PEP8 fixes

examples/densenet_cifar10.py

@@ -13,7 +13,6 @@ from keras.preprocessing.image import ImageDataGenerator
 from keras.utils import np_utils
 from keras_contrib.applications.densenet import DenseNet
 
-
 batch_size = 64
 nb_classes = 10
 nb_epoch = 300
@@ -27,7 +26,7 @@ depth = 40
 nb_dense_block = 3
 growth_rate = 12
 nb_filter = 16
-dropout_rate = 0.0 # 0.0 for data augmentation
+dropout_rate = 0.0  # 0.0 for data augmentation
 
 # Create the model (without loading weights)
 model = DenseNet(depth, nb_dense_block, growth_rate, nb_filter, dropout_rate=dropout_rate,
@@ -36,7 +35,7 @@ print("Model created")
 
 model.summary()
 
-optimizer = Adam(lr=1e-3) # Using Adam instead of SGD to speed up training
+optimizer = Adam(lr=1e-3)  # Using Adam instead of SGD to speed up training
 model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=["accuracy"])
 print("Finished compiling")
 
@@ -52,25 +51,26 @@ Y_train = np_utils.to_categorical(trainY, nb_classes)
 Y_test = np_utils.to_categorical(testY, nb_classes)
 
 generator = ImageDataGenerator(rotation_range=15,
-                               width_shift_range=5./32,
-                               height_shift_range=5./32)
+                               width_shift_range=5. / 32,
+                               height_shift_range=5. / 32)
 
 generator.fit(trainX, seed=0)
 
 weights_file = "DenseNet-40-12-CIFAR-10.h5"
 
-lr_reducer      = ReduceLROnPlateau(monitor='val_loss', factor=np.sqrt(0.1), 
-                                    cooldown=0, patience=10, min_lr=0.5e-6)
-early_stopper   = EarlyStopping(monitor='val_acc', min_delta=1e-4, patience=20)
-model_checkpoint= ModelCheckpoint(weights_file, monitor="val_acc", save_best_only=True,
-                                  save_weights_only=True,mode='auto')
+lr_reducer = ReduceLROnPlateau(monitor='val_loss', factor=np.sqrt(0.1),
+                               cooldown=0, patience=10, min_lr=0.5e-6)
+early_stopper = EarlyStopping(monitor='val_acc', min_delta=1e-4, patience=20)
+model_checkpoint = ModelCheckpoint(weights_file, monitor="val_acc", save_best_only=True,
+                                   save_weights_only=True, mode='auto')
 
 callbacks = [lr_reducer, early_stopper, model_checkpoint]
 
-model.fit_generator(generator.flow(trainX, Y_train, batch_size=batch_size), samples_per_epoch=len(trainX), nb_epoch=nb_epoch,
-                   callbacks=callbacks,
-                   validation_data=(testX, Y_test),
-                   nb_val_samples=testX.shape[0], verbose=2)
+model.fit_generator(generator.flow(trainX, Y_train, batch_size=batch_size), samples_per_epoch=len(trainX),
+                    nb_epoch=nb_epoch,
+                    callbacks=callbacks,
+                    validation_data=(testX, Y_test),
+                    nb_val_samples=testX.shape[0], verbose=2)
 
 yPreds = model.predict(testX)
 yPred = np.argmax(yPreds, axis=1)
@@ -81,4 +81,3 @@ accuracy = metrics.accuracy_score(yTrue, yPred) * 100
 error = 100 - accuracy
 print("Accuracy : ", accuracy)
 print("Error : ", error)
-