Pytorch LSTM 时间序列预测

import torchimport torch.nn as nnfrom torch.autograd import *import torch.optim as optimimport torch.nn.functional as Fimport matplotlib.pyplot as pltimport numpy as npdef SeriesGen(N):    x = torch.arange(1,N,0.01)    return torch.sin(x)def trainDataGen(seq,k):    dat = list()    L = len(seq)    for i in range(L-k-1):        indat = seq[i:i+k]        outdat = seq[i+1:i+k+1]        dat.append((indat,outdat))    return datdef ToVariable(x):    tmp = torch.FloatTensor(x)    return Variable(tmp)y = SeriesGen(10)dat = trainDataGen(y.numpy(),10)class LSTMpred(nn.Module):    def __init__(self,input_size,hidden_dim):        super(LSTMpred,self).__init__()        self.input_dim = input_size        self.hidden_dim = hidden_dim        self.lstm = nn.LSTM(input_size,hidden_dim)        self.hidden2out = nn.Linear(hidden_dim,1)        self.hidden = self.init_hidden()    def init_hidden(self):        return (Variable(torch.zeros(1, 1, self.hidden_dim)),                Variable(torch.zeros(1, 1, self.hidden_dim)))    def forward(self,seq):        lstm_out, self.hidden = self.lstm(            seq.view(len(seq), 1, -1), self.hidden)        outdat = self.hidden2out(lstm_out.view(len(seq),-1))        return outdatmodel = LSTMpred(1,6)loss_function = nn.MSELoss()optimizer = optim.SGD(model.parameters(), lr=0.01)for epoch in range(10):    print(epoch)    for seq, outs in dat[:700]:        seq = ToVariable(seq)        outs = ToVariable(outs)        #outs = torch.from_numpy(np.array([outs]))        optimizer.zero_grad()        model.hidden = model.init_hidden()        modout = model(seq)        loss = loss_function(modout, outs)        loss.backward()        optimizer.step()predDat = []for seq, trueVal in dat[700:]:    seq = ToVariable(seq)    trueVal = ToVariable(trueVal)    predDat.append(model(seq)[-1].data.numpy()[0])fig = plt.figure()plt.plot(y.numpy())plt.plot(range(700,890),predDat)plt.show()