The following are some tips for QAT.
- Experiments shows that QAT works better without dropout ops. This tool does
not support finetuning with dropouts at the moment and they should be
removed or disabled before running QAT. This can be done by setting
is_training=falsewhen using tf.layers or call
tf.keras.backend.set_learning_phase(0)when using tf.keras.layers.
- QAT is like float model training/finetuning, so the techniques for float model training/finetuning are also needed. The optimizer type and the learning rate curve are some important parameters to tune.