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Cross-entropy loss using tf.nn.sparse_softmax_cross_entropy_with_logits
.
tf.compat.v1.losses.sparse_softmax_cross_entropy(
labels,
logits,
weights=1.0,
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 shape [batch_size]
, then the loss weights apply to each
corresponding sample.
Returns | |
---|---|
Weighted loss Tensor of the same type as logits . If reduction is
NONE , this has the same shape as labels ; otherwise, it is scalar.
|
Raises | |
---|---|
ValueError
|
If the shapes of logits , labels , and weights are
incompatible, or if any of them are 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
.