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Questions tagged [exploding-gradient-problem]

For questions related to the exploding gradient problem, which is the numerical problem associated with the significant increase (or explosion) of the numbers of the gradient vector of an objective function with respect to the parameters of a neural network, which is being trained with a gradient-based optimization algorithm and backpropagation. There is also the related vanishing gradient problem, which arises when the numbers become very small.

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6 votes
1 answer

Why do ResNets avoid the vanishing gradient problem?

I read that, if we use the sigmoid or hyperbolic tangent activation functions in deep neural networks, we can have some problems with the vanishing of the gradient, and this is visible by the shapes ...
FraMan's user avatar
  • 179
8 votes
2 answers

How to deal with large (or NaN) neural network's weights?

My weights go from being between 0 and 1 at initialization to exploding into the tens of thousands in the next iteration. In the 3rd iteration, they become so large that only arrays of nan values are ...
FeedMeInformation's user avatar
5 votes
1 answer

What effect does batch norm have on the gradient?

Batch norm is a technique where they essentially standardize the activations at each layer, before passing it on to the next layer. Naturally, this will affect the gradient through the network. I have ...
information_interchange's user avatar
1 vote
1 answer

Mathematically speaking, Is it only the product operation used in the chain rule causing the vanishing or exploding gradient?

I am asking this question from the mathematical perspective of the vanishing and exploding gradient problems that we face generally during training deep neural networks. The chain rule of ...
hanugm's user avatar
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