message: Added also representative_dataset_gen function to an example.
diff: @@ -155,6 +155,11 @@ significantly, but only slightly increase model size.
  <pre>
  import tensorflow as tf
  converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
  +def representative_dataset_gen():
  + for _ in range(num_calibration_steps):
  + # Get sample input data as a numpy array in a method of your choosing.
  + yield [input]
  +converter.representative_dataset = representative_dataset_gen
  <b>converter.optimizations = [tf.lite.Optimize.DEFAULT]
  converter.target_spec.supported_ops = [tf.lite.constants.EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8]</b>
  tflite_quant_model = converter.convert()
  @@ -166,6 +171,11 @@ The following option should be added to the target_spec to allow this.
  <pre>
  import tensorflow as tf
  converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
  +def representative_dataset_gen():
  + for _ in range(num_calibration_steps):
  + # Get sample input data as a numpy array in a method of your choosing.
  + yield [input]
  +converter.representative_dataset = representative_dataset_gen
  converter.optimizations = [tf.lite.Optimize.DEFAULT]
  converter.target_spec.supported_ops = [tf.lite.constants.EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8,
  <b>tf.lite.OpsSet.TFLITE_BUILTINS</b>]
  