tf.math.minimum

Returns the min of x and y (i.e. x < y ? x : y) element-wise.

View aliases

Main aliases

tf.minimum

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.minimum

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
Extension types	Integrated gradients Client-efficient large-model federated learning via `federated_select` and sparse aggregation Neural machine translation with a Transformer and Keras

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.