tfp.experimental.distributions.marginal_fns.ps.minimum

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

tfp.experimental.distributions.marginal_fns.ps.minimum(
    x: Annotated[Any, TV_Minimum_T], y: Annotated[Any, TV_Minimum_T], name=None
) -> Annotated[Any, TV_Minimum_T]

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

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).

A Tensor. Has the same type as x.