randint_like¶
返回服从均匀分布的、范围在[low, high)的随机 Tensor，输出的形状与 x 的形状一致，当数据类型 dtype 为 None 时（默认），输出的数据类型与 x 的数据类型一致，当数据类型 dtype 不为 None 时，将输出用户指定的数据类型。当 high 为 None 时（默认），均匀采样的区间为[0, low)。
参数¶
x (Tensor) – 输入的多维 Tensor，数据类型可以是 bool，int32，int64，float16，float32，float64。输出 Tensor 的形状和
x相同。如果dtype为 None，则输出 Tensor 的数据类型与x相同。low (int，可选) - 要生成的随机值范围的下限，
low包含在范围中。当high为 None 时，均匀采样的区间为[0,low)。默认值为 0。high (int，可选) - 要生成的随机值范围的上限，
high不包含在范围中。默认值为 None，此时范围是[0,low)。dtype (str|np.dtype，可选) - 输出 Tensor 的数据类型，支持 bool，int32，int64，float16，float32，float64。当该参数值为 None 时，输出 Tensor 的数据类型与输入 Tensor 的数据类型一致。默认值为 None。
name (str，可选) - 具体用法请参见 Name，一般无需设置，默认值为 None。
返回¶
Tensor：从区间[
low，high)内均匀分布采样的随机 Tensor，形状为x.shape，数据类型为dtype。
代码示例¶
>>> import paddle
>>> # example 1:
>>> # dtype is None and the dtype of x is float32
>>> x = paddle.zeros((1,2)).astype("float32")
>>> out2 = paddle.randint_like(x, low=-5, high=5)
>>> print(out2)
Tensor(shape=[1, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0., 0.]])
>>> print(out2.dtype)
paddle.float32
>>> # example 2:
>>> # dtype is None and the dtype of x is float64
>>> x = paddle.zeros((1,2)).astype("float64")
>>> out2 = paddle.randint_like(x, low=-5, high=5)
>>> print(out2)
Tensor(shape=[1, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
[[ 4., -5.]])
>>> print(out2.dtype)
paddle.float64
>>> # example 3:
>>> # dtype is None and the dtype of x is int32
>>> x = paddle.zeros((1,2)).astype("int32")
>>> out3 = paddle.randint_like(x, low=-5, high=5)
>>> print(out3)
Tensor(shape=[1, 2], dtype=int32, place=Place(cpu), stop_gradient=True,
[[ 0, -4]])
>>> print(out3.dtype)
paddle.int32
>>> # example 4:
>>> # dtype is None and the dtype of x is int64
>>> x = paddle.zeros((1,2)).astype("int64")
>>> out4 = paddle.randint_like(x, low=-5, high=5)
>>> print(out4)
Tensor(shape=[1, 2], dtype=int64, place=Place(cpu), stop_gradient=True,
[[ 4, -3]])
>>> print(out4.dtype)
paddle.int64
>>> # example 5:
>>> # dtype is float64 and the dtype of x is float32
>>> x = paddle.zeros((1,2)).astype("float32")
>>> out5 = paddle.randint_like(x, low=-5, high=5, dtype="float64")
>>> print(out5)
Tensor(shape=[1, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
[[3., 1.]])
>>> print(out5.dtype)
paddle.float64
>>> # example 6:
>>> # dtype is bool and the dtype of x is float32
>>> x = paddle.zeros((1,2)).astype("float32")
>>> out6 = paddle.randint_like(x, low=-5, high=5, dtype="bool")
>>> print(out6)
Tensor(shape=[1, 2], dtype=bool, place=Place(cpu), stop_gradient=True,
[[False, True ]])
>>> print(out6.dtype)
paddle.bool
>>> # example 7:
>>> # dtype is int32 and the dtype of x is float32
>>> x = paddle.zeros((1,2)).astype("float32")
>>> out7 = paddle.randint_like(x, low=-5, high=5, dtype="int32")
>>> print(out7)
Tensor(shape=[1, 2], dtype=int32, place=Place(cpu), stop_gradient=True,
[[-2, -2]])
>>> print(out7.dtype)
paddle.int32
>>> # example 8:
>>> # dtype is int64 and the dtype of x is float32
>>> x = paddle.zeros((1,2)).astype("float32")
>>> out8 = paddle.randint_like(x, low=-5, high=5, dtype="int64")
>>> print(out8)
Tensor(shape=[1, 2], dtype=int64, place=Place(cpu), stop_gradient=True,
[[-5, 4]])
>>> print(out8.dtype)
paddle.int64
>>> # example 9:
>>> # dtype is int64 and the dtype of x is bool
>>> x = paddle.zeros((1,2)).astype("bool")
>>> out9 = paddle.randint_like(x, low=-5, high=5, dtype="int64")
>>> print(out9)
Tensor(shape=[1, 2], dtype=int64, place=Place(cpu), stop_gradient=True,
[[ 1, -2]])
>>> print(out9.dtype)
paddle.int64