Tensorflow: How to reduce memory footprint for inference only models?

During inference, we don't need to keep the activations from the previous layers as we propogate through the network. However, since we are not explicitly telling the program to discard them, it does not differentiate between training and inference passes. Is there a way -perhaps an easy flag,class,method- to do this kind of memory management in Tensorflow? Would simply using tf.stop_gradient work?