ML Community Day is November 9! Join us for updates from TensorFlow, JAX, and more Learn more

tf.keras.losses.LogCosh

Computes the logarithm of the hyperbolic cosine of the prediction error.

Inherits From: Loss

logcosh = log((exp(x) + exp(-x))/2), where x is the error y_pred - y_true.

Standalone usage:

y_true = [[0., 1.], [0., 0.]]
y_pred = [[1., 1.], [0., 0.]]
# Using 'auto'/'sum_over_batch_size' reduction type.
l = tf.keras.losses.LogCosh()
l(y_true, y_pred).numpy()
0.108
# Calling with 'sample_weight'.
l(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy()
0.087
# Using 'sum' reduction type.
l = tf.keras.losses.LogCosh(
    reduction=tf.keras.losses.Reduction.SUM)
l(y_true, y_pred).numpy()
0.217
# Using 'none' reduction type.
l = tf.keras.losses.LogCosh(
    reduction=tf.keras.losses.Reduction.NONE)
l(y_true, y_pred).numpy()
array([0.217, 0.], dtype=float32)

Usage with the compile() API:

model.compile(optimizer='sgd', loss=tf.keras.losses.LogCosh())

reduction Type of tf.keras.losses.Reduction to apply to loss. Default value is AUTO. AUTO indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to SUM_OVER_BATCH_SIZE. When used with tf.distribute.Strategy, outside of built-in training loops such as tf.keras compile and fit, using AUTO or SUM_OVER_BATCH_SIZE will raise an error. Please see this custom training tutorial for more details.
name Optional name for the instance. Defaults to 'log_cosh'.

Methods

from_config

View source

Instantiates a Loss from its config (output of get_config()).

Args
config Output of get_config().

Returns
A Loss instance.