tf.losses.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
.
labels
: The ground truth output tensor, same dimensions as 'predictions'.predictions
: The predicted outputs.weights
: Optional Tensor
whose rank is either 0, or the same rank as labels
, and must be broadcastable to labels
(i.e., all dimensions must be either 1
, or the same as the corresponding losses
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.Weighted loss float Tensor
. If reduction
is NONE
, this has the same shape as labels
; otherwise, it is scalar.
ValueError
: If the shape of predictions
doesn't match that of labels
or if the shape of weights
is invalid. Also if labels
or predictions
is None.
© 2018 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