Computes softmax cross entropy between
tf.compat.v2.nn.softmax_cross_entropy_with_logits( labels, logits, axis=-1, name=None )
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
labelsis 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
A common use case is to have logits and labels of shape
[batch_size, num_classes], but higher dimensions are supported, with the
axis argument specifying the class dimension.
labels must have the same dtype (either
Backpropagation will happen into both
labels. To disallow backpropagation into
labels, pass label tensors through
tf.stop_gradient before feeding it to this function.
Note that to avoid confusion, it is required to pass only named arguments to this function.
| || Each vector along the class dimension should hold a valid probability distribution e.g. for the case in which labels are of shape |
| ||Per-label activations, typically a linear output. These activation energies are interpreted as unnormalized log probabilities.|
| ||The class dimension. Defaulted to -1 which is the last dimension.|
| ||A name for the operation (optional).|
| A |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.