Does RNN suffer from vanishing gradient problem?

Does RNN suffer from vanishing gradient problem?

However, RNNs suffer from the problem of vanishing gradients, which hampers learning of long data sequences. The gradients carry information used in the RNN parameter update and when the gradient becomes smaller and smaller, the parameter updates become insignificant which means no real learning is done.

How do you stop exploding gradients?

Exploding gradients can be avoided in general by careful configuration of the network model, such as choice of small learning rate, scaled target variables, and a standard loss function.

Is there a RNN that solves the vanishing gradient problem?

But even with Relu, the vanishing gradient problem still exists. LSTM is a variant of RNN, which addresses the exploding and vanishing gradient problems. I might review it in another post.

Why is multiplying gradients in RNN a bad idea?

The gradient computation involves recurrent multiplication of W W. This multiplying by W W to each cell has a bad effect. Think like this: If you a scalar (number) and you multiply gradients by it over and over again for say 100 times, if that number > 1, it’ll explode the gradient and if < 1, it’ll vanish towards 0.

How are output and loss computed in RNN?

Simply said, RNN uses the same set of weights W, U to compute a hidden state h t (or s t) at each time step t; at next step t + 1 , the input for h t + 1 is the last hidden state h t, in conjunction with new time series data x t + 1. Output and loss are computed at each time step using weights V.

Why is exploding gradient problem not a problem?

This is called exploding gradient problem, and it basically makes the training unpractical. However, it turns out that exploding gradient is not that problematic, because: The problem is easy to notice and diagnose, because the derivative would become Nan very quickly and crash the program.