# Copyright 2020-2022 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
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# ============================================================================
"""Operators for random."""
from __future__ import absolute_import
from mindspore.common._decorator import deprecated
from mindspore._checkparam import Validator, Rel
from mindspore.common import dtype as mstype
from mindspore.ops.primitive import PrimitiveWithInfer, prim_attr_register, Primitive
from mindspore.ops._utils import get_broadcast_shape
class NonDeterministicInts(Primitive):
r"""
Generates some integers that match the given type.
Returns the tensor with the given shape, the random numbers in it drawn from the data range
that a given type can represent.
.. warning::
The value of `shape` must be greater than zero.
The number of elements of output can not exceed 1000000.
Args:
dtype (mindspore.dtype, optional): The date type of output. The supported values are: mstype.int32
and mstype.int64. Default: mstype.int64.
Inputs:
- **shape** (Tensor) - The shape of random tensor to be generated. The supported values are:
int32 and int64.
Outputs:
Tensor. Its shape is specified by the input `shape`. Its type is specified by `dtype`.
Raises:
TypeError: If `shape` is not a Tensor.
TypeError: If `dtype` is not mstype.int32 or mstype.int64.
ValueError: If `shape` has negative elements.
ValueError: If `shape` has less than 2 elements.
ValueError: If `shape` is not a 1-D tensor.
ValueError: If the number of elements of output is more than 1000000.
Supported Platforms:
``CPU``
Examples:
>>> shape = Tensor(np.array([2,2]), mstype.int32)
>>> ndints = ops.NonDeterministicInts(dtype=mstype.int32)
>>> output = ndints(shape)
>>> print(output)
[[13031056 -141954883 ]
[ 140364228 290834494 ]]
"""
@prim_attr_register
def __init__(self, dtype=mstype.int64):
"""Initialize NonDeterministicInts"""
self.dtype = dtype
self.add_prim_attr("max_length", 1000000)
self.init_prim_io_names(inputs=["shape"], outputs=["output"])
valid_values = (mstype.int32, mstype.int64)
Validator.check_type_name("dtype", dtype, valid_values, self.name)
self.add_prim_attr("side_effect_hidden", True)
class TruncatedNormal(Primitive):
"""
Returns a tensor of the specified shape filled with truncated normal values.
The generated values follow a normal distribution.
.. warning::
The value of `shape` must be greater than zero. The output length can not exceed 1000000.
Args:
seed (int, optional): An optional int. Defaults to 0. If either `seed` or `seed2` are set to be non-zero,
the seed is set by the given seed. Otherwise, it is seeded by a random seed.
seed2 (int, optional): An optional int. Defaults to 0. A second seed to avoid seed collision.
dtype (mindspore.dtype, optional): Specified output data type. Must be one of the following types:
mindspore.float16, mindspore.float32 and mindspore.float64. Default: mindspore.float32.
Inputs
- **shape** (Tensor) - The shape of random tensor to be generated. Its type must be one of the following types:
mindspore.int32 and mindspore.int64.
Outputs:
Tensor. Its shape is specified by the input `shape`. Its type is specified by `dtype`.
Its values are in [-2,2].
Raises:
TypeError: If `shape` is not a Tensor.
TypeError: If data type of `dtype` and `shape` are not allowed.
TypeError: If `seed` is not an integer.
ValueError: If `shape` elements are not positive.
ValueError: If `shape` is not a 1-D tensor.
ValueError: If the number of elements of output is more than 1000000.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> shape = Tensor(np.array([2, 2]), mstype.int32)
>>> seed = 0
>>> seed2 = 0
>>> truncated_normal = ops.TruncatedNormal(seed=seed, seed2=seed2)
>>> output = truncated_normal(shape)
>>> print(output)
[[ -1.303105 0.641905 ]
[ -0.917926 0.650655 ]]
"""
@prim_attr_register
def __init__(self, dtype=mstype.float32, seed=0, seed2=0):
"""Initialize TruncatedNormal"""
self.dtype = dtype
self.add_prim_attr("max_length", 1000000)
self.init_prim_io_names(inputs=["shape"], outputs=["output"])
Validator.check_value_type('seed', seed, [int], self.name)
Validator.check_value_type('seed2', seed2, [int], self.name)
valid_values = (mstype.float16, mstype.float32, mstype.float64)
Validator.check_type_name("dtype", dtype, valid_values, self.name)
self.add_prim_attr("side_effect_hidden", True)
[文档]class StandardNormal(Primitive):
r"""
Generates random numbers according to the standard Normal (or Gaussian) random number distribution.
Refer to :func:`mindspore.ops.standard_normal` for more details.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import ops
>>> shape = (3, 4)
>>> stdnormal = ops.StandardNormal(seed=2)
>>> output = stdnormal(shape)
>>> print(output)
[[-1.3031056 0.64198005 -0.65207404 -1.767485 ]
[-0.91792876 0.6508565 -0.9098478 -0.14092612]
[ 0.7806437 1.1585592 1.9676613 -0.00440959]]
"""
@prim_attr_register
def __init__(self, seed=0, seed2=0):
"""Initialize StandardNormal"""
self.init_prim_io_names(inputs=['shape'], outputs=['output'])
self.add_prim_attr("side_effect_hidden", True)
Validator.check_non_negative_int(seed, "seed", self.name)
Validator.check_non_negative_int(seed2, "seed2", self.name)
[文档]class StandardLaplace(Primitive):
r"""
Generates random numbers according to the Laplace random number distribution (mean=0, lambda=1).
It is defined as:
.. math::
\text{f}(x) = \frac{1}{2}\exp(-|x|),
Args:
seed (int): Random seed. Default: 0.
seed2 (int): Random seed2. Default: 0.
Inputs:
- **shape** (Union[tuple, Tensor]) - The shape of random tensor to be generated. Only constant value is allowed
when the input type is tuple. And the operator supports dynamic shape only when the input type is Tensor.
Outputs:
Tensor. The shape that the input 'shape' denotes. The dtype is float32.
Raises:
TypeError: If seed or seed2 is not an int.
TypeError: If shape is neither a tuple nor a Tensor.
ValueError: If seed or seed2 is not a non-negative int.
ValueError: If shape is a tuple containing non-positive items.
ValueError: If shape is a Tensor, and the rank of the Tensor is not equal to 1.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> shape = (4, 16)
>>> stdlaplace = ops.StandardLaplace(seed=2)
>>> output = stdlaplace(shape)
>>> result = output.shape
>>> print(result)
(4, 16)
"""
@prim_attr_register
def __init__(self, seed=0, seed2=0):
"""Initialize StandardLaplace"""
self.init_prim_io_names(inputs=['shape'], outputs=['output'])
self.add_prim_attr("side_effect_hidden", True)
Validator.check_non_negative_int(seed, "seed", self.name)
Validator.check_non_negative_int(seed2, "seed2", self.name)
class RandomGamma(Primitive):
r"""
Produces random positive floating-point values x, distributed according to probability density function:
.. note::
- Random seed: A set of regular random numbers can be obtained through some complex mathematical algorithms,
and the random seed is the initial value of this random number. If the random seed is the same, the random
number obtained will not change.
- Global random seed and operator-level random seed are not set: Use the default value as the random seed.
- Global random seed is set, but operator-level random seed is not set: A global random seed will splice
with a randomly generated seed.
- Global random seed is not set, operator-level random seed is set: The default global random seed is used,
and splices with the operator-level random seed.
- Both Global random and operator-level random seed are set: The global random seed will splice with the
operator-level random seed.
Args:
seed (int, optional): The operator-level random seed, used to generate random numbers,
must be non-negative. Default: 0.
seed2 (int, optional): The global random seed, which combines with the operator-level
random seed to determine the final generated random number, must be non-negative. Default: 0.
Inputs:
- **shape** (Tensor) - The shape of random tensor to be generated. It must be constant value.
- **alpha** (Tensor) - α is the shape parameter of RandomGamma distribution, it mainly determines the
shape of the graph curve. It must be greater than 0 and have date type float32.
Outputs:
Tensor. The shape should be equal to the concat shape between the input `shape` and `alpha`.
The dtype is the same type as alpha.
Raises:
TypeError: If data type of `seed` or `seed2` is not int.
TypeError: If `shape` or `alpha` is not a Tensor.
TypeError: If data type of `alpha` is not float32.
ValueError: If `shape` is not a constant value.
Supported Platforms:
``CPU``
Examples:
>>> shape = Tensor(np.array([3, 1, 2]), mstype.int32)
>>> alpha = Tensor(np.array([[3, 4], [5, 6]]), mstype.float32)
>>> gamma = ops.RandomGamma(seed=3)
>>> output = gamma(shape, alpha)
>>> result = output.shape
>>> print(result)
(3, 1, 2, 2, 2)
"""
@prim_attr_register
def __init__(self, seed=0, seed2=0):
"""Initialize Gamma"""
self.init_prim_io_names(inputs=['shape', 'alpha'], outputs=['output'])
self.add_prim_attr("side_effect_hidden", True)
Validator.check_non_negative_int(seed, "seed", self.name)
Validator.check_non_negative_int(seed2, "seed2", self.name)
class LogNormalReverse(Primitive):
r"""
Fills the elements of the input tensor with log normal values initialized by
given mean and std:
.. math::
\text{f}(x;1.0,2.0)=\frac{1}{x\delta \sqrt[]{2\pi} }e^{-\frac{(\ln x-\mu )^2}{2\delta ^2} }
Args:
mean (float, optional): the mean of normal distribution. With float data type.
Default: 2.0.
std (float, optional): the std of normal distribution. With float data type.
Default: 1.0.
Inputs:
- **input** (Tensor) - The tensor to be generated with log-normal distribution.
Must be one of the following types: float16, float32, float64.
Outputs:
Tensor. A Tensor with the same type and shape of input.
Raises:
TypeError: If `input` is not Tensor.
ValueError: If `input` is NULL.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> x = Tensor(np.random.randn(3,4),mstype.float64)
>>> mean = 2.0
>>> std = 1.0
>>> lognormalreverse = ops.LogNormalReverse(mean, std)
>>> output = lognormalreverse(x)
>>> result = output.shape
>>> print(result)
(3, 4)
"""
@prim_attr_register
def __init__(self, mean=1.0, std=2.0):
"""Initialize LogNormalReverse"""
Validator.check_value_type("mean", mean, [float], self.name)
Validator.check_value_type("std", std, [float], self.name)
self.add_prim_attr("side_effect_hidden", True)
class RandomGammaGrad(Primitive):
r"""
Computes the derivative of a random sample of Gamma with respect to alpha.:
Inputs:
- **alpha** (Tensor) - α is the shape parameter of RandomGamma distribution.
It must be greater than 0. Must be one of the following types: float32, float64.
- **sample** (Tensor) - The sample of random gamma tensor. Must be one of the
following types: float32, float64.
Outputs:
The dtype is the same type as alpha.
The output shape is derived from the input through broadcasting.
Raises:
TypeError: If data type of `alpha` and `sample` is not float32 or float64.
TypeError: If data type of `alpha` and `sample` is not same.
ValueError: If the shape last dim of `sample` and `alpha` is not equal.
Supported Platforms:
``GPU``
Examples:
>>> alpha = Tensor(np.array([1., 0.6, 3., 26.]), mstype.float32)
>>> sample = Tensor(np.array([6., 7, 11., 0.5]), mstype.float32)
>>> randomgammagrad = ops.RandomGammaGrad()
>>> output = randomgammagrad(alpha, sample)
>>> print(output)
[2.5142431 3.4334087 1.8847835 0.07780622]
"""
@prim_attr_register
def __init__(self):
"""Initialize RandomGammaGrad"""
self.init_prim_io_names(inputs=['alpha', 'sample'], outputs=['output'])
self.add_prim_attr("side_effect_hidden", True)
[文档]class Gamma(PrimitiveWithInfer):
r"""
Produces random positive floating-point values x, distributed according to probability density function:
.. math::
\text{P}(x|α,β) = \frac{\exp(-x/β)}{{β^α}\cdot{\Gamma(α)}}\cdot{x^{α-1}}
.. note::
- Random seed: A set of regular random numbers can be obtained through some complex mathematical algorithms,
and the random seed is the initial value of this random number. If the random seed is the same, the random
number obtained will not change.
- Global random seed and operator-level random seed are not set: Use the default value as the random seed.
- Global random seed is set, but operator-level random seed is not set: A global random seed will splice
with a randomly generated seed.
- Global random seed is not set, operator-level random seed is set: The default global random seed is used,
and splices with the operator-level random seed.
- Both Global random and operator-level random seed are set: The global random seed will splice with the
operator-level random seed.
Args:
seed (int): The operator-level random seed, used to generate random numbers, must be non-negative. Default: 0.
seed2 (int): The global random seed and it will combile with the operator-level random seed to determine the
final generated random number, must be non-negative. Default: 0.
Inputs:
- **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed.
- **alpha** (Tensor) - α is the shape parameter of Gamma distribution, which mainly determines the shape of
the curve. It must be greater than 0. The data type is float32.
- **beta** (Tensor) - β is the inverse scale parameter of the Gamma distribution, which mainly determines how
steep the curve is. It must be greater than 0. The data type is float32.
Outputs:
Tensor. The shape must be the broadcasted shape of Input "shape" and shapes of `alpha` and `beta`.
The dtype is float32.
Raises:
TypeError: If data type of `seed` or `seed2` is not int.
TypeError: If `alpha` or `beta` is not a Tensor.
TypeError: If data type of `alpha` or `beta` is not float32.
ValueError: If `shape` is not a constant value.
Supported Platforms:
``Ascend``
Examples:
>>> shape = (3, 1, 2)
>>> alpha = Tensor(np.array([[3, 4], [5, 6]]), mstype.float32)
>>> beta = Tensor(np.array([1.0]), mstype.float32)
>>> gamma = ops.Gamma(seed=3)
>>> output = gamma(shape, alpha, beta)
>>> result = output.shape
>>> print(result)
(3, 2, 2)
"""
@prim_attr_register
def __init__(self, seed=0, seed2=0):
"""Initialize RandomGamma"""
self.init_prim_io_names(
inputs=['shape', 'alpha', 'beta'], outputs=['output'])
self.add_prim_attr("side_effect_hidden", True)
Validator.check_non_negative_int(seed, "seed", self.name)
Validator.check_non_negative_int(seed2, "seed2", self.name)
def __infer__(self, shape, alpha, beta):
shape_v = shape["value"]
if shape_v is None:
raise ValueError(f"For '{self.name}', the 'shape' cannot be None.")
Validator.check_value_type("shape", shape_v, [tuple], self.name)
for i, shape_i in enumerate(shape_v):
Validator.check_positive_int(shape_i, f'shape[{i}]', self.name)
Validator.check_tensor_dtype_valid("alpha", alpha["dtype"], [mstype.float32], self.name)
Validator.check_tensor_dtype_valid("beta", beta["dtype"], [mstype.float32], self.name)
broadcast_shape = get_broadcast_shape(alpha['shape'], beta['shape'], self.name,
arg_name1="alpha", arg_name2="beta")
broadcast_shape = get_broadcast_shape(broadcast_shape, shape_v, self.name,
arg_name1="broadcast_alpha_beta", arg_name2="shape")
out = {
'shape': broadcast_shape,
'dtype': mstype.float32,
'value': None}
return out
class ParameterizedTruncatedNormal(Primitive):
r"""
Returns a tensor of the specified shape filled with truncated normal values.
When `shape` is :math:`(batch\_size, *)`, the shape of `mean`, `stdevs`,
`min` and `max` should be :math:`()` or :math:`(batch\_size, )`.
Note:
The value in tensor `min` must be strictly less than `max` at any position after broadcasting.
Args:
seed (int, optional): Random number seed. If either `seed` or `seed2` are set to be non-zero,
the seed is set by the given seed. Otherwise, it is seeded by a random seed. Default: 0.
seed2 (int, optional): A second seed to avoid seed collision. Default: 0.
Inputs:
- **shape** (Tensor) - The shape of random tensor to be generated. Its type must be one of the following types:
int32 and int64.
- **mean** (Tensor) - The parameter defines the mean of truncated normal distribution.
Its type must be one of the following types:float16, float32, float64.
- **stdevs** (Tensor) - The parameter defines the standard deviation for truncation of
the normal distribution. It must be greater than 0 and have the same type as means.
- **min** (Tensor) - The parameter defines the minimum of
truncated normal distribution. It must have the same type as means.
- **max** (Tensor) - The parameter defines the maximum of
truncated normal distribution. It must have the same type as means.
Outputs:
Tensor. Its shape is specified by the input `shape` and it must have the same type as means.
Raises:
TypeError: If data type of `shape`, `mean`, `stdevs`, `min` and `max` are not allowed.
TypeError: If `mean`, `stdevs`, `min`, `max` don't have the same type.
TypeError: If any of `shape`, `mean`, `stdevs`, `min` and `max` is not Tensor.
ValueError: When `shape` is :math:`(batch\_size, *)`, if the shape of `mean`, `stdevs`, `min` or `max`
is not :math:`()` or :math:`(batch\_size, )`.
ValueError: If `shape` elements are not positive.
ValueError: If `stdevs` elements are not positive.
ValueError: If `shape` has less than 2 elements.
ValueError: If `shape` is not a 1-D tensor.
Supported Platforms:
``CPU``
Examples:
>>> shape = Tensor(np.array([2, 3]), mstype.int32)
>>> mean = Tensor(np.array([0], mstype.float32))
>>> stdevs = Tensor(np.array([1], mstype.float32))
>>> min = Tensor(np.array([-100], mstype.float32))
>>> max = Tensor(np.array([100], mstype.float32))
>>> seed = 1
>>> seed2 = 2
>>> parameterized_truncated_normal = ops.ParameterizedTruncatedNormal(seed=seed, seed2=seed2)
>>> output = parameterized_truncated_normal(shape, mean, stdevs, min, max)
>>> print(output)
[[-0.54974616 -1.4028727 1.5827523 ]
[ 0.25759354 -1.9593946 -1.5078077 ]]
"""
@prim_attr_register
def __init__(self, seed=0, seed2=0):
"""Initialize ParameterizedTruncatedNormal"""
self.init_prim_io_names(
inputs=['shape', 'mean', 'stdevs', 'min', 'max'], outputs=['y'])
Validator.check_value_type('seed', seed, [int], self.name)
Validator.check_value_type('seed2', seed2, [int], self.name)
self.add_prim_attr("side_effect_hidden", True)
class Poisson(PrimitiveWithInfer):
r"""
Produces random non-negative integer values i. Distributed according to discrete probability function:
.. math::
\text{P}(i|μ) = \frac{\exp(-μ)μ^{i}}{i!}
Args:
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.
- **mean** (Tensor) - μ parameter the distribution was constructed with. The parameter defines mean number
of occurrences of the event. It must be greater than 0. With float32 data type.
Outputs:
Tensor. Its shape must be the broadcasted shape of `shape` and the shape of `mean`.
The dtype is int32.
Raises:
TypeError: If neither `seed` nor `seed2` is an int.
TypeError: If `shape` is not a tuple.
TypeError: If `mean` is not a Tensor whose dtype is not float32.
Supported Platforms:
deprecated
Examples:
>>> shape = (4, 1)
>>> mean = Tensor(np.array([5.0, 10.0]), mstype.float32)
>>> poisson = ops.Poisson(seed=5)
>>> output = poisson(shape, mean)
>>> result = output.shape
>>> print(result)
(4, 2)
"""
@deprecated("2.0", "mindspore.ops.operations.Poisson", False)
@prim_attr_register
def __init__(self, seed=0, seed2=0):
"""Initialize Poisson"""
self.init_prim_io_names(inputs=['shape', 'mean'], outputs=['output'])
self.add_prim_attr("side_effect_hidden", True)
Validator.check_non_negative_int(seed, "seed", self.name)
Validator.check_non_negative_int(seed2, "seed2", self.name)
def __infer__(self, shape, mean):
shape_v = shape["value"]
if shape_v is None:
raise ValueError(f"For '{self.name}', the 'shape' cannot be None.")
Validator.check_value_type("shape", shape_v, [tuple], self.name)
for i, shape_i in enumerate(shape_v):
Validator.check_positive_int(shape_i, f'shape[{i}]', self.name)
Validator.check_tensor_dtype_valid(
"mean", mean["dtype"], [mstype.float32], self.name)
broadcast_shape = get_broadcast_shape(
mean['shape'], shape_v, self.name, arg_name1="mean", arg_name2="shape")
out = {
'shape': broadcast_shape,
'dtype': mstype.int32,
'value': None}
return out
class RandomPoisson(Primitive):
r"""
Produces random non-negative values i, distributed according to discrete probability function:
.. math::
\text{P}(i|μ) = \frac{\exp(-μ)μ^{i}}{i!}
Args:
seed (int, optional): Random number seed. If either `seed` or `seed2` are set to be non-zero,
the seed is set by the given seed. Otherwise, it is seeded by a random seed. Default: 0.
seed2 (int, optional): A second seed to avoid seed collision. Default: 0.
dtype (mindspore.dtype, optional): The type of output. Default: mstype.int64.
Inputs:
- **shape** (Tensor) - The shape of random tensor to be generated, 1-D Tensor, whose dtype must be in
[int32, int64].
- **rate** (Tensor) - μ parameter the distribution was constructed with. The parameter defines mean number
of occurrences of the event. Its type must be in [float16, float32, float64, int32, int64].
Outputs:
Tensor. Its shape is :math:`(*shape, *rate.shape)`. Its type is specified by `dtype`.
Raises:
TypeError: If `shape` is not a Tensor or its dtype is not int32 or int64.
TypeError: If `dtype` is not int32 or int64.
ValueError: If `shape` is not a 1-D tensor.
ValueError: If `shape` elements are negative.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> shape = Tensor(np.array([2, 3]), mstype.int32)
>>> rate = Tensor(np.array([2, 2]), mstype.int32)
>>> seed = 0
>>> seed2 = 0
>>> random_poisson = ops.RandomPoisson(seed=seed, seed2=seed2)
>>> output = random_poisson(shape,rate)
>>> print(output.shape)
(2, 3, 2)
"""
@prim_attr_register
def __init__(self, seed=0, seed2=0, dtype=mstype.int64):
"""Initialize Poisson"""
self.init_prim_io_names(inputs=['shape', 'rate'], outputs=['output'])
Validator.check_value_type('seed', seed, [int], self.name)
Validator.check_value_type('seed2', seed2, [int], self.name)
valid_values = (mstype.int64, mstype.int32,
mstype.float16, mstype.float32, mstype.float64)
Validator.check_type_name("dtype", dtype, valid_values, self.name)
self.add_prim_attr("side_effect_hidden", True)
[文档]class RandomChoiceWithMask(Primitive):
"""
Generates a random sample as index tensor with a mask tensor from a given tensor.
Refer to :func:`mindspore.ops.choice_with_mask` for more details.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> rnd_choice_mask = ops.RandomChoiceWithMask()
>>> input_x = Tensor(np.ones(shape=[240000, 4]).astype(np.bool))
>>> output_y, output_mask = rnd_choice_mask(input_x)
>>> result = output_y.shape
>>> print(result)
(256, 2)
>>> result = output_mask.shape
>>> print(result)
(256,)
"""
@prim_attr_register
def __init__(self, count=256, seed=0, seed2=0):
"""Initialize RandomChoiceWithMask"""
Validator.check_value_type("count", count, [int], self.name)
Validator.check_positive_int(count, "count", self.name)
Validator.check_value_type('seed', seed, [int], self.name)
Validator.check_value_type('seed2', seed2, [int], self.name)
self.add_prim_attr("side_effect_hidden", True)
[文档]class RandomCategorical(PrimitiveWithInfer):
"""
Generates random samples from a given categorical distribution tensor.
Args:
dtype (mindspore.dtype): The type of output. Its value must be one of mindspore.int16,
mindspore.int32 and mindspore.int64. Default: mindspore.int64.
Inputs:
- **logits** (Tensor) - The input tensor. 2-D Tensor with shape [batch_size, num_classes].
- **num_sample** (int) - Number of sample to be drawn. Only constant values is allowed.
- **seed** (int) - Random seed. Default: 0. Only constant values is allowed.
Outputs:
- **output** (Tensor) - The output Tensor with shape [batch_size, num_samples].
Raises:
TypeError: If `dtype` is not one of the following: mindspore.int16, mindspore.int32, mindspore.int64.
TypeError: If `logits` is not a Tensor.
TypeError: If neither `num_sample` nor `seed` is an int.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> class Net(nn.Cell):
... def __init__(self, num_sample):
... super(Net, self).__init__()
... self.random_categorical = ops.RandomCategorical(mindspore.int64)
... self.num_sample = num_sample
... def construct(self, logits, seed=0):
... return self.random_categorical(logits, self.num_sample, seed)
...
>>> x = np.random.random((10, 5)).astype(np.float32)
>>> net = Net(8)
>>> output = net(Tensor(x))
>>> result = output.shape
>>> print(result)
(10, 8)
"""
@prim_attr_register
def __init__(self, dtype=mstype.int64):
"""Initialize RandomCategorical"""
self.dtype = dtype
valid_values = (mstype.int32, mstype.int16, mstype.int64)
Validator.check_type_name("dtype", dtype, valid_values, self.name)
self.init_prim_io_names(inputs=['logits', 'num_samples', 'seed'],
outputs=['output'])
self.add_prim_attr("side_effect_hidden", True)
[文档]class Multinomial(Primitive):
r"""
Returns a tensor sampled from the multinomial probability distribution located in the corresponding
row of tensor input.
Note:
The rows of input do not need to sum to one (in which case we use the values as weights),
but must be non-negative, finite and have a non-zero sum.
Args:
seed (int): Random seed, must be non-negative. Default: 0.
seed2 (int): Random seed2, must be non-negative. Default: 0.
dtype(dtype): The type of output, must be int32 or int64. Default: int32.
Inputs:
- **x** (Tensor) - the input tensor containing the cumsum of probabilities, must be 1 or 2
dimensions. Must be one of the following types: float16, float32, float64. CPU and GPU
supports x 1 or 2 dimensions and Ascend only supports 2 dimensions.
- **num_samples** (int) - number of samples to draw, must be a nonnegative number.
Outputs:
Tensor with the same rows as `x`, each row has num_samples sampled indices.
Raises:
TypeError: If neither `seed` nor `seed2` is an int.
TypeError: If `x` is not a Tensor whose dtype is float16, float32, float64.
TypeError: If dtype of `num_samples` is not int.
TypeError: If `dtype` is not int32 or int64.
ValueError: If `seed` or `seed2` is less than 0.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> x = Tensor([[0., 9., 4., 0.]], mstype.float32)
>>> multinomial = ops.Multinomial(seed=10)
>>> output = multinomial(x, 2)
>>> print(output) # run in CPU
[[1 1]]
"""
@prim_attr_register
def __init__(self, seed=0, seed2=0, dtype=mstype.int32):
"""Initialize Multinomial."""
Validator.check_non_negative_int(seed, "seed", self.name)
Validator.check_non_negative_int(seed2, "seed2", self.name)
self.init_prim_io_names(
inputs=['x', 'num_samples'], outputs=['output'])
Validator.check_value_type("dtype", dtype, [mstype.Type], self.name)
valid_values = (mstype.int64, mstype.int32)
Validator.check_type_name("dtype", dtype, valid_values, self.name)
self.add_prim_attr("side_effect_hidden", True)
class MultinomialWithReplacement(Primitive):
r"""
Returns a tensor where each row contains numsamples indices sampled from the multinomial distribution.
Note:
The rows of input do not need to sum to one (in which case we use the values as weights),
but must be non-negative, finite and have a non-zero sum.
Refer to :func:`mindspore.ops.multinomial_with_replacement` for more details.
Supported Platforms:
``Ascend`` ``CPU``
Examples:
>>> x = Tensor([[0., 9., 4., 0.]], mstype.float32)
>>> multinomialwithreplacement = ops.MultinomialWithReplacement(numsamples=2,replacement=True)
>>> output = multinomialwithreplacement(x, 2, 5)
>>> print(output)
[[1 1]]
"""
@prim_attr_register
def __init__(self, numsamples, replacement=False):
"""Initialize MultinomialWithReplacement."""
Validator.check_non_negative_int(numsamples, "numsamples", self.name)
Validator.check_value_type("replacement", replacement, [bool], self.name)
self.init_prim_io_names(inputs=['x', 'seed', 'offset'], outputs=['y'])
self.add_prim_attr("side_effect_hidden", True)
class RandomShuffle(Primitive):
r"""
Randomly shuffles a Tensor along its first dimension.
Args:
seed (int): Random seed. If `seed` or `seed2` is set to non-zero, the random number generator will be seeded
by the given seed. Otherwise, it will be seeded randomly. The seed must be non-negative. Default: 0.
seed2 (int): Random seed2, a second seed to avoid seed collision. If `seed` is 0, the `seed2` will be used as
the seed of the random generator. It must be non-negative. Default: 0.
Inputs:
- **x** (Tensor) - The Tensor need be shuffled.
Outputs:
Tensor. The shape and type are the same as the input `x`.
Raises:
TypeError: If data type of `seed` or `seed2` is not int.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> x = Tensor(np.array([1, 2, 3, 4]), mstype.float32)
>>> shuffle = ops.RandomShuffle(seed=1, seed2=1)
>>> output = shuffle(x)
>>> print(output.shape)
(4,)
"""
@prim_attr_register
def __init__(self, seed=0, seed2=0):
"""Initialize RandomShuffle"""
self.init_prim_io_names(inputs=['input_x'], outputs=['output'])
self.add_prim_attr("side_effect_hidden", True)
Validator.check_non_negative_int(seed, "seed", self.name)
Validator.check_non_negative_int(seed2, "seed2", self.name)