《PyTorch深度学习实践》第十讲卷积神经网络基础

一、

0、Feature Extraction���征提取器，Classification分类器

1、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。

2、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。

3、卷积层：保留图像的空间信息。

4、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)。

5、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

二、

import torchfrom torchvision import transformsfrom torchvision import datasetsfrom torch.utils.data import DataLoaderimport torch.nn.functional as Fimport torch.optim as optimimport matplotlib.pyplot as plt # prepare dataset batch_size = 64transform = 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        # print("x.shape",x.shape)        x = self.fc(x)         return x  model = Net()device = torch.device("cuda" if torch.cuda.is_available() else "cpu")model.to(device) # construct loss and optimizercriterion = 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        inputs, target = inputs.to(device), target.to(device)        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            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))    return correct/total  if __name__ == '__main__':    epoch_list = []    acc_list = []        for epoch in range(10):        train(epoch)        acc = test()        epoch_list.append(epoch)        acc_list.append(acc)        plt.plot(epoch_list,acc_list)    plt.ylabel('accuracy')    plt.xlabel('epoch')    plt.show()     

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。

运行结果：