tf.compat.v1.tpu.experimental.shared_embedding_columns

TPU version of tf.compat.v1.feature_column.shared_embedding_columns.

tf.compat.v1.tpu.experimental.shared_embedding_columns(
    categorical_columns,
    dimension,
    combiner='mean',
    initializer=None,
    shared_embedding_collection_name=None,
    max_sequence_lengths=None,
    learning_rate_fn=None,
    embedding_lookup_device=None,
    tensor_core_shape=None,
    use_safe_embedding_lookup=True
)

Note that the interface for tf.tpu.experimental.shared_embedding_columns is different from that of tf.compat.v1.feature_column.shared_embedding_columns: The following arguments are NOT supported: ckpt_to_load_from, tensor_name_in_ckpt, max_norm and trainable.

Use this function in place of tf.compat.v1.feature_column.shared_embedding_columns` when you want to use the TPU to accelerate your embedding lookups via TPU embeddings.

column_a = tf.feature_column.categorical_column_with_identity(...)
column_b = tf.feature_column.categorical_column_with_identity(...)
tpu_columns = tf.tpu.experimental.shared_embedding_columns(
    [column_a, column_b], 10)
...
def model_fn(features):
  dense_feature = tf.keras.layers.DenseFeature(tpu_columns)
  embedded_feature = dense_feature(features)
  ...

estimator = tf.estimator.tpu.TPUEstimator(
    model_fn=model_fn,
    ...
    embedding_config_spec=tf.estimator.tpu.experimental.EmbeddingConfigSpec(
        column=tpu_columns,
        ...))

categorical_columns A list of categorical columns returned from categorical_column_with_identity, weighted_categorical_column, categorical_column_with_vocabulary_file, categorical_column_with_vocabulary_list, sequence_categorical_column_with_identity, sequence_categorical_column_with_vocabulary_file, sequence_categorical_column_with_vocabulary_list
dimension An integer specifying dimension of the embedding, must be > 0.
combiner A string specifying how to reduce if there are multiple entries in a single row for a non-sequence column. For more information, see tf.feature_column.embedding_column.
initializer A variable initializer function to be used in embedding variable initialization. If not specified, defaults to tf.truncated_normal_initializer with mean 0.0 and standard deviation 1/sqrt(dimension).
shared_embedding_collection_name Optional name of the collection where shared embedding weights are added. If not given, a reasonable name will be chosen based on the names of categorical_columns. This is also used in variable_scope when creating shared embedding weights.
max_sequence_lengths An list of non-negative integers, either None or empty or the same length as the argument categorical_columns. Entries corresponding to non-sequence columns must be 0 and entries corresponding to sequence columns specify the max sequence length for the column. Any sequence shorter then this will be padded with 0 embeddings and any sequence longer will be truncated.
learning_rate_fn A function that takes global step and returns learning rate for the embedding table. If you intend to use the same learning rate for multiple embedding tables, please ensure that you pass the exact same python function to all calls of shared_embedding_columns, otherwise performence may suffer.
embedding_lookup_device The device on which to run the embedding lookup. Valid options are "cpu", "tpu_tensor_core", and "tpu_embedding_core". If specifying "tpu_tensor_core", a tensor_core_shape must be supplied. Defaults to "cpu". If not specified, the default behavior is embedding lookup on "tpu_embedding_core" for training and "cpu" for inference. Valid options for training : ["tpu_embedding_core", "tpu_tensor_core"] Valid options for serving : ["cpu", "tpu_tensor_core"] For training, tpu_embedding_core is good for large embedding vocab (>1M), otherwise, tpu_tensor_core is often sufficient. For serving, doing embedding lookup on tpu_tensor_core during serving is a way to reduce host cpu usage in cases where that is a bottleneck.
tensor_core_shape If supplied, a list of integers which specifies the intended dense shape to run embedding lookup for this feature on TensorCore. The batch dimension can be left None or -1 to indicate a dynamic shape. Only rank 2 shapes currently supported.
use_safe_embedding_lookup If true, uses safe_embedding_lookup_sparse instead of embedding_lookup_sparse. safe_embedding_lookup_sparse ensures there are no empty rows and all weights and ids are positive at the expense of extra compute cost. This only applies to rank 2 (NxM) shaped input tensors. Defaults to true, consider turning off if the above checks are not needed. Note that having empty rows will not trigger any error though the output result might be 0 or omitted.

A list of _TPUSharedEmbeddingColumnV2.

ValueError if dimension not > 0.
ValueError if initializer is specified but not callable.
ValueError if max_sequence_lengths is specified and not the same length as categorical_columns.
ValueError if max_sequence_lengths is positive for a non sequence column or 0 for a sequence column.