Class ActivationFunction4D

Namespace

NeuralNetworks.Operations.ActivationFunctions

Assembly

NeuralNetworks.dll

Defines an abstract 4D activation function operation applied element-wise to convolutional tensors.

public abstract class ActivationFunction4D : Operation4D

Inheritance

object

Operation

Operation<float[,,,], float[,,,]>

Operation4D

ActivationFunction4D

Derived

LeakyReLU4D

ReLU4D

Tanh4D

Inherited Members

Operation4D.EnsureSameShapeForInput(float[,,,], float[,,,])

Operation4D.EnsureSameShapeForOutput(float[,,,], float[,,,])

Operation<float[,,,], float[,,,]>.Input

Operation<float[,,,], float[,,,]>.Output

Operation<float[,,,], float[,,,]>.Forward(float[,,,], bool)

Operation<float[,,,], float[,,,]>.Backward(float[,,,])

Operation<float[,,,], float[,,,]>.EnsureSameShapeForInput(float[,,,], float[,,,])

Operation<float[,,,], float[,,,]>.EnsureSameShapeForOutput(float[,,,], float[,,,])

Operation<float[,,,], float[,,,]>.CalcOutput(bool)

Operation<float[,,,], float[,,,]>.CalcInputGradient(float[,,,])

Operation<float[,,,], float[,,,]>.Forward(object, bool)

Operation<float[,,,], float[,,,]>.Backward(object)

Operation<float[,,,], float[,,,]>.GetOutputType()

Operation<float[,,,], float[,,,]>.GetInputType()

Operation<float[,,,], float[,,,]>.CloneBase()

Operation<float[,,,], float[,,,]>.Clone()

Operation.SetRegistered()

Operation.GetOutputType()

Operation.GetInputType()

Operation.Forward(object, bool)

Operation.Backward(object)

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Remarks

Activation functions introduce non-linearity into neural network computations. This base class specifies the contract for operations that transform 4D tensors, typically shaped as

(batch, channels, height, width)

in convolutional pipelines. Concrete implementations should provide the forward activation and, where applicable, support gradient computation for backpropagation by deriving from Operation4D.