# reduce_min¶

`paddle.fluid.layers.``reduce_min`(input, dim=None, keep_dim=False, name=None)[source]

Computes the minimum of tensor elements over the given dimension.

Parameters
• input (Variable) – The input variable which is a Tensor, the data type is float32, float64, int32, int64.

• dim (list|int, optional) – The dimensions along which the minimum is computed. If `None`, compute the minimum over all elements of `input` and return a Tensor variable with a single element, otherwise must be in the range \([-rank(input), rank(input))\). If \(dim[i] < 0\), the dimension to reduce is \(rank + dim[i]\).

• keep_dim (bool, optional) – Whether to reserve the reduced dimension in the output Tensor. The result tensor will have one fewer dimension than the `input` unless `keep_dim` is true, default value is False.

• name (str, optional) – The default value is None. Normally there is no need for user to set this property. For more information, please refer to Name

Returns

Tensor, result of minimum on the specified dim of input tensor, it’s data type is the same as input’s Tensor.

Return type

Variable

Examples

```import paddle.fluid as fluid
# x is a Tensor variable with following elements:
#    [[0.2, 0.3, 0.5, 0.9]
#     [0.1, 0.2, 0.6, 0.7]]
# Each example is followed by the corresponding output tensor.
x = fluid.data(name='x', shape=[2, 4], dtype='float32')
fluid.layers.reduce_min(x)  # [0.1]
fluid.layers.reduce_min(x, dim=0)  # [0.1, 0.2, 0.5, 0.7]
fluid.layers.reduce_min(x, dim=-1)  # [0.2, 0.1]
fluid.layers.reduce_min(x, dim=1, keep_dim=True)  # [[0.2], [0.1]]

# y is a Tensor variable with shape [2, 2, 2] and elements as below:
#      [[[1.0, 2.0], [3.0, 4.0]],
#      [[5.0, 6.0], [7.0, 8.0]]]
# Each example is followed by the corresponding output tensor.
y = fluid.data(name='y', shape=[2, 2, 2], dtype='float32')
fluid.layers.reduce_min(y, dim=[1, 2]) # [1.0, 5.0]
fluid.layers.reduce_min(y, dim=[0, 1]) # [1.0, 2.0]
```