该程序演示将一个预训练好的模型在新数据集上重新fine-tuning的过程。我们冻结卷积层，只调整全连接层。

* 在MNIST数据集上使用前五个数字[0…4]训练一个卷积网络。
* 在后五个数字[5…9]用卷积网络做分类，冻结卷积层并且微调全连接层
一、变量初始化
now = datetime.datetime.now batch_size = 128 nb_classes = 5 nb_epoch = 5 #
输入图像的维度 img_rows, img_cols = 28, 28 # 使用卷积滤波器的数量 nb_filters = 32 # 用于max
pooling的pooling面积的大小 pool_size = 2 # 卷积核的尺度 kernel_size = (3,3) input_shape =
(img_rows, img_cols, 1)# 数据，在训练和测试数据集上混洗和拆分 (X_train, y_train), (X_test,
y_test) = mnist.load_data()X_train_lt5 = X_train[y_train 5] y_train_lt5 =
y_train[y_train 5]X_test_lt5 = X_test[y_test 5] y_test_lt5 = y_test[y_test
5]X_train_gte5 = X_train[y_train = 5] #使标签从0~4，故-5 y_train_gte5 =
y_train[y_train = 5] - 5X_test_gte5 = X_test[y_test = 5] y_test_gte5 =
y_test[y_test = 5] - 5
二、模型的训练函数
def train_model(model, train, test, nb_classes): #train[0]是图片，train[1]是标签
X_train = train[0].reshape((train[0].shape[0],) + input_shape)#1D+3D=4D X_test
= test[0].reshape((test[0].shape[0],) + input_shape) 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 ) Y_train = np_utils.to_categorical(train[1
], nb_classes) Y_test = np_utils.to_categorical(test[1], nb_classes)
model.compile(loss= categorical_crossentropy , optimizer= adadelta , metrics=[
accuracy ]) t = now() model.fit(X_train, Y_train, batch_size=batch_size,
nb_epoch=nb_epoch, verbose=1, validation_data=(X_test, Y_test)) print( Training
time: %s % (now() - t)) score = model.evaluate(X_test, Y_test, verbose=0)
print( Test score: , score[0]) print( Test accuracy: , score[1])
三、建立模型，构建卷积层（特征层）和全连接层（分类层）
feature_layers = [ Convolution2D(nb_filters, kernel_size, padding= valid ,
input_shape=input_shape), Activation( relu ), Convolution2D(nb_filters,
kernel_size), Activation( relu ), MaxPooling2D(pool_size=(pool_size,
pool_size)), Dropout(0.25), Flatten(), ] classification_layers = [ Dense(128),
Activation( relu ), Dropout(0.5), Dense(nb_classes), Activation( softmax ) ]
model = Sequential(feature_layers + classification_layers)
四、对模型进行预训练
train_model(model, (X_train_lt5, y_train_lt5), (X_test_lt5, y_test_lt5),
nb_classes)
五、冻结预训练模型的特征层
for l in feature_layers: l.trainable = False
六、fine_tuning分类层
train_model(model, (X_train_gte5, y_train_gte5), (X_test_gte5, y_test_gte5),
nb_classes)
源码地址：

https://github.com/Zheng-Wenkai/Keras_Demo
https://github.com/Zheng-Wenkai/Keras_Demo