diff --git a/keras_contrib/layers/convolutional.py b/keras_contrib/layers/convolutional.py
index a20d6a7..8495d38 100644
--- a/keras_contrib/layers/convolutional.py
+++ b/keras_contrib/layers/convolutional.py
@@ -458,4 +458,4 @@ class CosineConvolution2D(Layer):
 
 CosineConv2D = CosineConvolution2D
 get_custom_objects().update({"CosineConvolution2D": CosineConvolution2D})
-get_custom_objects().update({"CosineConv2D": CosineConv2D})
\ No newline at end of file
+get_custom_objects().update({"CosineConv2D": CosineConv2D})
diff --git a/tests/keras_contrib/layers/test_convolutional.py b/tests/keras_contrib/layers/test_convolutional.py
index 042a094..483d5cb 100644
--- a/tests/keras_contrib/layers/test_convolutional.py
+++ b/tests/keras_contrib/layers/test_convolutional.py
@@ -75,6 +75,7 @@ def test_deconvolution_3d():
                                    'subsample': subsample},
                            input_shape=(nb_samples, stack_size, kernel_dim1, kernel_dim2, kernel_dim3))
 
+
 @keras_test
 def test_cosineconvolution_2d():
     nb_samples = 2
@@ -85,30 +86,30 @@ def test_cosineconvolution_2d():
 
     for border_mode in _convolution_border_modes:
         for subsample in [(1, 1), (2, 2)]:
-          for bias_mode in [True, False]:
-            if border_mode == 'same' and subsample != (1, 1):
-                continue
+            for bias_mode in [True, False]:
+                if border_mode == 'same' and subsample != (1, 1):
+                    continue
 
-            layer_test(convolutional.CosineConvolution2D,
-                       kwargs={'nb_filter': nb_filter,
-                               'nb_row': 3,
-                               'nb_col': 3,
-                               'border_mode': border_mode,
-                               'subsample': subsample,
-                               'bias': bias_mode},
-                       input_shape=(nb_samples, nb_row, nb_col, stack_size))
+                layer_test(convolutional.CosineConvolution2D,
+                           kwargs={'nb_filter': nb_filter,
+                                   'nb_row': 3,
+                                   'nb_col': 3,
+                                   'border_mode': border_mode,
+                                   'subsample': subsample,
+                                   'bias': bias_mode},
+                           input_shape=(nb_samples, nb_row, nb_col, stack_size))
 
-            layer_test(convolutional.CosineConvolution2D,
-                       kwargs={'nb_filter': nb_filter,
-                               'nb_row': 3,
-                               'nb_col': 3,
-                               'border_mode': border_mode,
-                               'W_regularizer': 'l2',
-                               'b_regularizer': 'l2',
-                               'activity_regularizer': 'activity_l2',
-                               'subsample': subsample,
-                               'bias': bias_mode},
-                       input_shape=(nb_samples, nb_row, nb_col, stack_size))
+                layer_test(convolutional.CosineConvolution2D,
+                           kwargs={'nb_filter': nb_filter,
+                                   'nb_row': 3,
+                                   'nb_col': 3,
+                                   'border_mode': border_mode,
+                                   'W_regularizer': 'l2',
+                                   'b_regularizer': 'l2',
+                                   'activity_regularizer': 'activity_l2',
+                                   'subsample': subsample,
+                                   'bias': bias_mode},
+                           input_shape=(nb_samples, nb_row, nb_col, stack_size))
 
     dim_ordering = K.image_dim_ordering()
     assert dim_ordering in {'tf', 'th'}, 'dim_ordering must be in {tf, th}'
@@ -142,7 +143,5 @@ def test_cosineconvolution_2d():
     assert_allclose(out, -np.ones((1, 1, 1, 1), dtype=K.floatx()), atol=1e-5)
 
 
-
-
 if __name__ == '__main__':
     pytest.main([__file__])