这篇文章将为大家详细讲解有关keras如何实现VGG16 CIFAR10数据集，小编觉得挺实用的，因此分享给大家做个参考，希望大家阅读完这篇文章后可以有所收获。

我就废话不多说了，大家还是直接看代码吧！

import kerasfrom keras.datasets import cifar10from keras.preprocessing.image import ImageDataGeneratorfrom keras.models import Sequentialfrom keras.layers import Dense, Dropout, Activation, Flattenfrom keras.layers import Conv2D, MaxPooling2D, BatchNormalizationfrom keras import optimizersimport numpy as npfrom keras.layers.core import Lambdafrom keras import backend as Kfrom keras.optimizers import SGDfrom keras import regularizers #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) weight_decay = 0.0005nb_epoch=100batch_size=32 #layer1 32*32*3model = Sequential()model.add(Conv2D(64, (3, 3), padding='same',input_shape=(32,32,3),kernel_regularizer=regularizers.l2(weight_decay)))model.add(Activation('relu'))model.add(BatchNormalization())model.add(Dropout(0.3))#layer2 32*32*64model.add(Conv2D(64, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))model.add(Activation('relu'))model.add(BatchNormalization())model.add(MaxPooling2D(pool_size=(2, 2)))#layer3 16*16*64model.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*128model.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*128model.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*256model.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*256model.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*256model.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*512model.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*512model.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*512model.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*512model.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*512model.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*512model.add(Flatten())model.add(Dense(512,kernel_regularizer=regularizers.l2(weight_decay)))model.add(Activation('relu'))model.add(BatchNormalization())#layer15 512model.add(Dense(512,kernel_regularizer=regularizers.l2(weight_decay)))model.add(Activation('relu'))model.add(BatchNormalization())#layer16 512model.add(Dropout(0.5))model.add(Dense(10))model.add(Activation('softmax'))# 10 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)

补充知识：pytorch一步一步在VGG16上训练自己的数据集

准备数据集及加载，ImageFolder

在很多机器学习或者深度学习的任务中，往往我们要提供自己的图片。也就是说我们的数据集不是预先处理好的，像mnist，cifar10等它已经给你处理好了，更多的是原始的图片。比如我们以猫狗分类为例。在data文件下，有两个分别为train和val的文件夹。然后train下是cat和dog两个文件夹，里面存的是自己的图片数据，val文件夹同train。这样我们的数据集就准备好了。

ImageFolder能够以目录名作为标签来对数据集做划分，下面是pytorch中文文档中关于ImageFolder的介绍：

#对训练集做一个变换train_transforms = transforms.Compose([ transforms.RandomResizedCrop(224), #对图片尺寸做一个缩放切割 transforms.RandomHorizontalFlip(), #水平翻转 transforms.ToTensor(), #转化为张量 transforms.Normalize((.5, .5, .5), (.5, .5, .5)) #进行归一化])#对测试集做变换val_transforms = transforms.Compose([ transforms.Resize(256), transforms.RandomResizedCrop(224), transforms.ToTensor(), transforms.Normalize((.5, .5, .5), (.5, .5, .5))])train_dir = "G:/data/train" #训练集路径#定义数据集train_datasets = datasets.ImageFolder(train_dir, transform=train_transforms)#加载数据集train_dataloader = torch.utils.data.DataLoader(train_datasets, batch_size=batch_size, shuffle=True)val_dir = "G:/datat/val" val_datasets = datasets.ImageFolder(val_dir, transform=val_transforms)val_dataloader = torch.utils.data.DataLoader(val_datasets, batch_size=batch_size, shuffle=True)

迁移学习以VGG16为例

下面是迁移代码的实现：

class VGGNet(nn.Module): def __init__(self, num_classes=2): #num_classes，此处为 二分类值为2 super(VGGNet, self).__init__() net = models.vgg16(pretrained=True) #从预训练模型加载VGG16网络参数 net.classifier = nn.Sequential() #将分类层置空，下面将改变我们的分类层 self.features = net #保留VGG16的特征层 self.classifier = nn.Sequential( #定义自己的分类层 nn.Linear(512 * 7 * 7, 512), #512 * 7 * 7不能改变 ，由VGG16网络决定的，第二个参数为神经元个数可以微调 nn.ReLU(True), nn.Dropout(), nn.Linear(512, 128), nn.ReLU(True), nn.Dropout(), nn.Linear(128, num_classes), ) def forward(self, x): x = self.features(x) x = x.view(x.size(0), -1) x = self.classifier(x) return x

完整代码如下

from __future__ import print_function, divisionimport torchimport torch.nn as nnimport torch.nn.functional as Fimport torch.optim as optimfrom torchvision import datasets, transformsfrom torch.autograd import Variableimport numpy as npfrom torchvision import modelsbatch_size = 16learning_rate = 0.0002epoch = 10train_transforms = transforms.Compose([ transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize((.5, .5, .5), (.5, .5, .5))])val_transforms = transforms.Compose([ transforms.Resize(256), transforms.RandomResizedCrop(224), transforms.ToTensor(), transforms.Normalize((.5, .5, .5), (.5, .5, .5))])train_dir = './VGGDataSet/train'train_datasets = datasets.ImageFolder(train_dir, transform=train_transforms)train_dataloader = torch.utils.data.DataLoader(train_datasets, batch_size=batch_size, shuffle=True)val_dir = './VGGDataSet/val'val_datasets = datasets.ImageFolder(val_dir, transform=val_transforms)val_dataloader = torch.utils.data.DataLoader(val_datasets, batch_size=batch_size, shuffle=True)class VGGNet(nn.Module): def __init__(self, num_classes=3): super(VGGNet, self).__init__() net = models.vgg16(pretrained=True) net.classifier = nn.Sequential() self.features = net self.classifier = nn.Sequential( nn.Linear(512 * 7 * 7, 512), nn.ReLU(True), nn.Dropout(), nn.Linear(512, 128), nn.ReLU(True), nn.Dropout(), nn.Linear(128, num_classes), ) def forward(self, x): x = self.features(x) x = x.view(x.size(0), -1) x = self.classifier(x) return x#--------------------训练过程---------------------------------model = VGGNet()if torch.cuda.is_available(): model.cuda()params = [{'params': md.parameters()} for md in model.children() if md in [model.classifier]]optimizer = optim.Adam(model.parameters(), lr=learning_rate)loss_func = nn.CrossEntropyLoss()Loss_list = []Accuracy_list = []for epoch in range(100): print('epoch {}'.format(epoch + 1)) # training----------------------------- train_loss = 0. train_acc = 0. for batch_x, batch_y in train_dataloader: batch_x, batch_y = Variable(batch_x).cuda(), Variable(batch_y).cuda() out = model(batch_x) loss = loss_func(out, batch_y) train_loss += loss.data[0] pred = torch.max(out, 1)[1] train_correct = (pred == batch_y).sum() train_acc += train_correct.data[0] optimizer.zero_grad() loss.backward() optimizer.step() print('Train Loss: {:.6f}, Acc: {:.6f}'.format(train_loss / (len( train_datasets)), train_acc / (len(train_datasets)))) # evaluation-------------------------------- model.eval() eval_loss = 0. eval_acc = 0. for batch_x, batch_y in val_dataloader: batch_x, batch_y = Variable(batch_x, volatile=True).cuda(), Variable(batch_y, volatile=True).cuda() out = model(batch_x) loss = loss_func(out, batch_y) eval_loss += loss.data[0] pred = torch.max(out, 1)[1] num_correct = (pred == batch_y).sum() eval_acc += num_correct.data[0] print('Test Loss: {:.6f}, Acc: {:.6f}'.format(eval_loss / (len( val_datasets)), eval_acc / (len(val_datasets)))) Loss_list.append(eval_loss / (len(val_datasets))) Accuracy_list.append(100 * eval_acc / (len(val_datasets)))x1 = range(0, 100)x2 = range(0, 100)y1 = Accuracy_listy2 = Loss_listplt.subplot(2, 1, 1)plt.plot(x1, y1, 'o-')plt.title('Test accuracy vs. epoches')plt.ylabel('Test accuracy')plt.subplot(2, 1, 2)plt.plot(x2, y2, '.-')plt.xlabel('Test loss vs. epoches')plt.ylabel('Test loss')plt.show()# plt.savefig("accuracy_loss.jpg")

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