Computes sparse softmax cross entropy between `logits`

and `labels`

.

tf.compat.v1.nn.sparse_softmax_cross_entropy_with_logits( _sentinel=None, labels=None, logits=None, name=None )

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_v2`

.

A common use case is to have logits of shape `[batch_size, num_classes]`

and have labels of shape `[batch_size]`

, but higher dimensions are supported, in which case the `dim`

-th dimension is assumed to be of size `num_classes`

. `logits`

must have the dtype of `float16`

, `float32`

, or `float64`

, and `labels`

must have the dtype of `int32`

or `int64`

.

**Note that to avoid confusion, it is required to pass only named arguments to this function.**

Args | |
---|---|

`_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` | Per-label activations (typically a linear output) of shape `[d_0, d_1, ..., d_{r-1}, num_classes]` and dtype `float16` , `float32` , or `float64` . These activation energies are interpreted as unnormalized log probabilities. |

`name` | A name for the operation (optional). |

Returns | |
---|---|

A `Tensor` of the same shape as `labels` and of the same type as `logits` with the softmax cross entropy loss. |

Raises | |
---|---|

`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. |

© 2020 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/versions/r2.3/api_docs/python/tf/compat/v1/nn/sparse_softmax_cross_entropy_with_logits