randn¶
-
paddle.fluid.layers.
randn
(shape, out=None, dtype=None, device=None, stop_gradient=True, name=None)[source] This function returns a tensor filled with random numbers from a normal distribution with mean 0 and variance 1 (also called the standard normal distribution).
- Parameters
shape (list|tuple) – Shape of the generated random tensor.
out (Variable, optional) – Optional output which can be any created Variable that meets the requirements to store the result of operation. If the out is None, a new Variable wiil be returned to store the result. Default is None.
dtype (np.dtype|core.VarDesc.VarType|str, optional) – Data type of the output tensor, which can be float32, float64. if dtype is None , the data type of output tensor is float32 . Default is None.
device (str, optional) – Specific the output variable to be saved in cpu or gpu memory. Supported None, ‘cpu’, ‘gpu’. If it is None, the output variable will be automatically assigned devices. Default: None.
stop_gradient (bool, optional) – Indicating if we stop gradient from current(out) Variable. Default is True.
name (str, optional) – Normally there is no need for user to set this property. For more information, please refer to Name . Default is None.
- Returns
Random tensor whose data is drawn from a Gaussian distribution, dtype: flaot32 or float64 as specified.
- Return type:
Variable
- Raises
TypeError
– If the type of shape is not list or tuple.TypeError
– If the data type of dtype is not float32 or float64.ValueError
– If the length of shape is not bigger than 0.
Examples
# declarative mode import paddle.fluid as fluid data = fluid.layers.randn([2, 4]) place = fluid.CPUPlace() exe = fluid.Executor(place) res, = exe.run(fluid.default_main_program(), feed={}, fetch_list=[data]) print(res) # [[-1.4187592 0.7368311 -0.53748125 -0.0146909 ] # [-0.66294265 -1.3090698 0.1898754 -0.14065823]]
# imperative mode import paddle.fluid as fluid import paddle.fluid.dygraph as dg place = fluid.CPUPlace() with dg.guard(place) as g: x = fluid.layers.randn([2, 4]) x_np = x.numpy() print(x_np) # [[ 1.5149173 -0.26234224 -0.592486 1.4523455 ] # [ 0.04581212 -0.85345626 1.1687907 -0.02512913]]