tf.losses.log_loss
tf.losses.log_loss
tf.losses.log_loss
log_loss( labels, predictions, weights=1.0, epsilon=1e-07, scope=None, loss_collection=tf.GraphKeys.LOSSES, reduction=Reduction.SUM_BY_NONZERO_WEIGHTS )
Defined in tensorflow/python/ops/losses/losses_impl.py
.
Adds a Log Loss term to the training procedure.
weights
acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If weights
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 weights
vector. If the shape of weights
matches the shape of predictions
, then the loss of each measurable element of predictions
is scaled by the corresponding value of weights
.
Args:
-
labels
: The ground truth output tensor, same dimensions as 'predictions'. -
predictions
: The predicted outputs. -
weights
: OptionalTensor
whose rank is either 0, or the same rank aslabels
, and must be broadcastable tolabels
(i.e., all dimensions must be either1
, or the same as the correspondinglosses
dimension). -
epsilon
: A small increment to add to avoid taking a log of zero. -
scope
: The scope for the operations performed in computing the loss. -
loss_collection
: collection to which the loss will be added. -
reduction
: Type of reduction to apply to loss.
Returns:
Weighted loss float Tensor
. If reduction
is NONE
, this has the same shape as labels
; otherwise, it is scalar.
Raises:
-
ValueError
: If the shape ofpredictions
doesn't match that oflabels
or if the shape ofweights
is invalid.
© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/losses/log_loss