nz::nodes::calc::ELUNode Class Reference

Represents an Exponential Linear Unit (ELU) activation function node in a computational graph. More...

Inheritance diagram for nz::nodes::calc::ELUNode:

Collaboration diagram for nz::nodes::calc::ELUNode:

Public Member Functions
	ELUNode (Node *input, Tensor::value_type alpha=1.0f)
	Constructor to initialize an ELUNode for applying the ELU activation function.
void	forward () override
	Forward pass for the ELUNode to apply the ELU activation function.
void	backward () override
	Backward pass for the ELUNode to compute gradients.
Public Member Functions inherited from nz::nodes::Node
virtual void	print (std::ostream &os) const
	Prints the type, data, and gradient of the node.
void	dataInject (Tensor::value_type *data, bool grad=false) const
	Injects data into a relevant tensor object, optionally setting its gradient requirement.
template<typename Iterator >
void	dataInject (Iterator begin, Iterator end, const bool grad=false) const
	Injects data from an iterator range into the output tensor of the InputNode, optionally setting its gradient requirement.
void	dataInject (const std::initializer_list< Tensor::value_type > &data, bool grad=false) const
	Injects data from a std::initializer_list into the output tensor of the Node, optionally setting its gradient requirement.

Detailed Description

Represents an Exponential Linear Unit (ELU) activation function node in a computational graph.

The ELUNode class applies the Exponential Linear Unit (ELU) activation function to the input tensor. The ELU function is defined as:

ELU(x) = x, if x > 0
         alpha * (exp(x) - 1), if x <= 0

where alpha controls the value for negative inputs and smoothens the curve.

Key features:

• Forward Pass: Applies the ELU activation function element-wise to the input tensor. Positive values remain unchanged, while negative values are scaled exponentially with alpha.
• Backward Pass: Computes the gradient of the loss with respect to the input tensor. Gradients are propagated differently for positive and negative input values:

  ELU'(x) = 1, if x > 0
            alpha * exp(x), if x <= 0

• Shape Preservation: The output tensor has the same shape as the input tensor.
• Gradient Management: Automatically tracks gradients if required by the input tensor.

This class is part of the nz::nodes namespace and is often used to improve the learning dynamics in deep networks by reducing the vanishing gradient problem for negative inputs.

Note

• The alpha parameter defaults to 1.0, but can be customized during construction to control the behavior for negative inputs.
• Efficient GPU computations are performed for both forward and backward passes.

Usage Example:

// Example: Using ELUNode in a computational graph
InputNode input({3, 3}, true);  // Create an input node with shape {3, 3}
 
float data[] = {-1.0f, 0.0f, 1.0f, 2.0f, -2.0f, 3.0f, -3.0f, 4.0f, -4.0f};  // Sample input values
input.output->dataInject(data);  // Copy data to the input tensor
 
ELUNode elu_node(&input, 0.5f);  // Apply ELU activation with alpha = 0.5
elu_node.forward();  // Perform the forward pass
elu_node.backward();  // Propagate gradients in the backward pass
 
std::cout << "Output: " << *elu_node.output << std::endl;  // Print the result

See also

forward() for the ELU computation in the forward pass.

backward() for gradient computation in the backward pass.

Author

Mgepahmge (https://github.com/Mgepahmge)

Date

2024/12/05

Definition at line 2648 of file Nodes.cuh.

Constructor & Destructor Documentation

ELUNode()

nz::nodes::calc::ELUNode::ELUNode ( Node * input, Tensor::value_type alpha = 1.0f )

explicit

Constructor to initialize an ELUNode for applying the ELU activation function.

The constructor initializes an ELUNode, which applies the Exponential Linear Unit (ELU) activation function to an input tensor. It establishes a connection to the input node, initializes the output tensor, and sets the alpha parameter and node type.

Parameters

input	A pointer to the input node. Its output tensor will have the ELU activation applied.
alpha	The scaling parameter for negative input values. Defaults to 1.0.

• The input node is added to the inputs vector to establish the connection in the computational graph.
• The output tensor is initialized with the same shape as the input tensor, and its gradient tracking is determined based on the input tensor's requirements.
• The alpha parameter controls the scaling for negative input values, influencing the gradient flow and smoothness of the activation.
• The node's type is set to "ELU" to reflect its operation.

Note

• The ELU activation function is defined as:

  ELU(x) = x, if x > 0
           alpha * (exp(x) - 1), if x <= 0

• This node supports automatic gradient tracking if the input tensor requires gradients.

See also

forward() for the forward pass implementation.

backward() for gradient computation in the backward pass.

Author

Mgepahmge (https://github.com/Mgepahmge)

Date

2024/12/05

Definition at line 453 of file Nodes.cu.

Member Function Documentation

backward()

void nz::nodes::calc::ELUNode::backward ( )

overridevirtual

Backward pass for the ELUNode to compute gradients.

The backward() method computes the gradient of the loss with respect to the input tensor by applying the derivative of the ELU activation function. The gradient computation is based on the formula:

ELU'(x) = 1, if x > 0
          alpha * exp(x), if x <= 0

where alpha is the scaling parameter for negative input values.

• A CUDA kernel (ELUBackward) is launched to compute the gradients in parallel on the GPU.
• The derivative of the ELU function is applied element-wise to the input tensor's data and combined with the gradient of the output tensor to compute the input gradient.
• The alpha parameter, provided during construction, controls the gradient scale for negative input values.
• The computed gradient is stored in the gradient tensor of the input node.

Note

• Gradients are only computed and propagated if the input tensor's requiresGrad property is true.
• The shape of the gradient tensor matches that of the input tensor.

See also

forward() for the ELU computation in the forward pass.

Author

Mgepahmge (https://github.com/Mgepahmge)

Date

2024/12/05

Implements nz::nodes::Node.

Definition at line 467 of file Nodes.cu.

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forward()

void nz::nodes::calc::ELUNode::forward ( )

overridevirtual

Forward pass for the ELUNode to apply the ELU activation function.

The forward() method applies the Exponential Linear Unit (ELU) activation function element-wise to the input tensor. The result is stored in the output tensor. The ELU function is defined as:

ELU(x) = x, if x > 0
         alpha * (exp(x) - 1), if x <= 0

• A CUDA kernel (ExponentialLinearUnit) is launched to compute the activation function in parallel on the GPU.
• The grid and block dimensions are dynamically calculated based on the size of the output tensor to ensure efficient GPU utilization.
• The alpha parameter, provided during construction, scales the output for negative input values.

See also

backward() for the computation of gradients in the backward pass.

Note

• The shape of the output tensor matches that of the input tensor.

Author

Mgepahmge (https://github.com/Mgepahmge)

Date

2024/12/05

Implements nz::nodes::Node.

Definition at line 461 of file Nodes.cu.

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The documentation for this class was generated from the following files:

• D:/Users/Mgepahmge/Documents/C Program/NeuZephyr/include/NeuZephyr/Nodes.cuh
• D:/Users/Mgepahmge/Documents/C Program/NeuZephyr/src/Nodes.cu