VGG

AlexNet在Lenet的基础上增加了几个卷积层,改变了卷积核大小,每一层输出通道数目等,并且取得了很好的效果.但是并没有提出一个简单有效的思路.

VGG做到了这一点,提出了可以通过重复使⽤简单的基础块来构建深度学习模型的思路.

vgg的结构如下所示:

上图给出了不同层数的vgg的结构.也就是常说的vgg16,vgg19等等.

VGG BLOCK

vgg的设计思路是,通过不断堆叠3x3的卷积核,不断加深模型深度.vgg net证明了加深模型深度对提高模型的学习能力是一个很有效的手段.

看上图就能发现,连续的2个3x3卷积,感受野和一个5x5卷积是一样的,但是前者有两次非线性变换,后者只有一次!,这就是连续堆叠小卷积核能提高

模型特征学习的关键.此外,2个3x3的参数数量也比一个5x5少.(2x3x3 < 5x5)

vgg的基础组成模块,每一个卷积层都由n个3x3卷积后面接2x2的最大池化.池化层的步幅为2.从而卷积层卷积后,宽高不变,池化后,宽高减半.

我们可以有以下代码:

def make_layers(in_channels,cfg):

    layers = []

    previous_channel = in_channels #上一层的输出的channel数量

    for v in cfg:

        if v == 'M':

            layers.append(nn.MaxPool2d(kernel_size=2,stride=2))

        else:

            layers.append(nn.Conv2d(previous_channel,v,kernel_size=3,padding=1))

            layers.append(nn.ReLU())

            previous_channel = v

    conv = nn.Sequential(*layers)

    return conv

cfgs = {

    'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],

    'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],

    'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],

    'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],

}

cfgs定义了不同的vgg模型的结构,比如'A'代表vgg11．　数字代表卷积后的channel数. 'M'代表Maxpool

我们可以给出模型定义

class VGG(nn.Module):

    def __init__(self,input_channels,cfg,num_classes=10, init_weights=True):

        super(VGG, self).__init__()

        self.conv = make_layers(input_channels,cfg) # torch.Size([1, 512, 7, 7])

        self.fc = nn.Sequential(

            nn.Linear(512*7*7,4096),

            nn.ReLU(),

            nn.Linear(4096,4096),

            nn.ReLU(),

            nn.Linear(4096,num_classes)

        )

    def forward(self, img):

        feature = self.conv(img)

        output = self.fc(feature.view(img.shape[0], -1))

        return output

卷积层的输出可由以下测试代码得出

# conv = make_layers(1,cfgs['A'])

# X = torch.randn((1,1,224,224))

# out = conv(X)

# #print(out.shape)

加载数据

batch_size,num_workers=4,4

train_iter,test_iter = learntorch_utils.load_data(batch_size,num_workers,resize=224)

这里batch_size调到8我的显存就不够了...

定义模型

net = VGG(1,cfgs['A']).cuda()

定义损失函数

loss = nn.CrossEntropyLoss()

定义优化器

opt = torch.optim.Adam(net.parameters(),lr=0.001)

定义评估函数

def test():

    acc_sum = 0

    batch = 0

    for X,y in test_iter:

        X,y = X.cuda(),y.cuda()

        y_hat = net(X)

        acc_sum += (y_hat.argmax(dim=1) == y).float().sum().item()

        batch += 1

    #print('acc_sum %d,batch %d' % (acc_sum,batch))

    return 1.0*acc_sum/(batch*batch_size)

训练

num_epochs = 3

def train():

    for epoch in range(num_epochs):

        train_l_sum,batch,acc_sum = 0,0,0

        start = time.time()

        for X,y in train_iter:

            # start_batch_begin = time.time()

            X,y = X.cuda(),y.cuda()

            y_hat = net(X)

            acc_sum += (y_hat.argmax(dim=1) == y).float().sum().item()

            l = loss(y_hat,y)

            opt.zero_grad()

            l.backward()

            opt.step()

            train_l_sum += l.item()

            batch += 1

            mean_loss = train_l_sum/(batch*batch_size) #计算平均到每张图片的loss

            start_batch_end = time.time()

            time_batch = start_batch_end - start

            print('epoch %d,batch %d,train_loss %.3f,time %.3f' %

                (epoch,batch,mean_loss,time_batch))

        print('***************************************')

        mean_loss = train_l_sum/(batch*batch_size) #计算平均到每张图片的loss

        train_acc = acc_sum/(batch*batch_size)     #计算训练准确率

        test_acc = test()                           #计算测试准确率

        end = time.time()

        time_per_epoch =  end - start

        print('epoch %d,train_loss %f,train_acc %f,test_acc %f,time %f' %

                (epoch + 1,mean_loss,train_acc,test_acc,time_per_epoch))

train()

全连接层4096个神经元,参数太多,训练缓慢.4G的GTX 1050显卡,训练一个epoch大概一个多小时.

完整代码:https://github.com/sdu2011/learn_pytorch

batch=4,收敛极慢,迭代次数不够的话,欠拟合严重.在训练集上的train accuracy也很低.

由于全连接层的存在,参数极多,造成训练慢,显存占用多,导致batch_size调不大.模型修改为

class VGG(nn.Module):

    def __init__(self,input_channels,cfg,num_classes=10, init_weights=True):

        super(VGG, self).__init__()

        self.conv = make_layers(input_channels,cfg) # torch.Size([1, 512, 7, 7])

        self.fc = nn.Sequential(

            nn.Linear(512*7*7,512),

            nn.ReLU(inplace=True), #inplace作用:节省显存　https://www.cnblogs.com/wanghui-garcia/p/10642665.html

            nn.Dropout(p=0.5),

            nn.Linear(512,512),

            nn.ReLU(inplace=True),

            nn.Dropout(p=0.5),

            nn.Linear(512,num_classes)

        )

    def forward(self, img):

        feature = self.conv(img)

        output = self.fc(feature.view(img.shape[0], -1))

        return output

全连接层调整为512个神经元.batch_size调到16．训练快多了.

巴特西

从头学pytorch(十六):VGG NET

VGG