sparse_csr_tensor

paddle.sparse. sparse_csr_tensor ( crows, cols, values, shape, dtype=None, place=None, stop_gradient=True ) [source]

Constructs a sparse paddle.Tensor in CSR(Compressed Sparse Row) format according to the crows, cols and values. Currently, the crows and cols of each batch must be incremented.

Parameters

  • crows (list|tuple|ndarray|Tensor) – 1-D array, each element in the rows represents the starting position of the first non-zero element of each row in values. Can be a list, tuple, numpy.ndarray, paddle.Tensor.

  • cols (list|tuple|ndarray|Tensor) – 1-D array, the column of non-zero elements. Can be a list, tuple, numpy.ndarray, paddle.Tensor.

  • values (list|tuple|ndarray|Tensor) – 1-D array, the non-zero elements. 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. 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 from get_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 be cpu, gpu:x and gpu_pinned, where x 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 crows, cols and values .

Return type

Tensor

Examples

>>> import paddle

>>> crows = [0, 2, 3, 5]
>>> cols = [1, 3, 2, 0, 1]
>>> values = [1, 2, 3, 4, 5]
>>> dense_shape = [3, 4]
>>> csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape)
>>> print(csr)
Tensor(shape=[3, 4], dtype=paddle.int64, place=Place(cpu), stop_gradient=True,
       crows=[0, 2, 3, 5],
       cols=[1, 3, 2, 0, 1],
       values=[1, 2, 3, 4, 5])