链接

卷积神经网络基础

卷积神经网络高级部分

刘二大人笔记链接

刘二大人视频链接

补充

卷积conv2d

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convolution中的卷积核数量N和输入Channels相同，每N个卷积核计算组成一个输出数据output，每N个卷积核作为一个filters，M个filters组成M维outputs

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一共需要 $M （输出 C h a n n e l s ） ? N （输入 C h a n n e l s ）$ 个卷积核
一次卷积后输出的每个Channels的数据大小会变成 $inputs_{width}-kernel_{size}+1)*(inputs_{height}-kernel_{size}+1)$
可以用padding参数使每个Channels数据大小保持不变，padding = 1 表示增加一圈，就是边缘的两行两列。
卷积(convolution)后，C(Channels)可变可不变（一般都变），W(width)和H(Height)可变可不变，取决于是否padding和kernel的大小。

池化MaxPool2d

torch.nn.MaxPool2d(2)

subsampling(或pooling)后，Channels不变，W和H变

整体流程

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代码实现

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代码

import torch
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim

# 老样子准备数据
batch_size = 64
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])

train_dataset = datasets.MNIST(root=r'D:\code_management\pythonProject\dataset/mnist/', train=True, download=False,
                               transform=transform)
train_loader = DataLoader(train_dataset, shuffle=True, batch_size=batch_size)
test_dataset = datasets.MNIST(root=r'D:\code_management\pythonProject\dataset/dataset/mnist/', train=False,
                              download=False, transform=transform)
test_loader = DataLoader(test_dataset, shuffle=False, batch_size=batch_size)


# 设计神经网络
class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = torch.nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = torch.nn.Conv2d(10, 20, kernel_size=5)
        self.pooling = torch.nn.MaxPool2d(2)  # 只是用池化stride为2的池化层
        self.fc = torch.nn.Linear(320, 10)
        # 为320的计算过程(根据forward中的值进行)
        # 对于单个图像
        # 1*28*28 ---> 10*24*24 ---> 10*12*12 ---> 20*8*8 ---> 20*4*4 = 320
        # 全连接 : 320 ---> 10

    def forward(self, x):
        # flatten data from (n,1,28,28) to (n, 784)
        # 手写数据集只有一个channels,n为 batch_size
        batch_size = x.size(0)  # 取出batch_size
        x = F.relu(self.pooling(self.conv1(x)))  # 先卷积后池化
        x = F.relu(self.pooling(self.conv2(x)))
        x = x.view(batch_size, -1)  # 为进行全连接做准备，先从三维展成二维矩阵， -1 此处自动算出的是320
        x = self.fc(x)  # 全连接到10维度，一共10种
        return x


model = Net()
# 使用GPU还是CPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)


# 损失与优化方法
criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)


# 训练方法
def train(epoch):
    running_loss = 0.0
    for batch_idx, data in enumerate(train_loader, 0):
        inputs, target = data
        
        inputs, target = inputs.to(device), target.to(device)

        outputs = model(inputs)  # 训练
        loss = criterion(outputs, target)  # 算损失
        optimizer.zero_grad()  # 梯度清零
        loss.backward()  # 反向传播
        optimizer.step()  # 更新优化

        running_loss += loss.item()
        if batch_idx % 300 == 299:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, batch_idx + 1, running_loss / 300))
            running_loss = 0.0


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


if __name__ == '__main__':
    for epoch in range(10):
        train(epoch)
        test()