tf.math.minimum
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Returns the min of x and y (i.e. x < y ? x : y) element-wise.
tf.math.minimum(
x: Annotated[Any, tf.raw_ops.Any],
y: Annotated[Any, tf.raw_ops.Any],
name=None
) -> Annotated[Any, tf.raw_ops.Any]
Used in the notebooks
| Used in the guide |
Used in the tutorials |
|
|
|
Both inputs are number-type tensors (except complex). minimum expects that
both tensors have the same dtype.
Examples:
x = tf.constant([0., 0., 0., 0.])
y = tf.constant([-5., -2., 0., 3.])
tf.math.minimum(x, y)
<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-5., -2., 0., 0.], dtype=float32)>
Note that minimum supports broadcast semantics for x and y.
x = tf.constant([-5., 0., 0., 0.])
y = tf.constant([-3.])
tf.math.minimum(x, y)
<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-5., -3., -3., -3.], dtype=float32)>
The reduction version of this elementwise operation is tf.math.reduce_min
Args |
|---|
x
|
A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int8, uint8, int16, uint16, int32, uint32, int64, uint64.
|
y
|
A Tensor. Must have the same type as x.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A Tensor. Has the same type as x.
|
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Last updated 2024-04-26 UTC.
[null,null,["Last updated 2024-04-26 UTC."],[],[],null,["# tf.math.minimum\n\n\u003cbr /\u003e\n\nReturns the min of x and y (i.e. x \\\u003c y ? x : y) element-wise.\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tf.minimum`](https://www.tensorflow.org/api_docs/python/tf/math/minimum)\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.minimum`](https://www.tensorflow.org/api_docs/python/tf/math/minimum)\n\n\u003cbr /\u003e\n\n tf.math.minimum(\n x: Annotated[Any, ../../tf/raw_ops/Any],\n y: Annotated[Any, ../../tf/raw_ops/Any],\n name=None\n ) -\u003e Annotated[Any, ../../tf/raw_ops/Any]\n\n### Used in the notebooks\n\n| Used in the guide | Used in the tutorials |\n|----------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| - [Extension types](https://www.tensorflow.org/guide/extension_type) | - [Integrated gradients](https://www.tensorflow.org/tutorials/interpretability/integrated_gradients) - [Client-efficient large-model federated learning via \\`federated_select\\` and sparse aggregation](https://www.tensorflow.org/federated/tutorials/sparse_federated_learning) - [Neural machine translation with a Transformer and Keras](https://www.tensorflow.org/text/tutorials/transformer) |\n\nBoth inputs are number-type tensors (except complex). `minimum` expects that\nboth tensors have the same `dtype`.\n\n#### Examples:\n\n x = tf.constant([0., 0., 0., 0.])\n y = tf.constant([-5., -2., 0., 3.])\n tf.math.minimum(x, y)\n \u003ctf.Tensor: shape=(4,), dtype=float32, numpy=array([-5., -2., 0., 0.], dtype=float32)\u003e\n\nNote that `minimum` supports [broadcast semantics](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) for `x` and `y`. \n\n x = tf.constant([-5., 0., 0., 0.])\n y = tf.constant([-3.])\n tf.math.minimum(x, y)\n \u003ctf.Tensor: shape=(4,), dtype=float32, numpy=array([-5., -3., -3., -3.], dtype=float32)\u003e\n\nThe reduction version of this elementwise operation is [`tf.math.reduce_min`](../../tf/math/reduce_min)\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|--------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `x` | A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `uint8`, `int16`, `uint16`, `int32`, `uint32`, `int64`, `uint64`. |\n| `y` | A `Tensor`. Must have the same type as `x`. |\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| A `Tensor`. Has the same type as `x`. ||\n\n\u003cbr /\u003e"]]
