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tf.ragged.boolean_mask

TensorFlow 1 version View source on GitHub

Applies a boolean mask to data without flattening the mask dimensions.

tf.ragged.boolean_mask(
    data, mask, name=None
)

Returns a potentially ragged tensor that is formed by retaining the elements in data where the corresponding value in mask is True.

  • output[a1...aA, i, b1...bB] = data[a1...aA, j, b1...bB]

    Where j is the ith True entry of mask[a1...aA].

Note that output preserves the mask dimensions a1...aA; this differs from tf.boolean_mask, which flattens those dimensions.

Args:

  • data: A potentially ragged tensor.
  • mask: A potentially ragged boolean tensor. mask's shape must be a prefix of data's shape. rank(mask) must be known statically.
  • name: A name prefix for the returned tensor (optional).

Returns:

A potentially ragged tensor that is formed by retaining the elements in data where the corresponding value in mask is True.

  • rank(output) = rank(data).
  • output.ragged_rank = max(data.ragged_rank, rank(mask) - 1).

Raises:

  • ValueError: if rank(mask) is not known statically; or if mask.shape is not a prefix of data.shape.

Examples:

# Aliases for True & False so data and mask line up. 
T, F = (True, False) 
tf.ragged.boolean_mask(  # Mask a 2D Tensor. 
    data=[[1, 2, 3], [4, 5, 6], [7, 8, 9]], 
    mask=[[T, F, T], [F, F, F], [T, F, F]]).to_list() 
[[1, 3], [], [7]] 
tf.ragged.boolean_mask(  # Mask a 2D RaggedTensor. 
    tf.ragged.constant([[1, 2, 3], [4], [5, 6]]), 
    tf.ragged.constant([[F, F, T], [F], [T, T]])).to_list() 
[[3], [], [5, 6]] 
tf.ragged.boolean_mask(  # Mask rows of a 2D RaggedTensor. 
    tf.ragged.constant([[1, 2, 3], [4], [5, 6]]), 
    tf.ragged.constant([True, False, True])).to_list() 
[[1, 2, 3], [5, 6]]