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!4712 Fix bugs in random ops 

Merge pull request !4712 from peixu_ren/custom_pp_ops
This commit is contained in:
mindspore-ci-bot 2020-08-19 10:54:52 +08:00 committed by Gitee
parent 6868b9b6fa 2830d7046b
commit 3259dafa7e
2 changed files with 17 additions and 23 deletions
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20
mindspore/ops/composite/random_ops.py
View File

@ -35,12 +35,12 @@ def set_seed(seed):
random seed.
Args:
seed(Int): the graph-level seed value that to be set.
seed(Int): the graph-level seed value that to be set. Must be non-negative.
Examples:
>>> C.set_seed(10)
"""
const_utils.check_int_positive("seed", seed, "set_seed")
const_utils.check_non_negative("seed", seed, "set_seed")
global _GRAPH_SEED
_GRAPH_SEED = seed
@ -56,7 +56,7 @@ def get_seed():
Interger. The current graph-level seed.
Examples:
>>> C.get_seed(10)
>>> C.get_seed()
"""
return _GRAPH_SEED
@ -70,7 +70,7 @@ def normal(shape, mean, stddev, seed=0):
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.
Must be non-negative. Default: 0.
Returns:
Tensor. The shape should be the broadcasted shape of Input "shape" and shapes of mean and stddev.
@ -107,7 +107,7 @@ def uniform(shape, a, b, seed=0, dtype=mstype.float32):
It defines the maximum possibly generated value. With int32 or float32 data type.
If dtype is int32, only one number is allowed.
seed (int): Seed is used as entropy source for Random number engines generating pseudo-random numbers.
Default: 0.
Must be non-negative. Default: 0.
Returns:
Tensor. The shape should be the broadcasted shape of Input "shape" and shapes of a and b.
@ -151,7 +151,7 @@ def gamma(shape, alpha, beta, seed=0):
alpha (Tensor): The alpha α distribution parameter. With float32 data type.
beta (Tensor): The beta β distribution parameter. With float32 data type.
seed (int): Seed is used as entropy source for Random number engines generating pseudo-random numbers.
Default: 0.
Must be non-negative. Default: 0.
Returns:
Tensor. The shape should be the broadcasted shape of Input "shape" and shapes of alpha and beta.
@ -163,10 +163,6 @@ def gamma(shape, alpha, beta, seed=0):
>>> beta = Tensor(1.0, mstype.float32)
>>> output = C.gamma(shape, alpha, beta, seed=5)
"""
alpha_dtype = F.dtype(alpha)
beta_dtype = F.dtype(beta)
const_utils.check_tensors_dtype_same(alpha_dtype, mstype.float32, "gamma")
const_utils.check_tensors_dtype_same(beta_dtype, mstype.float32, "gamma")
const_utils.check_non_negative("seed", seed, "gamma")
seed1 = get_seed()
seed2 = seed
@ -182,7 +178,7 @@ def poisson(shape, mean, seed=0):
shape (tuple): The shape of random tensor to be generated.
mean (Tensor): The mean μ distribution parameter. With float32 data type.
seed (int): Seed is used as entropy source for Random number engines generating pseudo-random numbers.
Default: 0.
Must be non-negative. Default: 0.
Returns:
Tensor. The shape should be the broadcasted shape of Input "shape" and shapes of mean.
@ -193,8 +189,6 @@ def poisson(shape, mean, seed=0):
>>> mean = Tensor(1.0, mstype.float32)
>>> output = C.poisson(shape, mean, seed=5)
"""
mean_dtype = F.dtype(mean)
const_utils.check_tensors_dtype_same(mean_dtype, mstype.float32, "poisson")
const_utils.check_non_negative("seed", seed, "poisson")
seed1 = get_seed()
seed2 = seed

20
mindspore/ops/operations/random_ops.py
View File

@ -27,8 +27,8 @@ class StandardNormal(PrimitiveWithInfer):
Generates random numbers according to the standard Normal (or Gaussian) random number distribution.
Args:
seed (int): Random seed. Default: 0.
seed2 (int): Random seed2. Default: 0.
seed (int): Random seed. Must be non-negative. Default: 0.
seed2 (int): Random seed2. Must be non-negative. Default: 0.
Inputs:
- **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed.
@ -125,8 +125,8 @@ class Gamma(PrimitiveWithInfer):
\text{P}(x|α,β) = \frac{\exp(-x/β)}{{β^α}\cdot{\Gamma(α)}}\cdot{x^{α-1}},
Args:
seed (int): Random seed. Default: 0.
seed2 (int): Random seed2. Default: 0.
seed (int): Random seed. Must be non-negative. Default: 0.
seed2 (int): Random seed2. Must be non-negative. Default: 0.
Inputs:
- **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed.
@ -180,8 +180,8 @@ class Poisson(PrimitiveWithInfer):
\text{P}(i|μ) = \frac{\exp(-μ)μ^{i}}{i!},
Args:
seed (int): Random seed. Default: 0.
seed2 (int): Random seed2. Default: 0.
seed (int): Random seed. Must be non-negative. Default: 0.
seed2 (int): Random seed2. Must be non-negative. Default: 0.
Inputs:
- **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed.
@ -234,8 +234,8 @@ class UniformInt(PrimitiveWithInfer):
The number in tensor a should be strictly less than b at any position after broadcasting.
Args:
seed (int): Random seed. Default: 0.
seed2 (int): Random seed2. Default: 0.
seed (int): Random seed. Must be non-negative. Default: 0.
seed2 (int): Random seed2. Must be non-negative. Default: 0.
Inputs:
- **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed.
@ -287,8 +287,8 @@ class UniformReal(PrimitiveWithInfer):
Produces random floating-point values i, uniformly distributed on the interval [0, 1).
Args:
seed (int): Random seed. Default: 0.
seed2 (int): Random seed2. Default: 0.
seed (int): Random seed. Must be non-negative. Default: 0.
seed2 (int): Random seed2. Must be non-negative. Default: 0.
Inputs:
- **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed.