chainer.functions.stack¶

chainer.functions.stack(xs, axis=0)[source]¶

Concatenate variables along a new axis.

Parameters

xs (list of Variable or N-dimensional array) – Input variables to be concatenated. The variables must have the same shape.
axis (int) – The axis along which the arrays will be stacked. The axis parameter is acceptable when \(-ndim - 1 \leq axis \leq ndim\). (ndim is the dimension of input variables). When \(axis < 0\), the result is the same with \(ndim + 1 - |axis|\).

Returns

Output variable. Let x_1, x_2, ..., x_n and y be the input variables and the output variable, y[:, ..., 0, ..., :] is x_1, y[:, ..., 1, ..., :] is x_2 and y[:, ..., n-1, ..., :] is x_n (The indexed axis indicates the axis).

Return type

Variable

Example

>>> x1 = np.arange(0, 12).reshape(3, 4)
>>> x1.shape
(3, 4)
>>> x1
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])
>>> x2 = np.arange(12, 24).reshape(3, 4)
>>> x2.shape
(3, 4)
>>> x2
array([[12, 13, 14, 15],
       [16, 17, 18, 19],
       [20, 21, 22, 23]])
>>> y = F.stack([x1, x2], axis=0)
>>> y.shape
(2, 3, 4)
>>> y.array
array([[[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]],
<BLANKLINE>
       [[12, 13, 14, 15],
        [16, 17, 18, 19],
        [20, 21, 22, 23]]])
>>> y = F.stack([x1, x2], axis=1)
>>> y.shape
(3, 2, 4)
>>> y.array
array([[[ 0,  1,  2,  3],
        [12, 13, 14, 15]],
<BLANKLINE>
       [[ 4,  5,  6,  7],
        [16, 17, 18, 19]],
<BLANKLINE>
       [[ 8,  9, 10, 11],
        [20, 21, 22, 23]]])
>>> y = F.stack([x1, x2], axis=2)
>>> y.shape
(3, 4, 2)
>>> y.array
array([[[ 0, 12],
        [ 1, 13],
        [ 2, 14],
        [ 3, 15]],
<BLANKLINE>
       [[ 4, 16],
        [ 5, 17],
        [ 6, 18],
        [ 7, 19]],
<BLANKLINE>
       [[ 8, 20],
        [ 9, 21],
        [10, 22],
        [11, 23]]])
>>> y = F.stack([x1, x2], axis=-1)
>>> y.shape
(3, 4, 2)