首页 > 其他分享> > VGGNet学习笔记及仿真

VGGNet学习笔记及仿真

2021-08-06 13:05:34 作者：互联网

VGGNet学习笔记及仿真

文章目录

VGGNet学习笔记及仿真

引言

VGGNet（Visual Geometry Group）^[1]是2014年又一个经典的卷积神经网络，VGGNet的主要的想法是如何设计网络架构的问题，VGGNet在2014年获得了分类第二，定位第一的成绩，而分类第一的成绩由GoogLeNet^[2]获得。

VGGNet论文笔记

VGGNet架构

卷积层的输入是一个固定大小的 224 x 224 尺寸的RGB图像。唯一做的预处理就是减去平均的RBG值，在卷积层使用了非常小的感视野大小的过滤器3x3（这是捕捉左/右、上/下，中心概念的最小尺寸）。在其中一种配置中，还用到了 1 x 1 的卷积滤波器，可以看做是输入通道的线性变换。卷积的步长固定为1，卷积层的空间填充使得空间分辨率在卷积之后得到保留。
空间池化是由五个最大池化层实现的（不是所有的卷积层之后就是最大池化层），最大池化层是在步长为2，尺寸为2x2的滤波器下实现的。
卷积池化处理之后就是全连接层，前两个层各有4096个神经元，第三层是1000个神经单元，表示ILSVRC的1000中分类，最后一层是softmax层，网络中的全连接层的配置一致。

所有的隐藏层之后都紧跟着ReLU激活函数，在VGGNet中没有用到AlexNet^[3]中的LPN，VGGNet的作者认为这种规范化方法并不能提升模型的性能，反而会导致增加内存消耗和计算时间。

参数详解

请添加图片描述

VGGNet的不同的类型如上表所示，没一列表示不同配置的VGGNet，每个网络的深度都有所不同，从A的11个权重层（8个卷积层和3个全连接层）到网络E中的19个权重层（16个卷积层和3个全连接层）。

虽然VGGNet的深度很大，但是权重参数的数量并不比AlexNet^[3]的权重数量多，VGGNet每个配置的参数如下表2所示。

请添加图片描述

创新点

比起AlexNet等网络，VGGNet使用的是 3 x 3 的卷积核，两个 3 x 3 卷积层的串联相当于一个 5 x 5 的卷积层，3个 3 x 3 的卷积层相当于 7 x 7 的卷积层其他的优点是：

3个 3 x 3 卷积层的串联合并了三个非线性操作，而不是一个，这可以是的决策函数更有区分性
这样可以大大地减少参数量，3个 3 x 3卷积层的参数量只有 7 x 7 卷积层的一半左右
- 3个 3 x 3 卷积层有 C C C 个通道，那么3个卷积层总共的参数有： 3 ( 3 2 C 2 ) = 27 C 2 3(3^2C^2)=27C^2 3(32C2)=27C2
- 单个7 x 7卷积层的参数： 7 2 C 2 = 49 C 2 7^2C^2=49C^2 72C2=49C2

通过 3 x 3的滤波器分解，可以看做是一种对 7 x 7 卷积层的一种正则化

1 x 1 卷积层的引入是为了在不影响卷积层感受野的情况下增加决策函数非线性的方法。 1 x 1 卷积本质上是同一纬度空间上的线性投影（输入和输出通道的数量相同），整流函数也会引入额外的非线性。

训练细节

VGGNet的训练步骤遵循AlexNet的训练步骤^[3]（除了多尺度地对训练图像进行采样）

优化方法是（小批量动量梯度下降方法）mini-batch gradient descent with momentum
batch = 256
momentum = 0.9
L2 penalty = 0.5
前两个全连接层droupout = 0.5
learning rate = 1 0 − 2 10^{-2} 10−2
随机初始化的参数是从均值0，方差 1 0 − 2 10^{-2} 10−2的正态分布中采样，偏差为0
为了获得224 x 224 大小的输入图像，对重新缩放的图像进行随机裁剪（在每次SGD迭代的时候裁剪），为了扩充数据集，裁剪的图像还进行了水平翻转，具体的细节可以参考AlexNet网络预处理数据增强（data augmentation）部分^[3]

结果

结果对比如下图所示

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-1HiueHTP-1628224920791)(https://www.madao33.com/media/VGGNet学习笔记及仿真/comparison.png)]

VGGNet的结果是top-1 val error 23.7，top-5 val error 6.8, top-5 test error 6.8%

代码实现

以下代码参考^[4]

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision.transforms as transforms
import torchvision.datasets as dsets 
import torchvision
import time
import os

transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
transform_test = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
train_dataset = dsets.CIFAR10(root='./data/CIFAR10/', 
                            train=True, 
                            transform=transform_train,
                            download=True)
test_dataset = dsets.CIFAR10(root='./data/CIFAR10/', 
                           train=False, 
                           transform=transform_test)

Files already downloaded and verified

# reducing the dataset
reduced_train_dataset = []
for images, labels in train_dataset:
    if labels < 3:
        reduced_train_dataset.append((images, labels))       
reduced_test_dataset = []
for images, labels in test_dataset:
    if labels < 3:
        reduced_test_dataset.append((images, labels))

print("The number of training images : ", len(reduced_train_dataset))
print("The number of test images : ", len(reduced_test_dataset))

The number of training images :  15000
The number of test images :  3000

print('STEP 2: MAKING DATASET ITERABLE')
train_loader = torch.utils.data.DataLoader(dataset=reduced_train_dataset, 
                                           batch_size=128, 
                                           shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=reduced_test_dataset, 
                                          batch_size=100, 
                                          shuffle=False)
class_names = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

STEP 2: MAKING DATASET ITERABLE

Visualize a few images

import matplotlib.pyplot as plt%matplotlib inline  import numpy as np

def imshow(inp, title=None):

    """Imshow for Tensor."""
    inp = inp.numpy().transpose((1, 2, 0))
    mean = np.array([0.4914, 0.4822, 0.4465])
    std = np.array([0.2023, 0.1994, 0.2010])
    inp = std * inp + mean
    inp = np.clip(inp, 0, 1)
    plt.imshow(inp)
    if title is not None:
        plt.title(title)
    plt.pause(0.001)  # pause a bit so that plots are updated

train_loader_sample = torch.utils.data.DataLoader(dataset=reduced_train_dataset,                                            batch_size=4,                                            shuffle=True)# Get a batch of training datainputs, classes = next(iter(train_loader_sample))# Make a grid from batchout = torchvision.utils.make_grid(inputs)imshow(out, title=[class_names[x] for x in classes])

请添加图片描述

class VGG(nn.Module):

    def __init__(self):

        super(VGG, self).__init__()
        self.conv1 = nn.Sequential(

            nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2)

        )

        self.conv2 = nn.Sequential(

            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2)

        )

        self.conv3 = nn.Sequential(

            nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(),
            nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(),
            nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2)

        )

        self.conv4 = nn.Sequential(

            nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(),
            nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(),
            nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2)
        )

        self.conv5 = nn.Sequential(

            nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(),
            nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(),
            nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2)
        )
        self.fc1 = nn.Linear(512, 3)


    def forward(self, x):

        # Convolutions
        out = self.conv1(x)
        out = self.conv2(out)
        out = self.conv3(out)
        out = self.conv4(out)
        out = self.conv5(out)
        # Resize
        out = out.view(out.size(0), -1)
        out = self.fc1(out)
        return out

## Instantiate Model Class
model = VGG()
num_total_params = sum(p.numel() for p in model.parameters())
print("The number of parameters : ", num_total_params)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)

The number of parameters :  14724675

VGG(
  (conv1): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
    (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (5): ReLU()
    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (conv2): Sequential(
    (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
    (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (5): ReLU()
    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (conv3): Sequential(
    (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
    (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (5): ReLU()
    (6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (8): ReLU()
    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (conv4): Sequential(
    (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
    (3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (5): ReLU()
    (6): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (7): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (8): ReLU()
    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (conv5): Sequential(
    (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
    (3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (5): ReLU()
    (6): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (7): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (8): ReLU()
    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (fc1): Linear(in_features=512, out_features=3, bias=True)
)

## Loss/Optimizer
criterion = nn.CrossEntropyLoss()
learning_rate = 1e-2
momentum = 0.9
weight_decay = 5e-4
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum = momentum, weight_decay = weight_decay)

## Training
num_epochs = 50
iter = 0
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        images = images.to(device)
        labels = labels.to(device)
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
    correct = 0
    total = 0
    # Iterate through test dataset
    for images, labels in test_loader:
        images = images.to(device)
        labels = labels.to(device)
        outputs = model(images) 
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum()
    accuracy = 100 * correct.item() / total
    # Print Loss
    print('Epoch {}. Loss: {}. Accuracy: {}'.format(epoch, loss.item(), accuracy))
#############

Epoch 0. Loss: 0.5742393732070923. Accuracy: 83.66666666666667
Epoch 1. Loss: 0.40269795060157776. Accuracy: 86.63333333333334
Epoch 2. Loss: 0.2371339052915573. Accuracy: 88.5
Epoch 3. Loss: 0.44756925106048584. Accuracy: 88.23333333333333
Epoch 4. Loss: 0.39500197768211365. Accuracy: 88.8
Epoch 5. Loss: 0.22342568635940552. Accuracy: 90.76666666666667
Epoch 6. Loss: 0.05593661591410637. Accuracy: 91.23333333333333
Epoch 7. Loss: 0.011301316320896149. Accuracy: 90.63333333333334
Epoch 8. Loss: 0.04968326911330223. Accuracy: 92.16666666666667
Epoch 9. Loss: 0.12825727462768555. Accuracy: 90.73333333333333
Epoch 10. Loss: 0.008245733566582203. Accuracy: 92.16666666666667
Epoch 11. Loss: 0.19846688210964203. Accuracy: 91.8
Epoch 12. Loss: 0.06683117896318436. Accuracy: 91.46666666666667
Epoch 13. Loss: 0.06584116816520691. Accuracy: 92.3
Epoch 14. Loss: 0.07137342542409897. Accuracy: 91.96666666666667
Epoch 15. Loss: 0.0017633828101679683. Accuracy: 92.86666666666666
Epoch 16. Loss: 0.06365344673395157. Accuracy: 93.1
Epoch 17. Loss: 0.002621516352519393. Accuracy: 91.63333333333334
Epoch 18. Loss: 0.2748899459838867. Accuracy: 92.93333333333334
Epoch 19. Loss: 0.011702283285558224. Accuracy: 93.4
Epoch 20. Loss: 0.0003703629190567881. Accuracy: 92.66666666666667
Epoch 21. Loss: 0.0024486021138727665. Accuracy: 93.7
Epoch 22. Loss: 0.0356595404446125. Accuracy: 93.33333333333333
Epoch 23. Loss: 0.0003634128952398896. Accuracy: 92.76666666666667
Epoch 24. Loss: 0.04696182534098625. Accuracy: 92.56666666666666
Epoch 25. Loss: 0.09958905726671219. Accuracy: 92.7
Epoch 26. Loss: 0.17162220180034637. Accuracy: 92.36666666666666
Epoch 27. Loss: 0.009680134244263172. Accuracy: 92.93333333333334
Epoch 28. Loss: 0.08440369367599487. Accuracy: 93.5
Epoch 29. Loss: 0.06431729346513748. Accuracy: 93.26666666666667
Epoch 30. Loss: 0.009013362228870392. Accuracy: 93.46666666666667
Epoch 31. Loss: 0.18230386078357697. Accuracy: 93.4
Epoch 32. Loss: 0.0009615866583772004. Accuracy: 92.63333333333334
Epoch 33. Loss: 0.13079260289669037. Accuracy: 92.13333333333334
Epoch 34. Loss: 0.00011742366041289642. Accuracy: 93.26666666666667
Epoch 35. Loss: 0.13156653940677643. Accuracy: 93.13333333333334
Epoch 36. Loss: 0.07050688564777374. Accuracy: 92.36666666666666
Epoch 37. Loss: 0.0008148620836436749. Accuracy: 93.23333333333333
Epoch 38. Loss: 0.03399736061692238. Accuracy: 93.66666666666667
Epoch 39. Loss: 0.0019302064320072532. Accuracy: 93.36666666666666
Epoch 40. Loss: 0.0008526549208909273. Accuracy: 93.26666666666667
Epoch 41. Loss: 0.0002856892824638635. Accuracy: 93.23333333333333
Epoch 42. Loss: 0.001912760897539556. Accuracy: 92.9
Epoch 43. Loss: 0.007499492261558771. Accuracy: 93.26666666666667
Epoch 44. Loss: 0.00024754248443059623. Accuracy: 93.06666666666666
Epoch 45. Loss: 0.025322042405605316. Accuracy: 93.06666666666666
Epoch 46. Loss: 0.00015217023610603064. Accuracy: 93.03333333333333
Epoch 47. Loss: 0.0003425484464969486. Accuracy: 93.26666666666667
Epoch 48. Loss: 0.0007479854975827038. Accuracy: 93.9
Epoch 49. Loss: 0.009067901410162449. Accuracy: 92.93333333333334

参考文献

[1] Simonyan K , Zisserman A . Very Deep Convolutional Networks for Large-Scale Image Recognition[J]. Computer Science, 2014.

[2] Szegedy C , Liu W , Jia Y , et al. Going Deeper with Convolutions[J]. IEEE Computer Society, 2014.

[3] Krizhevsky A , Sutskever I , Hinton G . ImageNet Classification with Deep Convolutional Neural Networks[J]. Advances in neural information processing systems, 2012, 25(2).

[4] jays0606/VGGNet

标签：仿真,Loss,512,nn,VGGNet,笔记,Epoch,size,Accuracy
来源： https://blog.csdn.net/qq_36178962/article/details/119452298