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.
This is the model that I have defined :-
class SentimentAnalysis(nn.Module):
def __init__(self, vocab_size, embedding_dim):
super(SentimentAnalysis, self).__init__()
self.fc = nn.Linear(embedding_dim, 2)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
out1 = self.fc(x)
out2 = self.sigmoid(out2)
out3 = out3.mean(dim=1)
return out3
And this is my model training step :-
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
sentiment_analysis_model = SentimentAnalysis(len(train_vocabulary), 100)
sentiment_analysis_model = sentiment_analysis_model.to(device)
loss_fn = nn.BCELoss()
optimizer = torch.optim.Adam(sentiment_analysis_model.parameters(), lr=1e-3)
epochs = 5
losses = []
for epoch in range(epochs):
sentiment_analysis_model.train()
for batch_idx, batch in enumerate(train_loader):
input = batch[0].to(device)
target = batch[1].to(device)
optimizer.zero_grad()
out = sentiment_analysis_model(input)
target1 = []
for i in target:
if i == 0:
target1.append(torch.tensor([1.0, 0.0], requires_grad=True))
else:
target1.append(torch.tensor([0.0, 1.0], requires_grad=True))
target1 = torch.stack(target1)
loss = loss_fn(out, target1)
loss.backward()
optimizer.step()
losses.append(loss.item())
I’ve been stuck at this for more than a couple of days now, but unable to get any solution here. Any help would be so very much appreciated!
