莫烦pytorch学习笔记（七）——Optimizer优化器

  1 import torch
  2 
  3 import torch.utils.data as Data
  4 
  5 import torch.nn.functional as F
  6 
  7 from torch.autograd import Variable
  8 
  9 import matplotlib.pyplot as plt
 10 
 11  
 12 
 13 # 超参数
 14 
 15 LR = 0.01
 16 
 17 BATCH_SIZE = 32
 18 
 19 EPOCH = 12
 20 
 21  
 22 
 23 # 生成假数据
 24 
 25 # torch.unsqueeze() 的作用是将一维变二维，torch只能处理二维的数据
 26 
 27 x = torch.unsqueeze(torch.linspace(-1, 1, 1000), dim=1)  # x data (tensor), shape(100, 1)
 28 
 29 # 0.2 * torch.rand(x.size())增加噪点
 30 
 31 y = x.pow(2) + 0.1 * torch.normal(torch.zeros(*x.size()))
 32 
 33  
 34 
 35 # 输出数据图
 36 
 37 # plt.scatter(x.numpy(), y.numpy())
 38 
 39 # plt.show()
 40 
 41  
 42 
 43 torch_dataset = Data.TensorDataset(data_tensor=x, target_tensor=y)
 44 
 45 loader = Data.DataLoader(dataset=torch_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=0)
 46 
 47  
 48 
 49  
 50 
 51 class Net(torch.nn.Module):
 52 
 53     # 初始化
 54 
 55     def __init__(self):
 56 
 57         super(Net, self).__init__()
 58 
 59         self.hidden = torch.nn.Linear(1, 20)
 60 
 61         self.predict = torch.nn.Linear(20, 1)
 62 
 63  
 64 
 65     # 前向传递
 66 
 67     def forward(self, x):
 68 
 69         x = F.relu(self.hidden(x))
 70 
 71         x = self.predict(x)
 72 
 73         return x
 74 
 75  
 76 
 77 net_SGD = Net()
 78 
 79 net_Momentum = Net()
 80 
 81 net_RMSProp = Net()
 82 
 83 net_Adam = Net()
 84 
 85  
 86 
 87 nets = [net_SGD, net_Momentum, net_RMSProp, net_Adam]
 88 
 89  
 90 
 91 opt_SGD = torch.optim.SGD(net_SGD.parameters(), lr=LR)
 92 
 93 opt_Momentum = torch.optim.SGD(net_Momentum.parameters(), lr=LR, momentum=0.8)
 94 
 95 opt_RMSProp = torch.optim.RMSprop(net_RMSProp.parameters(), lr=LR, alpha=0.9)
 96 
 97 opt_Adam = torch.optim.Adam(net_Adam.parameters(), lr=LR, betas=(0.9, 0.99))
 98 
 99 optimizers = [opt_SGD, opt_Momentum, opt_RMSProp, opt_Adam]
100 
101  
102 
103 loss_func = torch.nn.MSELoss()
104 
105  
106 
107 loss_his = [[], [], [], []]  # 记录损失
108 
109  
110 
111 for epoch in range(EPOCH):
112 
113     print(epoch)
114 
115     for step, (batch_x, batch_y) in enumerate(loader):
116 
117         b_x = Variable(batch_x)
118 
119         b_y = Variable(batch_y)
120 
121  
122 
123         for net, opt,l_his in zip(nets, optimizers, loss_his):
124 
125             output = net(b_x)  # get output for every net
126 
127             loss = loss_func(output, b_y)  # compute loss for every net
128 
129             opt.zero_grad()  # clear gradients for next train
130 
131             loss.backward()  # backpropagation, compute gradients
132 
133             opt.step()  # apply gradients
134 
135             l_his.append(loss.data.numpy())  # loss recoder
136 
137 labels = ['SGD', 'Momentum', 'RMSprop', 'Adam']
138 
139 for i, l_his in enumerate(loss_his):
140 
141     plt.plot(l_his, label=labels[i])
142 
143 plt.legend(loc='best')
144 
145 plt.xlabel('Steps')
146 
147 plt.ylabel('Loss')
148 
149 plt.ylim((0, 0.2))
150 
151 plt.show()
152 
153         
154 
155  
156 
157