tf.contrib.framework.load_and_remap_matrix_initializer

tf.contrib.framework.load_and_remap_matrix_initializer(
    ckpt_path,
    old_tensor_name,
    new_row_vocab_size,
    new_col_vocab_size,
    old_row_vocab_size=-1,
    old_row_vocab_file=None,
    new_row_vocab_file=None,
    old_col_vocab_file=None,
    new_col_vocab_file=None,
    num_row_oov_buckets=0,
    num_col_oov_buckets=0,
    initializer=None,
    max_rows_in_memory=-1
)

Defined in tensorflow/python/training/checkpoint_ops.py.

Returns a var initializer for loading and remapping a 2-D (matrix) tensor.

The returned initializer loads a 2-D (matrix) Tensor with name old_tensor_name from the checkpoint at ckpt_path. It will reorder the rows/columns according to the specified vocab files and append additional out-of-vocabulary rows/columns according to the number of OOV buckets.

The format of the file at the {old,new}_{row,col}_vocab_file path should be a text file, with each line containing a single entity within the vocabulary. Let the function line_of(f, "x") return the 0-indexed line number of the entity "x" in file f, and the function entity_at(f, i) return the entity at line i of file f. Then, row i of the new output matrix will be taken from row line_of(old_row_vocab_file, entity_at(new_row_vocab_file, i)) of the old matrix. If any entity in new_row_vocab_file is not found in old_row_vocab_file, that row is considered a "missing" row, and its values will be initialized using the initializer arg. The same logic also applies for the columns.

For example, assuming that:

• old_row_vocab_file contains "mercury\nvenus\nmars"
• new_row_vocab_file contains "venus\njupiter\nmercury"
• old_col_vocab_file contains "good\nbetter\nbest"
• new_col_vocab_file contains "good\nbest\nfantastic"
• initializer returns the natural numbers [1, 2, 3, 4, ...]
• w(i, j) represents the value from row i, column j of the old matrix

Then the new output matrix will look like:

[[w(1, 0), w(1, 2), 1], [2, 3, 4], [w(0, 0), w(0, 2), 5]]

If we further specify that:

• num_row_oov_buckets == 2
• num_col_oov_buckets == 1

Then the new output matrix will look like:

[[w(1, 0), w(1, 2), 1, 12], [2, 3, 4, 13], [w(0, 0), w(0, 2), 5, 14], [6, 7, 8, 15], [9, 10, 11, 16]]

If {old,new}_row_vocab_file are None, we assume that the old and new row vocab files are the same, and no row remapping is done. If {old,new}_col_vocab_file are None, we assume that the old and new column vocab files are the same, and no column remapping is done.

The returned initializer only supports div-partitioning along the row axis. It does not support partitioning along the column axis or mod-partitioning.

NOTE: When this is used to warm-start variables, client code should use tf.lookup.index_table_from_tensor() like contrib/layers/python/layers/feature_column.py does, as opposed to tf.feature_to_id() - in order to ensure the underlying lookup tables are the same.

Args:

• ckpt_path: Path to the TensorFlow checkpoint (version 2, TensorBundle) from which the old matrix Tensor will be loaded.
• old_tensor_name: Name of the 2-D Tensor to load from checkpoint.
• new_row_vocab_size: int specifying the number of entries in new_row_vocab_file. If no row remapping is needed (no row vocab provided), this should be equal to the number of rows to load from the old matrix (which can theoretically be smaller than the number of rows in the old matrix).
• new_col_vocab_size: int specifying the number of entries in new_col_vocab_file. If no column remapping is needed (no column vocab provided), this should be equal to the number of columns in the old matrix.
• old_row_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 first old_row_vocab_size entries will be considered for remapping.Must be smaller than the length of old_row_vocab_file. NOTE: we do not provide an equivalent old_col_vocab_size for classes.
• old_row_vocab_file: A scalar Tensor of type string containing the path to the old row vocabulary file. Can be None, which represents no remapping on the row axis.
• new_row_vocab_file: A scalar Tensor of type string containing the path to the new row vocabulary file. Can be None, which represents no remapping on the row axis.
• old_col_vocab_file: A scalar Tensor of type string containing the path to the old column vocabulary file. Can be None, which represents no remapping on the column axis.
• new_col_vocab_file: A scalar Tensor of type string containing the path to the new column vocabulary file. Can be None, which represents no remapping on the column axis.
• num_row_oov_buckets: int specifying the number of out-of-vocabulary rows to append. Must be >= 0.
• num_col_oov_buckets: int specifying the number of out-of-vocabulary columns to append. Must be >= 0.
• initializer: Initializer function to initialize missing values. Accepts a 1-D tensor as the arg to specify the shape of the returned tensor. If None, defaults to using zeros_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 that should be used to initialize a (potentially partitioned) Variable whose complete shape is [new_row_vocab_size + num_row_oov_buckets, new_col_vocab_size + num_col_oov_buckets].

Raises:

• TypeError: If initializer is specified but not callable.