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tf.keras.losses.LogCosh

TensorFlow 1 version View source on GitHub

Class LogCosh

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

Aliases:

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

Usage:

l = tf.keras.losses.LogCosh()
loss = l([0., 1., 1.], [1., 0., 1.])
print('Loss: ', loss.numpy())  # Loss: 0.289

Usage with the compile API:

model = tf.keras.Model(inputs, outputs)
model.compile('sgd', loss=tf.keras.losses.LogCosh())

__init__

View source

__init__(
    reduction=losses_utils.ReductionV2.AUTO,
    name='logcosh'
)

Initialize self. See help(type(self)) for accurate signature.

Methods

__call__

View source

__call__(
    y_true,
    y_pred,
    sample_weight=None
)

Invokes the Loss instance.

Args:

  • y_true: Ground truth values. shape = [batch_size, d0, .. dN]
  • y_pred: The predicted values. shape = [batch_size, d0, .. dN]
  • sample_weight: Optional sample_weight acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If sample_weight is a tensor of size [batch_size], then the total loss for each sample of the batch is rescaled by the corresponding element in the sample_weight vector. If the shape of sample_weight is [batch_size, d0, .. dN-1] (or can be broadcasted to this shape), then each loss element of y_pred is scaled by the corresponding value of sample_weight. (Note ondN-1: all loss functions reduce by 1 dimension, usually axis=-1.)

Returns:

Weighted loss float Tensor. If reduction is NONE, this has shape [batch_size, d0, .. dN-1]; otherwise, it is scalar. (Note dN-1 because all loss functions reduce by 1 dimension, usually axis=-1.)

Raises:

  • ValueError: If the shape of sample_weight is invalid.

from_config

View source

from_config(
    cls,
    config
)

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

Args:

  • config: Output of get_config().

Returns:

A Loss instance.

get_config

View source

get_config()