tf.sequence_mask

tf.sequence_mask(
    lengths,
    maxlen=None,
    dtype=tf.bool,
    name=None
)

Defined in tensorflow/python/ops/array_ops.py.

See the guide: Tensor Transformations > Slicing and Joining

Returns a mask tensor representing the first N positions of each cell.

If lengths has shape [d_1, d_2, ..., d_n] the resulting tensor mask has dtype dtype and shape [d_1, d_2, ..., d_n, maxlen], with

mask[i_1, i_2, ..., i_n, j] = (j < lengths[i_1, i_2, ..., i_n])

Examples:

tf.sequence_mask([1, 3, 2], 5)  # [[True, False, False, False, False],
                                #  [True, True, True, False, False],
                                #  [True, True, False, False, False]]

tf.sequence_mask([[1, 3],[2,0]])  # [[[True, False, False],
                                  #   [True, True, True]],
                                  #  [[True, True, False],
                                  #   [False, False, False]]]

Args:

• lengths: integer tensor, all its values <= maxlen.
• maxlen: scalar integer tensor, size of last dimension of returned tensor. Default is the maximum value in lengths.
• dtype: output type of the resulting tensor.
• name: name of the op.

Returns:

A mask tensor of shape lengths.shape + (maxlen,), cast to specified dtype.

Raises:

• ValueError: if maxlen is not a scalar.