tf.contrib.losses.sparse_softmax_cross_entropy

tf.contrib.losses.sparse_softmax_cross_entropy(
    logits,
    labels,
    weights=1.0,
    scope=None
)

Defined in tensorflow/contrib/losses/python/losses/loss_ops.py.

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. Note that the order of the logits and labels arguments has been changed.

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.

Args:

• 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.

Returns:

A scalar Tensor representing the mean loss value.

Raises:

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