tf.raw_ops.LoadAndRemapMatrix

Loads a 2-D (matrix) Tensor with name old_tensor_name from the checkpoint

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.raw_ops.LoadAndRemapMatrix

tf.raw_ops.LoadAndRemapMatrix(
    ckpt_path, old_tensor_name, row_remapping, col_remapping, initializing_values,
    num_rows, num_cols, max_rows_in_memory=-1, name=None
)

at ckpt_path and potentially reorders its rows and columns using the specified remappings.

Most users should use one of the wrapper initializers (such as tf.contrib.framework.load_and_remap_matrix_initializer) instead of this function directly.

The remappings are 1-D tensors with the following properties:

• row_remapping must have exactly num_rows entries. Row i of the output matrix will be initialized from the row corresponding to index row_remapping[i] in the old Tensor from the checkpoint.
• col_remapping must have either 0 entries (indicating that no column reordering is needed) or num_cols entries. If specified, column j of the output matrix will be initialized from the column corresponding to index col_remapping[j] in the old Tensor from the checkpoint.
• A value of -1 in either of the remappings signifies a "missing" entry. In that case, values from the initializing_values tensor will be used to fill that missing row or column. If row_remapping has r missing entries and col_remapping has c missing entries, then the following condition must be true:

(r * num_cols) + (c * num_rows) - (r * c) == len(initializing_values)

The remapping tensors can be generated using the GenerateVocabRemapping op.

As an example, with row_remapping = [1, 0, -1], col_remapping = [0, 2, -1], initializing_values = [0.5, -0.5, 0.25, -0.25, 42], and w(i, j) representing the value from row i, column j of the old tensor in the checkpoint, the output matrix will look like the following:

[[w(1, 0), w(1, 2), 0.5], [w(0, 0), w(0, 2), -0.5], [0.25, -0.25, 42]]

Args
ckpt_path	A Tensor of type string. Path to the TensorFlow checkpoint (version 2, TensorBundle) from which the old matrix Tensor will be loaded.
old_tensor_name	A Tensor of type string. Name of the 2-D Tensor to load from checkpoint.
row_remapping	A Tensor of type int64. An int Tensor of row remappings (generally created by generate_vocab_remapping). Even if no row remapping is needed, this must still be an index-valued Tensor (e.g. [0, 1, 2, ...]), or a shifted index-valued Tensor (e.g. [8, 9, 10, ...], for partitioned Variables).
col_remapping	A Tensor of type int64. An int Tensor of column remappings (generally created by generate_vocab_remapping). May be a size-0 Tensor if only row remapping is to be done (e.g. column ordering is the same).
initializing_values	A Tensor of type float32. A float Tensor containing values to fill in for cells in the output matrix that are not loaded from the checkpoint. Length must be exactly the same as the number of missing / new cells.
num_rows	An int that is >= 0. Number of rows (length of the 1st dimension) in the output matrix.
num_cols	An int that is >= 1. Number of columns (length of the 2nd dimension) in the output matrix.
max_rows_in_memory	An optional int. Defaults to -1. 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.
name	A name for the operation (optional).

Returns
A Tensor of type float32.