tf.train.VocabInfo

Vocabulary information for warm-starting.

View aliases

Main aliases

tf.estimator.VocabInfo

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.estimator.VocabInfo, tf.compat.v1.train.VocabInfo, `tf.compat.v2.estimator.VocabInfo`

tf.train.VocabInfo(
    new_vocab, new_vocab_size, num_oov_buckets, old_vocab, old_vocab_size=-1,
    backup_initializer=None, axis=0
)

See 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.

Returns: A VocabInfo which represents the vocabulary information for warm-starting.

Raises: ValueError: axis is neither 0 or 1.

Example Usage:

      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.
  ```
  



<!-- Tabular view -->
 <table class="responsive fixed orange">
<colgroup><col width="214px"><col></colgroup>
<tr><th colspan="2"><h2 class="add-link">Attributes</h2></th></tr>

<tr>
<td>
`new_vocab`
</td>
<td>

</td>
</tr><tr>
<td>
`new_vocab_size`
</td>
<td>

</td>
</tr><tr>
<td>
`num_oov_buckets`
</td>
<td>

</td>
</tr><tr>
<td>
`old_vocab`
</td>
<td>

</td>
</tr><tr>
<td>
`old_vocab_size`
</td>
<td>

</td>
</tr><tr>
<td>
`backup_initializer`
</td>
<td>

</td>
</tr><tr>
<td>
`axis`
</td>
<td>

</td>
</tr>
</table>