本篇部落格介紹如何在pytorch中加速神經網路的訓練過程。

可以採用SGD、Momentum、AdaGrad、RMSProp、Adam等來加快神經網路的訓練過程。

示例程式碼：

import torch
import torch.utils.data as Data
import torch.nn.functional as F
from torch.autograd import Variable
import matplotlib.pyplot as plt

# 超引數
LR = 0.01
BATCH_SIZE = 32
EPOCH = 12

# 生成假資料
# torch.unsqueeze() 的作用是將一維變二維，torch只能處理二維的資料
x = torch.unsqueeze(torch.linspace(-1, 1, 1000), dim=1)  # x data (tensor), shape(100, 1)
# 0.2 * torch.rand(x.size())增加噪點
y = x.pow(2) + 0.1 * torch.normal(torch.zeros(*x.size()))

# 輸出資料圖
# plt.scatter(x.numpy(), y.numpy())
# plt.show()

torch_dataset = Data.TensorDataset(data_tensor=x, target_tensor=y)
loader = Data.DataLoader(dataset=torch_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=0)


class Net(torch.nn.Module):
    # 初始化
    def __init__(self):
        super(Net, self).__init__()
        self.hidden = torch.nn.Linear(1, 20)
        self.predict = torch.nn.Linear(20, 1)

    # 前向傳遞
    def forward(self, x):
        x = F.relu(self.hidden(x))
        x = self.predict(x)
        return x

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()

loss_his = [[], [], [], []]  # 記錄損失

for epoch in range(EPOCH):
    print(epoch)
    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, loss_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()  # apply gradients
            l_his.append(loss.data.numpy())  # loss recoder
labels = ['SGD', 'Momentum', 'RMSprop', 'Adam']
for i, l_his in enumerate(loss_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()
        


 

執行結果：