SoftmaxCrossEntropyWithLogits

public class SoftmaxCrossEntropyWithLogits

Public Constructors

SoftmaxCrossEntropyWithLogits()

Public Methods

static <T extends TNumber, U extends TNumber> Operand<T>
softmaxCrossEntropyWithLogits(Scope scope, Operand<U> labels, Operand<T> logits, int axis)
Computes softmax cross entropy between logits and labels.

Inherited Methods

Public Constructors

public SoftmaxCrossEntropyWithLogits ()

Public Methods

public static Operand<T> softmaxCrossEntropyWithLogits (Scope scope, Operand<U> labels, Operand<T> logits, int axis)

Computes softmax cross entropy between logits and labels.

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 ERROR(/org.tensorflow.op.NnOps#sparseSoftmaxCrossEntropyWithLogits)

Usage: 

   Operand<TFloat32> logits =
       tf.constant(new float[][] { {4.0F, 2.0F, 1.0F}, {0.0F, 5.0F, 1.0F} } );
   Operand<TFloat32> labels =
       tf.constant(new float[][] { {1.0F, 0.0F, 0.0F}, {0.0F, 0.8F, 0.2F} } );
   Operand<TFloat32> output =
       tf.nn.softmaxCrossEntropyWithLogits(labels, logits, -1);
   // output Shape = [2]
   // dataType = FLOAT (1)
   // values { 0.169846, 0.824745 }

Backpropagation will happen into both logits and labels. To disallow backpropagation into labels, pass label tensors through tf.stopGradient before feeding it to this function.

Parameters
scope current scope
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.
axis The class dimension. -1 is the last dimension.
Returns
  • 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.