tf.math.reduce_variance
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Computes the variance of elements across dimensions of a tensor.
tf.math.reduce_variance(
input_tensor, axis=None, keepdims=False, name=None
)
Reduces input_tensor along the dimensions given in axis.
Unless keepdims is true, the rank of the tensor is reduced by 1 for each
of the entries in axis, which must be unique. If keepdims is true, the
reduced dimensions are retained with length 1.
If axis is None, all dimensions are reduced, and a
tensor with a single element is returned.
For example:
x = tf.constant([[1., 2.], [3., 4.]])
tf.math.reduce_variance(x)
<tf.Tensor: shape=(), dtype=float32, numpy=1.25>
tf.math.reduce_variance(x, 0)
<tf.Tensor: shape=(2,), dtype=float32, numpy=array([1., 1.], ...)>
tf.math.reduce_variance(x, 1)
<tf.Tensor: shape=(2,), dtype=float32, numpy=array([0.25, 0.25], ...)>
Args |
|---|
input_tensor
|
The tensor to reduce. Should have real or complex type.
|
axis
|
The dimensions to reduce. If None (the default), reduces all
dimensions. Must be in the range [-rank(input_tensor),
rank(input_tensor)).
|
keepdims
|
If true, retains reduced dimensions with length 1.
|
name
|
A name scope for the associated operations (optional).
|
Returns |
|---|
The reduced tensor, of the same dtype as the input_tensor. Note, for
complex64 or complex128 input, the returned Tensor will be of type
float32 or float64, respectively.
|
Equivalent to np.var
Please note np.var has a dtype parameter that could be used to specify the
output type. By default this is dtype=float64. On the other hand,
tf.math.reduce_variance has aggressive type inference from input_tensor.
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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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26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.math.reduce_variance\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/python/ops/math_ops.py#L2550-L2610) |\n\nComputes the variance of elements across dimensions of a tensor. \n\n tf.math.reduce_variance(\n input_tensor, axis=None, keepdims=False, name=None\n )\n\nReduces `input_tensor` along the dimensions given in `axis`.\nUnless `keepdims` is true, the rank of the tensor is reduced by 1 for each\nof the entries in `axis`, which must be unique. If `keepdims` is true, the\nreduced dimensions are retained with length 1.\n\nIf `axis` is None, all dimensions are reduced, and a\ntensor with a single element is returned.\n\n#### For example:\n\n x = tf.constant([[1., 2.], [3., 4.]])\n tf.math.reduce_variance(x)\n \u003ctf.Tensor: shape=(), dtype=float32, numpy=1.25\u003e\n tf.math.reduce_variance(x, 0)\n \u003ctf.Tensor: shape=(2,), dtype=float32, numpy=array([1., 1.], ...)\u003e\n tf.math.reduce_variance(x, 1)\n \u003ctf.Tensor: shape=(2,), dtype=float32, numpy=array([0.25, 0.25], ...)\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|----------------|----------------------------------------------------------------------------------------------------------------------------------------------|\n| `input_tensor` | The tensor to reduce. Should have real or complex type. |\n| `axis` | The dimensions to reduce. If `None` (the default), reduces all dimensions. Must be in the range `[-rank(input_tensor), rank(input_tensor))`. |\n| `keepdims` | If true, retains reduced dimensions with length 1. |\n| `name` | A name scope for the associated operations (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| The reduced tensor, of the same dtype as the input_tensor. Note, for `complex64` or `complex128` input, the returned `Tensor` will be of type `float32` or `float64`, respectively. ||\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\nnumpy compatibility\n-------------------\n\n\u003cbr /\u003e\n\nEquivalent to np.var\n\nPlease note `np.var` has a `dtype` parameter that could be used to specify the\noutput type. By default this is `dtype=float64`. On the other hand,\n[`tf.math.reduce_variance`](../../tf/math/reduce_variance) has aggressive type inference from `input_tensor`.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e"]]
View source on GitHub