pytorch优化器

实验环境：pytorch1.5 以多元线性回归为例，分别采用自带优化器求解和手动求解。

自定义Dataset：

#自定义DataSet，需要继承Dataset类，并实现__getItem__和__len__方法 #模拟数据生成 w1, w2, w3, w4, b = 3, 4, 5, 6, 7 class MyDataSet(Dataset): def __init__(self): super(MyDataSet, self).__init__() #创建数据集 self.train_x = [] self.train_y = [] for i in range(0, 1000): self.train_x.append(torch.randint(1, 10, (4,)).tolist()) self.train_y.append(w1 * self.train_x[i][0] + w2 * self.train_x[i][1] + w3 * self.train_x[i][2] + w4 * self.train_x[i][3] + b) x_w = np.array(self.train_x).reshape((1000, 4)) x_b = np.ones((1000, 1)) self.train_x = torch.tensor(np.concatenate((x_w, x_b), axis=1), dtype=torch.float, requires_grad=True) self.train_y = torch.tensor(self.train_y, dtype=torch.float) def __getitem__(self, idx): return self.train_x[idx], self.train_y[idx] def __len__(self): return len(self.train_x)

利用SGB手动更新参数

import torch import numpy as np from torch.utils.data import Dataset, DataLoader from tqdm import tqdm #模拟数据生成 w1, w2, w3, w4, b = 3, 4, 5, 6, 7 def solve_with_sgd(): lr = 0.0001 epoch = 100000 batch_size = 64 w = torch.randn((5, 1), dtype=torch.float, requires_grad=True) my_dataset = MyDataSet() myloader = DataLoader(my_dataset, batch_size=batch_size,shuffle=True) for e in tqdm(range(epoch)): for x,y in myloader: #定义损失函数 loss = (1/(2 * len(x))) * ((x.mm(w) - y.view((-1,1))) ** 2).sum() loss.backward() with torch.no_grad(): w -= lr * w.grad w.grad.zero_() print(w) #sgd迭代100000次结果 # tensor([[3.0009], # [4.0007], # [5.0008], # [6.0008], # [6.9831]], requires_grad=True) solve_witg_sgd()

利用系统自带Adam优化器更新参数

初始化参数：

torch.optim.Adam(params, #params必须是一个可迭代对象 lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False) import torch import numpy as np from torch.utils.data import Dataset, DataLoader from tqdm import tqdm #模拟数据生成 w1, w2, w3, w4, b = 3, 4, 5, 6, 7 def solve_with_adam(): lr = 0.001 epoch = 10000 batch_size = 64 w = torch.randn((5, 1), dtype=torch.float, requires_grad=True) my_dataset = MyDataSet() myloader = DataLoader(my_dataset, batch_size=batch_size, shuffle=True) optimizer = torch.optim.Adam([w],lr = lr) #利用pytorch自带的MSE（均方误差）损失函数 loss = torch.nn.MSELoss(reduction = 'mean') for e in tqdm(range(epoch)): for x,y in myloader: optimizer.zero_grad() #定义损失函数 out = loss(x.mm(w),y.view((-1,1))) out.backward() #step()操作相当于对optimizer对象里面的所有参数进行w -= lr * w.grad。optimizer.zero_grad()类似 optimizer.step() print(w) #迭代10000次后的参数结果，和真实值完全一样 # tensor([[3.0000], # [4.0000], # [5.0000], # [6.0000], # [7.0000]], requires_grad=True) solve_with_adam()

参考

优化器 torch.optim.Optimizer