public final class
EnqueueTPUEmbeddingSparseBatch
An op that enqueues TPUEmbedding input indices from a SparseTensor.
This Op eases the porting of code that uses embedding_lookup_sparse(), although some Python preprocessing of the SparseTensor arguments to embedding_lookup_sparse() is required to produce the arguments to this Op, since only a single EnqueueTPUEmbeddingSparseBatch Op is allowed per training step.
The tensors at corresponding positions in the three input lists must have the same shape, i.e. rank 1 with dim_size() equal to the total number of lookups into the table described by the corresponding table_id.
Nested Classes
| class | EnqueueTPUEmbeddingSparseBatch.Options | Optional attributes for EnqueueTPUEmbeddingSparseBatch
|
|
Public Methods
| static EnqueueTPUEmbeddingSparseBatch.Options |
combiners(List<String> combiners)
|
| static <T extends Number, U extends Number, V extends Number> EnqueueTPUEmbeddingSparseBatch | |
| static EnqueueTPUEmbeddingSparseBatch.Options |
deviceOrdinal(Long deviceOrdinal)
|
Inherited Methods
Public Methods
public static EnqueueTPUEmbeddingSparseBatch.Options combiners (List<String> combiners)
Parameters
| combiners | A list of string scalars, one for each embedding table that specify how to normalize the embedding activations after weighted summation. Supported combiners are 'mean', 'sum', or 'sqrtn'. It is invalid to have the sum of the weights be 0 for 'mean' or the sum of the squared weights be 0 for 'sqrtn'. If combiners isn't passed, the default is to use 'sum' for all tables. |
|---|
public static EnqueueTPUEmbeddingSparseBatch create (Scope scope, Iterable<Operand<T>> sampleIndices, Iterable<Operand<U>> embeddingIndices, Iterable<Operand<V>> aggregationWeights, Operand<String> modeOverride, Options... options)
Factory method to create a class wrapping a new EnqueueTPUEmbeddingSparseBatch operation.
Parameters
| scope | current scope |
|---|---|
| sampleIndices | A list of rank 1 Tensors specifying the training example and feature to which the corresponding embedding_indices and aggregation_weights values belong. sample_indices[i] must equal b * nf + f, where nf is the number of features from the corresponding table, f is in [0, nf), and b is in [0, batch size). |
| embeddingIndices | A list of rank 1 Tensors, indices into the embedding tables. |
| aggregationWeights | A list of rank 1 Tensors containing per sample -- i.e. per (training example, feature) -- aggregation weights. |
| modeOverride | A string input that overrides the mode specified in the TPUEmbeddingConfiguration. Supported values are {'unspecified', 'inference', 'training', 'backward_pass_only'}. When set to 'unspecified', the mode set in TPUEmbeddingConfiguration is used, otherwise mode_override is used. |
| options | carries optional attributes values |
Returns
- a new instance of EnqueueTPUEmbeddingSparseBatch
public static EnqueueTPUEmbeddingSparseBatch.Options deviceOrdinal (Long deviceOrdinal)
Parameters
| deviceOrdinal | The TPU device to use. Should be >= 0 and less than the number of TPU cores in the task on which the node is placed. |
|---|