# tf.nn.sparse_softmax_cross_entropy_with_logits
|---|
| [ View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/nn_ops.py#L4397-L4455) |
Computes sparse softmax cross entropy between `logits` and `labels`.
tf.nn.sparse_softmax_cross_entropy_with_logits(
labels, logits, 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]
> **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`.
> [!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 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`.
logits = tf.constant([[2., -5., .5, -.1],
[0., 0., 1.9, 1.4],
[-100., 100., -100., -100.]])
labels = tf.constant([0, 3, 1])
tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=labels, logits=logits).numpy()
array([0.29750752, 1.1448325 , 0. ], dtype=float32)
To avoid confusion, passing only named arguments to this function is
recommended.
| ## Args ||
|---|---|
| `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 `float16`, `float32`, or `float64`. |
| `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. |