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Pytorch  LSTM 時間序列預測

https://github.com/pytorch/examples/blob/master/time_sequence_prediction/generate_sine_wave.py

Pytorch官網提供初學者入門的一個例子，有助於學習Pytorch時間序列預測。本例中使用兩個LSTMCell單元學習從不同相位開始的一些正弦波信號,LSTM網絡在學習了正弦波之後，試圖預測未來的信號值。

generate_sine_wave.py生成模擬數據：

# -*- coding: utf-8 -*-import numpy as npimport torchnp.random.seed(2)T = 20L = 1000N = 100x = np.empty((N, L), 'int64')x[:] = np.array(range(L)) + np.random.randint(-4 * T, 4 * T, N).reshape(N, 1)data = np.sin(x / 1.0 / T).astype('float64')torch.save(data, open('traindata.pt', 'wb'))

LSTM數據序列預測：

# -*- coding: utf-8 -*-from __future__ import print_functionimport torchimport torch.nn as nnimport torch.optim as optimimport numpy as npimport matplotlibmatplotlib.use('Agg')import matplotlib.pyplot as pltclass Sequence(nn.Module): def __init__(self): super(Sequence, self).__init__() self.lstm1 = nn.LSTMCell(1, 51) self.lstm2 = nn.LSTMCell(51, 51) self.linear = nn.Linear(51, 1) def forward(self, input, future = 0): outputs = [] h_t = torch.zeros(input.size(0), 51, dtype=torch.double) c_t = torch.zeros(input.size(0), 51, dtype=torch.double) h_t2 = torch.zeros(input.size(0), 51, dtype=torch.double) c_t2 = torch.zeros(input.size(0), 51, dtype=torch.double) for i, input_t in enumerate(input.chunk(input.size(1), dim=1)): h_t, c_t = self.lstm1(input_t, (h_t, c_t)) h_t2, c_t2 = self.lstm2(h_t, (h_t2, c_t2)) output = self.linear(h_t2) outputs += [output] for i in range(future):# if we should predict the future h_t, c_t = self.lstm1(output, (h_t, c_t)) h_t2, c_t2 = self.lstm2(h_t, (h_t2, c_t2)) output = self.linear(h_t2) outputs += [output] outputs = torch.stack(outputs, 1).squeeze(2) return outputsif __name__ == '__main__': # set random seed to 0 np.random.seed(0) torch.manual_seed(0) # load data and make training set data = torch.load('traindata.pt') input = torch.from_numpy(data[3:, :-1]) target = torch.from_numpy(data[3:, 1:]) test_input = torch.from_numpy(data[:3, :-1]) test_target = torch.from_numpy(data[:3, 1:]) # build the model seq = Sequence() seq.double() criterion = nn.MSELoss() # use LBFGS as optimizer since we can load the whole data to train optimizer = optim.LBFGS(seq.parameters(), lr=0.8) #begin to train for i in range(15): print('STEP: ', i) def closure(): optimizer.zero_grad() out = seq(input) loss = criterion(out, target) print('loss:', loss.item()) loss.backward() return loss optimizer.step(closure) # begin to predict, no need to track gradient here with torch.no_grad(): future = 1000 pred = seq(test_input, future=future) loss = criterion(pred[:, :-future], test_target) print('test loss:', loss.item()) y = pred.detach().numpy() # draw the result plt.figure(figsize=(30,10)) plt.title('Predict future values for time sequences\n(Dashlines are predicted values)', fontsize=30) plt.xlabel('x', fontsize=20) plt.ylabel('y', fontsize=20) plt.xticks(fontsize=20) plt.yticks(fontsize=20) def draw(yi, color): plt.plot(np.arange(input.size(1)), yi[:input.size(1)], color, linewidth = 2.0) plt.plot(np.arange(input.size(1), input.size(1) + future), yi[input.size(1):], color + ':', linewidth = 2.0) draw(y[0], 'r') draw(y[1], 'g') draw(y[2], 'b') plt.savefig('predict%d.pdf'%i) plt.close()

運行結果：