# tf.compat.v1.nn.softmax_cross_entropy_with_logits

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/nn_ops.py#L4179-L4242) |

Computes softmax cross entropy between `logits` and `labels`. (deprecated)

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

> [!WARNING]
> **Deprecated:** THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating:

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]
> **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`.

> [!WARNING]
> **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 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.**

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| ## Args ||
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| `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. |

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| ## 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`. ||

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