tf.losses.sparse_softmax_cross_entropy

tf.losses.sparse_softmax_cross_entropy(
    labels,
    logits,
    weights=1.0,
    scope=None,
    loss_collection=tf.GraphKeys.LOSSES,
    reduction=Reduction.SUM_BY_NONZERO_WEIGHTS
)

Defined in tensorflow/python/ops/losses/losses_impl.py.

Cross-entropy loss using tf.nn.sparse_softmax_cross_entropy_with_logits.

weights acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If weights is a tensor of shape [batch_size], then the loss weights apply to each corresponding sample.

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.
  • weights: Coefficients for the loss. This must be scalar or broadcastable to labels (i.e. same rank and each dimension is either 1 or the same).
  • scope: the scope for the operations performed in computing the loss.
  • loss_collection: collection to which the loss will be added.
  • reduction: Type of reduction to apply to loss.

Returns:

Weighted loss Tensor of the same type as logits. If reduction is NONE, this has the same shape as labels; otherwise, it is scalar.

Raises:

  • ValueError: If the shapes of logits, labels, and weights are incompatible, or if any of them are None.

@compatbility(eager) The loss_collection argument is ignored when executing eagerly. Consider holding on to the return value or collecting losses via a tf.keras.Model. @end_compatibility