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VGGNet------超經典神經網絡結構與PyTorch實作

VGGNet------超經典神經網絡結構與PyTorch實作

    • 1、VGGNet網絡結構
    • 2、VGGNet網絡結構的優缺點
    • 3、VGGNet網絡結構PyTorch實作

    在上文中詳細介紹了經典神經網絡 AlexNet，它為神經網絡的發展打開了一片天地。VGGNet可以說是經典中的經典，它是所有學習深度學習的同學們都必須熟知網絡。VGGNet是AlexNet的更新版，本文将詳細介紹VGGNet的網絡結構與相應的優缺點。

1、VGGNet網絡結構

    VGG有衆多版本，主要包括VGG11、VGG11-LRN、VGG13、VGG16-1、VGG16-3和VGG19，首先我們先大題看一下VGG的立體結構。

VGGNet------超經典神經網絡結構與PyTorch實作

    從上圖看，VGGNet與AlexNet相比好像除了增加了深度并沒有太特别的地方，依然是使用卷積、ReLU、池化、全連接配接等。但是，我們通過它的網絡細節就能發現他的高明之處，下圖為VGGNet的網絡結構圖。

VGGNet------超經典神經網絡結構與PyTorch實作

    對比AlexNet網絡結構，細心的同學會注意到以下幾個點：

    1、VGGNet基本上采用的都是3x3卷積核、2x2 MaxPooling，并沒有出現AlexNet中的11x11卷積核、5x5卷積核、3x3 MaxPooling等較大卷積和池化操作。

    2、VGGNet中是在卷積神經網絡中使用1x1卷積核，見網絡結構C中。1x1的卷積層的主要意義在于非線性變換和降維，在這裡則是非線性變換。

    3、VGGNet從網絡深度中看确實超過了AlexNet，同時，VGGNet同樣進過不同版本的實驗也驗證了增加網絡深度可以提高性能，但是也驗證了不斷地疊加絡深度并不能無限的提高性能，甚至還會出現退化。

2、VGGNet網絡結構的優缺點

優點

  1. 網絡結構簡單，統一使用3x3卷積核、2x2池化，使網絡結構也非正常整；
  2. 論文提出通過小尺寸濾波器（3x3）卷積層的組合代替大尺寸濾波器（如11x11、7x7等）卷積層，這樣不僅可以減少參數還得到更好的性能；
  3. 使用1x1卷積核，NiN與GoogleNet均使用了此結構，這對以後的深度學習算法影響深遠；
  4. 訓練使用了Multi-Scale訓練（多尺度訓練）的方法，此方法不僅能增加很多資料量，同時對于防止模型過拟合有很不錯的效果。

缺點

  1. 3個全連接配接層耗費了更多的計算資源，是以其訓練速度也相對較慢；
  2. 由于網絡深度的增加，VGG需要存儲的參數也多，是以訓練的模型相對較大；

3、VGGNet網絡結構PyTorch實作 

import torch.nn as nn
import torch.utils.model_zoo as model_zoo


__all__ = [
    'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
    'vgg19_bn', 'vgg19',
]


model_urls = {
    'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
    'vgg13': 'https://download.pytorch.org/models/vgg13-c768596a.pth',
    'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth',
    'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth',
    'vgg11_bn': 'https://download.pytorch.org/models/vgg11_bn-6002323d.pth',
    'vgg13_bn': 'https://download.pytorch.org/models/vgg13_bn-abd245e5.pth',
    'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth',
    'vgg19_bn': 'https://download.pytorch.org/models/vgg19_bn-c79401a0.pth',
}


class VGG(nn.Module):

    def __init__(self, features, num_classes=1000, init_weights=True):
        super(VGG, self).__init__()
        self.features = features
        self.classifier = nn.Sequential(
            nn.Linear(512 * 7 * 7, 4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, num_classes),
        )
        if init_weights:
            self._initialize_weights()

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x

    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(
                    m.weight, mode='fan_out', nonlinearity='relu')
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                nn.init.constant_(m.bias, 0)


def make_layers(cfg, batch_norm=False):
    layers = []
    in_channels = 3
    for v in cfg:
        if v == 'M':
            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
        else:
            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
            if batch_norm:
                layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
            else:
                layers += [conv2d, nn.ReLU(inplace=True)]
            in_channels = v
    return nn.Sequential(*layers)


cfg = {
    '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'],
}


def vgg11(pretrained=False, **kwargs):
    """VGG 11-layer model (configuration "A")

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    if pretrained:
        kwargs['init_weights'] = False
    model = VGG(make_layers(cfg['A']), **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['vgg11']))
    return model


def vgg11_bn(pretrained=False, **kwargs):
    """VGG 11-layer model (configuration "A") with batch normalization

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    if pretrained:
        kwargs['init_weights'] = False
    model = VGG(make_layers(cfg['A'], batch_norm=True), **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['vgg11_bn']))
    return model


def vgg13(pretrained=False, **kwargs):
    """VGG 13-layer model (configuration "B")

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    if pretrained:
        kwargs['init_weights'] = False
    model = VGG(make_layers(cfg['B']), **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['vgg13']))
    return model


def vgg13_bn(pretrained=False, **kwargs):
    """VGG 13-layer model (configuration "B") with batch normalization

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    if pretrained:
        kwargs['init_weights'] = False
    model = VGG(make_layers(cfg['B'], batch_norm=True), **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['vgg13_bn']))
    return model


def vgg16(pretrained=False, **kwargs):
    """VGG 16-layer model (configuration "D")

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    if pretrained:
        kwargs['init_weights'] = False
    model = VGG(make_layers(cfg['D']), **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['vgg16']))
    return model


def vgg16_bn(pretrained=False, **kwargs):
    """VGG 16-layer model (configuration "D") with batch normalization

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    if pretrained:
        kwargs['init_weights'] = False
    model = VGG(make_layers(cfg['D'], batch_norm=True), **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['vgg16_bn']))
    return model


def vgg19(pretrained=False, **kwargs):
    """VGG 19-layer model (configuration "E")

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    if pretrained:
        kwargs['init_weights'] = False
    model = VGG(make_layers(cfg['E']), **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['vgg19']))
    return model


def vgg19_bn(pretrained=False, **kwargs):
    """VGG 19-layer model (configuration 'E') with batch normalization

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    if pretrained:
        kwargs['init_weights'] = False
    model = VGG(make_layers(cfg['E'], batch_norm=True), **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['vgg19_bn']))
    return model
機器學習/深度學習 Pytorch VGGNet 結構詳解 優缺點 經典網絡 神經網絡
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