What happens if the weights are zero initialized?

What happens if the weights are zero initialized?

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.

Why the weights are initialized low and random in a deep network?

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. About the need for nondeterministic and randomized algorithms for challenging problems.

What if all the weights are initialized with the same value?

Now imagine that you initialize all weights to the same value (e.g. zero or one). In this case, each hidden unit will get exactly the same signal. E.g. if all weights are initialized to 1, each unit gets signal equal to sum of inputs (and outputs sigmoid(sum(inputs)) ).

Why neurons should not all be initialized for all weights to the same value?

If the gradients are equal then weights are going to be updated by the same amount. The weights attached to the same neuron, continue to remain the same throughout the training. Hence to break this symmetry the weights connected to the same neuron should not be initialized to the same value.

What can happen during backpropagation when all the weights are initialized to zero?

Forward feed: If all weights are 0’s, then the input to the 2nd layer will be the same for all nodes. The outputs of the nodes will be the same, though they will be multiplied by the next set of weights which will be 0, and so the inputs for the next layer will be zero etc., etc.

Is it OK to initialize the bias terms to 0?

It is possible and common to initialize the biases to be zero, since the asymmetry breaking is provided by the small random numbers in the weights.

What is weight initialization strategy for 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.

Why is Glorot initialized?

One common initialization scheme for deep NNs is called Glorot (also known as Xavier) Initialization. The idea is to initialize each weight with a small Gaussian value with mean = 0.0 and variance based on the fan-in and fan-out of the weight.

What happens if you set all the weights to 0 in a neural network with backpropagation?

Why is zero initialization not a good weight initialization process?

Zero initialization : This makes hidden units symmetric and continues for all the n iterations i.e. setting weights to 0 does not make it better than a linear model. An important thing to keep in mind is that biases have no effect what so ever when initialized with 0.

What is the typical value of weights associated with bias?

Weights control the signal (or the strength of the connection) between two neurons. In other words, a weight decides how much influence the input will have on the output. Biases, which are constant, are an additional input into the next layer that will always have the value of 1.

Why do we use weights in deep learning?

Weights and biases (commonly referred to as w and b) are the learnable parameters of a some machine learning models, including neural networks. Weights control the signal (or the strength of the connection) between two neurons. In other words, a weight decides how much influence the input will have on the output.

Is it possible to set all initial weights to zero?

Main problem with initialization of all weights to zero mathematically leads to either the neuron values are zero (for multi layers) or the delta would be zero. In one of the comments by @alfa in the above answers already a hint is provided, it is mentioned that the product of weights and delta needs to be zero.

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.

What should the initialization of Weights and bias be?

If all of the weights are the same, they will all have the same error and the model will not learn anything – there is no source of asymmetry between the neurons. What we could do, instead, is to keep the weights very close to zero but make them different by initializing them to small, non-zero numbers.

Why are hidden units not initialized to the same value?

It makes the hidden units symmetric and this problem is known as the symmetry problem. Hence to break this symmetry the weights connected to the same neuron should not be initialized to the same value. Never initialize all the weights to zero. Never initialize all the weights to the same value.