《Web安全之深度学习实战》笔记：第二章 卷积神经网络

本章通过cnn、alexnet、vgg来识别数据集。这一章与web安全相关较少，仅是展示卷积神经网络在图像领域的基本用法。

一、CNN算法

通过cnn识别mnist数据集

代码如下

import tflearn
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.normalization import local_response_normalization
from tflearn.layers.estimator import regression

# Data loading and preprocessing
import tflearn.datasets.mnist as mnist

import tflearn.datasets.oxflower17 as oxflower17


def cnn():
    X, Y, testX, testY = mnist.load_data('c://data/MNIST/', one_hot=True)
    X = X.reshape([-1, 28, 28, 1])
    testX = testX.reshape([-1, 28, 28, 1])

    # 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')

二、AlexNet

通过alexnet识别flower17数据集

 

代码如下

def alexnet():
    X, Y = oxflower17.load_data('c://data/17flowers/',one_hot=True, resize_pics=(227, 227))

    # Building 'AlexNet'
    network = input_data(shape=[None, 227, 227, 3])
    network = conv_2d(network, 96, 11, strides=4, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    network = conv_2d(network, 256, 5, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    network = conv_2d(network, 384, 3, activation='relu')
    network = conv_2d(network, 384, 3, activation='relu')
    network = conv_2d(network, 256, 3, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    network = fully_connected(network, 4096, activation='tanh')
    network = dropout(network, 0.5)
    network = fully_connected(network, 4096, activation='tanh')
    network = dropout(network, 0.5)
    network = fully_connected(network, 17, activation='softmax')
    network = regression(network, optimizer='momentum',
                         loss='categorical_crossentropy',
                         learning_rate=0.001)

    # Training
    model = tflearn.DNN(network, checkpoint_path='model_alexnet',
                        max_checkpoints=1, tensorboard_verbose=2)
    model.fit(X, Y, n_epoch=1000, validation_set=0.1, shuffle=True,
              show_metric=True, batch_size=64, snapshot_step=200,
              snapshot_epoch=False, run_id='alexnet')

三、VGG

通过VGG识别flower17数据集

 

代码如下

def vggnet():
    X, Y = oxflower17.load_data('c://data/17flowers/', one_hot=True,resize_pics=(227, 227))

    # Building 'VGG Network'
    network = input_data(shape=[None, 227, 227, 3])

    network = conv_2d(network, 64, 3, activation='relu')
    network = conv_2d(network, 64, 3, activation='relu')
    network = max_pool_2d(network, 2, strides=2)

    network = conv_2d(network, 128, 3, activation='relu')
    network = conv_2d(network, 128, 3, activation='relu')
    network = max_pool_2d(network, 2, strides=2)

    network = conv_2d(network, 256, 3, activation='relu')
    network = conv_2d(network, 256, 3, activation='relu')
    network = conv_2d(network, 256, 3, activation='relu')
    network = max_pool_2d(network, 2, strides=2)

    network = conv_2d(network, 512, 3, activation='relu')
    network = conv_2d(network, 512, 3, activation='relu')
    network = conv_2d(network, 512, 3, activation='relu')
    network = max_pool_2d(network, 2, strides=2)

    network = conv_2d(network, 512, 3, activation='relu')
    network = conv_2d(network, 512, 3, activation='relu')
    network = conv_2d(network, 512, 3, activation='relu')
    network = max_pool_2d(network, 2, strides=2)

    network = fully_connected(network, 4096, activation='relu')
    network = dropout(network, 0.5)
    network = fully_connected(network, 4096, activation='relu')
    network = dropout(network, 0.5)
    network = fully_connected(network, 17, activation='softmax')

    network = regression(network, optimizer='rmsprop',
                         loss='categorical_crossentropy',
                         learning_rate=0.0001)

    # Training
    model = tflearn.DNN(network, checkpoint_path='model_vgg',
                        max_checkpoints=1, tensorboard_verbose=0)
    model.fit(X, Y, n_epoch=500, shuffle=True,
              show_metric=True, batch_size=32, snapshot_step=500,
              snapshot_epoch=False, run_id='vgg')