SoftmaxCrossEntropyWithLogits

public final class SoftmaxCrossEntropyWithLogits

Computes softmax cross entropy cost and gradients to backpropagate.

Inputs are the logits, not probabilities.

Constants

String

OP_NAME

The name of this op, as known by TensorFlow core engine

Public Methods

Output<T>	backprop() backpropagated gradients (batch_size x num_classes matrix).
static <T extends TNumber> SoftmaxCrossEntropyWithLogits<T>	create(Scope scope, Operand<T> features, Operand<T> labels) Factory method to create a class wrapping a new SoftmaxCrossEntropyWithLogits operation.
Output<T>	loss() Per example loss (batch_size vector).

Inherited Methods

From class org.tensorflow.op.RawOp

final boolean	equals(Object obj)
final int	hashCode()
Operation	op() Return this unit of computation as a single Operation.
final String	toString()

From class java.lang.Object

boolean	equals(Object arg0)
final Class<?>	getClass()
int	hashCode()
final void	notify()
final void	notifyAll()
String	toString()
final void	wait(long arg0, int arg1)
final void	wait(long arg0)
final void	wait()

From interface org.tensorflow.op.Op

abstract ExecutionEnvironment	env() Return the execution environment this op was created in.
abstract Operation	op() Return this unit of computation as a single Operation.

Constants

public static final String OP_NAME

The name of this op, as known by TensorFlow core engine

Constant Value: "SoftmaxCrossEntropyWithLogits"

Public Methods

public Output<T> backprop ()

backpropagated gradients (batch_size x num_classes matrix).

public static SoftmaxCrossEntropyWithLogits<T> create (Scope scope, Operand<T> features, Operand<T> labels)

Factory method to create a class wrapping a new SoftmaxCrossEntropyWithLogits operation.

Parameters

scope	current scope
features	batch_size x num_classes matrix
labels	batch_size x num_classes matrix The caller must ensure that each batch of labels represents a valid probability distribution.

Returns

• a new instance of SoftmaxCrossEntropyWithLogits

public Output<T> loss ()

Per example loss (batch_size vector).