public class SparseSoftmaxCrossEntropyWithLogits

Public Constructors

Public Methods

static <T extends TNumber, U extends TNumber> Operand
sparseSoftmaxCrossEntropyWithLogits(Scope scope, Operand<T> labels, Operand<U> logits)
Computes sparse softmax cross entropy between logits and labels.

Inherited Methods

Public Constructors

public SparseSoftmaxCrossEntropyWithLogits ()

Public Methods

public static Operand sparseSoftmaxCrossEntropyWithLogits (Scope scope, Operand<T> labels, Operand<U> logits)

Computes sparse softmax cross entropy between logits and labels.

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.


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, ERROR(/org.tensorflow.op.NnOps#softmaxCrossEntropyWithLogits).


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 [batchSize, numClasses] and have labels of shape [batchSize], but higher dimensions are supported, in which case the dim-th dimension is assumed to be of size numClasses. logits must have the dataType of TFloat16, TFloat32 , or TFloat64, and labels must have the dtype of TInt32 or TInt64.

scope current scope
labels Tensor of shape [d_0, d_1, ..., d_{r-1}] (where r is rank of labels and result) and the dataType is TInt32 or TInt64. Each entry in labels must be an index in [0, numClasses). 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}, numClasses] and dataType of TFloat16, TFloat32, or TFloat64. These activation energies are interpreted as unnormalized log probabilities.
  • A Tensor of the same shape as labels and of the same type as logits with the softmax cross entropy loss.
IllegalArgumentException 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.