tf.contrib.framework.load_embedding_initializer(
ckpt_path,
embedding_tensor_name,
new_vocab_size,
embedding_dim,
old_vocab_file,
new_vocab_file,
old_vocab_size=-1,
num_oov_buckets=0,
initializer=None,
max_rows_in_memory=-1
)
Defined in tensorflow/python/training/checkpoint_ops.py
.
Returns a variable initializer for loading pre-trained embeddings.
Wrapper around load_and_remap_matrix_initializer()
specialized for loading
embedding weights and remapping according to the provided vocab files. See
docs for load_and_remap_matrix_initializer()
for more details.
NOTE: Only for use with div-partitioned variables / vocabularies.
Args:
ckpt_path
: Path to the TensorFlow checkpoint (version 2,TensorBundle
) from which the old matrixTensor
will be loaded.embedding_tensor_name
: Name of the 2-DTensor
to load from checkpoint.new_vocab_size
: Number of entries in the new vocab.embedding_dim
:int
specifying the dimension of the embedding vectors from the checkpoint. Must match the number of columns in the old embedding matrix.old_vocab_file
: A scalarTensor
of typestring
containing the path to the old vocabulary file.new_vocab_file
: A scalarTensor
of typestring
containing the path to the new vocabulary file.old_vocab_size
: The number of entries to consider in the old vocabulary. With the default value of -1, the entire old row vocabulary file will be used. Otherwise, only the firstold_vocab_size
entries will be considered for remapping.Must be smaller than the length ofold_row_vocab_file
.num_oov_buckets
:int
specifying the number of out-of-vocabulary buckets to use. Must be >= 0.initializer
: Initializer function that accepts a 1-D tensor as the arg to specify the shape of the returned tensor. IfNone
, defaults to usingtruncated_normal_initializer()
.max_rows_in_memory
:int
specifying the maximum number of rows to load from the checkpoint at once. If less than or equal to 0, the entire matrix will be loaded into memory. Setting this arg trades increased disk reads for lower memory usage.
Returns:
A variable initializer function.