《Web安全之深度学习实战》笔记:第五章 验证码识别

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本小节实际上就是用KNN、SVM、MLP、CNN识别mnist数据集,实际上这些在《web安全之机器学习入门》中都有讲过,而这些也是非常基础的应用。

整体设计如下

img

 源码如下所示

def do_knn_1d(x_train, y_train,x_test, y_test):
    print ("KNN and 1d")
    clf = neighbors.KNeighborsClassifier(n_neighbors=15)
    print (clf)
    clf.fit(x_train, y_train)
    y_pred = clf.predict(x_test)
    print (metrics.accuracy_score(y_test, y_pred))

二、SVM

整体设计如下

img

 

源码如下

def do_svm_1d(x_train, y_train,x_test, y_test):
    print ("SVM and 1d")
    clf = svm.SVC(decision_function_shape='ovo')
    print (clf)
    clf.fit(x_train, y_train)
    y_pred = clf.predict(x_test)
    print (metrics.accuracy_score(y_test, y_pred))

三、MLP

整体设计如下

img

 

源码如下

def do_dnn_1d(X, Y, testX, testY ):
    print ("DNN and 1d")

    # Building deep neural network
    input_layer = tflearn.input_data(shape=[None, 784])
    dense1 = tflearn.fully_connected(input_layer, 64, activation='tanh',
                                     regularizer='L2', weight_decay=0.001)
    dropout1 = tflearn.dropout(dense1, 0.8)
    dense2 = tflearn.fully_connected(dropout1, 64, activation='tanh',
                                     regularizer='L2', weight_decay=0.001)
    dropout2 = tflearn.dropout(dense2, 0.8)
    softmax = tflearn.fully_connected(dropout2, 10, activation='softmax')

    # Regression using SGD with learning rate decay and Top-3 accuracy
    sgd = tflearn.SGD(learning_rate=0.1, lr_decay=0.96, decay_step=1000)
    top_k = tflearn.metrics.Top_k(3)
    net = tflearn.regression(softmax, optimizer=sgd, metric=top_k,
                             loss='categorical_crossentropy')

    # Training
    model = tflearn.DNN(net, tensorboard_verbose=0)
    model.fit(X, Y, n_epoch=10, validation_set=(testX, testY),
              show_metric=True, run_id="mnist")

四、CNN

设计如下

img

 

源码如下

def do_cnn_2d(X, Y, testX, testY ):
    # Building convolutional network
    network = input_data(shape=[None, 28, 28, 1], name='input')
    network = conv_2d(network, 32, 3, activation='relu', regularizer="L2")
    network = max_pool_2d(network, 2)
    network = local_response_normalization(network)
    network = conv_2d(network, 64, 3, activation='relu', regularizer="L2")
    network = max_pool_2d(network, 2)
    network = local_response_normalization(network)
    network = fully_connected(network, 128, activation='tanh')
    network = dropout(network, 0.8)
    network = fully_connected(network, 256, activation='tanh')
    network = dropout(network, 0.8)
    network = fully_connected(network, 10, activation='softmax')
    network = regression(network, optimizer='adam', learning_rate=0.01,
                         loss='categorical_crossentropy', name='target')

    # Training
    model = tflearn.DNN(network, tensorboard_verbose=0)
    model.fit({'input': X}, {'target': Y}, n_epoch=5,
               validation_set=({'input': testX}, {'target': testY}),
               snapshot_step=100, show_metric=True, run_id='mnist')

五、总结

识别mnist数据集较基础,深度学习的识别结果稍好。

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