# tf.stack

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

### 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)
<tf.Tensor: shape=(2, 3), dtype=int32, numpy=
array([[1, 2, 3],
[4, 5, 6]], dtype=int32)>

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

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).

output A stacked Tensor with the same type as values.

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

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"必要な情報がない" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"複雑すぎる / 手順が多すぎる" },{ "type": "thumb-down", "id": "outOfDate", "label":"最新ではない" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"その他" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"わかりやすい" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"問題の解決に役立った" },{ "type": "thumb-up", "id": "otherUp", "label":"その他" }]