old_contents: # Copyright 2020 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Python utility script to generate unit test model data."""
  
  # Steps to regenerate model test data:
  # TODO(b/158011574): Do these steps in the script here instead of manually.
  # 1.) Run this script
  # 2.) Hexdump the model into a .h/.cc file:
  #       xxd -i /tmp/tf_micro_conv_test_model.tflite > /tmp/temp.cc
  # 3.) Copy/replace contents of temp.cc into desired header/source files (e.g.
  #     test_conv_model.h/.cc
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from absl import app
  import numpy as np
  import tensorflow.compat.v2 as tf
  
  
  def generate_conv_model():
    """Creates a basic Keras model and converts to tflite.
  
    This model does not make any relevant classifications. It only exists to
    generate a model that is designed to run on embedded devices.
    """
    input_shape = (16, 16, 1)
  
    model = tf.keras.models.Sequential()
    model.add(
        tf.keras.layers.Conv2D(16, 3, activation="relu", input_shape=input_shape))
    model.add(tf.keras.layers.Conv2D(32, 3, activation="relu"))
    model.add(tf.keras.layers.MaxPooling2D(2))
    model.add(tf.keras.layers.Flatten())
    model.add(tf.keras.layers.Dense(10))
    model.compile(
        optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])
    model.summary()
  
    # Test with random data
    data_x = np.random.rand(12, 16, 16, 1)
    data_y = np.random.randint(2, size=(12, 10))
    model.fit(data_x, data_y, epochs=5)
  
    def representative_dataset_gen():
      for _ in range(12):
        yield [np.random.rand(16, 16).reshape(1, 16, 16, 1).astype(np.float32)]
  
    # Now convert to a TFLite model with full int8 quantization:
    converter = tf.lite.TFLiteConverter.from_keras_model(model)
    converter.optimizations = [tf.lite.Optimize.DEFAULT]
    converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
    converter.experimental_new_converter = True
    converter.inference_input_type = tf.int8
    converter.inference_output_type = tf.int8
    converter.representative_dataset = representative_dataset_gen
  
    tflite_model = converter.convert()
    open("/tmp/tf_micro_conv_test_model.int8.tflite", "wb").write(tflite_model)
  
  
  def main(argv):
    del argv  # Unused for now
    generate_conv_model()
  
  
  if __name__ == "__main__":
    app.run(main)
  
new_contents: # Copyright 2020 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Python utility script to generate unit test model data."""
  
  # Steps to regenerate model test data:
  # TODO(b/158011574): Do these steps in the script here instead of manually.
  # 1.) Run this script
  # 2.) Hexdump the model into a .h/.cc file:
  #       xxd -i /tmp/tf_micro_conv_test_model.tflite > /tmp/temp.cc
  # 3.) Copy/replace contents of temp.cc into desired header/source files (e.g.
  #     test_conv_model.h/.cc
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from absl import app
  import numpy as np
  import tensorflow.compat.v2 as tf
  
  
  def generate_conv_model():
    """Creates a basic Keras model and converts to tflite.
  
    This model does not make any relevant classifications. It only exists to
    generate a model that is designed to run on embedded devices.
    """
    input_shape = (16, 16, 1)
  
    model = tf.keras.models.Sequential()
    model.add(
        tf.keras.layers.Conv2D(16, 3, activation="relu", input_shape=input_shape))
    model.add(tf.keras.layers.Conv2D(32, 3, activation="relu"))
    model.add(tf.keras.layers.MaxPooling2D(2))
    model.add(tf.keras.layers.Flatten())
    model.add(tf.keras.layers.Dense(10))
    model.compile(
        optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])
    model.summary()
  
    # Test with random data
    data_x = np.random.rand(12, 16, 16, 1)
    data_y = np.random.randint(2, size=(12, 10))
    model.fit(data_x, data_y, epochs=5)
  
    def representative_dataset_gen():
      for _ in range(12):
        yield [np.random.rand(16, 16).reshape(1, 16, 16, 1).astype(np.float32)]
  
    # Now convert to a TFLite model with full int8 quantization:
    converter = tf.lite.TFLiteConverter.from_keras_model(model)
    converter.optimizations = [tf.lite.Optimize.DEFAULT]
    converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
    converter.inference_input_type = tf.int8
    converter.inference_output_type = tf.int8
    converter.representative_dataset = representative_dataset_gen
  
    tflite_model = converter.convert()
    open("/tmp/tf_micro_conv_test_model.int8.tflite", "wb").write(tflite_model)
  
  
  def main(argv):
    del argv  # Unused for now
    generate_conv_model()
  
  
  if __name__ == "__main__":
    app.run(main)
  
subject: Remove explicit line "experimental_new_converter = True" the converter launched and is now the default.