简介
卷积神经网络(Convolutional Neural Network, CNN)是深度学习技术中极具代表的网络结构之一,在图像处理领域取得了很大的成功,在国际标准的ImageNet数据集上,许多成功的模型都是基于CNN的。
卷积神经网络CNN的结构一般包含这几个层:
- 输入层:用于数据的输入
- 卷积层:使用卷积核进行特征提取和特征映射
- 激励层:由于卷积也是一种线性运算,因此需要增加非线性映射
- 池化层:进行下采样,对特征图稀疏处理,减少数据运算量。
- 全连接层:通常在CNN的尾部进行重新拟合,减少特征信息的损失
- 输出层:用于输出结果
PyTorch实战
本文选用上篇的数据集MNIST手写数字识别实践CNN。
import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets, transforms from torch.autograd import Variable # Training settings batch_size = 64 # MNIST Dataset train_dataset = datasets.MNIST(root='./data/', train=True, transform=transforms.ToTensor(), download=True) test_dataset = datasets.MNIST(root='./data/', train=False, transform=transforms.ToTensor()) # Data Loader (Input Pipeline) train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True) test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False) class Net(nn.Module): def __init__(self): super(Net, self).__init__() # 输入1通道,输出10通道,kernel 5*5 self.conv1 = nn.Conv2d(1, 10, kernel_size=5) self.conv2 = nn.Conv2d(10, 20, kernel_size=5) self.mp = nn.MaxPool2d(2) # fully connect self.fc = nn.Linear(320, 10) def forward(self, x): # in_size = 64 in_size = x.size(0) # one batch # x: 64*10*12*12 x = F.relu(self.mp(self.conv1(x))) # x: 64*20*4*4 x = F.relu(self.mp(self.conv2(x))) # x: 64*320 x = x.view(in_size, -1) # flatten the tensor # x: 64*10 x = self.fc(x) return F.log_softmax(x) model = Net() optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5) def train(epoch): for batch_idx, (data, target) in enumerate(train_loader): data, target = Variable(data), Variable(target) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) loss.backward() optimizer.step() if batch_idx % 200 == 0: print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.data[0])) def test(): test_loss = 0 correct = 0 for data, target in test_loader: data, target = Variable(data, volatile=True), Variable(target) output = model(data) # sum up batch loss test_loss += F.nll_loss(output, target, size_average=False).data[0] # get the index of the max log-probability pred = output.data.max(1, keepdim=True)[1] correct += pred.eq(target.data.view_as(pred)).cpu().sum() test_loss /= len(test_loader.dataset) print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) for epoch in range(1, 10): train(epoch) test()
输出结果:
参考:https://github.com/hunkim/PyTorchZeroToAll
以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持脚本之家。