Computes softmax cross entropy between `logits`

and `labels`

. (deprecated)

tf.compat.v1.nn.softmax_cross_entropy_with_logits( _sentinel=None, labels=None, logits=None, dim=-1, name=None, axis=None )

Future major versions of TensorFlow will allow gradients to flow into the labels input on backprop by default.

See `tf.nn.softmax_cross_entropy_with_logits_v2`

.

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:While the classes are mutually exclusive, their probabilities need not be. All that is required is that each row of`labels`

is a valid probability distribution. If they are not, the computation of the gradient will be incorrect.

If using exclusive `labels`

(wherein one and only one class is true at a time), see `sparse_softmax_cross_entropy_with_logits`

.

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

, but higher dimensions are supported, with the `dim`

argument specifying the class dimension.

Backpropagation will happen only into `logits`

. To calculate a cross entropy loss that allows backpropagation into both `logits`

and `labels`

, see `tf.nn.softmax_cross_entropy_with_logits_v2`

.

**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` | Each vector along the class dimension should hold a valid probability distribution e.g. for the case in which labels are of shape `[batch_size, num_classes]` , each row of `labels[i]` must be a valid probability distribution. |

`logits` | Per-label activations, typically a linear output. These activation energies are interpreted as unnormalized log probabilities. |

`dim` | The class dimension. Defaulted to -1 which is the last dimension. |

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

`axis` | Alias for dim. |

Returns | |
---|---|

A `Tensor` that contains the softmax cross entropy loss. Its type is the same as `logits` and its shape is the same as `labels` except that it does not have the last dimension of `labels` . |

© 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/softmax_cross_entropy_with_logits