What does increasing hidden layers do?
It should be kept in mind that increasing hidden layers would also increase the complexity of the model and choosing hidden layers such as 8, 9, or in two digits may sometimes lead to overfitting. Once hidden layers have been decided the next task is to choose the number of nodes in each hidden layer.
Why is ReLU not sigmoid?
Advantage: Sigmoid: not blowing up activation. Relu : not vanishing gradient. Relu : More computationally efficient to compute than Sigmoid like functions since Relu just needs to pick max(0,x) and not perform expensive exponential operations as in Sigmoids.
Why do different layers of a neural network get stuck?
When we look closely, we’ll discover that the different layers in our deep network are learning at vastly different speeds. In particular, when later layers in the network are learning well, early layers often get stuck during training, learning almost nothing at all. This stuckness isn’t simply due to bad luck.
What is the purpose of the hidden layers?
“Hidden” layers really aren’t all that special… a hidden layer is really no more than any layer that isn’t input or output. So even a very simple 3 layer NN has 1 hidden layer. So I think the question isn’t really “How do hidden layers help?”
Why does learning slow down in gradient based learning?
Rather, we’ll discover there are fundamental reasons the learning slowdown occurs, connected to our use of gradient-based learning techniques. As we delve into the problem more deeply, we’ll learn that the opposite phenomenon can also occur: the early layers may be learning well, but later layers can become stuck.
When does vanishing gradient occur in machine learning?
If you do not carefully choose the range of the initial values for the weights, and if you do not control the range of the values of the weights during training, vanishing gradient would occur which is the main barrier to learning deep networks. The neural networks are trained using the gradient descent algorithm: