message: Removes a now-broken keras LayerNorm test that relied on the recently-removed params_axis argument to work.
diff: @@ -452,28 +452,6 @@ class LayerNormalizationTest(keras_parameterized.TestCase):
  self.assertEqual(layer.gamma.constraint, max_norm)
  self.assertEqual(layer.beta.constraint, max_norm)
  - @keras_parameterized.run_all_keras_modes
  - def test_layernorm_convnet(self):
  - if test.is_gpu_available(cuda_only=True):
  - with self.session(use_gpu=True):
  - model = keras.models.Sequential()
  - norm = keras.layers.LayerNormalization(
  - input_shape=(3, 4, 4))
  - model.add(norm)
  - model.compile(loss='mse',
  - optimizer=gradient_descent.GradientDescentOptimizer(0.01),
  - run_eagerly=testing_utils.should_run_eagerly())
  -
  - # centered on 5.0, variance 10.0
  - x = np.random.normal(loc=5.0, scale=10.0, size=(1000, 3, 4, 4))
  - model.fit(x, x, epochs=4, verbose=0)
  - out = model.predict(x)
  - out -= np.reshape(keras.backend.eval(norm.beta), (1, 3, 1, 1))
  - out /= np.reshape(keras.backend.eval(norm.gamma), (1, 3, 1, 1))
  -
  - np.testing.assert_allclose(np.mean(out, axis=(0, 2, 3)), 0.0, atol=1e-1)
  - np.testing.assert_allclose(np.std(out, axis=(0, 2, 3)), 1.0, atol=1e-1)
  -
  @keras_parameterized.run_all_keras_modes
  def test_layernorm_convnet_channel_last(self):
  model = keras.models.Sequential()
  