tf.keras.losses.logcosh

Logarithm of the hyperbolic cosine of the prediction error.

tf.keras.losses.logcosh(
    y_true, y_pred
)

Formula:

loss = mean(log(cosh(y_pred - y_true)), axis=-1)

Note that 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.

Example:

y_true = [[0., 1.], [0., 0.]]
y_pred = [[1., 1.], [0., 0.]]
loss = keras.losses.log_cosh(y_true, y_pred)
0.108

y_true Ground truth values with shape = [batch_size, d0, .. dN].
y_pred The predicted values with shape = [batch_size, d0, .. dN].

Logcosh error values with shape = [batch_size, d0, .. dN-1].