Tips for QAT - 2.5 English

The following are some tips for QAT.

Keras Model

For Keras models, please set backend.set_learning_phase(1) before creating the float train graph, and set backend.set_learning_phase(0) before creating the float evaluation graph. Moreover, backend.set_learning_phase() should be called after backend.clear_session(). Tensorflow1.x QAT APIs are designed for Tensorflow native training APIs. Using Keras model.fit() APIs in QAT may lead to some "nodes not executed" issues. It is recommended to use QAT APIs in Tensorflow2 quantization tool with Keras APIs.

Dropout

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=false when using tf.layers or call tf.keras.backend.set_learning_phase(0) when using tf.keras.layers.

Hyper-parameter

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.