MLLossFunctions
Library "MLLossFunctions"
Methods for Loss functions.
mse(expects, predicts) Mean Squared Error (MSE) " MSE = 1/N * sum((y - y')^2) ".
Parameters:
expects: float array, expected values.
predicts: float array, prediction values.
Returns: float
binary_cross_entropy(expects, predicts) Binary Cross-Entropy Loss (log).
Parameters:
expects: float array, expected values.
predicts: float array, prediction values.
Returns: float
Methods for Loss functions.
mse(expects, predicts) Mean Squared Error (MSE) " MSE = 1/N * sum((y - y')^2) ".
Parameters:
expects: float array, expected values.
predicts: float array, prediction values.
Returns: float
binary_cross_entropy(expects, predicts) Binary Cross-Entropy Loss (log).
Parameters:
expects: float array, expected values.
predicts: float array, prediction values.
Returns: float
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