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.
Declaration
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.