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tf.train.VocabInfo

Class VocabInfo

Vocabulary information for warm-starting.

Aliases:

  • Class tf.compat.v1.estimator.VocabInfo
  • Class tf.compat.v1.train.VocabInfo
  • Class tf.compat.v2.estimator.VocabInfo
  • Class tf.estimator.VocabInfo
  • Class tf.train.VocabInfo
View source on GitHub

See tf.estimator.WarmStartSettings for examples of using VocabInfo to warm-start.

Attributes:

  • new_vocab: [Required] A path to the new vocabulary file (used with the model to be trained).
  • new_vocab_size: [Required] An integer indicating how many entries of the new vocabulary will used in training.
  • num_oov_buckets: [Required] An integer indicating how many OOV buckets are associated with the vocabulary.
  • old_vocab: [Required] A path to the old vocabulary file (used with the checkpoint to be warm-started from).
  • old_vocab_size: [Optional] An integer indicating how many entries of the old vocabulary were used in the creation of the checkpoint. If not provided, the entire old vocabulary will be used.
  • backup_initializer: [Optional] A variable initializer used for variables corresponding to new vocabulary entries and OOV. If not provided, these entries will be zero-initialized.
  • axis: [Optional] Denotes what axis the vocabulary corresponds to. The default, 0, corresponds to the most common use case (embeddings or linear weights for binary classification / regression). An axis of 1 could be used for warm-starting output layers with class vocabularies.

    For example:

    embeddings_vocab_info = tf.VocabInfo( new_vocab='embeddings_vocab', new_vocab_size=100, num_oov_buckets=1, old_vocab='pretrained_embeddings_vocab', old_vocab_size=10000, backup_initializer=tf.compat.v1.truncated_normal_initializer( mean=0.0, stddev=(1 / math.sqrt(embedding_dim))), axis=0)

    softmax_output_layer_kernel_vocab_info = tf.VocabInfo( new_vocab='class_vocab', new_vocab_size=5, num_oov_buckets=0, # No OOV for classes. old_vocab='old_class_vocab', old_vocab_size=8, backup_initializer=tf.compat.v1.glorot_uniform_initializer(), axis=1)

    softmax_output_layer_bias_vocab_info = tf.VocabInfo( new_vocab='class_vocab', new_vocab_size=5, num_oov_buckets=0, # No OOV for classes. old_vocab='old_class_vocab', old_vocab_size=8, backup_initializer=tf.compat.v1.zeros_initializer(), axis=0)

    Currently, only axis=0 and axis=1 are supported.

Properties

new_vocab

new_vocab_size

num_oov_buckets

old_vocab

old_vocab_size

backup_initializer

axis