基於Theano的深度學習框架keras及配合SVM訓練模型

1.介紹
Keras是基於Theano的一個深度學習框架，它的設計參考了Torch，用Python語言編寫，是一個高度模組化的神經網路庫，支援GPU和CPU。keras官方文件地址 地址
2.流程
先使用CNN進行訓練，利用Theano函式將CNN全連線層的值取出來，給SVM進行訓練

3.結果示例
因為這裡只是一個演示keras&SVM的demo，未對引數進行過多的嘗試，結果一般

4.程式碼
由於keras文件、程式碼更新，目前網上很多程式碼都不能使用，下面貼上我的程式碼，可以直接執行

from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation,Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.optimizers import SGD
from keras.datasets import mnist
from keras.layers import BatchNormalization
from sklearn.svm import SVC
import theano
from keras.utils import np_utils


def 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 layer
model = Sequential()

#1 .use convolution,pooling,full connection
model.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 connection

model.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 reshape
X_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 svm
y_train_new = y_train[0:42000]
y_test_new = y_train[42000:]

#new train and test data
X_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_loss
print "val_accuracy: %f" %val_accuracy

#define theano funtion to get output of FC layer
get_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)