tf.nn.sparse_softmax_cross_entropy_with_logits( _sentinel=None, labels=None, logits=None, name=None )
Defined in tensorflow/python/ops/nn_ops.py
.
See the guide: Neural Network > Classification
Computes sparse softmax cross entropy between logits
and labels
.
Measures the probability error in discrete classification tasks in which the classes are mutually exclusive (each entry is in exactly one class). For example, each CIFAR-10 image is labeled with one and only one label: an image can be a dog or a truck, but not both.
NOTE: For this operation, the probability of a given label is considered exclusive. That is, soft classes are not allowed, and the labels
vector must provide a single specific index for the true class for each row of logits
(each minibatch entry). For soft softmax classification with a probability distribution for each entry, see softmax_cross_entropy_with_logits
.
WARNING: This op expects unscaled logits, since it performs a softmax
on logits
internally for efficiency. Do not call this op with the output of softmax
, as it will produce incorrect results.
A common use case is to have logits of shape [batch_size, num_classes]
and labels of shape [batch_size]
. But higher dimensions are supported.
Note that to avoid confusion, it is required to pass only named arguments to this function.
_sentinel
: Used to prevent positional parameters. Internal, do not use.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
.name
: A name for the operation (optional).A Tensor
of the same shape as labels
and of the same type as logits
with the softmax cross entropy loss.
ValueError
: If logits are scalars (need to have rank >= 1) or if the rank of the labels is not equal to the rank of the logits minus one.
© 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/nn/sparse_softmax_cross_entropy_with_logits