我正在使用 Keras 进行颜色去马赛克。我尝试过卷积神经网络，但它无法正常工作。我的代码是-：
from __future__ import print_function
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.optimizers import SGD
from keras.utils import np_utils
import bayer_pattern
from cnn.networks.model_define import CNNModel
import argparse
import numpy
from PIL import Image
ap = argparse.ArgumentParser()
ap.add_argument("-s", "--save-model", type=int, default=-1,
help="(optional) whether or not model should be saved to disk")
ap.add_argument("-l", "--load-model", type=int, default=-1,
help="(optional) whether or not pre-trained model should be loaded")
ap.add_argument("-w", "--weights", type=str,
help="(optional) path to weights file")
args = vars(ap.parse_args())
batch_size = 32
nb_classes = 10
nb_epoch = 200
data_augmentation = False
# input image dimensions
img_rows, img_cols = 32, 32
# the CIFAR10 images are RGB
img_channels = 3
# the data, shuffled and split between train and test sets
(y_train, y_train_train), (y_test, y_test_test) = cifar10.load_data()
print('X_train shape:', y_train.shape)
print('X_test shape:', y_test.shape)
print(y_train.shape[0], 'train samples')
print(y_test.shape[0], 'test samples')
# X_train.dump("X_train.dat")
# X_test.dump("X_test.dat")
# X_bayer_train = bayer_pattern.makeInputsCifar(X_train)
# X_bayer_test = bayer_pattern.makeInputsCifar(X_test)
# X_bayer_train.dump("X_bayer_train.dat")
# X_bayer_test.dump("X_bayer_test.dat")
X_bayer_train = numpy.load("X_bayer_train.dat")
X_bayer_test = numpy.load("X_bayer_test.dat")
model = Sequential()
model.add(Convolution2D(16, 5, 5, border_mode='same', input_shape=(img_channels, img_rows, img_cols)))
model.add(Activation("relu"))
model.add(Convolution2D(32, 5, 5, border_mode="same"))
model.add(Activation("relu"))
model.add(Convolution2D(64, 5, 5, border_mode="same"))
model.add(Activation("relu"))
model.add(Convolution2D(32, 5, 5, border_mode="same"))
model.add(Activation("relu"))
# model.add(Convolution2D(64, 5, 5, border_mode="same"))
# model.add(Activation("relu"))
# model.add(Convolution2D(64, 5, 5, border_mode="same"))
# model.add(Activation("relu"))
# model.add(Convolution2D(32, 5, 5, border_mode="same"))
# model.add(Activation("relu"))
# model.add(Convolution2D(32, 5, 5, border_mode="same"))
# model.add(Activation("relu"))
model.add(Convolution2D(16, 5, 5, border_mode="same"))
model.add(Activation("relu"))
model.add(Convolution2D(3, 5, 5, border_mode="same"))
model.add(Activation("relu"))
# for a mean squared error regression problem
model.compile(optimizer='rmsprop', loss='mse')
if not data_augmentation:
print('Not using data augmentation.')
model.fit(X_bayer_train, y_train,
batch_size=batch_size,
nb_epoch=nb_epoch,
validation_data=(X_bayer_test, y_test), shuffle=True)
score = model.evaluate(X_bayer_test, y_test, batch_size=batch_size)
print(score)
img = (model.predict(X_bayer_test[0:1]))[0].swapaxes(0,2).swapaxes(0,1)
imgans = (y_test[0:1])[0].swapaxes(0,2).swapaxes(0,1)
predicted = Image.fromarray(img, 'RGB')
predicted.save('predicted.png')
original = Image.fromarray(imgans, 'RGB')
original.save('original.png')
print(bayer_pattern.psnr(img,imgans))
print("[INFO] dumping weights to file...")
model.save('models/2Sept.h5')
else:
print('Using real-time data augmentation.')
# this will do preprocessing and realtime data augmentation
datagen = ImageDataGenerator(
featurewise_center=False, # set input mean to 0 over the dataset
samplewise_center=False, # set each sample mean to 0
featurewise_std_normalization=False, # divide inputs by std of the dataset
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)
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)
# fit the model on the batches generated by datagen.flow()
# model.fit_generator(datagen.flow(X_train, Y_train,
# batch_size=batch_size),
# samples_per_epoch=X_train.shape[0],
# nb_epoch=nb_epoch,
# validation_data=(X_test, Y_test))
model.fit_generator(datagen.flow(X_bayer_train, X_train,
batch_size=batch_size),
samples_per_epoch=X_bayer_train.shape[0],
nb_epoch=nb_epoch,
validation_data=(X_bayer_test, X_test))
print("[INFO] dumping weights to file...")
model.save_weights(args["weights"], overwrite=True)
有人可以为这个图像去马赛克问题推荐一些架构吗？目前我正在使用卷积神经网络和 cifar10 数据库。