tfp.experimental.distributions.marginal_fns.ps.reduce_min

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)>

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).

The reduced tensor.

numpy compatibility

Equivalent to np.min