What leads to the vanishing and exploding gradient problem in RNN?

In a network of n hidden layers, n derivatives will be multiplied together. If the derivatives are large then the gradient will increase exponentially as we propagate down the model until they eventually explode, and this is what we call the problem of exploding gradient .

Why vanishing gradient problem occurs in recurrent neural network RNN )?

The vanishing gradient problem occurs when the backpropagation algorithm moves back through all of the neurons of the neural net to update their weights. The actual factor that is multiplied through a recurrent neural network in the backpropagation algorithm is referred to by the mathematical variable Wrec .

What is exploding gradient problem in RNN?

Exploding gradients are a problem where large error gradients accumulate and result in very large updates to neural network model weights during training. This has the effect of your model being unstable and unable to learn from your training data.

What is gradient when we are talking about RNN?

What is ‘gradient’ when we are talking about RNN? A gradient measures how much the output of a function changes, if you change the inputs a little bit. The higher the gradient, the steeper the slope and the faster a model can learn. But if the slope is zero, the model stops to learning.

What is exploding gradient and vanishing gradient?

So here, in the situation where the value of the weights is larger than 1, that problem is called exploding gradient because it hampers the gradient descent algorithm. When the weights are less than 1 then it is called vanishing gradient because the value of the gradient becomes considerably small with time.

Does LSTM have exploding gradient problem?

Although LSTMs tend to not suffer from the vanishing gradient problem, they can have exploding gradients.