Class Adadelta

Adadelta is a more robust extension of Adagrad that adapts learning rates based on a moving window of gradient updates, instead of accumulating all past gradients. This way, Adadelta continues learning even when many updates have been done. Compared to Adagrad, in the original version of Adadelta you don't have to set an initial learning rate. In this version, initial learning rate and decay factor can be set, as in most other Keras optimizers.

Inheritance

System.Object

Keras

Base

Adadelta

Implements

System.IDisposable

Inherited Members

Base.Parameters

Base.None

Base.Init()

Base.ToPython()

Base.InvokeStaticMethod(Object, String, Dictionary<String, Object>)

Base.InvokeMethod(String, Dictionary<String, Object>)

Base.Item[String]

Keras.Instance

Keras.keras

Keras.keras2onnx

Keras.tfjs

Keras.Dispose()

Keras.ToTuple(Array)

Keras.ToList(Array)

System.Object.Equals(System.Object)

System.Object.Equals(System.Object, System.Object)

System.Object.GetHashCode()

System.Object.GetType()

System.Object.MemberwiseClone()

System.Object.ReferenceEquals(System.Object, System.Object)

System.Object.ToString()

Namespace: Keras.Optimizers

Assembly: Keras.dll

Syntax

public class Adadelta : Base, IDisposable

Constructors

Adadelta(Single, Single, Nullable<Single>, Single)

Initializes a new instance of the Adadelta class.

Declaration

public Adadelta(float lr = 1F, float rho = 0.95F, float? epsilon = default(float? ), float decay = 0F)

Parameters

Type	Name	Description
System.Single	lr	float >= 0. Initial learning rate, defaults to 1. It is recommended to leave it at the default value.
System.Single	rho	float >= 0. Adadelta decay factor, corresponding to fraction of gradient to keep at each time step.
System.Nullable<System.Single>	epsilon	float >= 0. Fuzz factor. If None, defaults to K.epsilon().
System.Single	decay	float >= 0. Initial learning rate decay.

Implements

System.IDisposable