Removed some exceptions from the pytest.ini (#368)

examples/cifar10_resnet.py

@@ -19,7 +19,8 @@
 
 
 weights_file = 'ResNet18v2-CIFAR-10.h5'
-lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.1), cooldown=0, patience=5, min_lr=0.5e-6)
+lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.1), cooldown=0,
+                               patience=5, min_lr=0.5e-6)
 early_stopper = EarlyStopping(min_delta=0.001, patience=10)
 csv_logger = CSVLogger('ResNet18v2-CIFAR-10.csv')
 model_checkpoint = ModelCheckpoint(weights_file, monitor='val_acc', save_best_only=True,
@@ -75,21 +76,22 @@
         samplewise_std_normalization=False,  # divide each input by its std
         zca_whitening=False,  # apply ZCA whitening
         rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)
-        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
-        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)
+        width_shift_range=0.1,  # randomly shift images horizontally
+        height_shift_range=0.1,  # randomly shift images vertically
         horizontal_flip=True,  # randomly flip images
         vertical_flip=False)  # randomly flip images
 
     # Compute quantities required for featurewise normalization
     # (std, mean, and principal components if ZCA whitening is applied).
     datagen.fit(X_train)
 
+    callbacks = [lr_reducer, early_stopper, csv_logger, model_checkpoint]
     # Fit the model on the batches generated by datagen.flow().
     model.fit_generator(datagen.flow(X_train, Y_train, batch_size=batch_size),
                         steps_per_epoch=X_train.shape[0] // batch_size,
                         validation_data=(X_test, Y_test),
                         epochs=nb_epoch, verbose=2,
-                        callbacks=[lr_reducer, early_stopper, csv_logger, model_checkpoint])
+                        callbacks=callbacks)
 
 scores = model.evaluate(X_test, Y_test, batch_size=batch_size)
 print('Test loss : ', scores[0])

examples/cifar10_wide_resnet.py

@@ -46,12 +46,14 @@
 
 model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])
 print('Finished compiling')
-
-model.fit_generator(generator.flow(trainX, trainY, batch_size=batch_size), steps_per_epoch=len(trainX) // batch_size,
+model_checkpoint = callbacks.ModelCheckpoint('WRN-28-8 Weights.h5',
+                                             monitor='val_acc',
+                                             save_best_only=True,
+                                             save_weights_only=True)
+model.fit_generator(generator.flow(trainX, trainY, batch_size=batch_size),
+                    steps_per_epoch=len(trainX) // batch_size,
                     epochs=epochs,
-                    callbacks=[
-                        callbacks.ModelCheckpoint('WRN-28-8 Weights.h5', monitor='val_acc', save_best_only=True,
-                                                  save_weights_only=True)],
+                    callbacks=[model_checkpoint],
                     validation_data=(testX, testY))
 
 scores = model.evaluate(testX, testY, batch_size)

pytest.ini

@@ -10,16 +10,12 @@ addopts=-v
 norecursedirs= build
 
 # PEP-8 The following are ignored:
-# E501 line too long (82 > 79 characters)
 # E402 module level import not at top of file - temporary measure to continue adding ros python packaged in sys.path
 # E731 do not assign a lambda expression, use a def
 
 pep8ignore=* E402 \
            * E731 \
            * W503
-           examples/cifar10_densenet.py E501 \
-           examples/cifar10_resnet.py E501 \
-           examples/cifar10_wide_resnet.py E501 \
            examples/improved_wgan.py E501 \
            keras_contrib/applications/densenet.py E501 \
            keras_contrib/applications/nasnet.py E501 \