# init(learningRate:beta1:beta2:timeStep:epsilon:gradientScale:regularizationScale:clipsGradientsTo:regularizationFunction:) Returns a new Adam optimizer object. ``` init(learningRate: Float, beta1: Float, beta2: Float, timeStep: Float, epsilon: Float, gradientScale: Float, regularizationScale: Float, clipsGradientsTo gradientBounds: ClosedRange? = nil, regularizationFunction: BNNSOptimizerRegularizationFunction) ``` ## Parameters `learningRate` A value that specifies the learning rate. `beta1` A value that specifies the first-moment constant, in the range `0` to `1`. `beta2` A value that specifies the second-moment constant, in the range `0` to `1`. `timeStep` A value that’s at least `1` and represents the optimizer’s current time. `epsilon` The epsilon value you use to improve numerical stability. `gradientScale` A value that specifies the gradient scaling factor. `regularizationScale` A value that specifies the regularization scaling factor. `gradientBounds` The values for the minimum and maximum gradients. `regularizationFunction` The variable that specifies the regularization function. --- Copyright © 2026 Apple Inc. All rights reserved. | [Terms of Use](https://www.apple.com/legal/internet-services/terms/site.html) | [Privacy Policy](https://www.apple.com/privacy/privacy-policy)