Contents
Why is better weight initialization important in neural networks?
Xavier proposed a better random weight initialization approach which also includes the size of the network (number of input and output neurons) while initializing weights. According to this approach, the weights should be inversely proportional to the square root of the number of neurons in the previous layer.
Can a neural network be stuck with a small random weight?
In this case, the equations of the learning algorithm would fail to make any changes to the network weights, and the model will be stuck. It is important to note that the bias weight in each neuron is set to zero by default, not a small random value.
What are the weights and biases of a neural network?
This article aims to provide an overview of what bias and weights are. The weights and bias are possibly the most important concept of a neural network. When the inputs are transmitted between neurons, the weights are applied to the inputs and passed into an activation function along with the bias.
How is a neural network trained on a training set?
When a neural network is trained on the training set, it is initialised with a set of weights. These weights are then optimised during the training period and the optimum weights are produced. A neuron first computes the weighted sum of the inputs.
What happens if initial weights are too big?
You can see that if the initial weights are too small (the standard deviation is small) the activations get choked, and that if they are too large the activations explode.
How to find the right weight in a neural network?
The middle value, that is approximately right can be found by setting the weights such that the variance of the activations and gradient updates stays approximately the same as you pass through the network. I wrote a blog post about weight initialization that goes into more detail, but the basic idea is as follows.
How big should the derivative of a logistic neuron be?
This means that if the input of a logistic neuron is 10 then, for a given training signal, the neuron will learn about 2200 times slower that if the input was 2. If you want the neuron to learn quickly, you either need to produce a huge training signal (such as with a cross-entropy loss function) or you want the derivative to be large.