李沐-动手学深度学习 VGG

1.VGG框架学习

import torch
from torch import nn
from d2l import torch as d2l

#定义了一个名为vgg_block的函数来实现一个VGG块
def vgg_block(num_convs,in_channels,out_channels):
    layers = []
    for _ in range(num_convs):
        layers.append(nn.Conv2d(in_channels,out_channels,
                                kernel_size=3,padding=1))
        layers.append(nn.ReLU())
        in_channels = out_channels
    layers.append(nn.MaxPool2d(kernel_size=2,stride=2))
    return nn.Sequential(*layers)

conv_arch = ((1,64),(1,128),(2,256),(2,512),(2,512))

#下面的代码实现了VGG-11。通过在conv_arch上执行for循环来简单实现。
def vgg(conv_arch):
    conv_blks = []
    in_channels = 1
    #卷积层部分
    for(num_convs,out_channels) in conv_arch:
        conv_blks.append(vgg_block(num_convs,in_channels,out_channels))
        in_channels = out_channels

    return nn.Sequential(
        *conv_blks,nn.Flatten(),
        #全连接层部分
        nn.Linear(out_channels*7*7,4096),nn.ReLU(),nn.Dropout(0.5),
        nn.Linear(4096,4096),nn.ReLU(),nn.Dropout(0.5),
        nn.Linear(4096,10))

net = vgg(conv_arch)

X = torch.randn(size=(1,1,224,224))
for blk in net:
    X = blk(X)
    print(blk.__class__.__name__,'output shape:\t',X.shape)

2训练模型

import torch
from torch import nn
from d2l import torch as d2l

#定义了一个名为vgg_block的函数来实现一个VGG块
def vgg_block(num_convs,in_channels,out_channels):
    layers = []
    for _ in range(num_convs):
        layers.append(nn.Conv2d(in_channels,out_channels,
                                kernel_size=3,padding=1))
        layers.append(nn.ReLU())
        in_channels = out_channels
    layers.append(nn.MaxPool2d(kernel_size=2,stride=2))
    return nn.Sequential(*layers)

conv_arch = ((1,64),(1,128),(2,256),(2,512),(2,512))

#下面的代码实现了VGG-11。通过在conv_arch上执行for循环来简单实现。
def vgg(conv_arch):
    conv_blks = []
    in_channels = 1
    #卷积层部分
    for(num_convs,out_channels) in conv_arch:
        conv_blks.append(vgg_block(num_convs,in_channels,out_channels))
        in_channels = out_channels

    return nn.Sequential(
        *conv_blks,nn.Flatten(),
        #全连接层部分
        nn.Linear(out_channels*7*7,4096),nn.ReLU(),nn.Dropout(0.5),
        nn.Linear(4096,4096),nn.ReLU(),nn.Dropout(0.5),
        nn.Linear(4096,10))

net = vgg(conv_arch)

'''
X = torch.randn(size=(1,1,224,224))
for blk in net:
    X = blk(X)
    print(blk.__class__.__name__,'output shape:\t',X.shape)
'''

#由于VGG-11比AlexNet计算量更大，因此构建了一个通道数较少的网络，足够用于训练Fashion-MNIST数据集。
ratio = 4
small_conv_arch = [(pair[0],pair[1] // ratio )for pair in conv_arch]
net = vgg(small_conv_arch)

#除了使用略高的学习率外，模型训练过程与 7.1节中的AlexNet类似
lr,num_epochs,batch_size = 0.05,10,128
train_iter,test_iter = d2l.load_data_fashion_mnist(batch_size,resize=224)
d2l.train_ch6(net,train_iter,test_iter,num_epochs,lr,d2l.try_gpu())
d2l.plt.show()