Method _getSequentialModel

tensorflow/lite/python/lite_test.py:1542–1566

(self, include_custom_layer=False)

    super(FromKerasFile, self).tearDown()

  def _getSequentialModel(self, include_custom_layer=False):
    model = keras.models.Sequential()
    model.add(keras.layers.Dense(2, input_shape=(3,)))
    if include_custom_layer:
      model.add(MyAddLayer(1.0))
    model.add(keras.layers.RepeatVector(3))
    model.add(keras.layers.TimeDistributed(keras.layers.Dense(3)))
    model.compile(
        loss=keras.losses.MSE,
        optimizer='sgd',
        metrics=[keras.metrics.categorical_accuracy],
        sample_weight_mode='temporal')
    x = np.random.random((1, 3))
    y = np.random.random((1, 3, 3))
    model.train_on_batch(x, y)
    model.predict(x)

    try:
      fd, self._keras_file = tempfile.mkstemp('.h5')
      keras.models.save_model(model, self._keras_file)
    finally:
      os.close(fd)

    if include_custom_layer:
      self._custom_objects = {'MyAddLayer': MyAddLayer}

  @parameterized.named_parameters(('_graph', context.graph_mode),
                                  ('_eager', context.eager_mode))