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.
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.
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/contrib/losses/sparse_softmax_cross_entropy