as_tensor

paddle. as_tensor ( data: TensorLike | NestedNumericSequence, dtype: DTypeLike | None = None, device: PlaceLike | None = None, requires_grad: bool = False, pin_memory: bool = False ) → paddle.Tensor [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.

We use the dtype conversion rules following this:
        Keep dtype
np.number ───────────► paddle.Tensor
                        (0-D Tensor)
            default_dtype
Python Number ───────────────► paddle.Tensor
                                (0-D Tensor)
            Keep dtype
np.ndarray ───────────► paddle.Tensor

Parameters

• 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 .

• device (CPUPlace|CUDAPinnedPlace|CUDAPlace|str, optional) – The place to allocate Tensor. Can be CPUPlace, CUDAPinnedPlace, CUDAPlace. Default: None, means global place. If device is string, It can be cpu, gpu:x and gpu_pinned, where x is the index of the GPUs.

• requires_grad (bool, optional) – Whether to block the gradient propagation of Autograd. Default: False.

• pin_memory (bool, optional) – If set, return tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False

Returns

A Tensor constructed from data .

Return type

Tensor

Examples

>>> import paddle

>>> type(paddle.tensor(1))
<class 'paddle.Tensor'>

>>> paddle.tensor(1)
Tensor(shape=[], dtype=int64, place=Place(cpu), stop_gradient=True,
1)

>>> x = paddle.tensor(1, requires_grad=True)
>>> print(x)
Tensor(shape=[], dtype=int64, place=Place(cpu), stop_gradient=False,
1)

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

>>> paddle.tensor([[0.1, 0.2], [0.3, 0.4]], device=paddle.CPUPlace(), requires_grad=True)
Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=False,
[[0.10000000, 0.20000000],
 [0.30000001, 0.40000001]])

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

>>> paddle.tensor([[1+1j, 2], [3+2j, 4]], dtype='complex64')
Tensor(shape=[2, 2], dtype=complex64, place=Place(cpu), stop_gradient=True,
[[(1+1j), (2+0j)],
 [(3+2j), (4+0j)]])