B站 刘二大人:卷积神经网络
1、卷积的含义选取一小块并将其改变形状。
单通道:
多通道:
一个 3*3卷积核,得到一个(5-3+1)*(5-3+1)单通道。N个卷积核得到N个通道。
2、padding *** 作
补零:
3、sride步长步长为2,相隔一次进行相乘:
4、maxpooling
最大池化:
5、简单卷积神经网络处理数字识别进行过程:
代码实现:
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) # 输入通道1,输出通道10,卷积核5 self.conv2 = torch.nn.Conv2d(10, 20, kernel_size=5) self.pooling = torch.nn.MaxPool2d(2) # 池化层,2*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) # 求出包大小,第零维度 x = F.relu(self.pooling(self.conv1(x))) # 先卷积->池化->relu x = F.relu(self.pooling(self.conv2(x))) x = x.view(batch_size, -1) # 自动算出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()
使用gpu跑代码:
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) # print("x.shape",x.shape) x = self.fc(x) return x model = Net() device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # 选择cuda, model.to(device) # 都放到cuda里面 # 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) # 迁入gpu 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) # 测试同样迁入gpu 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()6、效果的提升
如果是从3%错误率提升到2%,那就是足足提升了1/3!
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