Returns the min of x and y (i.e. x \< y ? x : y) element-wise. #### View aliases **Main aliases** [`tf.minimum`](https://www.tensorflow.org/api_docs/python/tf/math/minimum) **Compat aliases for migration** See [Migration guide](https://www.tensorflow.org/guide/migrate) for more details. [`tf.compat.v1.minimum`](https://www.tensorflow.org/api_docs/python/tf/math/minimum)
tf.math.minimum( x: Annotated[Any, https://www.tensorflow.org/api_docs/python/tf/raw_ops/Any], y: Annotated[Any, https://www.tensorflow.org/api_docs/python/tf/raw_ops/Any], name=None ) -> Annotated[Any, https://www.tensorflow.org/api_docs/python/tf/raw_ops/Any] ### Used in the notebooks | Used in the guide | Used in the tutorials | |---|---| | - [Extension types](https://www.tensorflow.org/guide/extension_type) | - [Integrated gradients](https://www.tensorflow.org/tutorials/interpretability/integrated_gradients) - [Neural machine translation with a Transformer and Keras](https://www.tensorflow.org/text/tutorials/transformer) | Both inputs are number-type tensors (except complex). `minimum` expects that both tensors have the same `dtype`. #### Examples: x = tf.constant([0., 0., 0., 0.]) y = tf.constant([-5., -2., 0., 3.]) tf.math.minimum(x, y) Note that `minimum` supports [broadcast semantics](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) for `x` and `y`. x = tf.constant([-5., 0., 0., 0.]) y = tf.constant([-3.]) tf.math.minimum(x, y) The reduction version of this elementwise operation is [`tf.math.reduce_min`](https://www.tensorflow.org/api_docs/python/tf/math/reduce_min)


| ## Args || |---|---| | `x` | A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `uint8`, `int16`, `uint16`, `int32`, `uint32`, `int64`, `uint64`. | | `y` | A `Tensor`. Must have the same type as `x`. | | `name` | A name for the operation (optional). |



| ## Returns || |---|---| | A `Tensor`. Has the same type as `x`. ||