paddle. to_tensor ( data, dtype=None, place=None, stop_gradient=True ) [source]

Constructs a paddle.Tensor from data , which can be scalar, tuple, list, numpy.ndarray, paddle.Tensor.

If the data is already a Tensor, copy will be performed and return a new tensor. If you only want to change stop_gradient property, please call Tensor.stop_gradient = stop_gradient directly.

  • data (scalar|tuple|list|ndarray|Tensor) – Initial data for the tensor. Can be a scalar, list, tuple, numpy.ndarray, paddle.Tensor.

  • 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.


A Tensor constructed from data .

Return type



import paddle

# <class 'paddle.Tensor'>

# Tensor(shape=[1], dtype=int64, place=CPUPlace, stop_gradient=True,
#        [1])

x = paddle.to_tensor(1, stop_gradient=False)
# Tensor(shape=[1], dtype=int64, place=CPUPlace, stop_gradient=False,
#        [1])

paddle.to_tensor(x)  # A new tensor will be created with default stop_gradient=True
# Tensor(shape=[1], dtype=int64, place=CPUPlace, stop_gradient=True,
#        [1])

paddle.to_tensor([[0.1, 0.2], [0.3, 0.4]], place=paddle.CPUPlace(), stop_gradient=False)
# Tensor(shape=[2, 2], dtype=float32, place=CPUPlace, stop_gradient=False,
#        [[0.10000000, 0.20000000],
#         [0.30000001, 0.40000001]])

type(paddle.to_tensor([[1+1j, 2], [3+2j, 4]], dtype='complex64'))
# <class 'paddle.Tensor'>

paddle.to_tensor([[1+1j, 2], [3+2j, 4]], dtype='complex64')
# Tensor(shape=[2, 2], dtype=complex64, place=CPUPlace, stop_gradient=True,
#        [[(1+1j), (2+0j)],
#         [(3+2j), (4+0j)]])