# tf.stack

Stacks a list of rank-`R` tensors into one rank-`(R+1)` tensor.

``````tf.stack(
values, axis=0, name='stack'
)
``````

### Used in the notebooks

Used in the guide Used in the tutorials

Packs the list of tensors in `values` into a tensor with rank one higher than each tensor in `values`, by packing them along the `axis` dimension. Given a list of length `N` of tensors of shape `(A, B, C)`;

if `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`. if `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`. Etc.

#### For example:

````x = tf.constant([1, 4]) `
`y = tf.constant([2, 5]) `
`z = tf.constant([3, 6]) `
`tf.stack([x, y, z]) `
`<tf.Tensor: shape=(3, 2), dtype=int32, numpy= `
`array([[1, 4], `
`       [2, 5], `
`       [3, 6]], dtype=int32)> `
```

tf.stack([x, y, z], axis=1)

This is the opposite of unstack. The numpy equivalent is `np.stack`

````np.array_equal(np.stack([x, y, z]), tf.stack([x, y, z])) `
`True `
```

#### Args:

• `values`: A list of `Tensor` objects with the same shape and type.
• `axis`: An `int`. The axis to stack along. Defaults to the first dimension. Negative values wrap around, so the valid range is `[-(R+1), R+1)`.
• `name`: A name for this operation (optional).

#### Returns:

• `output`: A stacked `Tensor` with the same type as `values`.

#### Raises:

• `ValueError`: If `axis` is out of the range [-(R+1), R+1).