chainer.functions.hstack¶

chainer.functions.hstack(xs)[source]¶

Concatenate variables horizontally (column wise).

Parameters: xs (list of Variable or N-dimensional array) – Input variables to be concatenated. The variables must have the same ndim. When the variables have the second axis (i.e. \(ndim \geq 2\)), the variables must have the same shape along all but the second axis. When the variables do not have the second axis(i.e. \(ndim < 2\)), the variables need not to have the same shape.
Returns: Output variable. When the input variables have the second axis (i.e. \(ndim \geq 2\)), the shapes of inputs and output are the same along all but the second axis. The length of second axis is the sum of the lengths of inputs’ second axis. When the variables do not have the second axis (i.e. \(ndim < 2\)), the shape of output is (N, ) (N is the sum of the input variables’ size).
Return type: Variable

Example

>>> x1 = np.array((1, 2, 3))
>>> x1.shape
(3,)
>>> x2 = np.array((2, 3, 4))
>>> x2.shape
(3,)
>>> y = F.hstack((x1, x2))
>>> y.shape
(6,)
>>> y.array
array([1, 2, 3, 2, 3, 4])
>>> 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, 18).reshape(3, 2)
>>> x2.shape
(3, 2)
>>> x2
array([[12, 13],
       [14, 15],
       [16, 17]])
>>> y = F.hstack([x1, x2])
>>> y.shape
(3, 6)
>>> y.array
array([[ 0,  1,  2,  3, 12, 13],
       [ 4,  5,  6,  7, 14, 15],
       [ 8,  9, 10, 11, 16, 17]])