分类网络
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import torchimport torch.nn.functional as Ffrom torch.autograd import Variableimport matplotlib.pyplot as plt# 构造数据n_data = torch.ones(100, 2)x0 = torch.normal(3*n_data, 1)x1 = torch.normal(-3*n_data, 1)# 标记为y0=0，y1=1两类标签y0 = torch.zeros(100)y1 = torch.ones(100)# 通过.cat连接数据x = torch.cat((x0, x1), 0).type(torch.FloatTensor)y = torch.cat((y0, y1), 0).type(torch.LongTensor)# .cuda()会将Variable数据迁入GPU中x, y = Variable(x).cuda(), Variable(y).cuda()# plt.scatter(x.data.cpu().numpy()[:, 0], x.data.cpu().numpy()[:, 1], c=y.data.cpu().numpy(), s=100, lw=0, cmap='RdYlBu')# plt.show()# 网络构造方法一class Net(torch.nn.Module): def __init__(self, n_feature, n_hidden, n_output): super(Net, self).__init__() # 隐藏层的输入和输出 self.hidden1 = torch.nn.Linear(n_feature, n_hidden) self.hidden2 = torch.nn.Linear(n_hidden, n_hidden) # 输出层的输入和输出 self.out = torch.nn.Linear(n_hidden, n_output) def forward(self, x): x = F.relu(self.hidden2(self.hidden1(x))) x = self.out(x) return x# 初始化一个网络，1个输入层，10个隐藏层，1个输出层net = Net(2, 10, 2)# 网络构造方法二'''net = torch.nn.Sequential( torch.nn.Linear(2, 10), torch.nn.Linear(10, 10), torch.nn.ReLU(), torch.nn.Linear(10, 2),)'''# .cuda()将网络迁入GPU中net.cuda()# 配置网络优化器optimizer = torch.optim.SGD(net.parameters(), lr=0.2)# SGD: torch.optim.SGD(net.parameters(), lr=0.01)# Momentum: torch.optim.SGD(net.parameters(), lr=0.01, momentum=0.8)# RMSprop: torch.optim.RMSprop(net.parameters(), lr=0.01, alpha=0.9)# Adam: torch.optim.Adam(net.parameters(), lr=0.01, betas=(0.9, 0.99))loss_func = torch.nn.CrossEntropyLoss()# 动态可视化plt.ion()plt.show()for t in range(300): print(t) out = net(x) loss = loss_func(out, y) optimizer.zero_grad() loss.backward() optimizer.step() if t % 5 == 0: plt.cla() prediction = torch.max(F.softmax(out, dim=0), 1)[1].cuda() # GPU中的数据无法被matplotlib利用，需要用.cpu()将数据从GPU中迁出到CPU中 pred_y = prediction.data.cpu().numpy().squeeze() target_y = y.data.cpu().numpy() plt.scatter(x.data.cpu().numpy()[:, 0], x.data.cpu().numpy()[:, 1], c=pred_y, s=100, lw=0, cmap='RdYlBu') accuracy = sum(pred_y == target_y) / 200 plt.text(1.5, -4, 'accuracy=%.2f' % accuracy, fontdict={'size':20, 'color':'red'}) plt.pause(0.1)plt.ioff()plt.show() |
回归网络
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import torchimport torch.nn.functional as Ffrom torch.autograd import Variableimport matplotlib.pyplot as plt# 构造数据x = torch.unsqueeze(torch.linspace(-1,1,100), dim=1)y = x.pow(2) + 0.2*torch.rand(x.size())# .cuda()会将Variable数据迁入GPU中x, y = Variable(x).cuda(), Variable(y).cuda()# plt.scatter(x.data.numpy(), y.data.numpy())# plt.show()# 网络构造方法一class Net(torch.nn.Module): def __init__(self, n_feature, n_hidden, n_output): super(Net, self).__init__() # 隐藏层的输入和输出 self.hidden = torch.nn.Linear(n_feature, n_hidden) # 输出层的输入和输出 self.predict = torch.nn.Linear(n_hidden, n_output) def forward(self, x): x = F.relu(self.hidden(x)) x = self.predict(x) return x # 初始化一个网络，1个输入层，10个隐藏层，1个输出层net = Net(1, 10, 1)# 网络构造方法二'''net = torch.nn.Sequential( torch.nn.Linear(1, 10), torch.nn.ReLU(), torch.nn.Linear(10, 1),)'''# .cuda()将网络迁入GPU中net.cuda()# 配置网络优化器optimizer = torch.optim.SGD(net.parameters(), lr=0.5)# SGD: torch.optim.SGD(net.parameters(), lr=0.01)# Momentum: torch.optim.SGD(net.parameters(), lr=0.01, momentum=0.8)# RMSprop: torch.optim.RMSprop(net.parameters(), lr=0.01, alpha=0.9)# Adam: torch.optim.Adam(net.parameters(), lr=0.01, betas=(0.9, 0.99))loss_func = torch.nn.MSELoss()# 动态可视化plt.ion()plt.show()for t in range(300): prediction = net(x) loss = loss_func(prediction, y) optimizer.zero_grad() loss.backward() optimizer.step() if t % 5 == 0 : plt.cla() # GPU中的数据无法被matplotlib利用，需要用.cpu()将数据从GPU中迁出到CPU中 plt.scatter(x.data.cpu().numpy(), y.data.cpu().numpy()) plt.plot(x.data.cpu().numpy(), prediction.data.cpu().numpy(), 'r-', lw=5) plt.text(0.5, 0, 'Loss=%.4f' % loss.item(), fontdict={'size':20, 'color':'red'}) plt.pause(0.1)plt.ioff()plt.show() |
以上这篇Pytorch 搭建分类回归神经网络并用GPU进行加速的例子就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/baishuiniyaonulia/article/details/100030943
