卷积神经网络笔记整理

 

1、前一部分叫做Feature Extraction，后一部分叫做classification

2、每一个卷积核它的通道数量要求和输入通道是一样的。这种卷积核的总数有多少个和你输出通道的数量是一样的。

3、卷积(convolution)后，C(Channels)变，W(width)和H(Height)可变可不变，取决于是否padding。subsampling(或pooling)后，C不变，W和H变。

4、卷积层：保留图像的空间信息。

5、卷积层要求输入输出是四维张量(B,C,W,H)，全连接层的输入与输出都是二维张量(B,Input_feature)。

6、卷积(线性变换)，激活函数(非线性变换)，池化；这个过程若干次后，view打平，进入全连接层

 

 

 

 

 

代码细节：

1、torch.nn.Conv2d(1,10,kernel_size=3,stride=2,bias=False)

1是指输入的Channel，灰色图像是1维的；10是指输出的Channel，也可以说第一个卷积层需要10个卷积核；kernel_size=3,卷积核大小是3x3；stride=2进行卷积运算时的步长，默认为1；bias=False卷积运算是否需要偏置bias，默认为False。padding = 0，卷积操作是否补0。

2、self.fc = torch.nn.Linear(320, 10)

这个320获取的方式，可以通过x = x.view(batch_size, -1) # print(x.shape)可得到(64,320),64指的是batch，320就是指要进行全连接操作时，输入的特征维度。

对MNIST dataset做多分类训练：

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

# prepare dataset

batch_size = 64
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])

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


# design model using class


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)
        self.fc = torch.nn.Linear(320, 10)

    def forward(self, x):
        # flatten data from (n,1,28,28) to (n, 784)
        batch_size = x.size(0)
        x = F.relu(self.pooling(self.conv1(x)))
        x = F.relu(self.pooling(self.conv2(x)))
        x = x.view(batch_size, -1)  # -1 此处自动算出的是320
        x = self.fc(x)

        return x


model = Net()

# construct loss and optimizer
criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)


# training cycle forward, backward, update


def train(epoch):
    running_loss = 0.0
    for batch_idx, data in enumerate(train_loader, 0):
        inputs, target = data
        optimizer.zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs, target)
        loss.backward()
        optimizer.step()

        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
            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()