Error, could you show me where is the problem for this error?
pytorch is latest version. Tried to change inputs and initial hidden state into Variable(), but it dose not work.
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*Traceback (most recent call last):
File " learnPytorch_lstm_short.py", line 81, in <module>
main()
File " learnPytorch_lstm_short.py", line 77, in main
neural_network()
File " learnPytorch_lstm_short.py", line 62, in neural_network
loss.backward()
File "/Users/xxx/opt/anaconda3/envs/torch_learn/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)
File "/Users/xxx/opt/anaconda3/envs/torch_learn/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward
Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
RuntimeError: Trying to backward through the graph a second time (or directly access saved tensors after they have already been freed). Saved intermediate values of the graph are freed when you call .backward() or autograd.grad(). Specify retain_graph=True if you need to backward through the graph a second time or if you need to access saved tensors after calling backward.*
The following is the code file:
from audioop import bias
import re
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
from torch.autograd import Variable
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.rnn = nn.LSTMCell(input_size = 10,
hidden_size = 10)
def forward(self, x, hx, cx ):
output = []
for i in range(x.shape[0]):
hx, cx = self.rnn(x[i], (hx, cx ))
output.append(hx)
output =torch.stack(output, dim=0)
return output, hx, cx
def neural_network():
net = Net()
net = net.float()
net.zero_grad()
criterion = nn.MSELoss() # nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
hx = Variable(torch.randn(3, 10))
cx = Variable(torch.randn(3, 10))
for epoch in range(1):
running_loss =0
for i in range(20):
inputs = Variable(torch.randn(10, 3, 10)) # time step, batch,hidden size
labels = Variable(torch.randn(10, 3, 10))
# print("input shape ", inputs.shape)
optimizer.zero_grad()
outputs, hx, cx = net(inputs, hx, cx)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 2 == 1:
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
print('Finished training ')
def main():
neural_network()
if __name__ == "__main__":
main()
