LogCoshLoss¶
Calculate the robust log-cosh loss between predictions and regression targets.
This is a robust alternative to the squared loss in regression problems, and is similar to the Huber loss. Log-cosh is the logarithm of the hyperbolic cosine, defined by the formula log(cosh(p-t)), and is twice differentiable, which can be useful in certain types of optimization problems, while Huber is computationally more efficient and has a tunable parameter that gives more control over the robustness regime. When configured to have the same transition point (delta in the HuberLoss node), the two losses have the same robustness behavior. The labels should have the same shape as the predictions or otherwise at least need to be of a broadcastable shape. Like all Loss nodes (except MeasureLoss), this node returns the per-example loss, which needs to be manually summed or averaged to get a total dataset/batch loss. If target values are omitted, they default to 0. Version 1.1.0
Ports/Properties¶
preds¶
Predictions.
- verbose name: Preds
- default value: None
- port type: DataPort
- value type: AnyNumeric (can be None)
- data direction: IN
targs¶
Target values.
- verbose name: Targs
- default value: None
- port type: DataPort
- value type: AnyNumeric (can be None)
- data direction: IN
output¶
Output.
- verbose name: Output
- default value: None
- port type: DataPort
- value type: AnyNumeric (can be None)
- data direction: OUT
set_breakpoint¶
Set a breakpoint on this node. If this is enabled, your debugger (if one is attached) will trigger a breakpoint.
- verbose name: Set Breakpoint (Debug Only)
- default value: False
- port type: BoolPort
- value type: bool (can be None)
metadata¶
User-definable meta-data associated with the node. Usually reserved for technical purposes.
- verbose name: Metadata
- default value: {}
- port type: DictPort
- value type: dict (can be None)
axis_pairing¶
How to pair axes of the preds and targs operands. In 'positional' mode, axes are paired by their position according to a right alignment, that is, the last axis of the first operand is paired with the last axis of the second operand, and so on, while any missing axes behave as if they were unnamed axes of length 1 (this is the same way plain n-dimensional arrays pair in Python/numpy). In 'matched' mode, axes are paired by their type and optionally label, where the axis order of the first operand is preserved in the output, optionally with additional axes that only occur in the second operand prepended on the left. The other operand then has its axes reordered to match. All axis classes are treated as distinct, except for the plain axis, which is treated as a wildcard axis that can pair with any other axis. The 'default' value resolves to a value that may be overridden in special contexts (e.g., an ambient Inference node) and otherwise resolves to the setting of the configuration variable default_axis_pairing, which is set to 'positional' in 2024.x. See also the 'label_handling' property for how labels are treated in this mode. Note that axis pairing can be subtle, and it is recommended to not blindly trust that the default behavior is always what the user intended.
- verbose name: Axis Pairing
- default value: default
- port type: EnumPort
- value type: str (can be None)
label_pairing¶
How to treat axis labels when pairing axes in 'matched' mode. In 'always' mode, labels are always considered significant, and axes with different labels are always considered distinct, which means that, if the two operands each have an axis of same type but with different labels, each operand will have a singleton axis inserted to pair with the respective axis in the other operand. In 'ignore' mode, labels are entirely ignored when pairing axes; this means that, if multiple axes of the same type occur in one or more operands, the last space axis in the first operand is paired with the last space axis in the second operand, etc. as in positional mode. In 'auto' mode, labels are only considered significant if they are necessary for distinguishing two or more axes of the same type in any of the operands, or if they occur on a plain axis.
- verbose name: Label Pairing
- default value: auto
- port type: EnumPort
- value type: str (can be None)
lists_as_arrays¶
Whether to treat lists as numeric arrays, as opposed to a recursive data structure. This is equivalent to converting any list operand(s) to arrays before passing them to the node. Broadly, enabling this allows for more efficient processing of large lists of either numbers, or lists of many smaller arrays, with some limitations and caveats, as follows. If one operand was a list and the other an array, an array will be returned, and if all were lists, a list will be returned (if the operand lists themselves contained arrays, the result is still a "pure" list of potentially nested lists of numbers). A limitation is that input lists may contain ONLY numbers or arrays of the same shape (e..g., no lists of blocks or packets, dictionaries, or irregular array shapes in this mode). In contrast, when the option is disabled, then if one operand is a list and the other is not a list (e.g., a number, array, block, packet, etc), then each element of the list is separately undergoing the operation while the other operand is held fixed. This has the consequence that a) the result will generally be a list if at least one operand is a list and b) the result will be mathematically different when one is a list and the other is an array, because the array will be separately combined with each list element. Note the default for this option was enabled by default in NeuroPype 2023. The option can also be set globally or in a context using the WithOptions node, for example for pipeline-wide backwards compatibility.
- verbose name: Lists As Arrays
- default value: False
- port type: BoolPort
- value type: bool (can be None)