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| 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 ¶
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.
Data Set Folder
Source folder that contains one or more data sets.
Data Set Base Name
The base name of a data set, excluding the .raw extension.
Kernel Folder
Missing description.
Kernel Base Name
Missing description.
Models Folder
Destination folder for trained models.
Model Base Name
The base name of a trained model, excluding the .onnx extension.
Logs Folder
Folder that contains one or more training logs.
Log Base Name
The base name of a training log, excluding the .txt extension.
Parameter Storage
Where the parameters of a trained model get stored on export.
Same File
The parameters of the trained model are saved into the ONNX file. This only works for models smaller than 2GB.
Same Directory
The parameters of the trained model are saved into different file in the same directory as the ONNX file. This is required for exporting models larger than 2GB.
Log to Standard Output
When on, information is written to the standard output during training. This does not stop the same information from being written out to log files.
Cache Mode
Determines how the processor node handles work items that report expected file results.
Automatic
If the expected result file exists on disk, the work item is marked as cooked without being scheduled. If the file does not exist on disk, the work item is scheduled as normal. If upstream work item dependencies write out new files during a cook, the cache files on work items in this node will also be marked as out-of-date.
Automatic (Ignore Upstream)
The same as Automatic, except upstream file writes do not invalidate cache files on work items in this node and this node will only check output files for its own work items.
Read Files
If the expected result file exists on disk, the work item is marked as cooked without being scheduled. Otherwise the work item is marked as failed.
Write Files
Work items are always scheduled and the expected result file is ignored even if it exists on disk.
Environment Path
The path to the python virtual environment in which the internal training script of this node is run.
Use Pip Cache
When on, 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.
TOP Scheduler Override
Overrides the TOP scheduler for this node.
Schedule When
When on, specifies 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, 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
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
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.
| See also |
