self.gru = nn.GRU(hidden_size, hidden_size, num_layers, batch_first=True) def forward(self, x, hidden=None): embedded = self.embedding(x) output, hidden = self.gru(embedded, hidden) #the output and the hidden is nan. return output, hidden.

Dec 2, 2017 · I’ve a GRU model: self.gru = nn.GRU(100, 900, 3).cuda(); self.gru2 = nn.GRU(900, 1536, 1).cuda() My problem is that my loss after around 20 iterations prints NaN or (in the rare case) stays constant.

Sep 21, 2020 · GRU doesn't prevent exploding gradient; it prevents vanishing gradient instead. So you can apply gradient clipping. You must train your scaler only with the training data and use it to transform test data.

When I use a GRU layer with recurrent dropout training loss (after couple of batches of first epoch) takes "value" of nan, while training accuracy (from the start of second epoch) takes the value of 0.

Mar 24, 2024 · I have a very simple GRU in pytorch that is failing because at some point, somewhere between 80 and 1000 steps, the gards become NaN, specially on the gru.weight_ih_l0. I have tried using torch.nn.utils.clip_grad_norm_ however does …

I've noticed that a frequent occurrence during training is NANs being introduced. Often times it seems to be introduced by weights in inner-product/fully-connected or convolution layers blowing up. Is this occurring because the gradient computation is blowing up?

Aug 30, 2021 · I am trying to run the most basic single-layer RNN with complex inputs on the nightly build of PyTorch (1.10.0-CPU). The problem is that the gradient always evaluates to NaN. I’ve tried all recurrent layers (RNN, GRU, LTSM) with the same result. Here is the model: def __init__( self, input_dim: int, output_dim: int, **kwargs. ):

I'm using autocast with GradScaler to train on mixed precision. For small dataset, it works fine. But when I trained on bigger dataset, after few epochs (3-4), the loss turns to nan. It is seq2seq, transformer model, using Adam optimizer, cross entropy criterion. diff = torch. sum ((output != target), axis=1) acc = torch. sum (diff == 0)

May 7, 2021 · I try to train a RNN network (seq2seq) with GRU and SRU cells. When training is done with GRU everything is ok, loss is decreasing an accuracy steadily rise. But when switch to GRU after few hours i got NAN in loss and norm of network params (hidden states, weight matrices) is nan.

Dec 24, 2023 · gru 是 rnn 的一个变种，它通过引入门控机制来控制信息的流动。如果 gru 中的参数设置不当，或者在训练过程中出现了问题，可能会导致梯度爆炸或消失问题，进而产生 nan 值。 一旦 gru 的输出中出现了 nan 值，这些值可能会作为 rnn 的输入。