基于Theano的深度学习框架keras及配合SVM训练模型 (非常好的思路：DL+DM)

1.介绍
Keras是基于Theano的一个深度学习框架，它的设计参考了Torch，用Python语言编写，是一个高度模块化的神经网络库，支持GPU和CPU。keras官方文档地址地址
2.流程
先使用CNN进行训练，利用Theano函数将CNN全连接层的值取出来，给SVM进行训练

3.结果示例
因为这里只是一个演示keras&SVM的demo，未对参数进行过多的尝试，结果一般

4.代码
由于keras文档、代码更新，目前网上很多代码都不能使用，下面贴上我的代码，可以直接运行

from keras.models import Sequentialfrom keras.layers.core import Dense, Dropout, Activation,Flattenfrom keras.layers.convolutional import Convolution2D, MaxPooling2Dfrom keras.optimizers import SGDfrom keras.datasets import mnistfrom keras.layers import BatchNormalizationfrom sklearn.svm import SVCimport theanofrom keras.utils import np_utilsdef svc(traindata,trainlabel,testdata,testlabel):    print("Start training SVM...")    svcClf = SVC(C=1.0,kernel="rbf",cache_size=3000)    svcClf.fit(traindata,trainlabel)    pred_testlabel = svcClf.predict(testdata)    num = len(pred_testlabel)    accuracy = len([1 for i in range(num) if testlabel[i]==pred_testlabel[i]])/float(num)    print("cnn-svm Accuracy:",accuracy)#each add as one layermodel = Sequential()#1 .use convolution,pooling,full connectionmodel.add(Convolution2D(5, 3, 3,border_mode='valid',input_shape=(1, 28, 28),activation='tanh'))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Convolution2D(10, 3, 3,activation='tanh'))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Flatten())model.add(Dense(100,activation='tanh')) #Full connectionmodel.add(Dense(10,activation='softmax'))#2 .just only user full connection# model.add(Dense(100,input_dim = 784, init='uniform',activation='tanh'))# model.add(Dense(100,init='uniform',activation='tanh'))# model.add(Dense(10,init='uniform',activation='softmax'))# sgd = SGD(lr=0.2, decay=1e-6, momentum=0.9, nesterov=True)model.compile(optimizer='sgd', loss='categorical_crossentropy')(X_train, y_train), (X_test, y_test) = mnist.load_data()#change data type,keras category need ont hot#2 reshape#X_train = X_train.reshape(X_train.shape[0],X_train.shape[1]*X_train.shape[2]) #X_train.shape[0] 60000 X_train.shape[1] 28  X_train.shape[2] 28#1 reshapeX_train = X_train.reshape(X_train.shape[0],1,X_train.shape[1],X_train.shape[2])Y_train = np_utils.to_categorical(y_train, 10)#new label for svmy_train_new = y_train[0:42000]y_test_new = y_train[42000:]#new train and test dataX_train_new = X_train[0:42000]X_test = X_train[42000:]Y_train_new = Y_train[0:42000]Y_test = Y_train[42000:]model.fit(X_train_new, Y_train_new, batch_size=200, nb_epoch=100,shuffle=True, verbose=1, show_accuracy=True, validation_split=0.2)print("Validation...")val_loss,val_accuracy = model.evaluate(X_test, Y_test, batch_size=1,show_accuracy=True)print "val_loss: %f" %val_lossprint "val_accuracy: %f" %val_accuracy#define theano funtion to get output of FC layerget_feature = theano.function([model.layers[0].input],model.layers[5].get_output(train=False),allow_input_downcast=False)FC_train_feature = get_feature(X_train_new)FC_test_feature = get_feature(X_test)svc(FC_train_feature,y_train_new,FC_test_feature,y_test_new)