tf.compat.v1.math.reduce_min

Computes the tf.math.minimum of elements across dimensions of a tensor. (deprecated arguments)

tf.compat.v1.math.reduce_min(
    input_tensor,
    axis=None,
    keepdims=None,
    name=None,
    reduction_indices=None,
    keep_dims=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.

>>> x = tf.constant([5, 1, 2, 4])
>>> tf.reduce_min(x)
<tf.Tensor: shape=(), dtype=int32, numpy=1>
>>> x = tf.constant([-5, -1, -2, -4])
>>> tf.reduce_min(x)
<tf.Tensor: shape=(), dtype=int32, numpy=-5>
>>> x = tf.constant([4, float('nan')])
>>> tf.reduce_min(x)
<tf.Tensor: shape=(), dtype=float32, numpy=nan>
>>> x = tf.constant([float('nan'), float('nan')])
>>> tf.reduce_min(x)
<tf.Tensor: shape=(), dtype=float32, numpy=nan>
>>> x = tf.constant([float('-inf'), float('inf')])
>>> tf.reduce_min(x)
<tf.Tensor: shape=(), dtype=float32, numpy=-inf>

See the numpy docs for np.amin and np.nanmin behavior.

input_tensorThe tensor to reduce. Should have real numeric type.
axisThe dimensions to reduce. If None (the default), reduces all dimensions. Must be in the range [-rank(input_tensor), rank(input_tensor)).
keepdimsIf true, retains reduced dimensions with length 1.
nameA name for the operation (optional).
reduction_indicesThe old (deprecated) name for axis.
keep_dimsDeprecated alias for keepdims.

The reduced tensor.