Source code for mars.tensor.merge.dstack

#!/usr/bin/env python
# -*- coding: utf-8 -*-
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from ..base import atleast_3d
from .concatenate import concatenate

[docs]def dstack(tup): """ Stack tensors in sequence depth wise (along third axis). This is equivalent to concatenation along the third axis after 2-D tensors of shape `(M,N)` have been reshaped to `(M,N,1)` and 1-D 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 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 third axis. 1-D or 2-D arrays must have the same shape. Returns ------- stacked : Tensor The array formed by stacking the given tensors, will be at least 3-D. 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]]]) """ return concatenate([atleast_3d(t) for t in tup], axis=2)