Apply boolean mask to tensor.

# Aliases:

• tf.compat.v2.boolean_mask

 tf.boolean_mask(
    tensor,
    mask,
    axis=None,
    name='boolean_mask'
)

Numpy equivalent is tensor[mask].

 # 1-D example
tensor = [0, 1, 2, 3]
mask = np.array([True, False, True, False])
boolean_mask(tensor, mask)  # [0, 2]

In general, 0 < dim(mask) = K <= dim(tensor), and mask's shape must match the first K dimensions of tensor's shape. We then have: boolean_mask(tensor, mask)[i, j1,...,jd] = tensor[i1,...,iKi1,...,iK,j1,...,jd] where (i1,...,iKi1,...,iK) is the ith True entry of mask (row-major order). The axis could be used with mask to indicate the axis to mask from. In that case, axis + dim(mask) <= dim(tensor) and mask's shape must match the first axis + dim(mask) dimensions of tensor's shape. tf.ragged.boolean_maskSee also: , which can be applied to both dense and ragged tensors, and can be used if you need to preserve the masked dimensions of tensor (rather than flattening them, as tf.boolean_mask does).

# Args:

• tensor: N-D tensor.
• mask: K-D boolean tensor, K <= N and K must be known statically.
• axis: A 0-D int Tensor representing the axis in tensor to mask from. By default, axis is 0 which will mask from the first dimension. Otherwise K + axis <= N.
• name: A name for this operation (optional).

# Returns:

(N-K+1)-dimensional tensor populated by entries in tensor corresponding to True values in mask.

# Raises:

• ValueError: If shapes do not conform.

# Examples:

 # 2-D example
tensor = [[1, 2], [3, 4], [5, 6]]
mask = np.array([True, False, True])
boolean_mask(tensor, mask)  # [[1, 2], [5, 6]]