通過可視化將4中Optimizer的性能進(jìn)行對(duì)比
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# -*- coding: utf-8 -*-
"""
@Time ： 2020/6/6 3:09 下午
@Auth ： Codewyf
@File ：2.5 Optimizer 優(yōu)化器.py
@IDE ：PyCharm
@Motto：Go Ahead Instead of Heasitating
"""
import torch
import torch.nn.functional as F
import torch.utils.data as Data
import matplotlib.pyplot as plt
from torch.autograd import Variable
# hyper parameters
LR = 0.01
BATCH_SIZE = 32
EPOCH = 12
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()
#
# optimizer = torch.optim.SGD()
torch_dataset = Data.TensorDataset(x,y)
#macOS
#loader = Data.DataLoader(dataset=torch_dataset, batch_size=BATCH_SIZE, shuffle=True,num_workers=2)
#windows
loader = Data.DataLoader(dataset=torch_dataset, batch_size=BATCH_SIZE, shuffle=True)
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
# different nets
net_SGD = Net()
net_Momentum = Net()
net_RMSprop = Net()
net_Adam = Net()
nets = [net_SGD, net_Momentum, net_RMSprop, net_Adam]
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]
loss_func = torch.nn.MSELoss()
losses_his = [[],[],[],[]] #record loss
for epoch in range(EPOCH):
print('Epoch: ', epoch)
for step, (b_x, b_y) in enumerate(loader):
for net, opt, l_his in zip(nets, optimizers, losses_his):
output = net(b_x) #get output for every net
loss = loss_func(output, b_y) #compute loss for every net
opt.zero_grad() #clear gradients for next train
loss.backward() #backpropagation, compute gradients
opt.step() #apple gradients
l_his.append(loss.data.numpy()) #把誤差放進(jìn)losses_his的列表里面
# SGD 是最普通的優(yōu)化器, 也可以說沒有加速效果, 而 Momentum 是 SGD 的改良版, 它加入了動(dòng)量原則.
# 后面的 RMSprop 又是 Momentum 的升級(jí)版. 而 Adam 又是 RMSprop 的升級(jí)版. 不過從這個(gè)結(jié)果中我們看到,
# Adam 的效果似乎比 RMSprop 要差一點(diǎn). 所以說并不是越先進(jìn)的優(yōu)化器, 結(jié)果越佳. 我們?cè)谧约旱脑囼?yàn)中可以嘗試不同的優(yōu)化器,
# 找到那個(gè)最適合你數(shù)據(jù)/網(wǎng)絡(luò)的優(yōu)化器.
labels = ['SGD', 'Momentum', 'RMSprop', 'Adam']
for i, l_his in enumerate(losses_his):
plt.plot(l_his, label=labels[i])
plt.legend(loc='best')
plt.xlabel('Steps')
plt.ylabel('Loss')
plt.ylim((0, 0.2))
plt.show()
