---
title: "init(learningRate:beta1:beta2:timeStep:epsilon:gradientScale:regularizationScale:gradientClipping:regularizationFunction:usesAMSGrad:)"
framework: accelerate
role: symbol
role_heading: Initializer
path: "accelerate/bnns/adamoptimizer/init(learningrate:beta1:beta2:timestep:epsilon:gradientscale:regularizationscale:gradientclipping:regularizationfunction:usesamsgrad:)"
---

# init(learningRate:beta1:beta2:timeStep:epsilon:gradientScale:regularizationScale:gradientClipping:regularizationFunction:usesAMSGrad:)

Returns a new Adam optimizer object with gradient clipped by value or clipped by norm.

## Declaration

```swift
init(learningRate: Float = 0.001, beta1: Float = 0.9, beta2: Float = 0.999, timeStep: Float, epsilon: Float = 1e-8, gradientScale: Float, regularizationScale: Float, gradientClipping: BNNS.GradientClipping, regularizationFunction: BNNSOptimizerRegularizationFunction, usesAMSGrad: Bool = false)
```

## 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.
- `gradientClipping`: The gradient clipping function and bounds.
- `regularizationFunction`: A value that specifies the regularization function.
- `usesAMSGrad`: A Boolean value that specifies whether the optimizer should use the AMSGrad variant.

## See Also

### Creating an Adam Optimizer

- [init(learningRate:beta1:beta2:timeStep:epsilon:gradientScale:regularizationScale:clipsGradientsTo:regularizationFunction:)](accelerate/bnns/adamoptimizer/init(learningrate:beta1:beta2:timestep:epsilon:gradientscale:regularizationscale:clipsgradientsto:regularizationfunction:).md)