tf.math.reduce_min | TensorFlow v2.16.1 (original) (raw)

Computes the tf.math.minimum of elements across dimensions of a tensor.

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Main aliases

tf.math.reduce_min(
    input_tensor, axis=None, keepdims=False, name=None
)

Used in the notebooks

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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)>

Args
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).

Returns
The reduced tensor.

numpy compatibility

Equivalent to np.min