tf.compat.v1.nn.sparse_softmax_cross_entropy_with_logits

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.