# tf.math.reduce_min

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

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

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