tf.keras.losses.MeanSquaredLogarithmicError

View source on GitHub

Computes the mean squared logarithmic error between y_true and y_pred.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.losses.MeanSquaredLogarithmicError, `tf.compat.v2.keras.losses.MeanSquaredLogarithmicError`, `tf.compat.v2.losses.MeanSquaredLogarithmicError`

tf.keras.losses.MeanSquaredLogarithmicError(
    reduction=losses_utils.ReductionV2.AUTO, name='mean_squared_logarithmic_error'
)

loss = square(log(y_true) - log(y_pred))

Usage:

msle = tf.keras.losses.MeanSquaredLogarithmicError()
loss = msle([0., 0., 1., 1.], [1., 1., 1., 0.])
print('Loss: ', loss.numpy())  # Loss: 0.36034

Usage with the compile API:

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

Methods

from_config

View source

@classmethod
from_config(
    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()

__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.