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Poisson

Description

Computes the Poisson loss between y_true and y_pred.​ Type : polymorphic.

 

 

 

Input parameters

 

Parameters : cluster,

reduction : enum, type of reduction to apply to the loss. In almost all cases this should be “Sum over Batch“.
sample weights : boolean, if enabled, adds an input for weighting each sample individually.

 

Output parameters

 

Loss : cluster, this cluster defines the loss function used for model training.

enum : enum, an enumeration indicating the loss type (e.g., MSE, CrossEntropy, etc.). If enum is set to CustomLoss, the custom class on the right will be used as the loss function. Otherwise, the selected loss will be applied with its default configuration.
 Class : object, a custom loss class instance.

Required data

 y_pred : array, predicted values.
 y_true : array, true values.

Use cases

Poisson loss is a loss function used in regression problems where the target values are counts or non-negative integers, typically following a Poisson distribution. This function measures the difference between predictions and actual data using the likelihood of events as predicted by a Poisson distribution.

It is particularly suited for models that forecast event rates, such as the number of website visits per hour or the number of sales per day.

Example

All these exemples are snippets PNG, you can drop these Snippet onto the block diagram and get the depicted code added to your VI (Do not forget to install Deep Learning library to run it).

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