pep8 fix

tests/keras_contrib/layers/test_normalization.py

@@ -10,6 +10,5 @@ from keras import backend as K
 from keras_contrib import backend as KC
 
 
-
 if __name__ == '__main__':
     pytest.main([__file__])

tests/keras_contrib/test_callbacks.py

@@ -1,14 +1,3 @@
-import os
-import sys
-import multiprocessing
-
-import numpy as np
-import pytest
-from csv import Sniffer
-from keras import optimizers
-
-np.random.seed(1337)
-
 from keras.models import Sequential
 from keras.layers.core import Dense
 from keras.utils.test_utils import get_test_data
@@ -17,6 +6,17 @@ from keras_contrib import backend as KC
 from keras.utils import np_utils
 from keras_contrib import callbacks
 
+import os
+import sys
+import multiprocessing
+
+import numpy as np
+import pytest
+from csv import Sniffer
+
+
+np.random.seed(1337)
+
 input_dim = 2
 nb_hidden = 4
 nb_class = 2

tests/keras_contrib/test_constraints.py

@@ -13,6 +13,5 @@ example_array = np.random.random((100, 100)) * 100. - 50.
 example_array[0, 0] = 0.  # 0 could possibly cause trouble
 
 
-
 if __name__ == '__main__':
     pytest.main([__file__])

tests/keras_contrib/test_initializations.py

@@ -1,9 +1,8 @@
-import pytest
-import numpy as np
-
 from keras import backend as K
 from keras_contrib import backend as KC
 from keras_contrib import initializations
+import pytest
+import numpy as np
 
 
 # 2D tensor test fixture

tests/keras_contrib/test_optimizers.py

@@ -1,13 +1,12 @@
 from __future__ import print_function
-import pytest
-import numpy as np
-np.random.seed(1337)
-
 from keras.utils.test_utils import get_test_data
 from keras.models import Sequential
 from keras.layers.core import Dense, Activation
 from keras.utils.np_utils import to_categorical
-from keras_contrib.optimizers import *
+from keras_contrib import optimizers
+import pytest
+import numpy as np
+np.random.seed(1337)
 
 
 (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000,

tests/keras_contrib/test_regularizers.py

@@ -1,7 +1,3 @@
-import pytest
-import numpy as np
-np.random.seed(1337)
-
 from keras.models import Sequential
 from keras.layers import Merge
 from keras.layers import Dense
@@ -12,6 +8,10 @@ from keras.layers import Embedding
 from keras.datasets import mnist
 from keras.utils import np_utils
 from keras_contrib import regularizers
+import pytest
+import numpy as np
+np.random.seed(1337)
+
 
 nb_classes = 10
 batch_size = 128