kl_alpha = (alpha - 1) * (1 - y) + 1
时间: 2023-12-18 12:03:33 浏览: 41
. The formula represents the calculation of the learning rate or step size for the update of the weights in a gradient descent algorithm.
In the formula, alpha represents the initial learning rate, y represents the output of the activation function for a particular input, and (alpha - 1) is a scaling factor. The term (1 - y) is the derivative of the activation function with respect to the input, which is used to adjust the learning rate based on the slope of the function at a particular point.
The value of kl_alpha determines the magnitude of the weight update, with larger values resulting in larger updates. A high learning rate can result in overshooting the optimum weights, while a low learning rate can lead to slow convergence. Therefore, it is important to choose an appropriate learning rate for the specific problem at hand.
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