BoostedTreesAggregateStats

public final class BoostedTreesAggregateStats

Aggregates the summary of accumulated stats for the batch.

The summary stats contains gradients and hessians accumulated for each node, feature dimension id and bucket.

Public Methods

Output<Float>	asOutput() Returns the symbolic handle of a tensor.
static BoostedTreesAggregateStats	create(Scope scope, Operand<Integer> nodeIds, Operand<Float> gradients, Operand<Float> hessians, Operand<Integer> feature, Long maxSplits, Long numBuckets) Factory method to create a class wrapping a new BoostedTreesAggregateStats operation.
Output<Float>	statsSummary() output Rank 4 Tensor (shape=[splits, feature_dimension, buckets, logits_dimension + hessian_dimension]) containing accumulated stats for each node, feature dimension and bucket.

Inherited Methods

From class org.tensorflow.op.PrimitiveOp

final boolean	equals(Object obj)
final int	hashCode()
Operation	op() Returns the underlying 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.Operand

abstract Output<Float>

asOutput() Returns the symbolic handle of a tensor.

Public Methods

public Output<Float> asOutput ()

Returns the symbolic handle of a tensor.

Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.

public static BoostedTreesAggregateStats create (Scope scope, Operand<Integer> nodeIds, Operand<Float> gradients, Operand<Float> hessians, Operand<Integer> feature, Long maxSplits, Long numBuckets)

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

Parameters

scope	current scope
nodeIds	int32; Rank 1 Tensor containing node ids for each example, shape [batch_size].
gradients	float32; Rank 2 Tensor (shape=[batch_size, logits_dimension]) with gradients for each example.
hessians	float32; Rank 2 Tensor (shape=[batch_size, hessian_dimension]) with hessians for each example.
feature	int32; Rank 2 feature Tensors (shape=[batch_size, feature_dimension]).
maxSplits	int; the maximum number of splits possible in the whole tree.
numBuckets	int; equals to the maximum possible value of bucketized feature.

Returns

• a new instance of BoostedTreesAggregateStats

public Output<Float> statsSummary ()

output Rank 4 Tensor (shape=[splits, feature_dimension, buckets, logits_dimension + hessian_dimension]) containing accumulated stats for each node, feature dimension and bucket.