message: Update quantization docs to use TFLiteConverter.from_saved_model() API instead of .from_keras_model() API
diff: @@ -34,7 +34,7 @@ weights from floating point to integer, which has 8-bits of precision:
  <pre>
  import tensorflow as tf
  -converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
  +converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
  <b>converter.optimizations = [tf.lite.Optimize.DEFAULT]</b>
  tflite_quant_model = converter.convert()
  </pre>
  @@ -68,7 +68,7 @@ the following steps:
  <pre>
  import tensorflow as tf
  -converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
  +converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
  <b>converter.optimizations = [tf.lite.Optimize.DEFAULT]
  def representative_dataset_gen():
  for _ in range(num_calibration_steps):
  @@ -96,7 +96,7 @@ the following steps:
  <pre>
  import tensorflow as tf
  -converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
  +converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
  converter.optimizations = [tf.lite.Optimize.DEFAULT]
  def representative_dataset_gen():
  for _ in range(num_calibration_steps):
  @@ -120,7 +120,7 @@ quantization of weights, use the following steps:
  <pre>
  import tensorflow as tf
  -converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
  +converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
  <b>converter.optimizations = [tf.lite.Optimize.DEFAULT]
  converter.target_spec.supported_types = [tf.float16]</b>
  tflite_quant_model = converter.convert()
  