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# init(learning_rate:beta1:beta2:time_step:epsilon:gradient_scale:regularization_scale:clip_gradients:clip_gradients_min:clip_gradients_max:regularization_func:)

Returns a new Adam optimizer fields structure from the specified parameters.

```
init(learning_rate: Float, beta1: Float, beta2: Float, time_step: Float, epsilon: Float, gradient_scale: Float, regularization_scale: Float, clip_gradients: Bool, clip_gradients_min: Float, clip_gradients_max: Float, regularization_func: BNNSOptimizerRegularizationFunction)
```

## Parameters

`learning_rate`

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.

`time_step`

A value that’s at least 1 and represents the optimizer’s current time.

`epsilon`

An addition for the division in the parameter update stage.

`gradient_scale`

A value that specifies the gradient scaling factor.

`regularization_scale`

A value that specifies the regularization scaling factor.

`clip_gradients`

A Boolean value that specifies whether to clip the gradient between minimum and maximum values.

`clip_gradients_min`

The values for the minimum gradient.

`clip_gradients_max`

The values for the maximum gradient.

`regularization_func`

The variable that specifies the regularization function.

---

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