mars.tensor.dstack¶

mars.tensor.
dstack
(tup)[source]¶ Stack tensors in sequence depth wise (along third axis).
This is equivalent to concatenation along the third axis after 2D tensors of shape (M,N) have been reshaped to (M,N,1) and 1D arrays of shape (N,) have been reshaped to (1,N,1). Rebuilds arrays divided by dsplit.
This function makes most sense for arrays with up to 3 dimensions. For instance, for pixeldata 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 third axis. 1D or 2D arrays must have the same shape.
 Returns
stacked – The array formed by stacking the given tensors, will be at least 3D.
 Return type
Tensor
See also
stack
Join a sequence of tensors along a new axis.
vstack
Stack along first axis.
hstack
Stack along second axis.
concatenate
Join a sequence of arrays along an existing axis.
dsplit
Split tensor along third axis.
Examples
>>> import mars.tensor as mt
>>> a = mt.array((1,2,3)) >>> b = mt.array((2,3,4)) >>> mt.dstack((a,b)).execute() array([[[1, 2], [2, 3], [3, 4]]])
>>> a = mt.array([[1],[2],[3]]) >>> b = mt.array([[2],[3],[4]]) >>> mt.dstack((a,b)).execute() array([[[1, 2]], [[2, 3]], [[3, 4]]])