(train_op)

    print(sess.run(accuracy))
    model_saver.save(sess, 'model/simple_model', global_step=100)
    coord.request_stop()
    coord.join(threads)

For evaluation

_, y_predict, accuracy = create_graph(test_input, test_label)

saver = tf.train.Saver()

with tf.Session() as sess:
    saver.restore(sess, tf.train.latest_checkpoint("./model/"))

    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)
    label_predict = sess.run([y_predict])