tf.compat.v1.tpu.experimental.embedding_column

View source on GitHub

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

tf.compat.v1.tpu.experimental.embedding_column(
    categorical_column,
    dimension,
    combiner='mean',
    initializer=None,
    max_sequence_length=0,
    learning_rate_fn=None,
    embedding_lookup_device=None,
    tensor_core_shape=None
)

Note that the interface for tf.tpu.experimental.embedding_column is different from that of tf.compat.v1.feature_column.embedding_column: 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.embedding_column when you want to use the TPU to accelerate your embedding lookups via TPU embeddings.

column = tf.feature_column.categorical_column_with_identity(...)
tpu_column = tf.tpu.experimental.embedding_column(column, 10)
...
def model_fn(features):
  dense_feature = tf.keras.layers.DenseFeature(tpu_column)
  embedded_feature = dense_feature(features)
  ...

estimator = tf.estimator.tpu.TPUEstimator(
    model_fn=model_fn,
    ...
    embedding_config_spec=tf.estimator.tpu.experimental.EmbeddingConfigSpec(
      column=[tpu_column],
      ...))

Args:

  • categorical_column: A categorical column 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.compat.v1.truncated_normal_initializer with mean 0.0 and standard deviation 1/sqrt(dimension).
  • max_sequence_length: An non-negative integer specifying the max sequence length. Any sequence shorter then this will be padded with 0 embeddings and any sequence longer will be truncated. This must be positive for sequence features and 0 for non-sequence features.
  • learning_rate_fn: A function that takes global step and returns learning rate for the embedding table.
  • 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. 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.

Returns:

A _TPUEmbeddingColumnV2.

Raises:

  • ValueError: if dimension not > 0.
  • ValueError: if initializer is specified but not callable.