PEP8 fixes in tests.

tests/integration_tests/test_temporal_data_tasks.py

@@ -108,8 +108,8 @@ def test_stacked_lstm_char_prediction():
     y = np.zeros((len(sentences), number_of_chars), dtype=np.bool)
     for i, sentence in enumerate(sentences):
         for t, char in enumerate(sentence):
-            X[i, t, ord(char)-ord('a')] = 1
+            X[i, t, ord(char) - ord('a')] = 1
-        y[i, ord(next_chars[i])-ord('a')] = 1
+        y[i, ord(next_chars[i]) - ord('a')] = 1
 
     # learn the alphabet with stacked LSTM
     model = Sequential([
@@ -123,7 +123,7 @@ def test_stacked_lstm_char_prediction():
     # prime the model with 'ab' sequence and let it generate the learned alphabet
     sentence = alphabet[:sequence_length]
     generated = sentence
-    for iteration in range(number_of_chars-sequence_length):
+    for iteration in range(number_of_chars - sequence_length):
         x = np.zeros((1, sequence_length, number_of_chars))
         for t, char in enumerate(sentence):
             x[0, t, ord(char) - ord('a')] = 1.

tests/keras/backend/test_backends.py

@@ -790,7 +790,7 @@ class TestBackend(object):
 
         # len max_time_steps array of batch_size x depth matrices
         inputs = ([input_prob_matrix_0[t, :][np.newaxis, :]
-                  for t in range(seq_len_0)] +  # Pad to max_time_steps = 8
+                   for t in range(seq_len_0)] +  # Pad to max_time_steps = 8
                   2 * [np.zeros((1, depth), dtype=np.float32)])
 
         inputs = KTF.variable(np.asarray(inputs).transpose((1, 0, 2)))
@@ -899,7 +899,7 @@ class TestBackend(object):
     def test_foldl(self):
         x = np.random.rand(10, 3).astype(np.float32)
         for K in [KTF, KTH]:
-            kx = K.eval(K.foldl(lambda a, b: a+b, x))
+            kx = K.eval(K.foldl(lambda a, b: a + b, x))
 
             assert (3,) == kx.shape
             assert_allclose(x.sum(axis=0), kx, atol=1e-05)
@@ -911,8 +911,8 @@ class TestBackend(object):
         # right to left we have no such problem and the result is larger
         x = np.array([1e-20, 1e-20, 10, 10, 10], dtype=np.float32)
         for K in [KTF, KTH]:
-            p1 = K.eval(K.foldl(lambda a, b: a*b, x))
+            p1 = K.eval(K.foldl(lambda a, b: a * b, x))
-            p2 = K.eval(K.foldr(lambda a, b: a*b, x))
+            p2 = K.eval(K.foldr(lambda a, b: a * b, x))
 
             assert p1 < p2
             assert 9e-38 < p2 <= 1e-37

tests/keras/engine/test_topology.py

@@ -9,6 +9,7 @@ from keras import backend as K
 from keras.models import model_from_json, model_from_yaml
 from keras.utils.test_utils import keras_test
 
+
 @keras_test
 def test_get_updates_for():
     a = Input(shape=(2,))
@@ -26,7 +27,7 @@ def test_get_losses_for():
     dense_layer = Dense(1)
     dense_layer.add_loss(0, inputs=a)
     dense_layer.add_loss(1, inputs=None)
-    
+
     assert dense_layer.get_losses_for(a) == [0]
     assert dense_layer.get_losses_for(None) == [1]
 

tests/keras/preprocessing/test_image.py

@@ -15,7 +15,7 @@ class TestImage:
         gray_images = []
         for n in range(8):
             bias = np.random.rand(img_w, img_h, 1) * 64
-            variance = np.random.rand(img_w, img_h, 1) * (255-64)
+            variance = np.random.rand(img_w, img_h, 1) * (255 - 64)
             imarray = np.random.rand(img_w, img_h, 3) * variance + bias
             im = Image.fromarray(imarray.astype('uint8')).convert('RGB')
             rgb_images.append(im)

tests/keras/test_callbacks.py

@@ -96,6 +96,7 @@ def test_ModelCheckpoint():
     os.remove(filepath.format(epoch=1))
     os.remove(filepath.format(epoch=3))
 
+
 def test_EarlyStopping():
     (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=train_samples,
                                                          nb_test=test_samples,

tests/keras/test_initializations.py

@@ -86,10 +86,10 @@ def test_identity(tensor_shape):
     if len(tensor_shape) > 2:
         with pytest.raises(Exception):
             _runner(initializations.identity, tensor_shape,
-                    target_mean=1./SHAPE[0], target_max=1.)
+                    target_mean=1. / SHAPE[0], target_max=1.)
     else:
         _runner(initializations.identity, tensor_shape,
-                target_mean=1./SHAPE[0], target_max=1.)
+                target_mean=1. / SHAPE[0], target_max=1.)
 
 
 @pytest.mark.parametrize('tensor_shape', [FC_SHAPE, CONV_SHAPE], ids=['FC', 'CONV'])

tests/keras/test_metrics.py

@@ -105,13 +105,13 @@ def test_top_k_categorical_accuracy():
     y_pred = K.variable(np.array([[0.3, 0.2, 0.1], [0.1, 0.2, 0.7]]))
     y_true = K.variable(np.array([[0, 1, 0], [1, 0, 0]]))
     success_result = K.eval(metrics.top_k_categorical_accuracy(y_true, y_pred,
-                            k=3))
+                                                               k=3))
     assert success_result == 1
     partial_result = K.eval(metrics.top_k_categorical_accuracy(y_true, y_pred,
-                            k=2))
+                                                               k=2))
     assert partial_result == 0.5
     failure_result = K.eval(metrics.top_k_categorical_accuracy(y_true, y_pred,
-                            k=1))
+                                                               k=1))
     assert failure_result == 0
 
 

tests/keras/wrappers/test_scikit_learn.py

@@ -49,6 +49,7 @@ def test_clasify_build_fn():
 
 def test_clasify_class_build_fn():
     class ClassBuildFnClf(object):
+
         def __call__(self, hidden_dims):
             return build_fn_clf(hidden_dims)
 
@@ -61,6 +62,7 @@ def test_clasify_class_build_fn():
 
 def test_clasify_inherit_class_build_fn():
     class InheritClassBuildFnClf(KerasClassifier):
+
         def __call__(self, hidden_dims):
             return build_fn_clf(hidden_dims)
 
@@ -110,6 +112,7 @@ def test_regression_build_fn():
 
 def test_regression_class_build_fn():
     class ClassBuildFnReg(object):
+
         def __call__(self, hidden_dims):
             return build_fn_reg(hidden_dims)
 
@@ -122,6 +125,7 @@ def test_regression_class_build_fn():
 
 def test_regression_inherit_class_build_fn():
     class InheritClassBuildFnReg(KerasRegressor):
+
         def __call__(self, hidden_dims):
             return build_fn_reg(hidden_dims)