卷积神经网络中的图像预处理在 Keras 与 Tflearn 中产生较低的准确度

我正在尝试将此 tflearn DCNN 样本（使用图像预处理和增强）转换为 keras：

Tflearn 示例：

import tflearn
from tflearn.data_utils import shuffle, to_categorical
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.estimator import regression
from tflearn.data_preprocessing import ImagePreprocessing
from tflearn.data_augmentation import ImageAugmentation

# Data loading and preprocessing
from tflearn.datasets import cifar10
(X, Y), (X_test, Y_test) = cifar10.load_data()
X, Y = shuffle(X, Y)
Y = to_categorical(Y, 10)
Y_test = to_categorical(Y_test, 10)

# Real-time data preprocessing
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()

# Real-time data augmentation
img_aug = ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_rotation(max_angle=25.)

# Convolutional network building
network = input_data(shape=[None, 32, 32, 3],
                     data_preprocessing=img_prep,
                     data_augmentation=img_aug)
network = conv_2d(network, 32, 3, activation='relu')
network = max_pool_2d(network, 2)
network = conv_2d(network, 64, 3, activation='relu')
network = conv_2d(network, 64, 3, activation='relu')
network = max_pool_2d(network, 2)
network = fully_connected(network, 512, activation='relu')
network = dropout(network, 0.5)
network = fully_connected(network, 10, activation='softmax')
network = regression(network, optimizer='adam',
                     loss='categorical_crossentropy',
                     learning_rate=0.001)

# Train using classifier
model = tflearn.DNN(network, tensorboard_verbose=0)
model.fit(X, Y, n_epoch=50, shuffle=True, validation_set=(X_test, Y_test),
          show_metric=True, batch_size=96, run_id='cifar10_cnn')

这在 50 个 epoch 后产生了以下结果：

Training Step: 26050  | total loss: 0.35260 | time: 144.306s
| Adam | epoch: 050 | loss: 0.35260 - acc: 0.8785 | val_loss: 0.64622 - val_acc: 0.8212 -- iter: 50000/50000

然后我尝试使用相同的 DCNN 层、参数和图像预处理/增强将其转换为 Keras：

import numpy as np
from keras.datasets import cifar10
from keras.callbacks import TensorBoard
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, UpSampling2D, AtrousConvolution2D
from keras.layers.advanced_activations import LeakyReLU, PReLU
from keras.utils import np_utils
from keras.preprocessing.image import ImageDataGenerator
from keras import backend as K
import matplotlib
from matplotlib import pyplot as plt

np.random.seed(1337)

batch_size = 96 # how many images to process at once
nb_classes = 10 # how many types of objects we can detect in this set
nb_epoch = 50 # how long we train the system
img_rows, img_cols = 32, 32 # image dimensions
nb_filters = 32 # number of convolutional filters to use
pool_size = (2, 2) # size of pooling area for max pooling
kernel_size = (3, 3) # convolution kernel size

(X_train, Y_train), (X_test, Y_test) = cifar10.load_data()
X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 3)
X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 3)
input_shape = (img_rows, img_cols, 3)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(Y_train, nb_classes)
Y_test = np_utils.to_categorical(Y_test, nb_classes)

datagen = ImageDataGenerator(featurewise_center=True,
                             featurewise_std_normalization=True,
                             horizontal_flip=True,
                             rotation_range=25)
datagen.fit(X_train)

model = Sequential()
model.add(Conv2D(nb_filters, kernel_size, padding='valid', input_shape=input_shape, activation='relu'))
model.add(MaxPooling2D(pool_size=pool_size))
model.add(Conv2D(nb_filters*2, kernel_size, activation='relu'))
model.add(Conv2D(nb_filters*2, kernel_size, activation='relu'))
model.add(MaxPooling2D(pool_size=pool_size))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes, activation='softmax'))

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

# Set up TensorBoard
tb = TensorBoard(log_dir='./logs')

history = model.fit_generator(datagen.flow(X_train, Y_train, batch_size=batch_size), epochs=nb_epoch, shuffle=True, verbose=1, validation_data=(X_test, Y_test), callbacks=[tb])
score = model.evaluate(X_test, Y_test, verbose=0)
print('Test score:', score[0])
print("Accuracy: %.2f%%" % (score[1]*100))

plt.plot(history.epoch,history.history['val_acc'],'-o',label='validation')
plt.plot(history.epoch,history.history['acc'],'-o',label='training')
plt.legend(loc=0)
plt.xlabel('epochs')
plt.ylabel('accuracy')
plt.grid(True)
plt.show()

这产生了更糟糕的验证准确性结果：

Epoch 50/50
521/521 [==============================] - 84s 162ms/step - loss: 0.4723 - acc: 0.8340 - val_loss: 3.2970 - val_acc: 0.2729
Test score: 3.2969648239135743
Accuracy: 27.29%

谁能帮我理解为什么？我是否误用/误解了 Keras 中的图像预处理/增强？