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Creates a cross-entropy loss using tf.nn.softmax_cross_entropy_with_logits_v2.

    onehot_labels, logits, weights=1.0, label_smoothing=0, scope=None,
    loss_collection=tf.GraphKeys.LOSSES, reduction=Reduction.SUM_BY_NONZERO_WEIGHTS

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.

If label_smoothing is nonzero, smooth the labels towards 1/num_classes: new_onehot_labels = onehot_labels * (1 - label_smoothing)

                    + label_smoothing / num_classes

Note that onehot_labels and logits must have the same shape, e.g. [batch_size, num_classes]. The shape of weights must be broadcastable to loss, whose shape is decided by the shape of logits. In case the shape of logits is [batch_size, num_classes], loss is a Tensor of shape [batch_size].


  • onehot_labels: One-hot-encoded labels.
  • logits: Logits outputs of the network.
  • weights: Optional Tensor that is broadcastable to loss.
  • label_smoothing: If greater than 0 then smooth the labels.
  • 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.


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


  • ValueError: If the shape of logits doesn't match that of onehot_labels or if the shape of weights is invalid or if weights is None. Also if onehot_labels or logits is None.

Eager Compatibility

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