tf.losses.sparse_softmax_cross_entropy( labels, logits, weights=1.0, scope=None, loss_collection=tf.GraphKeys.LOSSES, reduction=Reduction.SUM_BY_NONZERO_WEIGHTS )
Defined in tensorflow/python/ops/losses/losses_impl.py
.
Cross-entropy loss using tf.nn.sparse_softmax_cross_entropy_with_logits
.
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.
labels
: Tensor
of shape [d_0, d_1, ..., d_{r-1}]
(where r
is rank of labels
and result) and dtype int32
or int64
. Each entry in labels
must be an index in [0, num_classes)
. Other values will raise an exception when this op is run on CPU, and return NaN
for corresponding loss and gradient rows on GPU.logits
: Unscaled log probabilities of shape [d_0, d_1, ..., d_{r-1}, num_classes]
and dtype float32
or float64
.weights
: Coefficients for the loss. This must be scalar or broadcastable to labels
(i.e. same rank and each dimension is either 1 or the same).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 Tensor
of the same type as logits
. If reduction
is NONE
, this has the same shape as labels
; otherwise, it is scalar.
ValueError
: If the shapes of logits
, labels
, and weights
are incompatible, or if any of them are 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/sparse_softmax_cross_entropy