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 guides: Neural Network > Classification, Upgrade to TensorFlow 1.0 > Upgrading your code manually

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 that contains the softmax cross entropy loss. Its type is the same as logits and its shape is the same as labels except that it does not have the last dimension of labels.