tf.raw_ops.BoostedTreesSparseAggregateStats
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Aggregates the summary of accumulated stats for the batch.
tf.raw_ops.BoostedTreesSparseAggregateStats(
node_ids,
gradients,
hessians,
feature_indices,
feature_values,
feature_shape,
max_splits,
num_buckets,
name=None
)
The summary stats contains gradients and hessians accumulated for each node, bucket and dimension id.
Args |
|---|
node_ids
|
A Tensor of type int32.
int32; Rank 1 Tensor containing node ids for each example, shape [batch_size].
|
gradients
|
A Tensor of type float32.
float32; Rank 2 Tensor (shape=[batch_size, logits_dimension]) with gradients for each example.
|
hessians
|
A Tensor of type float32.
float32; Rank 2 Tensor (shape=[batch_size, hessian_dimension]) with hessians for each example.
|
feature_indices
|
A Tensor of type int32.
int32; Rank 2 indices of feature sparse Tensors (shape=[number of sparse entries, 2]).
Number of sparse entries across all instances from the batch. The first value is
the index of the instance, the second is dimension of the feature. The second axis
can only have 2 values, i.e., the input dense version of Tensor can only be matrix.
|
feature_values
|
A Tensor of type int32.
int32; Rank 1 values of feature sparse Tensors (shape=[number of sparse entries]).
Number of sparse entries across all instances from the batch. The first value is
the index of the instance, the second is dimension of the feature.
|
feature_shape
|
A Tensor of type int32.
int32; Rank 1 dense shape of feature sparse Tensors (shape=[2]).
The first axis can only have 2 values, [batch_size, feature_dimension].
|
max_splits
|
An int that is >= 1.
int; the maximum number of splits possible in the whole tree.
|
num_buckets
|
An int that is >= 1.
int; equals to the maximum possible value of bucketized feature + 1.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A tuple of Tensor objects (stats_summary_indices, stats_summary_values, stats_summary_shape).
|
stats_summary_indices
|
A Tensor of type int32.
|
stats_summary_values
|
A Tensor of type float32.
|
stats_summary_shape
|
A Tensor of type int32.
|
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Last updated 2023-10-06 UTC.
[null,null,["Last updated 2023-10-06 UTC."],[],[],null,["# tf.raw_ops.BoostedTreesSparseAggregateStats\n\n\u003cbr /\u003e\n\nAggregates the summary of accumulated stats for the batch.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.raw_ops.BoostedTreesSparseAggregateStats`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/BoostedTreesSparseAggregateStats)\n\n\u003cbr /\u003e\n\n tf.raw_ops.BoostedTreesSparseAggregateStats(\n node_ids,\n gradients,\n hessians,\n feature_indices,\n feature_values,\n feature_shape,\n max_splits,\n num_buckets,\n name=None\n )\n\nThe summary stats contains gradients and hessians accumulated for each node, bucket and dimension id.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `node_ids` | A `Tensor` of type `int32`. int32; Rank 1 Tensor containing node ids for each example, shape \\[batch_size\\]. |\n| `gradients` | A `Tensor` of type `float32`. float32; Rank 2 Tensor (shape=\\[batch_size, logits_dimension\\]) with gradients for each example. |\n| `hessians` | A `Tensor` of type `float32`. float32; Rank 2 Tensor (shape=\\[batch_size, hessian_dimension\\]) with hessians for each example. |\n| `feature_indices` | A `Tensor` of type `int32`. int32; Rank 2 indices of feature sparse Tensors (shape=\\[number of sparse entries, 2\\]). Number of sparse entries across all instances from the batch. The first value is the index of the instance, the second is dimension of the feature. The second axis can only have 2 values, i.e., the input dense version of Tensor can only be matrix. |\n| `feature_values` | A `Tensor` of type `int32`. int32; Rank 1 values of feature sparse Tensors (shape=\\[number of sparse entries\\]). Number of sparse entries across all instances from the batch. The first value is the index of the instance, the second is dimension of the feature. |\n| `feature_shape` | A `Tensor` of type `int32`. int32; Rank 1 dense shape of feature sparse Tensors (shape=\\[2\\]). The first axis can only have 2 values, \\[batch_size, feature_dimension\\]. |\n| `max_splits` | An `int` that is `\u003e= 1`. int; the maximum number of splits possible in the whole tree. |\n| `num_buckets` | An `int` that is `\u003e= 1`. int; equals to the maximum possible value of bucketized feature + 1. |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|-------------------------|-------------------------------|\n| A tuple of `Tensor` objects (stats_summary_indices, stats_summary_values, stats_summary_shape). ||\n| `stats_summary_indices` | A `Tensor` of type `int32`. |\n| `stats_summary_values` | A `Tensor` of type `float32`. |\n| `stats_summary_shape` | A `Tensor` of type `int32`. |\n\n\u003cbr /\u003e"]]
