W3cubDocs

/TensorFlow 1.15

tf.tpu.experimental.embedding_column

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

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.
Returns
A _TPUEmbeddingColumnV2.
Raises
ValueError if dimension not > 0.
ValueError if initializer is specified but not callable.

© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/tpu/experimental/embedding_column