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

org.tensorflow.op.PrimitiveOp
final boolean
equals(Object obj)
final int
hashCode()
Operation
op()
Returns the underlying Operation
final String
toString()
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()
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.