Enum ExpressionNodeType
Defines the different types of nodes that can exist in a computational graph.
public enum ExpressionNodeTypeFields
Add = 2A node that performs addition on its inputs.
For Beginners: An Add node takes two or more values and adds them together. For example, if one input is 3 and another is 4, the Add node outputs 7. In neural networks, addition is often used to combine different signals or to add a bias term to a weighted sum.
Constant = 0A node that represents a fixed numerical value that doesn't change during computation.
For Beginners: A Constant is simply a fixed number that doesn't change, like the number 5 or 3.14. In AI models, constants are often used for things like weights, biases, or other fixed parameters.
Divide = 5A node that performs division on its inputs.
For Beginners: A Divide node takes two values and divides the first by the second. For example, if the inputs are 10 and 2, the Divide node outputs 5. In AI models, division might be used for normalization (adjusting values to a standard scale) or calculating ratios between values.
Multiply = 4A node that performs multiplication on its inputs.
For Beginners: A Multiply node takes two or more values and multiplies them together. For example, if one input is 3 and another is 4, the Multiply node outputs 12. In neural networks, multiplication is commonly used to apply weights to input values, controlling how much influence each input has.
Subtract = 3A node that performs subtraction on its inputs.
For Beginners: A Subtract node takes two values and subtracts the second from the first. For example, if the inputs are 7 and 3, the Subtract node outputs 4. In AI models, subtraction might be used in calculating differences or errors between predicted and actual values.
Variable = 1A node that represents a value that can change during computation, such as an input or parameter.
For Beginners: A Variable is a value that can change, like a placeholder that gets filled in with different values. In AI models, variables often represent the input data (like images or text) or values that get updated during training (like weights that the model is learning).
Remarks
For Beginners: A computational graph is a way to represent mathematical operations as a network of connected nodes. Think of it like a recipe with steps: some nodes are ingredients (constants and variables), while others are actions (like add, subtract). This is how AI models internally organize calculations. Each node in the graph performs a specific operation on its inputs and passes the result to the next node.