AlexNet

1.网络架构

如图所示可见其结构为：

pytorch对网络层的删除直接删除 pytorch 网络结构_神经网络

AlexNet网络共八层，五层卷积层和三层全连接层。这是一个非常经典的设计，为后续神经网络的发展提供了极大的贡献。

2.pytorch网络设计

网络设计部分做了一些小的修改，目的是为了适配minist的3x28x28的输入图片大小。

网络构造代码部分：

class AlexNet(nn.Module):
    def __init__(self):
        super(AlexNet, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(3, 96, 11, 1, 5),  # in_channels, out_channels, kernel_size, stride, padding
            nn.ReLU(),
            nn.MaxPool2d(3, 1),  # kernel_size, stride 26x26
            # 减少卷积窗口，使用填充为2来使输入输出大小一致
            nn.Conv2d(96, 256, 5, 1, 2),
            nn.ReLU(),
            nn.MaxPool2d(4, 2),  # 12x12
            # 下面接三个卷积层
            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(4, 2)  # 5x5
        )
        self.fc = nn.Sequential(
            nn.Linear(256 * 5 * 5, 4096),
            nn.Dropout(0.5),
            nn.Linear(4096, 4096),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(4096, 10),
        )

    def forward(self, img):
        img.shape[0]
        # img.resize_(3,224,224)
        feature = self.conv(img)
        output = self.fc(feature.view(img.shape[0], -1))
        return output

3.网络测试

一些基础设置与上一篇文章一致，还是贴一下代码。

网络测试部分我使用的是minist数据集，为了贴近真实（主要是方便我自己懂），在下载了数据集之后将其转为了图片数据集，更为直观。数据集分为train 和test两部分，在测试中需要做如下配置：

1.依赖资源引入

draw_tool是一个自己编写的绘制loss，acc的画图库，device使用了我电脑的1050ti显卡。

import torch
from matplotlib import pyplot as plt
import torch.optim as optim
from torch.autograd import Variable
import torch.nn.functional as F
from torch import nn
from torchsummary import summary
from torchvision import transforms
from torch.utils.data import Dataset, DataLoader
from PIL import Image
import draw_tool

root = "F:/pycharm/dataset/mnist/MNIST/"
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
draw = draw_tool.draw_tool()

2.数据集的读取和分类

#加载图片
def default_loader(path):
    return Image.open(path).convert('RGB')

#构造标注和图片相关
class MyDataset(Dataset):
    def __init__(self, txt, transform=None, target_transform=None, loader=default_loader):
        fh = open(txt, 'r')
        imgs = []
        for line in fh:
            line = line.strip('\n')
            line = line.rstrip()
            words = line.split()
            imgs.append((words[0], int(words[1])))
        self.imgs = imgs
        self.transform = transform
        self.target_transform = target_transform
        self.loader = loader

    def __getitem__(self, index):
        fn, label = self.imgs[index]
        img = self.loader(fn)
        if self.transform is not None:
            img = self.transform(img)
        return img, label

    def __len__(self):
        return len(self.imgs)


train_data = MyDataset(txt=root + 'rawtrain.txt', transform=transforms.ToTensor())
test_data = MyDataset(txt=root + 'rawtest.txt', transform=transforms.ToTensor())
train_loader = DataLoader(dataset=train_data, batch_size=31, shuffle=True)
test_loader = DataLoader(dataset=test_data, batch_size=31, shuffle=True)
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))
                                ])

3.模型训练设置

model = AlexNet()
#使用softmax分类
criterion = torch.nn.CrossEntropyLoss()
#设置随机梯度下降 学习率和L2正则
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
#使用GPU训练
model = model.to(device)

4.训练

每训练一个epoch 做一次平均loss train acc test acc的计算绘制

def train(epoch):
    running_loss = 0.0
    num_correct = 0.0
    total = 0
    correct = 0
    total = 0
    test_acc = 0.0
    # train

    for batch_idx, data in enumerate(train_loader, 0):
        inputs, target = data
        inputs = inputs.to(device)
        target = target.to(device)
        optimizer.zero_grad()
        # forward + backward + update
        outputs = model(inputs)
        loss = criterion(outputs, target)

        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        _, predicted = torch.max(outputs.data, dim=1)
        total += target.size(0)
        num_correct += (predicted == target).sum().item()
    # #test
    # with torch.no_grad():
    #     for data in test_loader:
    #         images, labels = data
    #         images = images.to(device)
    #         labels = labels.to(device)
    #         outputs = model(images)
    #         _, predicted = torch.max(outputs.data, dim=1)
    #         total += labels.size(0)
    #
    #         correct += (predicted == labels).sum().item()

    print('[%d, %5d] loss: %.3f' % (epoch + 1, batch_idx + 1, running_loss / len(train_loader)))
    # print('Accuracy on test set: %d %%' % (100 * correct / total))
    # test_acc=100 * correct / total
    test_acc = test()
    acc = (num_correct / len(train_loader.dataset) * 100)
    print("num_correct=")
    print(acc)
    running_loss /= len(train_loader)
    draw.new_data(running_loss, acc, test_acc, 2)
    draw.draw()

def test():
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            images, labels = data
            images = images.to(device)
            labels = labels.to(device)
            outputs = model(images)
            _, predicted = torch.max(outputs.data, dim=1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    test_acc = 100 * correct / total
    print('Accuracy on test set: ', test_acc, '%')
    return test_acc

5.结果统计

if __name__ == '__main__':
    for epoch in range(20):
        train(epoch)

    torch.save(model.state_dict(), "minist_last.pth")
    draw.show()

pytorch对网络层的删除直接删除 pytorch 网络结构_pytorch对网络层的删除直接删除_02

从图中效果可以看到随着训练次数的增加，loss在不断下降，train acc 和test acc 也在慢慢收敛，最终达到了train acc=97% test acc=96%的效果。但与之前上一文的训练有一样的问题所在，不知道为什么中途的test acc会突然下降，这里就不在往下继续训练了，网络变得更为复杂并不代表精度一定会上升，反而对于简单数据的预测来说，只会更差。