import torch
import torch.utils.data as Data
import torch.nn.functional as F
import matplotlib.pyplot as plt
%matplotlib inline
# torch.manual_seed(1)    # reproducible

LR =0.01
BATCH_SIZE =32
EPOCH =12

# fake dataset
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()

# put dateset into torch dataset
torch_dataset= Data.TensorDataset(x, 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

net_SGD         = Net()
net_Momentum    = Net()
net_RMSprop     = Net()
net_Adam        = Net()
nets =[net_SGD, net_Momentum, net_RMSprop, net_Adam]

# different optimizers
    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

    # training
    for epoch in range(EPOCH):
        print('Epoch: ', epoch)
        for step, (b_x, b_y) in enumerate(loader):          # for each training step
            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()                     # apply gradients
                l_his.append(loss.data.numpy())     # 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()