tf.nn.softmax_cross_entropy_with_logits

tf.nn.softmax_cross_entropy_with_logits(
    _sentinel=None,
    labels=None,
    logits=None,
    dim=-1,
    name=None
)

Defined in tensorflow/python/ops/nn_ops.py.

See the guide: Neural Network > Classification

Computes softmax cross entropy between logits and labels. (deprecated)

THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating:

Future major versions of TensorFlow will allow gradients to flow into the labels input on backprop by default.

See tf.nn.softmax_cross_entropy_with_logits_v2.

Measures the probability error in discrete classification tasks in which the classes are mutually exclusive (each entry is in exactly one class). For example, each CIFAR-10 image is labeled with one and only one label: an image can be a dog or a truck, but not both.

NOTE: While the classes are mutually exclusive, their probabilities need not be. All that is required is that each row of labels is a valid probability distribution. If they are not, the computation of the gradient will be incorrect.

If using exclusive labels (wherein one and only one class is true at a time), see sparse_softmax_cross_entropy_with_logits.

WARNING: This op expects unscaled logits, since it performs a softmax on logits internally for efficiency. Do not call this op with the output of softmax, as it will produce incorrect results.

A common use case is to have logits and labels of shape [batch_size, num_classes], but higher dimensions are supported, with the dim argument specifying the class dimension.

Backpropagation will happen only into logits. To calculate a cross entropy loss that allows backpropagation into both logits and labels, see tf.nn.softmax_cross_entropy_with_logits_v2.

Note that to avoid confusion, it is required to pass only named arguments to this function.

Args:

  • _sentinel: Used to prevent positional parameters. Internal, do not use.
  • labels: Each vector along the class dimension should hold a valid probability distribution e.g. for the case in which labels are of shape [batch_size, num_classes], each row of labels[i] must be a valid probability distribution.
  • logits: Unscaled log probabilities.
  • dim: The class dimension. Defaulted to -1 which is the last dimension.
  • name: A name for the operation (optional).

Returns:

A Tensor of the same shape as labels and of the same type as logits with the softmax cross entropy loss.