Computes the tf.math.minimum
of elements across dimensions of a tensor.
tfp.experimental.distributions.marginal_fns.ps.reduce_min(
input_tensor, axis=None, keepdims=False, name=None
)
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)>
Returns | |
---|---|
The reduced tensor. |
numpy compatibility
Equivalent to np.min