machinelearning_notebook/demo_code/3_CNN_VGG16.py

import sys
sys.path.append('..')

import numpy as np
import torch
from torch import nn
from torch.autograd import Variable
from torchvision.datasets import CIFAR10


def vgg_block(num_convs, in_channels, out_channels):
    net = [nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), \
           nn.ReLU(True)] # 定义第一层

    for i in range(num_convs-1): # 定义后面的很多层
        net.append(nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1))
        net.append(nn.ReLU(True))
        
    net.append(nn.MaxPool2d(2, 2)) # 定义池化层
    return nn.Sequential(*net)
    
def vgg_stack(num_convs, channels):
    net = []
    for n, c in zip(num_convs, channels):
        in_c = c[0]
        out_c = c[1]
        net.append(vgg_block(n, in_c, out_c))
    return nn.Sequential(*net)
    
vgg_net = vgg_stack((2, 2, 3, 3, 3), \
                    ((3, 64), (64, 128), (128, 256), (256, 512), (512, 512)))


class vgg(nn.Module):
    def __init__(self):
        super(vgg, self).__init__()
        self.feature = vgg_net
        self.fc = nn.Sequential(
            nn.Linear(512, 100),
            nn.ReLU(True),
            nn.Linear(100, 10)
        )
    def forward(self, x):
        x = self.feature(x)
        x = x.view(x.shape[0], -1)
        x = self.fc(x)
        return x