Class MaxNorm

MaxNorm weight constraint. Constrains the weights incident to each hidden unit to have a norm less than or equal to a desired value.

Inheritance

System.Object

Keras

Base

MaxNorm

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.Constraints

Assembly: Keras.dll

Syntax

public class MaxNorm : Base, IDisposable

Constructors

MaxNorm(Single, Int32)

Initializes a new instance of the MaxNorm class.

Declaration

public MaxNorm(float max_value = 2F, int axis = 0)

Parameters

Type	Name	Description
System.Single	max_value	the maximum norm for the incoming weights.
System.Int32	axis	integer, axis along which to calculate weight norms. For instance, in a Dense layer the weight matrix has shape (input_dim, output_dim), set axis to 0 to constrain each weight vector of length (input_dim,). In a Conv2D layer with data_format="channels_last", the weight tensor has shape (rows, cols, input_depth, output_depth), set axis to [0, 1, 2] to constrain the weights of each filter tensor of size (rows, cols, input_depth).

Implements

System.IDisposable