Is it a good idea to initialize the weights of a deep neural network to zero?

Is it a good idea to initialize the weights of a deep neural network to zero?

Initializing all the weights with zeros leads the neurons to learn the same features during training. Thus, both neurons will evolve symmetrically throughout training, effectively preventing different neurons from learning different things.

What is the impact of weight in artificial neural network?

In Neural network, some inputs are provided to an artificial neuron, and with each input a weight is associated. Weight increases the steepness of activation function. This means weight decide how fast the activation function will trigger whereas bias is used to delay the triggering of the activation function.

What is the problem with all zero initialisation of weights in a neural net?

Zero initialization: If all the weights are initialized to zeros, the derivatives will remain same for every w in W[l]. As a result, neurons will learn same features in each iterations. This problem is known as network failing to break symmetry. And not only zero, any constant initialization will produce a poor result.

How are weights calculated in neural networks?

You can find the number of weights by counting the edges in that network. To address the original question: In a canonical neural network, the weights go on the edges between the input layer and the hidden layers, between all hidden layers, and between hidden layers and the output layer.

When do random seeds get used in neural networks?

In general (barring any special cases that I’m unaware of), a Neural Network should behave deterministically after training; if you give it the same input, it should provide the same output, your random seed should no longer have influence after training. Thanks for contributing an answer to Artificial Intelligence Stack Exchange!

Why do you initialize a neural network with random weights?

The weights of artificial neural networks must be initialized to small random numbers. This is because this is an expectation of the stochastic optimization algorithm used to train the model, called stochastic gradient descent. To understand this approach to problem solving,…

When to use a feed forward neural network?

Whenever you deal with huge amounts of data and you want to solve a supervised learning task with a feed-forward neural network, solutions based on backpropagation are much more feasible. The reason for this is, that for a complex neural network, the number of free parameters is very high.

What are the advantages and disadvantages of neural networks?

That said, helpful guidelines on how to better understand when you should use which type of algorithm never hurts. The main advantage of neural networks lies in their ability to outperform nearly every other machine learning algorithm, but this comes with some disadvantages that we will discuss and lay our focus on during this post.