tf.contrib.losses.sparse_softmax_cross_entropy(*args, **kwargs)

tf.contrib.losses.sparse_softmax_cross_entropy(*args, **kwargs)

See the guide: Losses (contrib) > Loss operations for use in neural networks.

Cross-entropy loss using tf.nn.sparse_softmax_cross_entropy_with_logits. (deprecated)

THIS FUNCTION IS DEPRECATED. It will be removed after 2016-12-30. Instructions for updating: Use tf.losses.sparse_softmax_cross_entropy instead.

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 size [batch_size], then the loss weights apply to each corresponding sample.


  • logits: [batch_size, num_classes] logits outputs of the network .
  • labels: [batch_size, 1] or [batch_size] labels of dtype int32 or int64 in the range [0, num_classes).
  • weights: Coefficients for the loss. The tensor must be a scalar or a tensor of shape [batch_size] or [batch_size, 1].
  • scope: the scope for the operations performed in computing the loss.


A scalar Tensor representing the mean loss value.


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

Defined in tensorflow/python/util/