What is the RProp algorithm's weight update rule?Select one:a.The weight update is proportional to the derivative of the error functionb.The weight update is inversely proportional to the derivative of the error functionc.The weight update is proportional to the second derivative of the error functiond.The weight update is inversely proportional to the second derivative of the error function
Question
What is the RProp algorithm's weight update rule?Select one:a.The weight update is proportional to the derivative of the error functionb.The weight update is inversely proportional to the derivative of the error functionc.The weight update is proportional to the second derivative of the error functiond.The weight update is inversely proportional to the second derivative of the error function
Solution
The RProp (Resilient Propagation) algorithm's weight update rule is not directly dependent on the value of the gradient. Instead, it only utilizes the sign of the partial derivative of the error function. Therefore, none of the options a, b, c, or d accurately describe the weight update rule of the RProp algorithm.
In RProp, the size of the weight update is determined by a separate update value. If the sign of the gradient does not change from the last iteration, this update value increases, leading to faster convergence. If the sign of the gradient changes, the update value decreases to avoid overshooting the minimum. This makes the RProp algorithm resilient to the negative effects of the second order derivative, which can cause problems in other gradient descent algorithms.
Similar Questions
What is the RProp algorithm's learning rate update rule?Select one:a.The learning rate is updated based on the difference between the current and previous iteration's gradientb.The learning rate is updated based on the difference between the current and previous iteration's weightsc.The learning rate is updated based on the difference between the current and previous iteration's errord.The learning rate is updated based on the difference between the current and previous iteration's Hessian matrix
How does the RProp algorithm adjust the learning rate?Select one:a.It uses a fixed learning rate regardless of the errorb.It increases the learning rate if the error decreases and decreases the learning rate if the error increasesc.It increases the learning rate if the error increases and decreases the learning rate if the error decreasesd.It uses a predetermined set of learning rates for each iteration
What is the process of adjusting the weights and biases based on the error in the output layer called?Select one:a.Backpropagationb.None of the abovec.Forward propagationd.Activation
What is the weight update rule in backpropagation?Select one:a.W(i, j) = W(i, j) - alpha * delta(i) * output(j)b.W(i, j) = W(i, j) + alpha * delta(i) * output(j)c.W(i, j) = W(i, j) / alpha * delta(i) * output(j)d.W(i, j) = W(i, j) * alpha * delta(i) * output(j)
The weights are kept constant to avoid overfitting The weights are adjusted proportionally based on the error gradient The weights are increased by a fixed amount The weights are decreased by a fixed amount
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