旧版本中：
from keras.layers import merge
merge6 = merge([layer1,layer2], mode = 'concat', concat_axis = 3)
新版本中：
from keras.layers.merge import concatenate
merge = concatenate([layer1, layer2], axis=3)
补充知识：keras输入数据的方法：model.fit和model.fit_generator
1.第一种，普通的不用数据增强的
from keras.datasets import mnist,cifar10，cifar100 (X_trAIn, y_train), (X_valid, Y_valid) = cifar10.load_data() model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, shuffle=True, verbose=1, validation_data=(X_valid, Y_valid), )
2.第二种，带数据增强的 ImageDataGenerator，可以旋转角度、平移等操作。
from keras.preprocessing.image import ImageDataGenerator
(trainX, trainY), (testX, testY) = cifar100.load_data()
trainX = trainX.astype('float32')
testX = testX.astype('float32')
trainX /= 255.
testX /= 255.
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)
generator.fit(trainX, seed=0)
model.fit_generator(generator.flow(trainX, Y_train, batch_size=batch_size),
steps_per_epoch=len(trainX) // batch_size, epochs=nb_epoch,
callbacks=callbacks,
validation_data=(testX, Y_test),
validation_steps=testX.shape[0] // batch_size, verbose=1)
