video learn url

image.png

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

# hyper parameters
LR = 0.01
BATCH_SIZE = 32
EPOCH = 12

# 回歸數(shù)據(jù)
x = torch.unsqueeze(torch.linspace(-1,1,1000),dim=1)
y = x.pow(2) + 0.1*torch.normal(torch.zeros(*x.size()))

# plot dataset
# 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 = 2,
)

# default network
class Net(torch.nn.Module):
    def __init__(self):
        super(Net,self).__init__()
        self.hidden = torch.nn.Linear(1,20) # hidden layer
        self.predict = torch.nn.Linear(20,1) # output layer

    def forward(self,x):
        x = F.relu(self.hidden(x)) # activation function for hidden layer
        x = self.predict(x) # linear output
        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 = [[],[],[],[]]

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,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() # claer gradients for net train
            loss.backward() # backpropagation, compute gradients
            opt.step() # apply gradients
            l_his.append(loss.data[0]) # loss recoder

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()