分类网络
import torch
import torch.nn.functional as F
from torch.autograd import Variable
import 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
import torch import torch.nn.functional as F from torch.autograd import Variable import 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()
