change lr to 0.8 to fix the issue for 0.2

Author: boathit

time_sequence_prediction/train.py

@@ -1,6 +1,6 @@
 from __future__ import print_function
 import torch
-import torch.nn as nn 
+import torch.nn as nn
 from torch.autograd import Variable
 import torch.optim as optim
 import numpy as np
@@ -49,7 +49,7 @@ def forward(self, input, future = 0):
  seq.double()
  criterion = nn.MSELoss()
  # use LBFGS as optimizer since we can load the whole data to train
- optimizer = optim.LBFGS(seq.parameters())
+ optimizer = optim.LBFGS(seq.parameters(), lr=0.8)
  #begin to train
  for i in range(15):
  print('STEP: ', i)
@@ -69,7 +69,7 @@ def closure():
  y = pred.data.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.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)
@@ -82,4 +82,3 @@ def draw(yi, color):
  draw(y[2], 'b')
  plt.savefig('predict%d.pdf'%i)
  plt.close()
-