Configuration data for one embedding feature.
View aliases
Compat aliases for migration
See Migration guide for more details.
tf.tpu.experimental.embedding.FeatureConfig(
table: tf.tpu.experimental.embedding.TableConfig
,
max_sequence_length: int = 0,
validate_weights_and_indices: bool = True,
output_shape: Optional[Union[List[int], tf.TensorShape
]] = None,
name: Optional[Text] = None
)
Used in the notebooks
Used in the guide | Used in the tutorials |
---|---|
This class holds the configuration data for a single embedding feature. The
main use is to assign features to tf.tpu.experimental.embedding.TableConfig
s
via the table parameter:
table_config_one = tf.tpu.experimental.embedding.TableConfig(
vocabulary_size=...,
dim=...)
table_config_two = tf.tpu.experimental.embedding.TableConfig(
vocabulary_size=...,
dim=...)
feature_config = {
'feature_one': tf.tpu.experimental.embedding.FeatureConfig(
table=table_config_one),
'feature_two': tf.tpu.experimental.embedding.FeatureConfig(
table=table_config_one),
'feature_three': tf.tpu.experimental.embedding.FeatureConfig(
table=table_config_two)}
embedding = tf.tpu.experimental.embedding.TPUEmbedding(
feature_config=feature_config,
batch_size=...
optimizer=tf.tpu.experimental.embedding.Adam(0.1))
The above configuration has 2 tables, and three features. The first two features will be looked up in the first table and the third feature will be looked up in the second table.
You can also specify the output shape for each feature. The output shape should be the expected activation shape excluding the table dimension. For dense and sparse tensor, the output shape should be the same as the input shape excluding the last dimension. For ragged tensor, the output shape can mismatch the input shape.
When feeding features into embedding.enqueue
they can be tf.Tensor
s,
tf.SparseTensor
s or tf.RaggedTensor
s. When the argument
max_sequence_length
is 0, the default, you should expect a output of
embedding.dequeue
for this feature of shape (batch_size, dim)
. If
max_sequence_length
is greater than 0, the feature is embedded as a sequence
and padded up to the given length. The shape of the output for this feature
will be (batch_size, max_sequence_length, dim)
.
Args | |
---|---|
table
|
An instance of tf.tpu.experimental.embedding.TableConfig ,
describing the table in which this feature should be looked up.
|
max_sequence_length
|
If positive, the feature is a sequence feature with the corresponding maximum sequence length. If the sequence is longer than this, it will be truncated. If 0, the feature is not a sequence feature. |
validate_weights_and_indices
|
If true, uses safe_embedding_lookup during serving which ensures there are no empty rows and all weights and ids are positive at the expense of extra compute cost. |
output_shape
|
Optional argument to config the output shape of the feature
activation. If provided, the feature feeding to the embedding.enqueue
has to match the shape (for ragged tensor, the input shape and output
shape can mismatch). If not provided, the shape can be either provided
to the embedding.build or auto detected at the runtime.
|
name
|
An optional string used to name the table. Must be defined if running on SparseCore. |
Raises | |
---|---|
ValueError
|
if table is not an instance of
tf.tpu.experimental.embedding.TableConfig .
|
ValueError
|
if max_sequence_length not an integer or is negative.
|