Annex v0.2.1 Annex.Cost.MeanSquaredError View Source

MeanSquaredError is the module that encapsulates the calculation of the mean squared error equation.

The mean squared error is a calculation that describes the penalty (Cost) of the distance between points that highly penalizes large values and slightly penalizes small values by applying the square (x * x) of the difference between the expected values (labels) and the predicted values (predictions) of a sequence/network/model, summing the squares and taking the mean/average.

MeanSquaredError is the default Cost for Annex. MeanSquaredError enables a neural network to more quickly "learn" by associating a higher error penalty for higher error values. Basically, the more incorrect a prediction the higher the penalty by a power of 2.

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Functions

Callback implementation for Annex.Cost.calculate/1.

Link to this section Functions

Callback implementation for Annex.Cost.calculate/1.