# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Operations for random number generatos."""
from .. import operations as P
from .. import functional as F
from ..primitive import constexpr
from .multitype_ops import _constexpr_utils as const_utils
from ...common import dtype as mstype
# set graph-level RNG seed
_GRAPH_SEED = 0
@constexpr
def set_seed(seed):
global _GRAPH_SEED
_GRAPH_SEED = seed
@constexpr
def get_seed():
return _GRAPH_SEED
[docs]def normal(shape, mean, stddev, seed=0):
"""
Generates random numbers according to the Normal (or Gaussian) random number distribution.
It is defined as:
Args:
shape (tuple): The shape of random tensor to be generated.
mean (Tensor): The mean μ distribution parameter, which specifies the location of the peak.
With float32 data type.
stddev (Tensor): The deviation σ distribution parameter. With float32 data type.
seed (int): Seed is used as entropy source for Random number engines generating pseudo-random numbers.
Default: 0.
Returns:
Tensor. The shape should be the broadcasted shape of Input "shape" and shapes of mean and stddev.
The dtype is float32.
Examples:
>>> shape = (4, 16)
>>> mean = Tensor(1.0, mstype.float32)
>>> stddev = Tensor(1.0, mstype.float32)
>>> C.set_seed(10)
>>> output = C.normal(shape, mean, stddev, seed=5)
"""
mean_dtype = F.dtype(mean)
stddev_dtype = F.dtype(stddev)
const_utils.check_tensors_dtype_same(mean_dtype, mstype.float32, "normal")
const_utils.check_tensors_dtype_same(stddev_dtype, mstype.float32, "normal")
seed1 = get_seed()
seed2 = seed
stdnormal = P.StandardNormal(seed1, seed2)
rnd = stdnormal(shape)
value = rnd * stddev + mean
return value