mars.tensor.vstack#
- mars.tensor.vstack(tup)[源代码]#
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.
- 参数
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.
- 返回
stacked – The tensor formed by stacking the given tensors, will be at least 2-D.
- 返回类型
Tensor
参见
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.
实际案例
>>> 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]])