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|
Logarithm of the hyperbolic cosine of the prediction error.
tf.keras.losses.logcosh(
y_true, y_pred
)
log(cosh(x)) is approximately equal to (x ** 2) / 2 for small x and
to abs(x) - log(2) for large x. This means that 'logcosh' works mostly
like the mean squared error, but will not be so strongly affected by the
occasional wildly incorrect prediction.
Arguments | |
|---|---|
y_true
|
tensor of true targets. |
y_pred
|
tensor of predicted targets. |
Returns | |
|---|---|
| Tensor with one scalar loss entry per sample. |