public final class
EnqueueTPUEmbeddingSparseTensorBatch
Eases the porting of code that uses tf.nn.embedding_lookup_sparse().
sample_indices[i], embedding_indices[i] and aggregation_weights[i] correspond to the ith feature. table_ids[i] indicates which embedding table to look up ith feature.
The tensors at corresponding positions in the three input lists (sample_indices, embedding_indices and aggregation_weights) 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 feature.
Nested Classes
| class | EnqueueTPUEmbeddingSparseTensorBatch.Options | Optional attributes for EnqueueTPUEmbeddingSparseTensorBatch
|
|
Constants
| String | OP_NAME | The name of this op, as known by TensorFlow core engine |
Public Methods
| static EnqueueTPUEmbeddingSparseTensorBatch.Options |
combiners(List<String> combiners)
|
| static <T extends TNumber, U extends TNumber, V extends TNumber> EnqueueTPUEmbeddingSparseTensorBatch |
create(Scope scope, Iterable<Operand<T>> sampleIndices, Iterable<Operand<U>> embeddingIndices, Iterable<Operand<V>> aggregationWeights, Operand<TString> modeOverride, List<Long> tableIds, Options... options)
Factory method to create a class wrapping a new EnqueueTPUEmbeddingSparseTensorBatch operation.
|
| static EnqueueTPUEmbeddingSparseTensorBatch.Options |
deviceOrdinal(Long deviceOrdinal)
|
| static EnqueueTPUEmbeddingSparseTensorBatch.Options |
maxSequenceLengths(List<Long> maxSequenceLengths)
|
Inherited Methods
Constants
public static final String OP_NAME
The name of this op, as known by TensorFlow core engine
Constant Value:
"EnqueueTPUEmbeddingSparseTensorBatch"
Public Methods
public static EnqueueTPUEmbeddingSparseTensorBatch.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 EnqueueTPUEmbeddingSparseTensorBatch create (Scope scope, Iterable<Operand<T>> sampleIndices, Iterable<Operand<U>> embeddingIndices, Iterable<Operand<V>> aggregationWeights, Operand<TString> modeOverride, List<Long> tableIds, Options... options)
Factory method to create a class wrapping a new EnqueueTPUEmbeddingSparseTensorBatch operation.
Parameters
| scope | current scope |
|---|---|
| sampleIndices | A list of rank 1 Tensors specifying the training example to which the corresponding embedding_indices and aggregation_weights values belong. It corresponds to sp_ids.indices[:,0] in embedding_lookup_sparse(). |
| embeddingIndices | A list of rank 1 Tensors, indices into the embedding tables. It corresponds to sp_ids.values in embedding_lookup_sparse(). |
| aggregationWeights | A list of rank 1 Tensors containing per training example aggregation weights. It corresponds to sp_weights.values in embedding_lookup_sparse(). |
| 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. |
| tableIds | A list of integers specifying the identifier of the embedding table (offset of TableDescriptor in the TPUEmbeddingConfiguration) to lookup the corresponding input. The ith input is looked up using table_ids[i]. The size of the table_ids list must be equal to that of sample_indices, embedding_indices and aggregation_weights. |
| options | carries optional attributes values |
Returns
- a new instance of EnqueueTPUEmbeddingSparseTensorBatch
public static EnqueueTPUEmbeddingSparseTensorBatch.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. |
|---|