How to handle different queue batch size and feed value batch size in tensorflow?

Question

My code used to work on tensorflow 0.6, but it no longer works on the lastest tensorflow.

I would like to perform inference every few training iterations. My training data is pulled from a queue, my inference data is from feed_dict. The training batch size is 128 while the inference batch size is 1. What Should I do to make the network accept the two different batch sizes?

batch_size = 128
x_batch = tf.placeholder("float", [None, 100])
q = tf.FIFOQueue(10, [tf.float32], shapes=[[batch_size, 100]])
enqueue_op = q.enqueue([x_batch])

# during training
x = q.dequeue() # dequeue operation

# network definition, takes x as input, and output y
......

# during inference
x_array_of_batch_size_1 = .. # a 1x100 numpy array
sess.run([y], feed_dict={x: x_array_of_batch_size_1))

I got the following error:

ValueError: Cannot feed value of shape (1, 100) for Tensor u'fifo_queue_Dequeue:0', which has shape '(128, 100)'

Answer 1

We added this check recently to prevent errors (and add a few optimization opportunities). You can make your program work again by changing the declaration of x to use the new tf.placeholder_with_default() op:

x = tf.placeholder_with_default(q.dequeue(), shape=[None, 100])