Description of TF Lite’s Toco converter args for quantization aware training

You should never need to manually set the quantization stats.

Have you tried the post-training-quantization tutorials?

https://www.tensorflow.org/lite/performance/post_training_integer_quant

Basically they set the quantization options:

converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.uint8
converter.inference_output_type = tf.uint8

Then they pass a “representative dataset” to the converter, so that the converter can run the model a few batches to gather the necessary statistics:

def representative_data_gen():
  for input_value in mnist_ds.take(100):
    yield [input_value]

converter.representative_dataset = representative_data_gen

While there are options for quantized training, it’s always easier to to do post-training quantization.