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# tf.range

Creates a sequence of numbers.

``````tf.range(limit, delta=1, dtype=None, name='range')
tf.range(start, limit, delta=1, dtype=None, name='range')
``````

### Used in the notebooks

Used in the guide Used in the tutorials

Creates a sequence of numbers that begins at `start` and extends by increments of `delta` up to but not including `limit`.

The dtype of the resulting tensor is inferred from the inputs unless it is provided explicitly.

Like the Python builtin `range`, `start` defaults to 0, so that `range(n) = range(0, n)`.

#### For example:

````start = 3`
`limit = 18`
`delta = 3`
`tf.range(start, limit, delta)`
`<tf.Tensor: shape=(5,), dtype=int32,`
`numpy=array([ 3,  6,  9, 12, 15], dtype=int32)>`
```
````start = 3`
`limit = 1`
`delta = -0.5`
`tf.range(start, limit, delta)`
`<tf.Tensor: shape=(4,), dtype=float32,`
`numpy=array([3. , 2.5, 2. , 1.5], dtype=float32)>`
```
````limit = 5`
`tf.range(limit)`
`<tf.Tensor: shape=(5,), dtype=int32,`
`numpy=array([0, 1, 2, 3, 4], dtype=int32)>`
```

`start` A 0-D `Tensor` (scalar). Acts as first entry in the range if `limit` is not None; otherwise, acts as range limit and first entry defaults to 0.
`limit` A 0-D `Tensor` (scalar). Upper limit of sequence, exclusive. If None, defaults to the value of `start` while the first entry of the range defaults to 0.
`delta` A 0-D `Tensor` (scalar). Number that increments `start`. Defaults to 1.
`dtype` The type of the elements of the resulting tensor.
`name` A name for the operation. Defaults to "range".

An 1-D `Tensor` of type `dtype`.

#### Numpy Compatibility

Equivalent to np.arange