TensorFlow Model Guidelines

In order to make full use of AIMET features, there are several guidelines users should follow when defining TensorFlow models.

If model has BatchNormalization (BN) layers

If model has BatchNormalization (BN) layers, then user should set it’s trainble flag to False and recompile the model before AIMET usage. This is one of the limitations with TensorFlow 2.x but If you are using TensorFlow 1.x, then this step is not required:

...
model = Model()
from aimet_tensorflow.utils.graph import update_keras_bn_ops_trainable_flag
model = update_keras_bn_ops_trainable_flag(model, load_save_path="./", trainable=False)
aimet_tensorflow.utils.graph.update_keras_bn_ops_trainable_flag(model, trainable, load_save_path)[source]

helper method to update Keras BN ops trainable state in a given keras model.

Parameters:
  • model (Model) – Keras model to be updated with BN ops trainable flag

  • trainable (bool) – bool flag to indicate trainable to be set to true or false

  • load_save_path (str) – temp folder to perform load/save, cleans up file created

Return type:

Model

Returns:

updated keras model

If model has Recurrent (RNN, LSTM etc.) layers

Recurrent layers (RNN, LSTM) are not supported with TensorFlow 2.x and only supported with TensorFlow 1.x.