卷积神经网络基础

 全连接的网络将图片的的本身二维空间结构进行了破坏，而这些空间结构是有用的，因此，要定义新的操作图像的计算节点，因此引入了卷积神经网络，能够尊重图像的空间结构，他的组成为：卷积层、池化层和归一化处理

卷积层

 将图像与过滤器进行卷积，“在空间上滑过图像，计算点积
1.过滤器的通道数和输入的通道数相同，输出的通道数和过滤器的数量相同
2. 对于每一次的卷积，可以发现图片的W和H都变小了，为了解决特征图收缩的问题，我们增加了padding，在原始图像的周围添加0（最常用），称作零填充
3. 如果图片的分辨率很大的话，每一将过滤器移动一个像素，那么就需要很多次才能把图像收缩，因此引入了超参数Stride，它是过滤器每次移动的步长

池化层

 它实际上是一种形式的降采样。有多种不同形式的非线性池化函数，而其中“最大池化（Max pooling）”是最为常见的。它是将输入的图像划分为若干个矩形区域，对每个子区域输出最大值。直觉上，这种机制能够有效地原因在于，在发现一个特征之后，它的精确位置远不及它和其他特征的相对位置的关系重要。池化层会不断地减小数据的空间大小，因此参数的数量和计算量也会下降，这在一定程度上也控制了过拟合。
常见池化层

• 平均池化（average pooling）：计算图像区域的平均值作为该区域池化后的值。

• 最大池化（max pooling）:选图像区域的最大值作为该区域池化后的值。
  

课上代码

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

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):
    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)
    x = self.fc (x)
    return x

model = Net ()

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

def train (epoch):
  running_loss = 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 += (labels == predicted).sum().item()
  
  print('accuracy on test set: %d %% ' % (100*correct/total))



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

GPU版本代码

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
import matplotlib.pyplot as plt
 
# 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
        # 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 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
        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()