chainer.functions.dstack

chainer.functions.dstack(xs)[source]

Concatenate variables along third axis (depth wise).

Parameters:xs (list of Variable or numpy.ndarray or cupy.ndarray) – Input variables to be concatenated. The variables must have the same ndim. When the variables have the third axis (i.e. \(ndim \geq 3\)), the variables must have the same shape along all but the third axis. When the variables do not have the third axis(i.e. \(ndim < 3\)), the variables must have the same shape.
Returns:Output variable. When the input variables have the third axis (i.e. \(ndim \geq 3\)), the shapes of inputs and output are the same along all but the third axis. The length of third axis is the sum of the lengths of inputs’ third axis. When the shape of variables are (N1, N2) (i.e. \(ndim = 2\)), the shape of output is (N1, N2, 2). When the shape of variables are (N1,) (i.e. \(ndim = 1\)), the shape of output is (1, N1, 2). When the shape of variables are () (i.e. \(ndim = 0\)), the shape of output is (1, 1, 2).
Return type:Variable

Example

>>> x1 = np.array((1, 2, 3))
>>> x1.shape
(3,)
>>> x2 = np.array((2, 3, 4))
>>> x2.shape
(3,)
>>> y = F.dstack((x1, x2))
>>> y.shape
(1, 3, 2)
>>> y.data
array([[[1, 2],
        [2, 3],
        [3, 4]]])
>>> x1 = np.arange(0, 6).reshape(3, 2)
>>> x1.shape
(3, 2)
>>> x1
array([[0, 1],
       [2, 3],
       [4, 5]])
>>> x2 = np.arange(6, 12).reshape(3, 2)
>>> x2.shape
(3, 2)
>>> x2
array([[ 6,  7],
       [ 8,  9],
       [10, 11]])
>>> y = F.dstack([x1, x2])
>>> y.shape
(3, 2, 2)
>>> y.data
array([[[ 0,  6],
        [ 1,  7]],

       [[ 2,  8],
        [ 3,  9]],

       [[ 4, 10],
        [ 5, 11]]])
>>> x1 = np.arange(0, 12).reshape(3, 2, 2)
>>> x2 = np.arange(12, 18).reshape(3, 2, 1)
>>> y = F.dstack([x1, x2])
>>> y.shape
(3, 2, 3)
>>> y.data
array([[[ 0,  1, 12],
        [ 2,  3, 13]],

       [[ 4,  5, 14],
        [ 6,  7, 15]],

       [[ 8,  9, 16],
        [10, 11, 17]]])