动手学pytorch-经典卷积神经网络模型

Posted 2021-03-09 54hys

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经典卷积神经网络

1.LeNet

技术图片

卷积层块里的基本单位是卷积层后接平均池化层：卷积层用来识别图像里的空间模式，如线条和物体局部，之后的平均池化层则用来降低卷积层对位置的敏感性。卷积层块由两个这样的基本单位重复堆叠构成。在卷积层块中，每个卷积层都使用5×5的窗口，并在输出上使用sigmoid激活函数。第一个卷积层输出通道数为6，第二个卷积层输出通道数则增加到16。全连接层块含3个全连接层。它们的输出个数分别是120、84和10，其中10为输出的类别个数。

class LeNet(nn.Module):
    def __init__(self, *, channels, fig_size, num_class):
        super(LeNet, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(channels, 6, 5, padding=2),
            nn.Sigmoid(),
            nn.AvgPool2d(2, 2),
            nn.Conv2d(6, 16, 5),
            nn.Sigmoid(),
            nn.AvgPool2d(2, 2),
        )
        ##经过卷积和池化层后的图像大小
        fig_size = (fig_size - 5 + 1 + 4 ) // 1
        fig_size = (fig_size - 2 + 2) // 2
        fig_size = (fig_size - 5 + 1) // 1
        fig_size = (fig_size - 2 + 2) // 2
        self.fc = nn.Sequential(
            nn.Flatten(),
            nn.Linear(16 * fig_size * fig_size, 120),
            nn.Sigmoid(),
            nn.Linear(120, 84),
            nn.Sigmoid(),
            nn.Linear(84, num_class),
        )
    def forward(self, X):
        conv_features = self.conv(X)
        output = self.fc(conv_features)
        return output

2.AlexNet

技术图片

首次证明了学习到的特征可以超越??设计的特征，从而?举打破计算机视觉研究的前状。
特征：

8层变换，其中有5层卷积和2层全连接隐藏层，以及1个全连接输出层。
将sigmoid激活函数改成了更加简单的ReLU激活函数。
用Dropout来控制全连接层的模型复杂度。
引入数据增强，如翻转、裁剪和颜色变化，从而进一步扩大数据集来缓解过拟合。

class AlexNet(nn.Module):
    def __init__(self,*, channels, fig_size, num_class):
        super(AlexNet, self).__init__()
        self.dropout = 0.5
        self.conv = nn.Sequential(
            nn.Conv2d(channels, 96, 11, 4),
            nn.ReLU(),
            nn.MaxPool2d(3, 2),
            nn.Conv2d(96, 256, 5, 1, 2),
            nn.ReLU(),
            nn.MaxPool2d(3, 2),
            nn.Conv2d(256, 384, 3, 1, 1),
            nn.ReLU(),
            nn.Conv2d(384, 384, 3, 1, 1),
            nn.ReLU(),
            nn.Conv2d(384, 256, 3, 1, 1),
            nn.ReLU(),
            nn.MaxPool2d(3, 2),
        )
        ##经过卷积和池化层后的图像大小
        fig_size = (fig_size - 11 + 4) // 4
        fig_size = (fig_size - 3 + 2) // 2
        fig_size = (fig_size - 5 + 1 + 4) // 1
        fig_size = (fig_size - 3 + 2) // 2
        fig_size = (fig_size - 3 + 1 + 2) // 1
        fig_size = (fig_size - 3 + 1 + 2) // 1
        fig_size = (fig_size - 3 + 1 + 2) // 1
        fig_size = (fig_size - 3 + 2) // 2 
        self.fc = nn.Sequential(
            nn.Linear(256 * fig_size * fig_size, 4096),
            nn.ReLU(),
            nn.Dropout(p = self.dropout),
            nn.Linear(4096, 4096),
            nn.ReLU(),
            nn.Dropout(p = self.dropout),
            nn.Linear(4096, num_class),
        )
    
    def forward(self, X):
        conv_features = self.conv(X)
        output = self.fc(conv_features.view(X.shape[0], -1))
        return output

3.Vgg

技术图片

VGG：通过重复使?简单的基础块来构建深度模型。
Block:数个相同的填充为1、窗口形状为(3 imes 3)的卷积层,接上一个步幅为2、窗口形状为(2 imes 2)的最大池化层。卷积层保持输入的高和宽不变，而池化层则对其减半。

class VggBlock(nn.Module):
    def __init__(self, conv_arch):
        super(VggBlock, self).__init__()
        num_convs, in_channels, out_channels = conv_arch
        self.conv = nn.Sequential()
        for i in range(num_convs):
            self.conv.add_module(f'conv_{i+1}', nn.Conv2d(in_channels, out_channels, 3, padding=1))
            in_channels = out_channels
        self.conv.add_module('maxpool', nn.MaxPool2d(2, 2))
    
    def forward(self, X):
        return self.conv(X)

class Vgg11(nn.Module):
    def __init__(self, *, channels, fig_size, num_class):
        super(Vgg11, self).__init__()
        self.dropout = 0.5
        self.conv_arch = [(1, channels, 64), (1, 64, 128), (2, 128, 256), (2, 256, 512), (2, 512, 512)]
        self.fc_neuros = 4096

        self.vgg_blocks = nn.Sequential()
        for i, conv_arch in enumerate(self.conv_arch):
            self.vgg_blocks.add_module(f'vbb_block{i+1}', VggBlock(conv_arch))

        fig_size = fig_size // (2 ** len(self.conv_arch))
        fc_features = self.conv_arch[-1][-1] * fig_size * fig_size
        self.fc = nn.Sequential(
            nn.Flatten(),
            nn.Linear(fc_features, self.fc_neuros),
            nn.ReLU(),
            nn.Dropout(p = self.dropout),
            nn.Linear(self.fc_neuros, self.fc_neuros),
            nn.ReLU(),
            nn.Dropout(p = self.dropout),
            nn.Linear(self.fc_neuros, num_class),
        )
    
    def forward(self, X):
        conv_features = self.vgg_blocks(X)
        output = self.fc(conv_features)
        return output

4.Nin

技术图片

1×1卷积核作用
1.放缩通道数：通过控制卷积核的数量达到通道数的放缩。
2.增加非线性。1×1卷积核的卷积过程相当于全连接层的计算过程，并且还加入了非线性激活函数，从而可以增加网络的非线性。
3.计算参数少

class NinBlock(nn.Module):
    def __init__(self, conv_arch):
        # conv_arch : (in_channels, out_channels, kernel_size, stride, padding)
        super(NinBlock, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(*conv_arch),
            nn.ReLU(),
            nn.Conv2d(conv_arch[1], conv_arch[1], kernel_size=1),
            nn.ReLU(),
            nn.Conv2d(conv_arch[1], conv_arch[1], kernel_size=1),
            nn.ReLU(),
        )
    
    def forward(self, X):
        return self.conv(X)

class GlobalAvgPool2d(nn.Module):
    def __init__(self):
        super(GlobalAvgPool2d, self).__init__()
    
    def forward(self, X):
        return F.avg_pool2d(X, kernel_size = X.size()[2:])

class Nin(nn.Module):
    def __init__(self, *, channels, fig_size, num_class):
        super(Nin, self).__init__()
        self.dropout = 0.5
        self.conv_arch = [(channels, 96, 11, 4, 0), (96, 256, 5, 1, 2), 
                          (256, 384, 3, 1, 1), (384, num_class, 3, 1, 1)]
        self.nin_blocks = nn.Sequential()
        for i, conv_arch in enumerate(self.conv_arch[:-1]):
            self.nin_blocks.add_module(f'nin_block_{i+1}', NinBlock(conv_arch))
            self.nin_blocks.add_module(f'max_pool_{i+1}', nn.MaxPool2d(3, 2))
        self.nin_blocks.add_module('dropout', nn.Dropout(p = self.dropout))
        self.nin_blocks.add_module(f'nin_block_{len(self.conv_arch)}', NinBlock(self.conv_arch[-1]))
        self.global_avg_pool = GlobalAvgPool2d()
        self.flatten = nn.Flatten()
    
    def forward(self, X):
        conv_features = self.nin_blocks(X)
        avg_pool = self.global_avg_pool(conv_features)
        return self.flatten(avg_pool)

5.GoogleNet

技术图片

由Inception基础块组成。
Inception块相当于?个有4条线路的??络。它通过不同窗口形状的卷积层和最?池化层来并?抽取信息，并使?1×1卷积层减少通道数从而降低模型复杂度。
可以?定义的超参数是每个层的输出通道数，我们以此来控制模型复杂度。

技术图片

class Inception(nn.Module):
    def __init__(self, conv_arch):
        super(Inception, self).__init__()
        in_channels, c1, c2, c3, c4 = conv_arch
        self.path_1 = nn.Conv2d(in_channels, c1, kernel_size = 1)
        self.path_2 = nn.Sequential(
            nn.Conv2d(in_channels, c2[0], kernel_size = 1),
            nn.ReLU(),
            nn.Conv2d(c2[0], c2[1], kernel_size = 3, padding = 1),
        )
        self.path_3 = nn.Sequential(
            nn.Conv2d(in_channels, c3[0], kernel_size = 1),
            nn.ReLU(),
            nn.Conv2d(c3[0], c3[1], kernel_size = 5, padding=2),
        )
        self.path_4 = nn.Sequential(
            nn.MaxPool2d(kernel_size = 3, stride=1, padding=1),
            nn.Conv2d(in_channels, c4, kernel_size=1),
        )

    def forward(self, X):
        p1 = F.relu(self.path_1(X))
        p2 = F.relu(self.path_2(X))
        p3 = F.relu(self.path_3(X))
        p4 = F.relu(self.path_4(X))
        return torch.cat((p1, p2, p3, p4), dim = 1)

class GoogleNet(nn.Module):
    def __init__(self, *, channels, fig_size, num_class):
        super(GoogleNet, self).__init__()
        self.b1 = nn.Sequential(
            nn.Conv2d(channels, 64, 7, 2, 3),
            nn.ReLU(),
            nn.MaxPool2d(3, 2, 1),
        )
        self.b2 = nn.Sequential(
            nn.Conv2d(64, 64, 1),
            nn.Conv2d(64, 192, 3, padding=1),
            nn.MaxPool2d(3, 2, 1),
        )
        self.b3 = nn.Sequential(
            Inception([192, 64, (96, 128), (16, 32), 32]),
            Inception([256, 128, (128, 192), (32, 96), 64]),
            nn.MaxPool2d(3, 2, 1),
        )
        self.b4 = nn.Sequential(
            Inception([480, 192, (96, 208), (16, 48), 64]),
            Inception([512, 160, (112, 224), (24, 64), 64]),
            Inception([512, 128, (128, 256), (24, 64), 64]),
            Inception([512, 112, (144, 288), (32, 64), 64]),
            Inception([528, 256, (160, 320), (32, 128), 128]),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        )
        self.b5 = nn.Sequential(
            Inception([832, 256, (160, 320), (32, 128), 128]),
            Inception([832, 384, (192, 384), (48, 128), 128]),
            GlobalAvgPool2d(),
        )
        self.fc = nn.Sequential(
            nn.Flatten(),
            nn.Linear(1024, num_class),
        )
        self.Inception_blocks = nn.Sequential(self.b3, self.b4, self.b5)

    def forward(self, X):
        conv_features = self.b1(X)
        conv_features = self.b2(conv_features)
        incep_features = self.Inception_blocks(conv_features)
        return self.fc(incep_features)

fig_size = 224
channels = 3
num_class = 10
X = torch.ones([10,channels, fig_size, fig_size])
# nin = Nin(channels = channels, fig_size = fig_size, num_class = num_class)
# output = nin(X)

# vgg11 = Vgg11(channels = channels, fig_size = fig_size, num_class = num_class)
# output = vgg11(X)

# googlenet = GoogleNet(channels = channels, fig_size = fig_size, num_class = num_class)
# output = googlenet(X)

# lenet = LeNet(fig_size=fig_size, num_class=num_class, channels=channels)
# output = lenet(X)

# alexnet = AlexNet(fig_size=fig_size, num_class=num_class,channels = channels)
# output = alexnet(X)

print(output.shape)

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