update to torch 0.4

tutorial-contents/403_RNN_regressor.py

@@ -3,13 +3,12 @@ View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/
 My Youtube Channel: https://www.youtube.com/user/MorvanZhou
 
 Dependencies:
-torch: 0.1.11
+torch: 0.4
 matplotlib
 numpy
 """
 import torch
 from torch import nn
-from torch.autograd import Variable
 import numpy as np
 import matplotlib.pyplot as plt
 
@@ -69,19 +68,19 @@ h_state = None      # for initial hidden state
 plt.figure(1, figsize=(12, 5))
 plt.ion()           # continuously plot
 
-for step in range(60):
+for step in range(100):
     start, end = step * np.pi, (step+1)*np.pi   # time range
     # use sin predicts cos
     steps = np.linspace(start, end, TIME_STEP, dtype=np.float32)
     x_np = np.sin(steps)    # float32 for converting torch FloatTensor
     y_np = np.cos(steps)
 
-    x = Variable(torch.from_numpy(x_np[np.newaxis, :, np.newaxis]))    # shape (batch, time_step, input_size)
-    y = Variable(torch.from_numpy(y_np[np.newaxis, :, np.newaxis]))
+    x = torch.from_numpy(x_np[np.newaxis, :, np.newaxis])    # shape (batch, time_step, input_size)
+    y = torch.from_numpy(y_np[np.newaxis, :, np.newaxis])
 
     prediction, h_state = rnn(x, h_state)   # rnn output
     # !! next step is important !!
-    h_state = Variable(h_state.data)        # repack the hidden state, break the connection from last iteration
+    h_state = h_state.data        # repack the hidden state, break the connection from last iteration
 
     loss = loss_func(prediction, y)         # cross entropy loss
     optimizer.zero_grad()                   # clear gradients for this training step