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Vocabulary information for warm-starting.
Compat aliases for migration
See Migration guide for more details.
tf.estimator.VocabInfo( new_vocab, new_vocab_size, num_oov_buckets, old_vocab, old_vocab_size=-1, backup_initializer=None, axis=0 )
tf.estimator.WarmStartSettings for examples of using
VocabInfo to warm-start.
Args: 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.
VocabInfo which represents the vocabulary information for warm-starting.
axis is neither 0 or 1.
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,