chainer.functions.dstack¶
-
chainer.functions.
dstack
(xs)[source]¶ Concatenate variables along third axis (depth wise).
Parameters: xs (list of Variable
ornumpy.ndarray
orcupy.ndarray
) – Input variables to be concatenated. The variables must have the samendim
. 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]]])