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What is momentum term in neural network?
Momentum in neural networks is a variant of the stochastic gradient descent. It replaces the gradient with a momentum which is an aggregate of gradients as very well explained here. It is also the common name given to the momentum factor, as in your case.
What is back propagation momentum?
One method that has been proposed is a slight modification of the backpropagation algorithm so that it includes a momentum term. Applied to backpropagation, the concept of momentum is that previous changes in the weights should influence the current direction of movement in weight space.
Why is momentum used with backpropagation?
In this paper, the momentum backpropagation algorithm (MOBP), an improved algorithm of the traditional BP method, was used for logging data processing. In MOBP, momentum is introduced to the traditional BP algorithm to increase the speed of convergence, without compromising its capability in solving nonlinear problems.
How does the momentum of a neural network work?
Momentum is where we add a temporal element into our equation for updating the parameters of a neural network – that is, an element of time. This time element increases the momentum of the ball by some amount. This amount is called gamma γ γ, which is usually initialized to 0.9 0.9.
How does the back propagation algorithm work in a neural network?
When the gradient is negative, increase in weight decreases the error. When the gradient is positive, decrease in weight decreases the error. How does back propagation algorithm work? The goal of back propagation algorithm is to optimize the weights so that the neural network can learn how to correctly map arbitrary inputs to outputs.
How does the momentum term for backpropagation algorithm work?
The momentum introduces some balancing in the update between the eigenvectors associated to lower and larger eigenvalues. Thanks for contributing an answer to Computer Science Stack Exchange! Please be sure to answer the question.
Is the problem of backpropagation a fundamental problem?
Backprop is not the fundamental problem, it is network structures. Neural networks should not be limited to a feedforward configuration* . Backprop can only train feedforward networks and will remain one of the best solutions as long as networks are feedforward.