keras实现多种分类网络的方法-创新互联
不懂keras实现多种分类网络的方法?其实想解决这个问题也不难,下面让小编带着大家一起学习怎么去解决,希望大家阅读完这篇文章后大所收获。
成都创新互联是一家集网站建设,鄂尔多斯企业网站建设,鄂尔多斯品牌网站建设,网站定制,鄂尔多斯网站建设报价,网络营销,网络优化,鄂尔多斯网站推广为一体的创新建站企业,帮助传统企业提升企业形象加强企业竞争力。可充分满足这一群体相比中小企业更为丰富、高端、多元的互联网需求。同时我们时刻保持专业、时尚、前沿,时刻以成就客户成长自我,坚持不断学习、思考、沉淀、净化自己,让我们为更多的企业打造出实用型网站。Keras应该是最简单的一种深度学习框架了,入门非常的简单.
简单记录一下keras实现多种分类网络:如AlexNet、Vgg、ResNet
采用kaggle猫狗大战的数据作为数据集.
由于AlexNet采用的是LRN标准化,Keras没有内置函数实现,这里用batchNormalization代替
收件建立一个model.py的文件,里面存放着alexnet,vgg两种模型,直接导入就可以了
#coding=utf-8 from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D, BatchNormalization from keras.layers import * from keras.layers.advanced_activations import LeakyReLU,PReLU from keras.models import Model def keras_batchnormalization_relu(layer): BN = BatchNormalization()(layer) ac = PReLU()(BN) return ac def AlexNet(resize=227, classes=2): model = Sequential() # 第一段 model.add(Conv2D(filters=96, kernel_size=(11, 11), strides=(4, 4), padding='valid', input_shape=(resize, resize, 3), activation='relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='valid')) # 第二段 model.add(Conv2D(filters=256, kernel_size=(5, 5), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='valid')) # 第三段 model.add(Conv2D(filters=384, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(Conv2D(filters=384, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(Conv2D(filters=256, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='valid')) # 第四段 model.add(Flatten()) model.add(Dense(4096, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(4096, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(1000, activation='relu')) model.add(Dropout(0.5)) # Output Layer model.add(Dense(classes,activation='softmax')) # model.add(Activation('softmax')) return model def AlexNet2(inputs, classes=2, prob=0.5): ''' 自己写的函数,尝试keras另外一种写法 :param inputs: 输入 :param classes: 类别的个数 :param prob: dropout的概率 :return: 模型 ''' # Conv2D(32, (3, 3), dilation_rate=(2, 2), padding='same')(inputs) print "input shape:", inputs.shape conv1 = Conv2D(filters=96, kernel_size=(11, 11), strides=(4, 4), padding='valid')(inputs) conv1 = keras_batchnormalization_relu(conv1) print "conv1 shape:", conv1.shape pool1 = MaxPool2D(pool_size=(3, 3), strides=(2, 2))(conv1) print "pool1 shape:", pool1.shape conv2 = Conv2D(filters=256, kernel_size=(5, 5), padding='same')(pool1) conv2 = keras_batchnormalization_relu(conv2) print "conv2 shape:", conv2.shape pool2 = MaxPool2D(pool_size=(3, 3), strides=(2, 2))(conv2) print "pool2 shape:", pool2.shape conv3 = Conv2D(filters=384, kernel_size=(3, 3), padding='same')(pool2) conv3 = PReLU()(conv3) print "conv3 shape:", conv3.shape conv4 = Conv2D(filters=384, kernel_size=(3, 3), padding='same')(conv3) conv4 = PReLU()(conv4) print "conv4 shape:", conv4 conv5 = Conv2D(filters=256, kernel_size=(3, 3), padding='same')(conv4) conv5 = PReLU()(conv5) print "conv5 shape:", conv5 pool3 = MaxPool2D(pool_size=(3, 3), strides=(2, 2))(conv5) print "pool3 shape:", pool3.shape dense1 = Flatten()(pool3) dense1 = Dense(4096, activation='relu')(dense1) print "dense2 shape:", dense1 dense1 = Dropout(prob)(dense1) # print "dense1 shape:", dense1 dense2 = Dense(4096, activation='relu')(dense1) print "dense2 shape:", dense2 dense2 = Dropout(prob)(dense2) # print "dense2 shape:", dense2 predict= Dense(classes, activation='softmax')(dense2) model = Model(inputs=inputs, outputs=predict) return model def vgg13(resize=224, classes=2, prob=0.5): model = Sequential() model.add(Conv2D(64, (3, 3), strides=(1, 1), input_shape=(resize, resize, 3), padding='same', activation='relu', kernel_initializer='uniform')) model.add(Conv2D(64, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(128, (3, 2), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform')) model.add(Conv2D(128, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform')) model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform')) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform')) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Flatten()) model.add(Dense(4096, activation='relu')) model.add(Dropout(prob)) model.add(Dense(4096, activation='relu')) model.add(Dropout(prob)) model.add(Dense(classes, activation='softmax')) return model def vgg16(resize=224, classes=2, prob=0.5): model = Sequential() model.add(Conv2D(64, (3, 3), strides=(1, 1), input_shape=(resize, resize, 3), padding='same', activation='relu', kernel_initializer='uniform')) model.add(Conv2D(64, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(128, (3, 2), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform')) model.add(Conv2D(128, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform')) model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform')) model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform')) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform')) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform')) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform')) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Flatten()) model.add(Dense(4096, activation='relu')) model.add(Dropout(prob)) model.add(Dense(4096, activation='relu')) model.add(Dropout(prob)) model.add(Dense(classes, activation='softmax')) return model
另外有需要云服务器可以了解下创新互联scvps.cn,海内外云服务器15元起步,三天无理由+7*72小时售后在线,公司持有idc许可证,提供“云服务器、裸金属服务器、高防服务器、香港服务器、美国服务器、虚拟主机、免备案服务器”等云主机租用服务以及企业上云的综合解决方案,具有“安全稳定、简单易用、服务可用性高、性价比高”等特点与优势,专为企业上云打造定制,能够满足用户丰富、多元化的应用场景需求。
文章题目:keras实现多种分类网络的方法-创新互联
网页路径:http://pwwzsj.com/article/cehcoh.html