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
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.
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.
stacked – The tensor formed by stacking the given tensors, will be at least 2-D.
Join a sequence of tensors along a new axis.
Stack tensors in sequence horizontally (column wise).
Stack tensors in sequence depth wise (along third dimension).
Join a sequence of tensors along an existing axis.
Split tensor into a list of multiple sub-arrays vertically.
Assemble tensors from blocks.
>>> import mars.tensor as mt
>>> a = mt.array([1, 2, 3])
>>> b = mt.array([2, 3, 4])
array([[1, 2, 3],
[2, 3, 4]])
>>> a = mt.array([, , ])
>>> b = mt.array([, , ])