How can we avoid vanishing gradient problem in RNN?

How can we avoid vanishing gradient problem in RNN?

In case of vanishing gradient, you can:

  1. initialize weights so that the potential for vanishing gradient is minimized;
  2. have Echo State Networks that are designed to solve the vanishing gradient problem;
  3. have Long Short-Term Memory Networks (LSTMs).

What is the vanishing gradient problem in the RNN?

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.

Why is vanishing gradient a problem?

In machine learning, the vanishing gradient problem is encountered when training artificial neural networks with gradient-based learning methods and backpropagation. The problem is that in some cases, the gradient will be vanishingly small, effectively preventing the weight from changing its value.

How do you avoid exploding gradient problems?

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.

Can CNN replace Lstm?

That is because recently there’s literature that points out that CNN can achieve what LSTM has been used for and great at, namely predicting sequences, but in a much faster, more computationally efficient manner.

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.

How is the vanishing gradient problem related to WREC?

To sum up, if wrec is small, you have vanishing gradient problem, and if wrec is large, you have exploding gradient problem. For the vanishing gradient problem, the further you go through the network, the lower your gradient is and the harder it is to train the weights, which has a domino effect on all of the further weights throughout the network.

Which is more problematic exploding gradient or vanishing gradient?

Vanishing gradient is more problematic than exploding gradient, because it is a general problem not only to RNN, but also to any deep neural network with many layers. Because the derivative of previous layers depends on that of later layers, it is hard to learn previous layers if later layers have small derivative.

Why are LSTMs Stop Your gradients from vanishing?

LSTMs: The Gentle Giants On their surface, LSTMs (and related architectures such as GRUs) seems like wonky, overly complex contraptions. Indeed, at first it Why LSTMs Stop Your Gradients From Vanishing: A View from the Backwards Pass | weberna’s blog