Log-cosh loss is a loss function that is used to solve regression problems. Log-cosh is calculated as the average logarithm of the hyperbolic cosine of the differences between the predicted and actual values.
The formula to calculate the log-cosh:
n– the number of data points.
y– the actual value of the data point. Also known as true value.
ŷ– the predicted value of the data point. This value is returned by model.
Let’s say we have the following sets of numbers:
|actual values of ||4||-1.5||5||2|
|predicted values of ||3.5||1||5||3|
Here is example how log-cosh can be calculated using these numbers:
TensorFlow 2 allows to calculate the log-cosh. It can be done by using
from tensorflow import keras yActual = [4, -1.5, 5, 2] yPredicted = [3.5, 1, 5, 3] logcoshObject = keras.losses.LogCosh() logcoshTensor = logcoshObject(yActual, yPredicted) logcosh = logcoshTensor.numpy() print(logcosh)
Log-cosh also can be calculated by using
logcoshTensor = keras.losses.log_cosh(yActual, yPredicted) logcosh = logcoshTensor.numpy()