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What is 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 .
How do you address a vanishing gradient problem?
Solutions: The simplest solution is to use other activation functions, such as ReLU, which doesn’t cause a small derivative. Residual networks are another solution, as they provide residual connections straight to earlier layers.
How do we counter vanishing gradient problems in recurrent neural network?
How to Fix Exploding Gradients?
- Re-Design the Network Model. In deep neural networks, exploding gradients may be addressed by redesigning the network to have fewer layers.
- Use Long Short-Term Memory Networks.
- Use Gradient Clipping.
- Use Weight Regularization.
How does a recurrent neural network solve the vanishing gradient problem?
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 nature of recurrent neural networks means that the cost function computed at a deep layer of the neural net will be used to change the weights of neurons at shallower layers.
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.
How is the vanishing gradient problem used in machine learning?
In Machine Learning, the Vanishing Gradient Problem is encountered while training Neural Networks with gradient-based methods (example, Back Propagation). This problem makes it hard to learn and tune the parameters of the earlier layers in the network.
How does ResNet solve the vanishing gradient problem?
The ResNet architecture, shown below, should now make perfect sense as to how it would not allow the vanishing gradient problem to occur. ResNet stands for Residual Network. These skip connections act as gradient superhighways, allowing the gradient to flow unhindered.