tf.boolean_mask
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Apply boolean mask to tensor.
tf.boolean_mask(
tensor, mask, axis=None, name='boolean_mask'
)
Numpy equivalent is tensor[mask].
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.
See also: tf.ragged.boolean_mask, 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).
Examples:
tensor = [0, 1, 2, 3] # 1-D example
mask = np.array([True, False, True, False])
tf.boolean_mask(tensor, mask)
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([0, 2], dtype=int32)>
tensor = [[1, 2], [3, 4], [5, 6]] # 2-D example
mask = np.array([True, False, True])
tf.boolean_mask(tensor, mask)
<tf.Tensor: shape=(2, 2), dtype=int32, numpy=
array([[1, 2],
[5, 6]], dtype=int32)>
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]]
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Last updated 2024-04-26 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26 UTC."],[],[],null,["# tf.boolean_mask\n\n\u003cbr /\u003e\n\n|-------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/python/ops/array_ops.py#L1505-L1562) |\n\nApply boolean mask to tensor. \n\n tf.boolean_mask(\n tensor, mask, axis=None, name='boolean_mask'\n )\n\nNumpy equivalent is `tensor[mask]`.\n\nIn general, `0 \u003c dim(mask) = K \u003c= dim(tensor)`, and `mask`'s shape must match\nthe first K dimensions of `tensor`'s shape. We then have:\n`boolean_mask(tensor, mask)[i, j1,...,jd] = tensor[i1,...,iK,j1,...,jd]`\nwhere `(i1,...,iK)` is the ith `True` entry of `mask` (row-major order).\nThe `axis` could be used with `mask` to indicate the axis to mask from.\nIn that case, `axis + dim(mask) \u003c= dim(tensor)` and `mask`'s shape must match\nthe first `axis + dim(mask)` dimensions of `tensor`'s shape.\n\nSee also: [`tf.ragged.boolean_mask`](../tf/ragged/boolean_mask), which can be applied to both dense and\nragged tensors, and can be used if you need to preserve the masked dimensions\nof `tensor` (rather than flattening them, as [`tf.boolean_mask`](../tf/boolean_mask) does).\n\n#### Examples:\n\n tensor = [0, 1, 2, 3] # 1-D example\n mask = np.array([True, False, True, False])\n tf.boolean_mask(tensor, mask)\n \u003ctf.Tensor: shape=(2,), dtype=int32, numpy=array([0, 2], dtype=int32)\u003e\n\n tensor = [[1, 2], [3, 4], [5, 6]] # 2-D example\n mask = np.array([True, False, True])\n tf.boolean_mask(tensor, mask)\n \u003ctf.Tensor: shape=(2, 2), dtype=int32, numpy=\n array([[1, 2],\n [5, 6]], dtype=int32)\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|----------|------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `tensor` | N-D Tensor. |\n| `mask` | K-D boolean Tensor, K \\\u003c= N and K must be known statically. |\n| `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 \\\u003c= N. |\n| `name` | A name for this operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| (N-K+1)-dimensional tensor populated by entries in `tensor` corresponding to `True` values in `mask`. ||\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|--------------|---------------------------|\n| `ValueError` | If shapes do not conform. |\n\n\u003cbr /\u003e\n\n#### Examples:\n\n # 2-D example\n tensor = [[1, 2], [3, 4], [5, 6]]\n mask = np.array([True, False, True])\n boolean_mask(tensor, mask) # [[1, 2], [5, 6]]"]]
View source on GitHub