``````tf.boolean_mask(
tensor,
axis=None
)
``````

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

See the guide: Tensor Transformations > Slicing and Joining

Apply boolean mask to tensor. Numpy equivalent is `tensor[mask]`.

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

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,...,iK,j1,...,jd]` where `(i1,...,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.

#### Args:

• `tensor`: N-D tensor.
• `mask`: K-D boolean tensor, K <= N and K must be known statically.
• `name`: A name for this operation (optional).
• `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.

#### 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]]