tf.compat.v1.nn.softmax_cross_entropy_with_logits

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

tf.compat.v1.nn.softmax_cross_entropy_with_logits(
    labels=None, logits=None, dim=-1, name=None, axis=None
)

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.

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

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.

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 Per-label activations, typically a linear output. These activation energies are interpreted as unnormalized log probabilities.
dim The class dimension. Defaulted to -1 which is the last dimension.
name A name for the operation (optional).
axis Alias for dim.

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.