diff options
author | A. Unique TensorFlower <gardener@tensorflow.org> | 2018-08-29 18:21:31 -0700 |
---|---|---|
committer | TensorFlower Gardener <gardener@tensorflow.org> | 2018-08-29 18:25:18 -0700 |
commit | 98dd0cd1539c8831ff2527895dd3025c7f12b187 (patch) | |
tree | 6c583fa069d4626112dbcb0eec466a7de108d030 /tensorflow/go | |
parent | 356433df3b29fd3db817c98044b1617cebf11982 (diff) |
Go: Update generated wrapper functions for TensorFlow ops.
PiperOrigin-RevId: 210829888
Diffstat (limited to 'tensorflow/go')
-rw-r--r-- | tensorflow/go/op/wrappers.go | 70 |
1 files changed, 35 insertions, 35 deletions
diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index 0aba0393af..986f198c44 100644 --- a/tensorflow/go/op/wrappers.go +++ b/tensorflow/go/op/wrappers.go @@ -26671,41 +26671,6 @@ func LatencyStatsDataset(scope *Scope, input_dataset tf.Output, tag tf.Output, o return op.Output(0) } -// Runs multiple additive regression ensemble predictors on input instances and -// -// computes the update to cached logits. It is designed to be used during training. -// It traverses the trees starting from cached tree id and cached node id and -// calculates the updates to be pushed to the cache. -// -// Arguments: -// -// cached_tree_ids: Rank 1 Tensor containing cached tree ids which is the starting -// tree of prediction. -// cached_node_ids: Rank 1 Tensor containing cached node id which is the starting -// node of prediction. -// bucketized_features: A list of rank 1 Tensors containing bucket id for each -// feature. -// logits_dimension: scalar, dimension of the logits, to be used for partial logits -// shape. -// -// Returns Rank 2 Tensor containing logits update (with respect to cached -// values stored) for each example.Rank 1 Tensor containing new tree ids for each example.Rank 1 Tensor containing new node ids in the new tree_ids. -func BoostedTreesTrainingPredict(scope *Scope, tree_ensemble_handle tf.Output, cached_tree_ids tf.Output, cached_node_ids tf.Output, bucketized_features []tf.Output, logits_dimension int64) (partial_logits tf.Output, tree_ids tf.Output, node_ids tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"logits_dimension": logits_dimension} - opspec := tf.OpSpec{ - Type: "BoostedTreesTrainingPredict", - Input: []tf.Input{ - tree_ensemble_handle, cached_tree_ids, cached_node_ids, tf.OutputList(bucketized_features), - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - // MapSizeAttr is an optional argument to MapSize. type MapSizeAttr func(optionalAttr) @@ -31918,3 +31883,38 @@ func BoostedTreesDeserializeEnsemble(scope *Scope, tree_ensemble_handle tf.Outpu } return scope.AddOperation(opspec) } + +// Runs multiple additive regression ensemble predictors on input instances and +// +// computes the update to cached logits. It is designed to be used during training. +// It traverses the trees starting from cached tree id and cached node id and +// calculates the updates to be pushed to the cache. +// +// Arguments: +// +// cached_tree_ids: Rank 1 Tensor containing cached tree ids which is the starting +// tree of prediction. +// cached_node_ids: Rank 1 Tensor containing cached node id which is the starting +// node of prediction. +// bucketized_features: A list of rank 1 Tensors containing bucket id for each +// feature. +// logits_dimension: scalar, dimension of the logits, to be used for partial logits +// shape. +// +// Returns Rank 2 Tensor containing logits update (with respect to cached +// values stored) for each example.Rank 1 Tensor containing new tree ids for each example.Rank 1 Tensor containing new node ids in the new tree_ids. +func BoostedTreesTrainingPredict(scope *Scope, tree_ensemble_handle tf.Output, cached_tree_ids tf.Output, cached_node_ids tf.Output, bucketized_features []tf.Output, logits_dimension int64) (partial_logits tf.Output, tree_ids tf.Output, node_ids tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"logits_dimension": logits_dimension} + opspec := tf.OpSpec{ + Type: "BoostedTreesTrainingPredict", + Input: []tf.Input{ + tree_ensemble_handle, cached_tree_ids, cached_node_ids, tf.OutputList(bucketized_features), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} |