tf.math.reduce_min
Stay organized with collections
Save and categorize content based on your preferences.
Computes the tf.math.minimum of elements across dimensions of a tensor.
tf.math.reduce_min(
input_tensor, axis=None, keepdims=False, name=None
)
Used in the notebooks
| Used in the guide |
Used in the tutorials |
|
|
|
This is the reduction operation for the elementwise tf.math.minimum op.
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:
a = tf.constant([
[[1, 2], [3, 4]],
[[1, 2], [3, 4]]
])
tf.reduce_min(a)
<tf.Tensor: shape=(), dtype=int32, numpy=1>
Choosing a specific axis returns minimum element in the given axis:
b = tf.constant([[1, 2, 3], [4, 5, 6]])
tf.reduce_min(b, axis=0)
<tf.Tensor: shape=(3,), dtype=int32, numpy=array([1, 2, 3], dtype=int32)>
tf.reduce_min(b, axis=1)
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([1, 4], dtype=int32)>
Setting keepdims to True retains the dimension of input_tensor:
tf.reduce_min(a, keepdims=True)
<tf.Tensor: shape=(1, 1, 1), dtype=int32, numpy=array([[[1]]], dtype=int32)>
tf.math.reduce_min(a, axis=0, keepdims=True)
<tf.Tensor: shape=(1, 2, 2), dtype=int32, numpy=
array([[[1, 2],
[3, 4]]], dtype=int32)>
Args |
|---|
input_tensor
|
The tensor to reduce. Should have real numeric 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 for the operation (optional).
|
Returns |
|---|
|
The reduced tensor.
|
Equivalent to np.min
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates. Some content is licensed under the numpy license.
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_min\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#L2836-L2897) |\n\nComputes the [`tf.math.minimum`](../../tf/math/minimum) of elements across dimensions of a tensor.\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tf.reduce_min`](https://www.tensorflow.org/api_docs/python/tf/math/reduce_min)\n\n\u003cbr /\u003e\n\n tf.math.reduce_min(\n input_tensor, axis=None, keepdims=False, name=None\n )\n\n### Used in the notebooks\n\n| Used in the guide | Used in the tutorials |\n|--------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| - [Ragged tensors](https://www.tensorflow.org/guide/ragged_tensor) | - [Integrated gradients](https://www.tensorflow.org/tutorials/interpretability/integrated_gradients) - [Intro to Autoencoders](https://www.tensorflow.org/tutorials/generative/autoencoder) - [MoViNet for streaming action recognition](https://www.tensorflow.org/hub/tutorials/movinet) - [TensorFlow Ranking Keras pipeline for distributed training](https://www.tensorflow.org/ranking/tutorials/ranking_dnn_distributed) |\n\nThis is the reduction operation for the elementwise [`tf.math.minimum`](../../tf/math/minimum) op.\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 a = tf.constant([\n [[1, 2], [3, 4]],\n [[1, 2], [3, 4]]\n ])\n tf.reduce_min(a)\n \u003ctf.Tensor: shape=(), dtype=int32, numpy=1\u003e\n\nChoosing a specific axis returns minimum element in the given axis: \n\n b = tf.constant([[1, 2, 3], [4, 5, 6]])\n tf.reduce_min(b, axis=0)\n \u003ctf.Tensor: shape=(3,), dtype=int32, numpy=array([1, 2, 3], dtype=int32)\u003e\n tf.reduce_min(b, axis=1)\n \u003ctf.Tensor: shape=(2,), dtype=int32, numpy=array([1, 4], dtype=int32)\u003e\n\nSetting `keepdims` to `True` retains the dimension of `input_tensor`: \n\n tf.reduce_min(a, keepdims=True)\n \u003ctf.Tensor: shape=(1, 1, 1), dtype=int32, numpy=array([[[1]]], dtype=int32)\u003e\n tf.math.reduce_min(a, axis=0, keepdims=True)\n \u003ctf.Tensor: shape=(1, 2, 2), dtype=int32, numpy=\n array([[[1, 2],\n [3, 4]]], dtype=int32)\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 numeric 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 for the operation (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. ||\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\nnumpy compatibility\n-------------------\n\n\u003cbr /\u003e\n\nEquivalent to np.min\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e"]]
View source on GitHub