#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sun Sep 30 17:12:12 2018這是用keras搭建的vgg16網絡 這是很經典的cnn,在圖像和時間序列分析方面有很多的應用 @author: lg """ #################import keras from keras.datasets import cifar10 from keras.preprocessing.image import ImageDataGenerator from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Conv2D, MaxPooling2D, BatchNormalization from keras import optimizers import numpy as np from keras.layers.core import Lambda from keras import backend as K from keras.optimizers import SGD from keras import regularizers from keras.models import load_model#import data (x_train, y_train), (x_test, y_test) = cifar10.load_data() x_train = x_train.astype('float32') x_test = x_test.astype('float32') y_train = keras.utils.to_categorical(y_train, 10) y_test = keras.utils.to_categorical(y_test, 10)#用于正則化時權重降低的速度 weight_decay = 0.0005 nb_epoch=100 batch_size=32#layer1 32*32*3 model = Sequential() #第一個 卷積層 的卷積核的數目是32 ，卷積核的大小是3*3，stride沒寫，默認應該是1*1 #對于stride=1*1,并且padding ='same',這種情況卷積后的圖像shape與卷積前相同，本層后shape還是32*32 model.add(Conv2D(64, (3, 3), padding='same', input_shape=(32,32,3),kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) #進行一次歸一化 model.add(BatchNormalization()) model.add(Dropout(0.3)) #layer2 32*32*64 model.add(Conv2D(64, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) #下面兩行代碼是等價的，#keras Pool層有個奇怪的地方，stride,默認是(2*2), #padding默認是valid，在寫代碼是這些參數還是最好都加上,這一步之后,輸出的shape是16*16*64 #model.add(MaxPooling2D(pool_size=(2, 2))) model.add(MaxPooling2D(pool_size=(2, 2),strides=(2,2),padding='same') ) #layer3 16*16*64 model.add(Conv2D(128, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) #layer4 16*16*128 model.add(Conv2D(128, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2))) #layer5 8*8*128 model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) #layer6 8*8*256 model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) #layer7 8*8*256 model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2))) #layer8 4*4*256 model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) #layer9 4*4*512 model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) #layer10 4*4*512 model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2))) #layer11 2*2*512 model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) #layer12 2*2*512 model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) #layer13 2*2*512 model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.5)) #layer14 1*1*512 model.add(Flatten()) model.add(Dense(512,kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) #layer15 512 model.add(Dense(512,kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) #layer16 512 model.add(Dropout(0.5)) model.add(Dense(10)) model.add(Activation('softmax')) # 10 model.summary() sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) model.compile(loss='categorical_crossentropy', optimizer=sgd,metrics=['accuracy'])model.fit(x_train,y_train,epochs=nb_epoch, batch_size=batch_size,validation_split=0.1, verbose=1)model.save('my_model_bp.h5')

總結

以上是生活随笔為你收集整理的keras实现 vgg16的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。