sparse_coo_tensor¶
- paddle.sparse. sparse_coo_tensor ( indices, values, shape=None, dtype=None, place=None, stop_gradient=True ) [source]
-
Constructs a sparse
paddle.Tensor
in coordinate format according to the indices and values of the specified non-zero elements.- Parameters
-
indices (list|tuple|ndarray|Tensor) – the indices of non-zero elements. Can be a list, tuple, numpy.ndarray, paddle.Tensor. The indices must be 2-D.
values (list|tuple|ndarray|Tensor) – Initial values for the tensor. Can be a scalar, list, tuple, numpy.ndarray, paddle.Tensor.
shape (list|tuple, optional) – The shape of the sparse tensor also represents the shape of original dense tensor. If not provided the smallest shape will be inferred to hold all elements.
dtype (str|np.dtype, optional) – The desired data type of returned tensor. Can be ‘bool’ , ‘float16’ , ‘float32’ , ‘float64’ , ‘int8’ , ‘int16’ , ‘int32’ , ‘int64’ , ‘uint8’, ‘complex64’ , ‘complex128’. Default: None, infers dtype from
data
except for python float number which gets dtype fromget_default_type
.place (CPUPlace|CUDAPinnedPlace|CUDAPlace|str, optional) – The place to allocate Tensor. Can be CPUPlace, CUDAPinnedPlace, CUDAPlace. Default: None, means global place. If
place
is string, It can becpu
,gpu:x
andgpu_pinned
, wherex
is the index of the GPUs.stop_gradient (bool, optional) – Whether to block the gradient propagation of Autograd. Default: True.
- Returns
-
A Tensor constructed from
indices
andvalues
. - Return type
-
Tensor
- Raises
-
TypeError – If the data type of
values
is not list, tuple, numpy.ndarray, paddle.TensorValueError – If
values
is tuple|list, it can’t contain nested tuple|list with different lengths , such as: [[1, 2], [3, 4, 5]]. If theindices
is not a 2-D.TypeError – If
dtype
is not bool, float16, float32, float64, int8, int16, int32, int64, uint8, complex64, complex128ValueError – If
place
is not paddle.CPUPlace, paddle.CUDAPinnedPlace, paddle.CUDAPlace or specified pattern string.
Examples:
import paddle from paddle.fluid.framework import _test_eager_guard with _test_eager_guard(): indices = [[0, 1, 2], [1, 2, 0]] values = [1.0, 2.0, 3.0] dense_shape = [2, 3] coo = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape) # print(coo) # Tensor(shape=[2, 3], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True, # indices=[[0, 1, 2], # [1, 2, 0]], # values=[1., 2., 3.])