gaussian_random

paddle.fluid.layers.gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32')[source]

Generate a random tensor whose data is drawn from a Gaussian distribution.

Parameters

  • shape (tuple[int] | list[int] | Variable | list[Variable]) – Shape of the generated random tensor.

  • mean (float) – Mean of the random tensor, defaults to 0.0.

  • std (float) – Standard deviation of the random tensor, defaults to 1.0.

  • seed (int) – (int, default 0) Random seed of generator.0 means use system wide seed.Note that if seed is not 0, this operator will always generate the same random numbers every time

  • dtype (np.dtype | core.VarDesc.VarType | str) – Output data type, float32 or float64.

Returns

Random tensor whose data is drawn from a Gaussian distribution, dtype: flaot32 or float64 as specified.

Return type

Variable

Examples

import paddle.fluid as fluid

# example 1:
# attr shape is a list which doesn't contain tensor Variable.
result_1 = fluid.layers.gaussian_random(shape=[3, 4])

# example 2:
# attr shape is a list which contains tensor Variable.
dim_1 = fluid.layers.fill_constant([1],"int64",3)
dim_2 = fluid.layers.fill_constant([1],"int32",5)
result_2 = fluid.layers.gaussian_random(shape=[dim_1, dim_2])

# example 3:
# attr shape is a Variable, the data type must be int64 or int32.
var_shape = fluid.data(name='var_shape', shape=[2], dtype="int64")
result_3 = fluid.layers.gaussian_random(var_shape)
var_shape_int32 = fluid.data(name='var_shape_int32', shape=[2], dtype="int32")
result_4 = fluid.layers.gaussian_random(var_shape_int32)

# declarative mode
import numpy as np
from paddle import fluid

x = fluid.layers.gaussian_random((2, 3), std=2., seed=10)

place = fluid.CPUPlace()
exe = fluid.Executor(place)
start = fluid.default_startup_program()
main = fluid.default_main_program()

exe.run(start)
x_np, = exe.run(main, feed={}, fetch_list=[x])

x_np
# array([[2.3060477, 2.676496 , 3.9911983],
#        [0.9990833, 2.8675377, 2.2279181]], dtype=float32)

# imperative mode
import numpy as np
from paddle import fluid
import paddle.fluid.dygraph as dg

place = fluid.CPUPlace()
with dg.guard(place) as g:
    x = fluid.layers.gaussian_random((2, 4), mean=2., dtype="float32", seed=10)
    x_np = x.numpy()
x_np
# array([[2.3060477 , 2.676496  , 3.9911983 , 0.9990833 ],
#        [2.8675377 , 2.2279181 , 0.79029655, 2.8447366 ]], dtype=float32)