ML Regression Kernel TOP node

Trains a neural network for regression.

On this page	Overview Parameters
Since	20.5

Overview ¶

This is a generic ML training node that can solve a specific class of regression problems. In contrast to ML Train Regression, this node doesn’t train a fully general neural network but uses a more specific model. For more details, see ML Regression Kernel.

Parameters ¶

Model

Weight Decay

The model trained by this node minimizes a quadratic loss. Weight Decay is a coefficient that scales a sum of squared weights (excluding bias parameters) that is a summand of this loss. Increasing the Weight Decay may improve the generalization of the model, while sacrificing how closely it fits the example targets. Increasing the value of this parameter may also help you get a more stable solution in cases that are difficult to train.

Error Threshold

This is a relative error threshold that is used to detect cases where the solution is not accurate enough. If the error of the computed model exceeds this threshold, then the node will display an error. This threshold doesn’t really do anything except generating errors when it is exceeded. This is to avoid surprises when an accurate model cannot be computed.

Kernel Type

This determines the type of kernel function that the model is based on. The kernel maps each pair of input components to a value.

Gaussian

Exponential function of a scaled, negated squared distance of two inputs.

Polynomial

Takes the dot product of two inputs, adds a constant to it and then raises the result to a specified degree.

Sigmoid

Takes the dot product of two inputs, multiplies that by a constant, adds another constant, and then applies a hyperbolic tangent.

Width

When in Gaussian, missing description.

Polynomial Offset

When in Polynomial, missing description.

Polynomial Offset

When in Polynomial, missing description.

Sigmoid Scale

When in Sigmoid, missing description.

Sigmoid Offset

When in Sigmoid, missing description.

Execution

Use Pip Cache

When enabled, pip will attempt to use cached packages on the local system instead of downloading them every time. This can improve the installation times when repeatedly installing the same Python package in different virtual environments.

Device

Environment Path

The path to the python virtual environment in which the internal training script of this node is run.

Schedulers

TOP Scheduler Override

This parameter overrides the TOP scheduler for this node.

Schedule When

When enabled, this parameter can be used to specify an expression that determines which work items from the node should be scheduled. If the expression returns zero for a given work item, that work item will immediately be marked as cooked instead of being queued with a scheduler. If the expression returns a non-zero value, the work item is scheduled normally.

Work Item Label

Determines how the node should label its work items. This parameter allows you to assign non-unique label strings to your work items which are then used to identify the work items in the attribute panel, task bar, and scheduler job names.

Use Default Label

The work items in this node will use the default label from the TOP network, or have no label if the default is unset.

Inherit From Upstream Item

The work items inherit their labels from their parent work items.

Custom Expression

The work item label is set to the Label Expression custom expression which is evaluated for each item.

Node Defines Label

The work item label is defined in the node’s internal logic.

Label Expression

When on, this parameter specifies a custom label for work items created by this node. The parameter can be an expression that includes references to work item attributes or built-in properties. For example, $OS: @pdg_frame will set the label of each work item based on its frame value.

Work Item Priority

This parameter determines how the current scheduler prioritizes the work items in this node.

Inherit From Upstream Item

The work items inherit their priority from their parent items. If a work item has no parent, its priority is set to 0.

Custom Expression

The work item priority is set to the value of Priority Expression.

Node Defines Priority

The work item priority is set based on the node’s own internal priority calculations.

This option is only available on the Python Processor TOP, ROP Fetch TOP, and ROP Output TOP nodes. These nodes define their own prioritization schemes that are implemented in their node logic.

Priority Expression

This parameter specifies an expression for work item priority. The expression is evaluated for each work item in the node.

This parameter is only available when Work Item Priority is set to Custom Expression.