Is random weight assignment better than assigning weights to the units in the hidden layer?

Is random weight assignment better than assigning weights to the units in the hidden layer?

If the weights are zero, complexity of the whole deep net would be the same as that of a single neuron and the predictions would be nothing better than random. Nodes that are side-by-side in a hidden layer connected to the same inputs must have different weights for the learning algorithm to update the weights.

Why do we scale the initialization depending on layer size?

Why Initialize Weights The aim of weight initialization is to prevent layer activation outputs from exploding or vanishing during the course of a forward pass through a deep neural network.

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.

Which is better 0 or 0 weight initialization?

Assigning random values to weights is better than just 0 assignment. But there is one thing to keep in my mind is that what happens if weights are initialized high values or very low values and what is a reasonable initialization of weight values.

How are random numbers used in weight initialization?

Historically, weight initialization involved using small random numbers, although over the last decade, more specific heuristics have been developed that use information, such as the type of activation function that is being used and the number of inputs to the node.

How is weight initialization used in deep learning?

Weight initialization is a procedure to set the weights of a neural network to small random values that define the starting point for the optimization (learning or training) of the neural network model. … training deep models is a sufficiently difficult task that most algorithms are strongly affected by the choice of initialization.