View source on GitHub |
Adds a Log Loss term to the training procedure.
tf.compat.v1.losses.log_loss(
labels,
predictions,
weights=1.0,
epsilon=1e-07,
scope=None,
loss_collection=ops.GraphKeys.LOSSES,
reduction=Reduction.SUM_BY_NONZERO_WEIGHTS
)
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
.
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 of predictions doesn't match that of labels or
if the shape of weights is invalid. Also if labels or predictions
is None.
|
eager compatibility
The loss_collection
argument is ignored when executing eagerly. Consider
holding on to the return value or collecting losses via a tf.keras.Model
.