Method testSequentialModel

tensorflow/lite/python/lite_test.py:1570–1605

Test a Sequential tf.keras model with default inputs.

(self, test_context)

  @parameterized.named_parameters(('_graph', context.graph_mode),
                                  ('_eager', context.eager_mode))
  def testSequentialModel(self, test_context):
    """Test a Sequential tf.keras model with default inputs."""
    with test_context():
      self._getSequentialModel()

      converter = lite.TFLiteConverter.from_keras_model_file(self._keras_file)
      tflite_model = converter.convert()
      self.assertTrue(tflite_model)

    # Check tensor details of converted model.
    interpreter = Interpreter(model_content=tflite_model)
    interpreter.allocate_tensors()

    input_details = interpreter.get_input_details()
    self.assertLen(input_details, 1)
    self.assertEqual('dense_input', input_details[0]['name'])
    self.assertEqual(np.float32, input_details[0]['dtype'])
    self.assertTrue(([1, 3] == input_details[0]['shape']).all())
    self.assertEqual((0., 0.), input_details[0]['quantization'])

    output_details = interpreter.get_output_details()
    self.assertLen(output_details, 1)
    self.assertEqual(np.float32, output_details[0]['dtype'])
    self.assertTrue(([1, 3, 3] == output_details[0]['shape']).all())
    self.assertEqual((0., 0.), output_details[0]['quantization'])

    # Check inference of converted model.
    input_data = np.array([[1, 2, 3]], dtype=np.float32)
    interpreter.set_tensor(input_details[0]['index'], input_data)
    interpreter.invoke()
    tflite_result = interpreter.get_tensor(output_details[0]['index'])

    keras_model = keras.models.load_model(self._keras_file)
    keras_result = keras_model.predict(input_data)

    np.testing.assert_almost_equal(tflite_result, keras_result, 5)

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