tf.nn.softmax_cross_entropy_with_logits( _sentinel=None, labels=None, logits=None, dim=-1, name=None )
See the guide: Neural Network > Classification
Computes softmax cross entropy between
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.
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
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
WARNING: This op expects unscaled logits, since it performs a
logits internally for efficiency. Do not call this op with the
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
dim argument specifying the class dimension.
Backpropagation will happen only into
logits. To calculate a cross entropy
loss that allows backpropagation into both
Note that to avoid confusion, it is required to pass only named arguments to this function.
_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).
Tensor of the same shape as
labels and of the same type as
with the softmax cross entropy loss.