  
  @pytest.mark.parametrize("dummy", [None], ids=[get_backend().upper()])
  def test_adabelief(dummy):  # pylint:disable=unused-argument
      """ Test for custom Adam optimizer """
      _test_optimizer(optimizers.AdaBelief(), target=0.5)
 
</s> remove     _test_optimizer(k_optimizers.Adam(), target=0.5)
    _test_optimizer(k_optimizers.Adam(decay=1e-3), target=0.5)
 </s> add     _test_optimizer(k_optimizers.Adam(), target=0.45)
    _test_optimizer(k_optimizers.Adam(decay=1e-3), target=0.45) </s> remove     assert ((_BACKEND == "cpu" and keras.__version__ in ("2.3.0-tf", "2.4.0")) or
 </s> add     assert ((_BACKEND == "cpu" and keras.__version__ in ("2.3.0-tf", "2.4.0",
                                                         "2.6.0", "2.7.0", "2.8.0")) or </s> remove 

@pytest.mark.parametrize('dummy', [None], ids=[get_backend().upper()])
def test_dssim_channels_last(dummy):  # pylint:disable=unused-argument
    """ Basic test for DSSIM Loss """
    prev_data = K.image_data_format()
    K.set_image_data_format('channels_last')
    for input_dim, kernel_size in zip([32, 33], [2, 3]):
        input_shape = [input_dim, input_dim, 3]
        var_x = np.random.random_sample(4 * input_dim * input_dim * 3)
        var_x = var_x.reshape([4] + input_shape)
        var_y = np.random.random_sample(4 * input_dim * input_dim * 3)
        var_y = var_y.reshape([4] + input_shape)

        model = Sequential()
        model.add(Conv2D(32, (3, 3), padding='same', input_shape=input_shape,
                         activation='relu'))
        model.add(Conv2D(3, (3, 3), padding='same', input_shape=input_shape,
                         activation='relu'))
        adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-8)
        model.compile(loss=losses.DSSIMObjective(kernel_size=kernel_size),
                      metrics=['mse'],
                      optimizer=adam)
        model.fit(var_x, var_y, batch_size=2, epochs=1, shuffle='batch')

        # Test same
        x_1 = K.constant(var_x, 'float32')
        x_2 = K.constant(var_x, 'float32')
        dssim = losses.DSSIMObjective(kernel_size=kernel_size)
        assert_allclose(0.0, K.eval(dssim(x_1, x_2)), atol=1e-4)

        # Test opposite
        x_1 = K.zeros([4] + input_shape)
        x_2 = K.ones([4] + input_shape)
        dssim = losses.DSSIMObjective(kernel_size=kernel_size)
        assert_allclose(0.5, K.eval(dssim(x_1, x_2)), atol=1e-4)

    K.set_image_data_format(prev_data)
 </s> add  </s> remove     def call(self, inputs, **kwargs):  # pylint:disable=unused-argument
 </s> add     def call(self, inputs, *args, **kwargs): </s> remove     def call(self, inputs, **kwargs):  # pylint:disable=unused-argument
 </s> add     def call(self, inputs, *args, **kwargs):
    _test_optimizer(optimizers.AdaBelief(), target=0.45)
  
  @pytest.mark.parametrize("dummy", [None], ids=[get_backend().upper()])
  def test_adabelief(dummy):  # pylint:disable=unused-argument
      """ Test for custom Adam optimizer """
    _test_optimizer(optimizers.AdaBelief(), target=0.45)
keep keep keep keep replace
 Update code to support Tensorflow versions up to 2.8 (#1213)

* Update maximum tf version in setup + requirements

* - bump max version of tf version in launcher
- standardise tf version check

* update keras get_custom_objects  for tf>2.6

* bugfix: force black text in GUI file dialogs (linux)

* dssim loss - Move to stock tf.ssim function

* Update optimizer imports for compatibility

* fix logging for tf2.8

* Fix GUI graphing for TF2.8

* update tests

* bump requirements.txt versions

* Remove limit on nvidia-ml-py

* Graphing bugfixes
  - Prevent live graph from displaying if data not yet available

* bugfix: Live graph. Collect loss labels correctly

* fix: live graph - swallow inconsistent loss errors

* Bugfix: Prevent live graph from clearing during training

* Fix graphing for AMD 
tests/lib/model/optimizers_test.py