# mars.tensor.vstack¶

mars.tensor.vstack(tup)[source]

Stack tensors in sequence vertically (row wise).

This is equivalent to concatenation along the first axis after 1-D tensors of shape (N,) have been reshaped to (1,N). Rebuilds tensors divided by vsplit.

This function makes most sense for tensors with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r/g/b channels (third axis). The functions concatenate, stack and block provide more general stacking and concatenation operations.

Parameters

tup (sequence of tensors) – The tensors must have the same shape along all but the first axis. 1-D tensors must have the same length.

Returns

stacked – The tensor formed by stacking the given tensors, will be at least 2-D.

Return type

Tensor

stack

Join a sequence of tensors along a new axis.

hstack

Stack tensors in sequence horizontally (column wise).

dstack

Stack tensors in sequence depth wise (along third dimension).

concatenate

Join a sequence of tensors along an existing axis.

vsplit

Split tensor into a list of multiple sub-arrays vertically.

block

Assemble tensors from blocks.

Examples

>>> import mars.tensor as mt

>>> a = mt.array([1, 2, 3])
>>> b = mt.array([2, 3, 4])
>>> mt.vstack((a,b)).execute()
array([[1, 2, 3],
[2, 3, 4]])

>>> a = mt.array([[1], [2], [3]])
>>> b = mt.array([[2], [3], [4]])
>>> mt.vstack((a,b)).execute()
array([[1],
[2],
[3],
[2],
[3],
[4]])