详解PyTorch批训练及优化器比较

import torch import torch.utils.data as Data  torch.manual_seed(1) # 设定随机数种子  BATCH_SIZE = 5  x = torch.linspace(1, 10, 10) y = torch.linspace(0.5, 5, 10)  # 将数据转换为torch的dataset格式 torch_dataset = Data.TensorDataset(data_tensor=x, target_tensor=y)  # 将torch_dataset置入Dataloader中 loader = Data.DataLoader(   dataset=torch_dataset,   batch_size=BATCH_SIZE, # 批大小   # 若dataset中的样本数不能被batch_size整除的话，最后剩余多少就使用多少   shuffle=True, # 是否随机打乱顺序   num_workers=2, # 多线程读取数据的线程数   )  for epoch in range(3):   for step, (batch_x, batch_y) in enumerate(loader):     print('Epoch:', epoch, '|Step:', step, '|batch_x:',        batch_x.numpy(), '|batch_y', batch_y.numpy()) ''''' shuffle=True Epoch: 0 |Step: 0 |batch_x: [ 6. 7. 2. 3. 1.] |batch_y [ 3.  3.5 1.  1.5 0.5] Epoch: 0 |Step: 1 |batch_x: [ 9. 10.  4.  8.  5.] |batch_y [ 4.5 5.  2.  4.  2.5] Epoch: 1 |Step: 0 |batch_x: [ 3.  4.  2.  9. 10.] |batch_y [ 1.5 2.  1.  4.5 5. ] Epoch: 1 |Step: 1 |batch_x: [ 1. 7. 8. 5. 6.] |batch_y [ 0.5 3.5 4.  2.5 3. ] Epoch: 2 |Step: 0 |batch_x: [ 3. 9. 2. 6. 7.] |batch_y [ 1.5 4.5 1.  3.  3.5] Epoch: 2 |Step: 1 |batch_x: [ 10.  4.  8.  1.  5.] |batch_y [ 5.  2.  4.  0.5 2.5]  shuffle=False Epoch: 0 |Step: 0 |batch_x: [ 1. 2. 3. 4. 5.] |batch_y [ 0.5 1.  1.5 2.  2.5] Epoch: 0 |Step: 1 |batch_x: [ 6.  7.  8.  9. 10.] |batch_y [ 3.  3.5 4.  4.5 5. ] Epoch: 1 |Step: 0 |batch_x: [ 1. 2. 3. 4. 5.] |batch_y [ 0.5 1.  1.5 2.  2.5] Epoch: 1 |Step: 1 |batch_x: [ 6.  7.  8.  9. 10.] |batch_y [ 3.  3.5 4.  4.5 5. ] Epoch: 2 |Step: 0 |batch_x: [ 1. 2. 3. 4. 5.] |batch_y [ 0.5 1.  1.5 2.  2.5] Epoch: 2 |Step: 1 |batch_x: [ 6.  7.  8.  9. 10.] |batch_y [ 3.  3.5 4.  4.5 5. ] ''' 

classtorch.utils.data.TensorDataset(data_tensor, target_tensor)

import torch import torch.utils.data as Data import torch.nn.functional as F from torch.autograd import Variable import matplotlib.pyplot as plt torch.manual_seed(1) # 设定随机数种子  # 定义超参数 LR = 0.01 # 学习率 BATCH_SIZE = 32 # 批大小 EPOCH = 12 # 迭代次数  x = torch.unsqueeze(torch.linspace(-1, 1, 1000), dim=1) y = x.pow(2) + 0.1*torch.normal(torch.zeros(*x.size()))  #plt.scatter(x.numpy(), y.numpy()) #plt.show()  # 将数据转换为torch的dataset格式 torch_dataset = Data.TensorDataset(data_tensor=x, target_tensor=y) # 将torch_dataset置入Dataloader中 loader = Data.DataLoader(dataset=torch_dataset, batch_size=BATCH_SIZE,              shuffle=True, num_workers=2)  class Net(torch.nn.Module):   def __init__(self):     super(Net, self).__init__()     self.hidden = torch.nn.Linear(1, 20)     self.predict = torch.nn.Linear(20, 1)    def forward(self, x):     x = F.relu(self.hidden(x))     x = self.predict(x)     return x  # 为每个优化器创建一个Net net_SGD = Net() net_Momentum = Net() net_RMSprop = Net() net_Adam = Net()  nets = [net_SGD, net_Momentum, net_RMSprop, net_Adam]  # 初始化优化器 opt_SGD = torch.optim.SGD(net_SGD.parameters(), lr=LR) opt_Momentum = torch.optim.SGD(net_Momentum.parameters(), lr=LR, momentum=0.8) opt_RMSprop = torch.optim.RMSprop(net_RMSprop.parameters(), lr=LR, alpha=0.9) opt_Adam = torch.optim.Adam(net_Adam.parameters(), lr=LR, betas=(0.9, 0.99))  optimizers = [opt_SGD, opt_Momentum, opt_RMSprop, opt_Adam]  # 定义损失函数 loss_function = torch.nn.MSELoss() losses_history = [[], [], [], []] # 记录training时不同神经网络的loss值  for epoch in range(EPOCH):   print('Epoch:', epoch + 1, 'Training...')   for step, (batch_x, batch_y) in enumerate(loader):     b_x = Variable(batch_x)     b_y = Variable(batch_y)      for net, opt, l_his in zip(nets, optimizers, losses_history):       output = net(b_x)       loss = loss_function(output, b_y)       opt.zero_grad()       loss.backward()       opt.step()       l_his.append(loss.data[0])  labels = ['SGD', 'Momentum', 'RMSprop', 'Adam']  for i, l_his in enumerate(losses_history):   plt.plot(l_his, label=labels[i]) plt.legend(loc='best') plt.xlabel('Steps') plt.ylabel('Loss') plt.ylim((0, 0.2)) plt.show() 

x = [1, 2, 3] y = [4, 5, 6] z = [7, 8, 9] xyz = zip(x, y, z) print xyz [(1, 4, 7), (2, 5, 8), (3, 6, 9)]  x = [1, 2, 3] x = zip(x) print x [(1,), (2,), (3,)]  x = [1, 2, 3] y = [4, 5, 6, 7] xy = zip(x, y) print xy [(1, 4), (2, 5), (3, 6)]