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Use Torch Variable to Change Data Types #81

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7 changes: 4 additions & 3 deletions tutorial-contents/403_RNN_regressor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,7 @@
"""
import torch
from torch import nn
from torch.autograd import Variable
import numpy as np
import matplotlib.pyplot as plt

Expand Down Expand Up @@ -81,12 +82,12 @@ def forward(self, x, h_state):
x_np = np.sin(steps)
y_np = np.cos(steps)

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])
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]))

prediction, h_state = rnn(x, h_state) # rnn output
# !! next step is important !!
h_state = h_state.data # repack the hidden state, break the connection from last iteration
h_state = Variable(h_state.data) # repack the hidden state, break the connection from last iteration

loss = loss_func(prediction, y) # calculate loss
optimizer.zero_grad() # clear gradients for this training step
Expand Down