Contents
- 1 What is the vanishing gradient problem and how do you deal with this?
- 2 Why is vanishing gradients and outputs a more common problem in basic RNNs compared to feed forward networks?
- 3 What is the effect of the vanishing gradient problem?
- 4 How does batch normalization solve the vanishing gradient problem?
What is the vanishing gradient problem and how do you deal with this?
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.
Why is vanishing gradients and outputs a more common problem in basic RNNs compared to feed forward networks?
Why is vanishing gradients and outputs a more common problem in basic RNNs compared to feed forward networks? Since RNNs use the same weights for each iteration, it will also have the same effect on the input every time. RNNs often solve exploding gradients by using tanh activation functions and gradient clipping.
What is the effect of the vanishing gradient problem?
Vanishing gradient problem. This has the effect of multiplying n of these small numbers to compute gradients of the “front” layers in an n -layer network, meaning that the gradient (error signal) decreases exponentially with n while the front layers train very slowly.
Why are small gradients more difficult to train?
This problem of very small gradients is known as the vanishing gradient problem. The vanishing gradient problem particularly affects the lower layers of the network and makes them more difficult to train. Similarly, if the gradient associated with a weight becomes extremely large the updates to the weight will also be large.
How does Relu solve the vanishing gradient problem?
Rectifiers such as ReLU suffer less from the vanishing gradient problem, because they only saturate in one direction. Behnke relied only on the sign of the gradient ( Rprop) when training his Neural Abstraction Pyramid to solve problems like image reconstruction and face localization.
How does batch normalization solve the vanishing gradient problem?
As stated before, the problem arises when a large input space is mapped to a small one, causing the derivatives to disappear. In Image 1, this is most clearly seen at when |x| is big. Batch normalization reduces this problem by simply normalizing the input so |x| doesn’t reach the outer edges of the sigmoid function.