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!3394 Three distribution class instances: Exponential, Geometric Uniform distribution 

Merge pull request !3394 from XunDeng/pp_poc_v3
This commit is contained in:
mindspore-ci-bot 2020-07-28 22:21:29 +08:00 committed by Gitee
parent 3cac1bb9f8 292f2de0cf
commit 46ae7879d4
20 changed files with 3673 additions and 663 deletions
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8
mindspore/nn/distribution/__init__.py
View File

@ -21,7 +21,13 @@ The high-level components(Distributions) used to construct the probabilistic net
from .distribution import Distribution
from .normal import Normal
from .bernoulli import Bernoulli
from .exponential import Exponential
from .uniform import Uniform
from .geometric import Geometric
__all__ = ['Distribution',
'Normal',
'Bernoulli',]
'Bernoulli',
'Exponential',
'Uniform',
'Geometric',]

9
mindspore/nn/distribution/_utils/__init__.py
View File

@ -17,8 +17,11 @@ Distribution operation utility functions.
"""
from .utils import *
__all__ = ['check_scalar', 'convert_to_batch', 'cast_to_tensor',
'calc_batch_size', 'check_greater',
__all__ = ['convert_to_batch',
'cast_to_tensor',
'check_greater',
'check_greater_equal_zero',
'check_greater_zero',
'calc_broadcast_shape_from_param',
'check_scalar_from_param', 'check_prob']
'check_scalar_from_param',
'check_prob']

65
mindspore/nn/distribution/_utils/utils.py
View File

@ -20,17 +20,10 @@ from ....common.tensor import Tensor
from ....common.parameter import Parameter
from ....common import dtype as mstype
def check_scalar(value):
"""
Check if input value is a scalar.
"""
return np.isscalar(value)
def cast_to_tensor(t, dtype=mstype.float32):
"""
Cast an user input value into a Tensor of dtype.
If the input t is of type Parameter, t is directly returned as a Parameter.
Args:
t (int, float, list, numpy.ndarray, Tensor, Parameter): object to be cast to Tensor.
@ -54,22 +47,10 @@ def cast_to_tensor(t, dtype=mstype.float32):
return t
if isinstance(t, (list, np.ndarray)):
return Tensor(t, dtype=dtype)
if check_scalar(t):
if np.isscalar(t):
return Tensor([t], dtype=dtype)
raise RuntimeError("Input type is not supported.")
def calc_batch_size(batch_shape):
"""
Calculate the size of a given batch_shape.
Args:
batch_shape (tuple): batch shape to be calculated.
Returns:
int.
"""
return int(np.prod(batch_shape))
def convert_to_batch(t, batch_shape, dtype):
"""
Convert a Tensor to a given batch shape.
@ -87,15 +68,9 @@ def convert_to_batch(t, batch_shape, dtype):
"""
if isinstance(t, Parameter):
return t
t = cast_to_tensor(t, dtype)
if t.shape != batch_shape:
mul = calc_batch_size(batch_shape) // t.size()
if (calc_batch_size(batch_shape) % t.size()) != 0:
raise RuntimeError("Cannot cast the tensor to the given batch shape.")
temp = list(t.asnumpy()) * mul
temp = np.reshape(temp, batch_shape)
return Tensor(temp, dtype)
return t
if isinstance(t, Tensor):
return Tensor(np.broadcast_to(t.asnumpy(), batch_shape), dtype=dtype)
return Tensor(np.broadcast_to(t, batch_shape), dtype=dtype)
def check_scalar_from_param(params):
"""
@ -107,9 +82,11 @@ def check_scalar_from_param(params):
Notes: String parameters are excluded.
"""
for value in params.values():
if isinstance(value, Parameter):
return False
if isinstance(value, (str, type(params['dtype']))):
continue
elif check_scalar(value):
elif np.isscalar(value):
continue
else:
return False
@ -157,6 +134,26 @@ def check_greater_equal_zero(value, name):
value = value.default_input
comp = np.less(value.asnumpy(), np.zeros(value.shape))
if comp.any():
raise ValueError(f'{name} should be greater than ot equal to zero.')
def check_greater_zero(value, name):
"""
Check if the given Tensor is strictly greater than zero.
Args:
value (Tensor, Parameter): value to be checked.
name (str) : name of the value.
Raises:
ValueError: if the input value is less than or equal to zero.
"""
if isinstance(value, Parameter):
if isinstance(value.default_input, MetaTensor):
return
value = value.default_input
comp = np.less(np.zeros(value.shape), value.asnumpy())
if not comp.all():
raise ValueError(f'{name} should be greater than zero.')
def check_greater(a, b, name_a, name_b):
@ -164,14 +161,16 @@ def check_greater(a, b, name_a, name_b):
Check if Tensor b is strictly greater than Tensor a.
Args:
a (Tensor): input tensor a.
b (Tensor): input tensor b.
a (Tensor, Parameter): input tensor a.
b (Tensor, Parameter): input tensor b.
name_a (str): name of Tensor_a.
name_b (str): name of Tensor_b.
Raises:
ValueError: if b is less than or equal to a
"""
if isinstance(a, Parameter) or isinstance(b, Parameter):
return
comp = np.less(a.asnumpy(), b.asnumpy())
if not comp.all():
raise ValueError(f'{name_a} should be less than {name_b}')

205
mindspore/nn/distribution/bernoulli.py
View File

@ -14,29 +14,75 @@
# ============================================================================
"""Bernoulli Distribution"""
from mindspore.ops import operations as P
from mindspore.ops import composite as C
from .distribution import Distribution
from ._utils.utils import cast_to_tensor, check_prob
from ...common import dtype as mstype
class Bernoulli(Distribution):
"""
Example class: Bernoulli Distribution.
Bernoulli Distribution.
Args:
probs (int, float, list, numpy.ndarray, Tensor, Parameter): probability of 1 as outcome.
probs (float, list, numpy.ndarray, Tensor, Parameter): probability of 1 as outcome.
seed (int): seed to use in sampling. Default: 0.
dtype (mindspore.dtype): type of the distribution. Default: mstype.int32.
name (str): name of the distribution. Default: Bernoulli.
Note:
probs should be proper probabilities (0 <= p <= 1).
Dist_spec_args is probs.
Examples:
>>> # To initialize a Bernoulli distribution which has equal probability of getting 1 and 0
>>> b = nn.Bernoulli(0.5, dtype = mstype.int32)
>>> # The following create two independent Bernoulli distributions
>>> b = nn.Bernoulli([0.7, 0.2], dtype = mstype.int32)
>>> # To initialize a Bernoulli distribution of prob 0.5
>>> n = nn.Bernoulli(0.5, dtype=mstype.int32)
>>>
>>> # The following creates two independent Bernoulli distributions
>>> n = nn.Bernoulli([0.5, 0.5], dtype=mstype.int32)
>>>
>>> # A Bernoulli distribution can be initilized without arguments
>>> # In this case, probs must be passed in through construct.
>>> n = nn.Bernoulli(dtype=mstype.int32)
>>>
>>> # To use Bernoulli distribution in a network
>>> class net(Cell):
>>> def __init__(self):
>>> super(net, self).__init__():
>>> self.b1 = nn.Bernoulli(0.5, dtype=mstype.int32)
>>> self.b2 = nn.Bernoulli(dtype=mstype.int32)
>>>
>>> # All the following calls in construct are valid
>>> def construct(self, value, probs_b, probs_a):
>>>
>>> # Similar calls can be made to other probability functions
>>> # by replacing 'prob' with the name of the function
>>> ans = self.b1('prob', value)
>>> # Evaluate with the respect to distribution b
>>> ans = self.b1('prob', value, probs_b)
>>>
>>> # probs must be passed in through construct
>>> ans = self.b2('prob', value, probs_a)
>>>
>>> # Functions 'sd', 'var', 'entropy' have the same usage like 'mean'
>>> # Will return [0.0]
>>> ans = self.b1('mean')
>>> # Will return mean_b
>>> ans = self.b1('mean', probs_b)
>>>
>>> # probs must be passed in through construct
>>> ans = self.b2('mean', probs_a)
>>>
>>> # Usage of 'kl_loss' and 'cross_entropy' are similar
>>> ans = self.b1('kl_loss', 'Bernoulli', probs_b)
>>> ans = self.b1('kl_loss', 'Bernoulli', probs_b, probs_a)
>>>
>>> # Additional probs_a must be passed in through construct
>>> ans = self.b2('kl_loss', 'Bernoulli', probs_b, probs_a)
>>>
>>> # Sample Usage
>>> ans = self.b1('sample')
>>> ans = self.b1('sample', (2,3))
>>> ans = self.b1('sample', (2,3), probs_b)
>>> ans = self.b2('sample', (2,3), probs_a)
"""
def __init__(self,
@ -50,29 +96,34 @@ class Bernoulli(Distribution):
param = dict(locals())
super(Bernoulli, self).__init__(dtype, name, param)
if probs is not None:
self._probs = cast_to_tensor(probs)
check_prob(self._probs)
self._probs = cast_to_tensor(probs, dtype=mstype.float32)
check_prob(self.probs)
else:
self._probs = probs
self.seed = seed
# ops needed for the class
self.log = P.Log()
self.add = P.TensorAdd()
self.mul = P.Mul()
self.sqrt = P.Sqrt()
self.realdiv = P.RealDiv()
self.shape = P.Shape()
self.const = P.ScalarToArray()
self.less = P.Less()
self.cast = P.Cast()
self.const = P.ScalarToArray()
self.dtypeop = P.DType()
self.erf = P.Erf()
self.fill = P.Fill()
self.log = P.Log()
self.less = P.Less()
self.shape = P.Shape()
self.select = P.Select()
self.sq = P.Square()
self.sqrt = P.Sqrt()
self.uniform = P.UniformReal(seed=seed)
def extend_repr(self):
str_info = f'probs = {self._probs}'
if self.is_scalar_batch:
str_info = f'probs = {self.probs}'
else:
str_info = f'batch_shape = {self._broadcast_shape}'
return str_info
@property
def probs(self):
"""
Returns the probability for the outcome is 1.
@ -85,7 +136,21 @@ class Bernoulli(Distribution):
MEAN(B) = probs1
"""
if name == 'mean':
return self._probs if probs1 is None else probs1
return self.probs if probs1 is None else probs1
return None
def _mode(self, name='mode', probs1=None):
r"""
.. math::
MODE(B) = 1 if probs1 > 0.5 else = 0
"""
if name == 'mode':
probs1 = self.probs if probs1 is None else probs1
prob_type = self.dtypeop(probs1)
zeros = self.fill(prob_type, self.shape(probs1), 0.0)
ones = self.fill(prob_type, self.shape(probs1), 1.0)
comp = self.less(0.5, probs1)
return self.select(comp, ones, zeros)
return None
def _var(self, name='var', probs1=None):
@ -93,10 +158,35 @@ class Bernoulli(Distribution):
.. math::
VAR(B) = probs1 * probs0
"""
if name in ('sd', 'var'):
probs1 = self._probs if probs1 is None else probs1
probs0 = self.add(1, -1 * probs1)
return self.mul(probs0, probs1)
if name in self._variance_functions:
probs1 = self.probs if probs1 is None else probs1
probs0 = 1.0 - probs1
return probs0 * probs1
return None
def _entropy(self, name='entropy', probs=None):
r"""
.. math::
H(B) = -probs0 * \log(probs0) - probs1 * \log(probs1)
"""
if name == 'entropy':
probs1 = self.probs if probs is None else probs
probs0 = 1 - probs1
return -1 * (probs0 * self.log(probs0)) - (probs1 * self.log(probs1))
return None
def _cross_entropy(self, name, dist, probs1_b, probs1_a=None):
"""
Evaluate cross_entropy between Bernoulli distributions.
Args:
name (str): name of the funtion.
dist (str): type of the distributions. Should be "Bernoulli" in this case.
probs1_b (Tensor): probs1 of distribution b.
probs1_a (Tensor): probs1 of distribution a. Default: self.probs.
"""
if name == 'cross_entropy' and dist == 'Bernoulli':
return self._entropy(probs=probs1_a) + self._kl_loss(name, dist, probs1_b, probs1_a)
return None
def _prob(self, name, value, probs=None):
@ -106,17 +196,43 @@ class Bernoulli(Distribution):
Args:
name (str): name of the function. Should be "prob" when passed in from construct.
value (Tensor): a Tensor composed of only zeros and ones.
probs (Tensor): probability of outcome is 1. Default: self._probs.
probs (Tensor): probability of outcome is 1. Default: self.probs.
.. math::
pmf(k) = probs1 if k = 1;
pmf(k) = probs0 if k = 0;
"""
if name in ('prob', 'log_prob'):
probs1 = self._probs if probs is None else probs
probs0 = self.add(1, -1 * probs1)
return self.add(self.mul(probs1, value),
self.mul(probs0, self.add(1, -1 * value)))
if name in self._prob_functions:
probs1 = self.probs if probs is None else probs
probs0 = 1.0 - probs1
return (probs1 * value) + (probs0 * (1.0 - value))
return None
def _cdf(self, name, value, probs=None):
r"""
cdf of Bernoulli distribution.
Args:
name (str): name of the function.
value (Tensor): value to be evaluated.
probs (Tensor): probability of outcome is 1. Default: self.probs.
.. math::
cdf(k) = 0 if k < 0;
cdf(k) = probs0 if 0 <= k <1;
cdf(k) = 1 if k >=1;
"""
if name in self._cdf_survival_functions:
probs1 = self.probs if probs is None else probs
prob_type = self.dtypeop(probs1)
value = value * self.fill(prob_type, self.shape(probs1), 1.0)
probs0 = 1.0 - probs1 * self.fill(prob_type, self.shape(value), 1.0)
comp_zero = self.less(value, 0.0)
comp_one = self.less(value, 1.0)
zeros = self.fill(prob_type, self.shape(value), 0.0)
ones = self.fill(prob_type, self.shape(value), 1.0)
less_than_zero = self.select(comp_zero, zeros, probs0)
return self.select(comp_one, less_than_zero, ones)
return None
def _kl_loss(self, name, dist, probs1_b, probs1_a=None):
@ -124,21 +240,20 @@ class Bernoulli(Distribution):
Evaluate bernoulli-bernoulli kl divergence, i.e. KL(a||b).
Args:
name (str): name of the funtion. Should always be "kl_loss" when passed in from construct.
name (str): name of the funtion.
dist (str): type of the distributions. Should be "Bernoulli" in this case.
probs1_b (Tensor): probs1 of distribution b.
probs1_a (Tensor): probs1 of distribution a. Default: self._probs.
probs1_a (Tensor): probs1 of distribution a. Default: self.probs.
.. math::
KL(a||b) = probs1_a * \log(\fract{probs1_a}{probs1_b}) +
probs0_a * \log(\fract{probs0_a}{probs0_b})
"""
if name == 'kl_loss' and dist == 'Bernoulli':
probs1_a = self._probs if probs1_a is None else probs1_a
probs0_a = self.add(1, -1 * probs1_a)
probs0_b = self.add(1, -1 * probs1_b)
return self.add(probs1_a * self.log(self.realdiv(probs1_a, probs1_b)),
probs0_a * self.log(self.realdiv(probs0_a, probs0_b)))
if name in self._divergence_functions and dist == 'Bernoulli':
probs1_a = self.probs if probs1_a is None else probs1_a
probs0_a = 1.0 - probs1_a
probs0_b = 1.0 - probs1_b
return probs1_a * self.log(probs1_a / probs1_b) + probs0_a * self.log(probs0_a / probs0_b)
return None
def _sample(self, name, shape=(), probs=None):
@ -148,21 +263,17 @@ class Bernoulli(Distribution):
Args:
name (str): name of the function. Should always be 'sample' when passed in from construct.
shape (tuple): shape of the sample. Default: ().
probs (Tensor): probs1 of the samples. Default: self._probs.
probs (Tensor): probs1 of the samples. Default: self.probs.
Returns:
Tensor, shape is shape + batch_shape.
"""
if name == 'sample':
probs1 = self._probs if probs is None else probs
batch_shape = self.shape(probs1)
sample_shape = shape + batch_shape
mean_zero = self.const(0.0)
sd_one = self.const(1.0)
sqrt_two = self.sqrt(self.const(2.0))
sample_norm = C.normal(sample_shape, mean_zero, sd_one, self.seed)
sample_uniform = 0.5 * (1 + self.erf(self.realdiv(sample_norm, sqrt_two)))
probs1 = self.probs if probs is None else probs
l_zero = self.const(0.0)
h_one = self.const(1.0)
sample_uniform = self.uniform(shape + self.shape(probs1), l_zero, h_one)
sample = self.less(sample_uniform, probs1)
sample = self.cast(sample, self._dtype)
sample = self.cast(sample, self.dtype)
return sample
return None

373
mindspore/nn/distribution/distribution.py
View File

@ -14,8 +14,7 @@
# ============================================================================
"""basic"""
from ..cell import Cell
from ._utils.utils import calc_broadcast_shape_from_param
from ._utils.utils import calc_broadcast_shape_from_param, check_scalar_from_param
class Distribution(Cell):
"""
@ -29,19 +28,18 @@ class Distribution(Cell):
Note:
Derived class should override operations such as ,_mean, _prob,
and _log_prob. Functions should be called through construct when
used inside a network in the form of function name followed by
arguments.
used inside a network. Arguments should be passed in through *args
in the form of function name followed by additional arguments.
Functions such as cdf and prob, require a value to be passed in while
functions such as mean and sd do not require arguments other than name.
Examples:
>>> class MyNormalDistribution(Distribution):
>>> def __init__(self):
>>> super(MyDistribution, self).__init__()
>>> self._mean_value = Tensor([2.0,3.0])
>>> self._sd_value = Tensor([2.0,3.0])
>>>
>>> def _mean(self):
>>> return self._mean_value
Dist_spec_args are unique for each type of distribution. For example, mean and sd
are the dist_spec_args for a Normal distribution.
For all functions, passing in dist_spec_args, are optional.
Passing in the additional dist_spec_args will make the result to be evaluated with
new distribution specified by the dist_spec_args. But it won't change the
original distribuion.
"""
def __init__(self,
dtype,
@ -61,12 +59,40 @@ class Distribution(Cell):
self._parameters[k] = param[k]
# some attributes
self._broadcast_shape = calc_broadcast_shape_from_param(
self._parameters)
self.parameters)
self._is_scalar_batch = check_scalar_from_param(self.parameters)
# set the function to call according to the derived class's attributes
self._set_prob()
self._set_log_prob()
self._set_sd()
self._set_var()
self._set_cdf()
self._set_survival()
self._set_log_cdf()
self._set_log_survival()
self._set_cross_entropy()
self._prob_functions = ('prob', 'log_prob')
self._cdf_survival_functions = ('cdf', 'log_cdf', 'survival_function', 'log_survival')
self._variance_functions = ('var', 'sd')
self._divergence_functions = ('kl_loss', 'cross_entropy')
@property
def name(self):
return self._name
@property
def dtype(self):
return self._dtype
@property
def parameters(self):
return self._parameters
@property
def is_scalar_batch(self):
return self._is_scalar_batch
def _set_prob(self):
"""
@ -74,8 +100,8 @@ class Distribution(Cell):
"""
if hasattr(self, '_prob'):
self._call_prob = self._prob
elif hasattr(self, '_log_likelihood'):
self._call_prob = self._calc_prob_from_log_likelihood
elif hasattr(self, '_log_prob'):
self._call_prob = self._calc_prob_from_log_prob
def _set_sd(self):
"""
@ -86,45 +112,100 @@ class Distribution(Cell):
elif hasattr(self, '_var'):
self._call_sd = self._calc_sd_from_var
def _set_var(self):
"""
Set variance based on the availability of _sd and _var.
"""
if hasattr(self, '_var'):
self._call_var = self._var
elif hasattr(self, '_sd'):
self._call_var = self._calc_var_from_sd
def _set_log_prob(self):
"""
Set log probability based on the availability of _prob and _log_likelihood.
Set log probability based on the availability of _prob and _log_prob.
"""
if hasattr(self, '_log_likelihood'):
self._call_log_prob = self._log_likelihood
if hasattr(self, '_prob'):
if hasattr(self, '_log_prob'):
self._call_log_prob = self._log_prob
elif hasattr(self, '_prob'):
self._call_log_prob = self._calc_log_prob_from_prob
def log_likelihood(self, *args):
def _set_cdf(self):
"""
Evaluate the log probability at the given value.
Set cdf based on the availability of _cdf and _log_cdf and survival_functions.
"""
if hasattr(self, '_cdf'):
self._call_cdf = self._cdf
elif hasattr(self, '_log_cdf'):
self._call_cdf = self._calc_cdf_from_log_cdf
elif hasattr(self, '_survival_function'):
self._call_cdf = self._calc_cdf_from_survival
elif hasattr(self, '_log_survival'):
self._call_cdf = self._calc_cdf_from_log_survival
def _set_survival(self):
"""
Set survival function based on the availability of _survival function and _log_survival
and _call_cdf.
"""
if hasattr(self, '_survival_function'):
self._call_survival = self._survival_function
elif hasattr(self, '_log_survival'):
self._call_survival = self._calc_survival_from_log_survival
elif hasattr(self, '_call_cdf'):
self._call_survival = self._calc_survival_from_call_cdf
def _set_log_cdf(self):
"""
Set log cdf based on the availability of _log_cdf and _call_cdf.
"""
if hasattr(self, '_log_cdf'):
self._call_log_cdf = self._log_cdf
elif hasattr(self, '_call_cdf'):
self._call_log_cdf = self._calc_log_cdf_from_call_cdf
def _set_log_survival(self):
"""
Set log survival based on the availability of _log_survival and _call_survival.
"""
if hasattr(self, '_log_survival'):
self._call_log_survival = self._log_survival
elif hasattr(self, '_call_survival'):
self._call_log_survival = self._calc_log_survival_from_call_survival
def _set_cross_entropy(self):
"""
Set log survival based on the availability of _cross_entropy.
"""
if hasattr(self, '_cross_entropy'):
self._call_cross_entropy = self._cross_entropy
def log_prob(self, *args):
"""
Evaluate the log probability(pdf or pmf) at the given value.
Note:
value is casted to Tensor for further calculation.
Returns:
Tensor, shape is the broadcast_shape of the distribution.
Args must include name of the function and value.
Dist_spec_args are optional.
"""
return self._call_log_prob(*args)
def _calc_prob_from_log_likelihood(self, *args):
def _calc_prob_from_log_prob(self, *args):
r"""
Evaluate prob from log probability.
.. math::
probability(x) = \exp(log_likehood(x))
"""
return self.exp(self._log_likelihood(*args))
return self.exp(self._log_prob(*args))
def prob(self, *args):
"""
Evaluate the prob (pdf or pmf) at given value.
Evaluate the probability (pdf or pmf) at given value.
Note:
value is casted to Tensor for further calculation.
Returns:
Tensor, shape is the broadcast_shape of the distribution.
Args must include name of the function and value.
Dist_spec_args are optional.
"""
return self._call_prob(*args)
@ -137,33 +218,154 @@ class Distribution(Cell):
"""
return self.log(self._prob(*args))
def kl_loss(self, **kwargs):
def cdf(self, *args):
"""
Evaluate the KL divergence. Parameters of the second distribution should be
passed in through **kwargs.
Evaluate the cdf at given value.
Returns:
Tensor, shape is the broadcast_shape of the distribution and input distribution.
Note:
Args must include name of the function and value.
Dist_spec_args are optional.
"""
return self._kl_loss(**kwargs)
return self._call_cdf(*args)
def mean(self, **kwargs):
def _calc_cdf_from_log_cdf(self, *args):
r"""
Evaluate cdf from log_cdf.
.. math::
cdf(x) = \exp(log_cdf(x))
"""
return self.exp(self._log_cdf(*args))
def _calc_cdf_from_survival(self, *args):
r"""
Evaluate cdf from survival function.
.. math::
cdf(x) = 1 - (survival_function(x))
"""
return 1.0 - self._survival_function(*args)
def _calc_cdf_from_log_survival(self, *args):
r"""
Evaluate cdf from log survival function.
.. math::
cdf(x) = 1 - (\exp(log_survival(x)))
"""
return 1.0 - self.exp(self._log_survival(*args))
def log_cdf(self, *args):
"""
Evaluate the log cdf at given value.
Note:
Args must include name of the function and value.
Dist_spec_args are optional.
"""
return self._call_log_cdf(*args)
def _calc_log_cdf_from_call_cdf(self, *args):
r"""
Evaluate log cdf from cdf.
.. math::
log_cdf(x) = \log(cdf(x))
"""
return self.log(self._call_cdf(*args))
def survival_function(self, *args):
"""
Evaluate the survival function at given value.
Note:
Args must include name of the function and value.
Dist_spec_args are optional.
"""
return self._call_survival(*args)
def _calc_survival_from_call_cdf(self, *args):
r"""
Evaluate survival function from cdf.
.. math::
survival_function(x) = 1 - (cdf(x))
"""
return 1.0 - self._call_cdf(*args)
def _calc_survival_from_log_survival(self, *args):
r"""
Evaluate survival function from log survival function.
.. math::
survival(x) = \exp(survival_function(x))
"""
return self.exp(self._log_survival(*args))
def log_survival(self, *args):
"""
Evaluate the log survival function at given value.
Note:
Args must include name of the function and value.
Dist_spec_args are optional.
"""
return self._call_log_survival(*args)
def _calc_log_survival_from_call_survival(self, *args):
r"""
Evaluate log survival function from survival function.
.. math::
log_survival(x) = \log(survival_function(x))
"""
return self.log(self._call_survival(*args))
def kl_loss(self, *args):
"""
Evaluate the KL divergence, i.e. KL(a||b).
Note:
Args must include name of the function, type of the distribution, parameters of distribution b.
Parameters for distribution a are optional.
"""
return self._kl_loss(*args)
def mean(self, *args):
"""
Evaluate the mean.
Returns:
Tensor, shape is the broadcast_shape of the distribution.
Note:
Args must include the name of function. Dist_spec_args are optional.
"""
return self._mean(**kwargs)
return self._mean(*args)
def sd(self, **kwargs):
def mode(self, *args):
"""
Evaluate the mode.
Note:
Args must include the name of function. Dist_spec_args are optional.
"""
return self._mode(*args)
def sd(self, *args):
"""
Evaluate the standard deviation.
Returns:
Tensor, shape is the broadcast_shape of the distribution.
Note:
Args must include the name of function. Dist_spec_args are optional.
"""
return self._call_sd(**kwargs)
return self._call_sd(*args)
def var(self, *args):
"""
Evaluate the variance.
Note:
Args must include the name of function. Dist_spec_args are optional.
"""
return self._call_var(*args)
def _calc_sd_from_var(self, *args):
r"""
@ -174,27 +376,96 @@ class Distribution(Cell):
"""
return self.sqrt(self._var(*args))
def _calc_var_from_sd(self, *args):
r"""
Evaluate log probability from probability.
.. math::
VAR(x) = STD(x) ^ 2
"""
return self.sq(self._sd(*args))
def entropy(self, *args):
"""
Evaluate the entropy.
Note:
Args must include the name of function. Dist_spec_args are optional.
"""
return self._entropy(*args)
def cross_entropy(self, *args):
"""
Evaluate the cross_entropy between distribution a and b.
Note:
Args must include name of the function, type of the distribution, parameters of distribution b.
Parameters for distribution a are optional.
"""
return self._call_cross_entropy(*args)
def _calc_cross_entropy(self, *args):
r"""
Evaluate cross_entropy from entropy and kl divergence.
.. math::
H(X, Y) = H(X) + KL(X||Y)
"""
return self._entropy(*args) + self._kl_loss(*args)
def sample(self, *args):
"""
Sampling function.
Args:
*args (list): arguments passed in through construct.
Note:
Args must include name of the function.
Shape of the sample and dist_spec_args are optional.
"""
return self._sample(*args)
def construct(self, *inputs):
"""
Override construct in Cell.
Args:
*inputs: inputs[0] is always the name of the function.
Note:
Names of supported functions:
'prob', 'log_prob', 'cdf', 'log_cdf', 'survival_function', 'log_survival'
'var', 'sd', 'entropy', 'kl_loss', 'cross_entropy', 'sample'.
Notes:
Always raise RuntimeError as Distribution should not be called directly.
Args:
*inputs (list): inputs[0] is always the name of the function.
"""
if inputs[0] == 'log_prob':
return self._call_log_prob(*inputs)
if inputs[0] == 'prob':
return self._call_prob(*inputs)
if inputs[0] == 'cdf':
return self._call_cdf(*inputs)
if inputs[0] == 'log_cdf':
return self._call_log_cdf(*inputs)
if inputs[0] == 'survival_function':
return self._call_survival(*inputs)
if inputs[0] == 'log_survival':
return self._call_log_survival(*inputs)
if inputs[0] == 'kl_loss':
return self._kl_loss(*inputs)
if inputs[0] == 'mean':
return self._mean(*inputs)
if inputs[0] == 'mode':
return self._mode(*inputs)
if inputs[0] == 'sd':
return self._call_sd(*inputs)
if inputs[0] == 'var':
return self._call_var(*inputs)
if inputs[0] == 'entropy':
return self._entropy(*inputs)
if inputs[0] == 'cross_entropy':
return self._call_cross_entropy(*inputs)
if inputs[0] == 'sample':
return self._sample(*inputs)
return None

268
mindspore/nn/distribution/exponential.py Normal file
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@ -0,0 +1,268 @@
# 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.
# ============================================================================
"""Exponential Distribution"""
import numpy as np
from mindspore.ops import operations as P
from .distribution import Distribution
from ...common import dtype as mstype
from ._utils.utils import cast_to_tensor, check_greater_zero
class Exponential(Distribution):
"""
Example class: Exponential Distribution.
Args:
rate (float, list, numpy.ndarray, Tensor, Parameter): inverse scale.
seed (int): seed to use in sampling. Default: 0.
dtype (mindspore.dtype): type of the distribution. Default: mstype.float32.
name (str): name of the distribution. Default: Exponential.
Note:
rate should be strictly greater than 0.
Dist_spec_args is rate.
Examples:
>>> # To initialize an Exponential distribution of rate 0.5
>>> n = nn.Exponential(0.5, dtype=mstype.float32)
>>>
>>> # The following creates two independent Exponential distributions
>>> n = nn.Exponential([0.5, 0.5], dtype=mstype.float32)
>>>
>>> # A Exponential distribution can be initilized without arguments
>>> # In this case, rate must be passed in through construct.
>>> n = nn.Exponential(dtype=mstype.float32)
>>>
>>> # To use Exponential distribution in a network
>>> class net(Cell):
>>> def __init__(self):
>>> super(net, self).__init__():
>>> self.e1 = nn.Exponential(0.5, dtype=mstype.float32)
>>> self.e2 = nn.Exponential(dtype=mstype.float32)
>>>
>>> # All the following calls in construct are valid
>>> def construct(self, value, rate_b, rate_a):
>>>
>>> # Similar calls can be made to other probability functions
>>> # by replacing 'prob' with the name of the function
>>> ans = self.e1('prob', value)
>>> # Evaluate with the respect to distribution b
>>> ans = self.e1('prob', value, rate_b)
>>>
>>> # Rate must be passed in through construct
>>> ans = self.e2('prob', value, rate_a)
>>>
>>> # Functions 'sd', 'var', 'entropy' have the same usage with 'mean'
>>> # Will return [0.0]
>>> ans = self.e1('mean')
>>> # Will return mean_b
>>> ans = self.e1('mean', rate_b)
>>>
>>> # Rate must be passed in through construct
>>> ans = self.e2('mean', rate_a)
>>>
>>> # Usage of 'kl_loss' and 'cross_entropy' are similar
>>> ans = self.e1('kl_loss', 'Exponential', rate_b)
>>> ans = self.e1('kl_loss', 'Exponential', rate_b, rate_a)
>>>
>>> # Additional rate must be passed in through construct
>>> ans = self.e2('kl_loss', 'Exponential', rate_b, rate_a)
>>>
>>> # Sample Usage
>>> ans = self.e1('sample')
>>> ans = self.e1('sample', (2,3))
>>> ans = self.e1('sample', (2,3), rate_b)
>>> ans = self.e2('sample', (2,3), rate_a)
"""
def __init__(self,
rate=None,
seed=0,
dtype=mstype.float32,
name="Exponential"):
"""
Constructor of Exponential distribution.
"""
param = dict(locals())
super(Exponential, self).__init__(dtype, name, param)
if rate is not None:
self._rate = cast_to_tensor(rate, mstype.float32)
check_greater_zero(self._rate, "rate")
else:
self._rate = rate
self.minval = np.finfo(np.float).tiny
# ops needed for the class
self.const = P.ScalarToArray()
self.dtypeop = P.DType()
self.exp = P.Exp()
self.fill = P.Fill()
self.less = P.Less()
self.log = P.Log()
self.select = P.Select()
self.shape = P.Shape()
self.sqrt = P.Sqrt()
self.sq = P.Square()
self.uniform = P.UniformReal(seed=seed)
def extend_repr(self):
if self.is_scalar_batch:
str_info = f'rate = {self.rate}'
else:
str_info = f'batch_shape = {self._broadcast_shape}'
return str_info
@property
def rate(self):
"""
Return rate of the distribution.
"""
return self._rate
def _mean(self, name='mean', rate=None):
r"""
.. math::
MEAN(EXP) = \fract{1.0}{\lambda}.
"""
if name == 'mean':
rate = self.rate if rate is None else rate
return 1.0 / rate
return None
def _mode(self, name='mode', rate=None):
r"""
.. math::
MODE(EXP) = 0.
"""
if name == 'mode':
rate = self.rate if rate is None else rate
return self.fill(self.dtype, self.shape(rate), 0.)
return None
def _sd(self, name='sd', rate=None):
r"""
.. math::
sd(EXP) = \fract{1.0}{\lambda}.
"""
if name in self._variance_functions:
rate = self.rate if rate is None else rate
return 1.0 / rate
return None
def _entropy(self, name='entropy', rate=None):
r"""
.. math::
H(Exp) = 1 - \log(\lambda).
"""
rate = self.rate if rate is None else rate
if name == 'entropy':
return 1.0 - self.log(rate)
return None
def _cross_entropy(self, name, dist, rate_b, rate_a=None):
"""
Evaluate cross_entropy between Exponential distributions.
Args:
name (str): name of the funtion. Should always be "cross_entropy" when passed in from construct.
dist (str): type of the distributions. Should be "Exponential" in this case.
rate_b (Tensor): rate of distribution b.
rate_a (Tensor): rate of distribution a. Default: self.rate.
"""
if name == 'cross_entropy' and dist == 'Exponential':
return self._entropy(rate=rate_a) + self._kl_loss(name, dist, rate_b, rate_a)
return None
def _prob(self, name, value, rate=None):
r"""
pdf of Exponential distribution.
Args:
Args:
name (str): name of the function.
value (Tensor): value to be evaluated.
rate (Tensor): rate of the distribution. Default: self.rate.
Note:
Value should be greater or equal to zero.
.. math::
pdf(x) = rate * \exp(-1 * \lambda * x) if x >= 0 else 0
"""
if name in self._prob_functions:
rate = self.rate if rate is None else rate
prob = rate * self.exp(-1. * rate * value)
zeros = self.fill(self.dtypeop(prob), self.shape(prob), 0.0)
comp = self.less(value, zeros)
return self.select(comp, zeros, prob)
return None
def _cdf(self, name, value, rate=None):
r"""
cdf of Exponential distribution.
Args:
name (str): name of the function.
value (Tensor): value to be evaluated.
rate (Tensor): rate of the distribution. Default: self.rate.
Note:
Value should be greater or equal to zero.
.. math::
cdf(x) = 1.0 - \exp(-1 * \lambda * x) if x >= 0 else 0
"""
if name in self._cdf_survival_functions:
rate = self.rate if rate is None else rate
cdf = 1.0 - self.exp(-1. * rate * value)
zeros = self.fill(self.dtypeop(cdf), self.shape(cdf), 0.0)
comp = self.less(value, zeros)
return self.select(comp, zeros, cdf)
return None
def _kl_loss(self, name, dist, rate_b, rate_a=None):
"""
Evaluate exp-exp kl divergence, i.e. KL(a||b).
Args:
name (str): name of the funtion.
dist (str): type of the distributions. Should be "Exponential" in this case.
rate_b (Tensor): rate of distribution b.
rate_a (Tensor): rate of distribution a. Default: self.rate.
"""
if name in self._divergence_functions and dist == 'Exponential':
rate_a = self.rate if rate_a is None else rate_a
return self.log(rate_a) - self.log(rate_b) + rate_b / rate_a - 1.0
return None
def _sample(self, name, shape=(), rate=None):
"""
Sampling.
Args:
name (str): name of the function.
shape (tuple): shape of the sample. Default: ().
rate (Tensor): rate of the distribution. Default: self.rate.
Returns:
Tensor, shape is shape + batch_shape.
"""
if name == 'sample':
rate = self.rate if rate is None else rate
minval = self.const(self.minval)
maxval = self.const(1.0)
sample = self.uniform(shape + self.shape(rate), minval, maxval)
return -self.log(sample) / rate
return None

288
mindspore/nn/distribution/geometric.py Normal file
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@ -0,0 +1,288 @@
# 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.
# ============================================================================
"""Geometric Distribution"""
import numpy as np
from mindspore.ops import operations as P
from .distribution import Distribution
from ._utils.utils import cast_to_tensor, check_prob
from ...common import dtype as mstype
class Geometric(Distribution):
"""
Geometric Distribution.
It represents k+1 Bernoulli trials needed to get one success, k is the number of failures.
Args:
probs (float, list, numpy.ndarray, Tensor, Parameter): probability of success.
seed (int): seed to use in sampling. Default: 0.
dtype (mindspore.dtype): type of the distribution. Default: mstype.int32.
name (str): name of the distribution. Default: Geometric.
Note:
probs should be proper probabilities (0 <= p <= 1).
Dist_spec_args is probs.
Examples:
>>> # To initialize a Geometric distribution of prob 0.5
>>> n = nn.Geometric(0.5, dtype=mstype.int32)
>>>
>>> # The following creates two independent Geometric distributions
>>> n = nn.Geometric([0.5, 0.5], dtype=mstype.int32)
>>>
>>> # A Geometric distribution can be initilized without arguments
>>> # In this case, probs must be passed in through construct.
>>> n = nn.Geometric(dtype=mstype.int32)
>>>
>>> # To use Geometric distribution in a network
>>> class net(Cell):
>>> def __init__(self):
>>> super(net, self).__init__():
>>> self.g1 = nn.Geometric(0.5, dtype=mstype.int32)
>>> self.g2 = nn.Geometric(dtype=mstype.int32)
>>>
>>> # Tthe following calls are valid in construct
>>> def construct(self, value, probs_b, probs_a):
>>>
>>> # Similar calls can be made to other probability functions
>>> # by replacing 'prob' with the name of the function
>>> ans = self.g1('prob', value)
>>> # Evaluate with the respect to distribution b
>>> ans = self.g1('prob', value, probs_b)
>>>
>>> # Probs must be passed in through construct
>>> ans = self.g2('prob', value, probs_a)
>>>
>>> # Functions 'sd', 'var', 'entropy' have the same usage with 'mean'
>>> # Will return [0.0]
>>> ans = self.g1('mean')
>>> # Will return mean_b
>>> ans = self.g1('mean', probs_b)
>>>
>>> # Probs must be passed in through construct
>>> ans = self.g2('mean', probs_a)
>>>
>>> # Usage of 'kl_loss' and 'cross_entropy' are similar
>>> ans = self.g1('kl_loss', 'Geometric', probs_b)
>>> ans = self.g1('kl_loss', 'Geometric', probs_b, probs_a)
>>>
>>> # Additional probs must be passed in through construct
>>> ans = self.g2('kl_loss', 'Geometric', probs_b, probs_a)
>>>
>>> # Sample Usage
>>> ans = self.g1('sample')
>>> ans = self.g1('sample', (2,3))
>>> ans = self.g1('sample', (2,3), probs_b)
>>> ans = self.g2('sample', (2,3), probs_a)
"""
def __init__(self,
probs=None,
seed=0,
dtype=mstype.int32,
name="Geometric"):
"""
Constructor of Geometric distribution.
"""
param = dict(locals())
super(Geometric, self).__init__(dtype, name, param)
if probs is not None:
self._probs = cast_to_tensor(probs, dtype=mstype.float32)
check_prob(self._probs)
else:
self._probs = probs
self.minval = np.finfo(np.float).tiny
# ops needed for the class
self.const = P.ScalarToArray()
self.dtypeop = P.DType()
self.fill = P.Fill()
self.floor = P.Floor()
self.issubclass = P.IsSubClass()
self.less = P.Less()
self.log = P.Log()
self.pow = P.Pow()
self.select = P.Select()
self.shape = P.Shape()
self.sq = P.Square()
self.sqrt = P.Sqrt()
self.uniform = P.UniformReal(seed=seed)
def extend_repr(self):
if self.is_scalar_batch:
str_info = f'probs = {self.probs}'
else:
str_info = f'batch_shape = {self._broadcast_shape}'
return str_info
@property
def probs(self):
"""
Returns the probability for the outcome is 1.
"""
return self._probs
def _mean(self, name='mean', probs1=None):
r"""
.. math::
MEAN(Geo) = \fratc{1 - probs1}{probs1}
"""
if name == 'mean':
probs1 = self.probs if probs1 is None else probs1
return (1. - probs1) / probs1
return None
def _mode(self, name='mode', probs1=None):
r"""
.. math::
MODE(Geo) = 0
"""
if name == 'mode':
probs1 = self.probs if probs1 is None else probs1
return self.fill(self.dtypeop(probs1), self.shape(probs1), 0.)
return None
def _var(self, name='var', probs1=None):
r"""
.. math::
VAR(Geo) = \fract{1 - probs1}{probs1 ^ {2}}
"""
if name in self._variance_functions:
probs1 = self.probs if probs1 is None else probs1
return (1.0 - probs1) / self.sq(probs1)
return None
def _entropy(self, name='entropy', probs=None):
r"""
.. math::
H(Geo) = \fract{-1 * probs0 \log_2 (1-probs0)\ - prob1 * \log_2 (1-probs1)\ }{probs1}
"""
if name == 'entropy':
probs1 = self.probs if probs is None else probs
probs0 = 1.0 - probs1
return (-probs0 * self.log(probs0) - probs1 * self.log(probs1)) / probs1
return None
def _cross_entropy(self, name, dist, probs1_b, probs1_a=None):
r"""
Evaluate cross_entropy between Geometric distributions.
Args:
name (str): name of the funtion. Should always be "cross_entropy" when passed in from construct.
dist (str): type of the distributions. Should be "Geometric" in this case.
probs1_b (Tensor): probability of success of distribution b.
probs1_a (Tensor): probability of success of distribution a. Default: self.probs.
"""
if name == 'cross_entropy' and dist == 'Geometric':
return self._entropy(probs=probs1_a) + self._kl_loss(name, dist, probs1_b, probs1_a)
return None
def _prob(self, name, value, probs=None):
r"""
pmf of Geometric distribution.
Args:
name (str): name of the function. Should be "prob" when passed in from construct.
value (Tensor): a Tensor composed of only natural numbers.
probs (Tensor): probability of success. Default: self.probs.
.. math::
pmf(k) = probs0 ^k * probs1 if k >= 0;
pmf(k) = 0 if k < 0.
"""
if name in self._prob_functions:
probs1 = self.probs if probs is None else probs
dtype = self.dtypeop(value)
if self.issubclass(dtype, mstype.int_):
pass
elif self.issubclass(dtype, mstype.float_):
value = self.floor(value)
else:
return None
pmf = self.pow((1.0 - probs1), value) * probs1
zeros = self.fill(self.dtypeop(probs1), self.shape(pmf), 0.0)
comp = self.less(value, zeros)
return self.select(comp, zeros, pmf)
return None
def _cdf(self, name, value, probs=None):
r"""
cdf of Geometric distribution.
Args:
name (str): name of the function.
value (Tensor): a Tensor composed of only natural numbers.
probs (Tensor): probability of success. Default: self.probs.
.. math::
cdf(k) = 1 - probs0 ^ (k+1) if k >= 0;
cdf(k) = 0 if k < 0.
"""
if name in self._cdf_survival_functions:
probs1 = self.probs if probs is None else probs
probs0 = 1.0 - probs1
dtype = self.dtypeop(value)
if self.issubclass(dtype, mstype.int_):
pass
elif self.issubclass(dtype, mstype.float_):
value = self.floor(value)
else:
return None
cdf = 1.0 - self.pow(probs0, value + 1.0)
zeros = self.fill(self.dtypeop(probs1), self.shape(cdf), 0.0)
comp = self.less(value, zeros)
return self.select(comp, zeros, cdf)
return None
def _kl_loss(self, name, dist, probs1_b, probs1_a=None):
r"""
Evaluate Geometric-Geometric kl divergence, i.e. KL(a||b).
Args:
name (str): name of the funtion.
dist (str): type of the distributions. Should be "Geometric" in this case.
probs1_b (Tensor): probability of success of distribution b.
probs1_a (Tensor): probability of success of distribution a. Default: self.probs.
.. math::
KL(a||b) = \log(\fract{probs1_a}{probs1_b}) + \fract{probs0_a}{probs1_a} * \log(\fract{probs0_a}{probs0_b})
"""
if name in self._divergence_functions and dist == 'Geometric':
probs1_a = self.probs if probs1_a is None else probs1_a
probs0_a = 1.0 - probs1_a
probs0_b = 1.0 - probs1_b
return self.log(probs1_a / probs1_b) + (probs0_a / probs1_a) * self.log(probs0_a / probs0_b)
return None
def _sample(self, name, shape=(), probs=None):
"""
Sampling.
Args:
name (str): name of the function. Should always be 'sample' when passed in from construct.
shape (tuple): shape of the sample. Default: ().
probs (Tensor): probability of success. Default: self.probs.
Returns:
Tensor, shape is shape + batch_shape.
"""
if name == 'sample':
probs = self.probs if probs is None else probs
minval = self.const(self.minval)
maxval = self.const(1.0)
sample_uniform = self.uniform(shape + self.shape(probs), minval, maxval)
return self.floor(self.log(sample_uniform) / self.log(1.0 - probs))
return None

177
mindspore/nn/distribution/normal.py
View File

@ -23,24 +23,70 @@ from ...context import get_context
class Normal(Distribution):
"""
Example class: Normal distribution.
Normal distribution.
Args:
mean (int, float, list, numpy.ndarray, Tensor, Parameter): mean of the Gaussian distribution.
sd (int, float, list, numpy.ndarray, Tensor, Parameter): stddev of the Gaussian distribution.
mean (int, float, list, numpy.ndarray, Tensor, Parameter): mean of the Normal distribution.
sd (int, float, list, numpy.ndarray, Tensor, Parameter): stddev of the Normal distribution.
seed (int): seed to use in sampling. Default: 0.
dtype (mindspore.dtype): type of the distribution. Default: mstype.float32.
name (str): name of the distribution. Default: Normal.
Note:
Standard deviation should be greater than zero.
Dist_spec_args are mean and sd.
Examples:
>>> # To initialize a normal distribution of mean 3.0 and standard deviation 4.0
>>> n = nn.Normal(3.0, 4.0, dtype=mstype.float32)
>>> # The following create two independent normal distributions
>>> n = nn.Normal([3.0, 3.0], [4.0, 4.0], dtype=mstype.float32)
>>> # To initialize a Normal distribution of mean 3.0 and standard deviation 4.0
>>> n = nn.Normal(3.0, 4.0, dtype=mstype.float32)
>>>
>>> # The following creates two independent Normal distributions
>>> n = nn.Normal([3.0, 3.0], [4.0, 4.0], dtype=mstype.float32)
>>>
>>> # A normal distribution can be initilize without arguments
>>> # In this case, mean and sd must be passed in through construct.
>>> n = nn.Normal(dtype=mstype.float32)
>>>
>>> # To use normal in a network
>>> class net(Cell):
>>> def __init__(self):
>>> super(net, self).__init__():
>>> self.n1 = nn.Normal(0.0, 1.0, dtype=mstype.float32)
>>> self.n2 = nn.Normal(dtype=mstype.float32)
>>>
>>> # The following calls are valid in construct
>>> def construct(self, value, mean_b, sd_b, mean_a, sd_a):
>>>
>>> # Similar calls can be made to other probability functions
>>> # by replacing 'prob' with the name of the function
>>> ans = self.n1('prob', value)
>>> # Evaluate with the respect to distribution b
>>> ans = self.n1('prob', value, mean_b, sd_b)
>>>
>>> # mean and sd must be passed in through construct
>>> ans = self.n2('prob', value, mean_a, sd_a)
>>>
>>> # Functions 'sd', 'var', 'entropy' have the same usage with 'mean'
>>> # Will return [0.0]
>>> ans = self.n1('mean')
>>> # Will return mean_b
>>> ans = self.n1('mean', mean_b, sd_b)
>>>
>>> # mean and sd must be passed in through construct
>>> ans = self.n2('mean', mean_a, sd_a)
>>>
>>> # Usage of 'kl_loss' and 'cross_entropy' are similar
>>> ans = self.n1('kl_loss', 'Normal', mean_b, sd_b)
>>> ans = self.n1('kl_loss', 'Normal', mean_b, sd_b, mean_a, sd_a)
>>>
>>> # Additional mean and sd must be passed in through construct
>>> ans = self.n2('kl_loss', 'Normal', mean_b, sd_b, mean_a, sd_a)
>>>
>>> # Sample Usage
>>> ans = self.n1('sample')
>>> ans = self.n1('sample', (2,3))
>>> ans = self.n1('sample', (2,3), mean_b, sd_b)
>>> ans = self.n2('sample', (2,3), mean_a, sd_a)
"""
def __init__(self,
@ -64,27 +110,29 @@ class Normal(Distribution):
self.seed = seed
#ops needed for the class
self.exp = P.Exp()
self.add = P.TensorAdd()
self.mul = P.Mul()
self.sq = P.Square()
self.log = P.Log()
self.sqrt = P.Sqrt()
self.realdiv = P.RealDiv()
self.expm1 = P.Expm1() if get_context('device_target') == 'Ascend' else self._expm1_by_step
self.shape = P.Shape()
self.zeroslike = P.ZerosLike()
self.const = P.ScalarToArray()
self.erf = P.Erf()
self.exp = P.Exp()
self.expm1 = P.Expm1() if get_context('device_target') == 'Ascend' else self._expm1_by_step
self.fill = P.Fill()
self.log = P.Log()
self.shape = P.Shape()
self.sq = P.Square()
self.sqrt = P.Sqrt()
self.zeroslike = P.ZerosLike()
def extend_repr(self):
str_info = f'mean = {self._mean_value}, standard deviation = {self._sd_value}'
if self.is_scalar_batch:
str_info = f'mean = {self._mean_value}, standard deviation = {self._sd_value}'
else:
str_info = f'batch_shape = {self._broadcast_shape}'
return str_info
def _expm1_by_step(self, x):
"""
Expm1 ops under GPU context.
"""
return self.add(self.exp(x), -1)
return self.exp(x) - 1.0
def _mean(self, name='mean', mean=None, sd=None):
"""
@ -95,29 +143,92 @@ class Normal(Distribution):
return mean
return None
def _mode(self, name='mode', mean=None, sd=None):
"""
Mode of the distribution.
"""
if name == 'mode':
mean = self._mean_value if mean is None or sd is None else mean
return mean
return None
def _sd(self, name='sd', mean=None, sd=None):
"""
Standard deviation of the distribution.
"""
if name in ('sd', 'var'):
if name in self._variance_functions:
sd = self._sd_value if mean is None or sd is None else sd
return sd
return None
def _log_likelihood(self, name, value, mean=None, sd=None):
def _entropy(self, name='entropy', sd=None):
r"""
Evaluate entropy.
.. math::
H(X) = \log(\sqrt(numpy.e * 2. * numpy.pi * \sq(\sigma)))
"""
if name == 'entropy':
sd = self._sd_value if sd is None else sd
return self.log(self.sqrt(np.e * 2. * np.pi * self.sq(sd)))
return None
def _cross_entropy(self, name, dist, mean_b, sd_b, mean_a=None, sd_a=None):
r"""
Evaluate cross_entropy between normal distributions.
Args:
name (str): name of the funtion passed in from construct. Should always be "cross_entropy".
dist (str): type of the distributions. Should be "Normal" in this case.
mean_b (Tensor): mean of distribution b.
sd_b (Tensor): standard deviation distribution b.
mean_a (Tensor): mean of distribution a. Default: self._mean_value.
sd_a (Tensor): standard deviation distribution a. Default: self._sd_value.
"""
if name == 'cross_entropy' and dist == 'Normal':
return self._entropy(sd=sd_a) + self._kl_loss(name, dist, mean_b, sd_b, mean_a, sd_a)
return None
def _log_prob(self, name, value, mean=None, sd=None):
r"""
Evaluate log probability.
Args:
name (str): name of the funtion passed in from construct.
value (Tensor): value to be evaluated.
mean (Tensor): mean of the distribution. Default: self._mean_value.
sd (Tensor): standard deviation the distribution. Default: self._sd_value.
.. math::
L(x) = -1* \fract{(x - \mu)^2}{2. * \sigma^2} - \log(\sqrt(2* \pi * \sigma^2))
"""
if name in ('prob', 'log_prob'):
if name in self._prob_functions:
mean = self._mean_value if mean is None else mean
sd = self._sd_value if sd is None else sd
unnormalized_log_prob = -1. * self.realdiv(self.sq(self.add(value, -1. * mean)),
2. * self.sq(sd))
unnormalized_log_prob = -1. * (self.sq(value - mean)) / (2. * self.sq(sd))
neg_normalization = -1. * self.log(self.sqrt(2. * np.pi * self.sq(sd)))
return self.add(unnormalized_log_prob, neg_normalization)
return unnormalized_log_prob + neg_normalization
return None
def _cdf(self, name, value, mean=None, sd=None):
r"""
Evaluate cdf of given value.
Args:
name (str): name of the funtion passed in from construct. Should always be "cdf".
value (Tensor): value to be evaluated.
mean (Tensor): mean of the distribution. Default: self._mean_value.
sd (Tensor): standard deviation the distribution. Default: self._sd_value.
.. math::
cdf(x) = 0.5 * (1+ Erf((x - \mu) / ( \sigma * \sqrt(2))))
"""
if name in self._cdf_survival_functions:
mean = self._mean_value if mean is None else mean
sd = self._sd_value if sd is None else sd
sqrt2 = self.sqrt(self.const(2.0))
adjusted = (value - mean) / (sd * sqrt2)
return 0.5 * (1.0 + self.erf(adjusted))
return None
def _kl_loss(self, name, dist, mean_b, sd_b, mean_a=None, sd_a=None):
@ -125,7 +236,7 @@ class Normal(Distribution):
Evaluate Normal-Normal kl divergence, i.e. KL(a||b).
Args:
name (str): name of the funtion passed in from construct. Should always be "kl_loss".
name (str): name of the funtion passed in from construct.
dist (str): type of the distributions. Should be "Normal" in this case.
mean_b (Tensor): mean of distribution b.
sd_b (Tensor): standard deviation distribution b.
@ -136,12 +247,12 @@ class Normal(Distribution):
KL(a||b) = 0.5 * (\fract{MEAN(a)}{STD(b)} - \fract{MEAN(b)}{STD(b)}) ^ 2 +
0.5 * EXPM1(2 * (\log(STD(a)) - \log(STD(b))) - (\log(STD(a)) - \log(STD(b)))
"""
if name == 'kl_loss' and dist == 'Normal':
if name in self._divergence_functions and dist == 'Normal':
mean_a = self._mean_value if mean_a is None else mean_a
sd_a = self._sd_value if sd_a is None else sd_a
diff_log_scale = self.add(self.log(sd_a), - self.log(sd_b))
squared_diff = self.sq(self.add(self.realdiv(mean_a, sd_b), - self.realdiv(mean_b, sd_b)))
return self.add(self.add(0.5 * squared_diff, 0.5 * self.expm1(2 * diff_log_scale)), - diff_log_scale)
diff_log_scale = self.log(sd_a) - self.log(sd_b)
squared_diff = self.sq(mean_a / sd_b - mean_b / sd_b)
return 0.5 * squared_diff + 0.5 * self.expm1(2 * diff_log_scale) - diff_log_scale
return None
def _sample(self, name, shape=(), mean=None, sd=None):
@ -160,11 +271,11 @@ class Normal(Distribution):
if name == 'sample':
mean = self._mean_value if mean is None else mean
sd = self._sd_value if sd is None else sd
batch_shape = self.shape(self.add(self.zeroslike(mean), self.zeroslike(sd)))
batch_shape = self.shape(self.zeroslike(mean) + self.zeroslike(sd))
sample_shape = shape + batch_shape
mean_zero = self.const(0.0)
sd_one = self.const(1.0)
sample_norm = C.normal(sample_shape, mean_zero, sd_one, self.seed)
sample = self.add(mean, self.mul(sample_norm, sd))
sample = mean + sample_norm * sd
return sample
return None

304
mindspore/nn/distribution/uniform.py Normal file
View File

@ -0,0 +1,304 @@
# 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.
# ============================================================================
"""Uniform Distribution"""
from mindspore.ops import operations as P
from .distribution import Distribution
from ...common import dtype as mstype
from ._utils.utils import convert_to_batch, check_greater
class Uniform(Distribution):
"""
Example class: Uniform Distribution.
Args:
low (int, float, list, numpy.ndarray, Tensor, Parameter): lower bound of the distribution.
high (int, float, list, numpy.ndarray, Tensor, Parameter): upper bound of the distribution.
seed (int): seed to use in sampling. Default: 0.
dtype (mindspore.dtype): type of the distribution. Default: mstype.float32.
name (str): name of the distribution. Default: Uniform.
Note:
low should be stricly less than high.
Dist_spec_args are high and low.
Examples:
>>> # To initialize a Uniform distribution of mean 3.0 and standard deviation 4.0
>>> n = nn.Uniform(0.0, 1.0, dtype=mstype.float32)
>>>
>>> # The following creates two independent Uniform distributions
>>> n = nn.Uniform([0.0, 0.0], [1.0, 2.0], dtype=mstype.float32)
>>>
>>> # A Uniform distribution can be initilized without arguments
>>> # In this case, high and low must be passed in through construct.
>>> n = nn.Uniform(dtype=mstype.float32)
>>>
>>> # To use Uniform in a network
>>> class net(Cell):
>>> def __init__(self)
>>> super(net, self).__init__():
>>> self.u1 = nn.Uniform(0.0, 1.0, dtype=mstype.float32)
>>> self.u2 = nn.Uniform(dtype=mstype.float32)
>>>
>>> # All the following calls in construct are valid
>>> def construct(self, value, low_b, high_b, low_a, high_a):
>>>
>>> # Similar calls can be made to other probability functions
>>> # by replacing 'prob' with the name of the function
>>> ans = self.u1('prob', value)
>>> # Evaluate with the respect to distribution b
>>> ans = self.u1('prob', value, low_b, high_b)
>>>
>>> # High and low must be passed in through construct
>>> ans = self.u2('prob', value, low_a, high_a)
>>>
>>> # Functions 'sd', 'var', 'entropy' have the same usage with 'mean'
>>> # Will return [0.0]
>>> ans = self.u1('mean')
>>> # Will return low_b
>>> ans = self.u1('mean', low_b, high_b)
>>>
>>> # High and low must be passed in through construct
>>> ans = self.u2('mean', low_a, high_a)
>>>
>>> # Usage of 'kl_loss' and 'cross_entropy' are similar
>>> ans = self.u1('kl_loss', 'Uniform', low_b, high_b)
>>> ans = self.u1('kl_loss', 'Uniform', low_b, high_b, low_a, high_a)
>>>
>>> # Additional high and low must be passed in through construct
>>> ans = self.u2('kl_loss', 'Uniform', low_b, high_b, low_a, high_a)
>>>
>>> # Sample Usage
>>> ans = self.u1('sample')
>>> ans = self.u1('sample', (2,3))
>>> ans = self.u1('sample', (2,3), low_b, high_b)
>>> ans = self.u2('sample', (2,3), low_a, high_a)
"""
def __init__(self,
low=None,
high=None,
seed=0,
dtype=mstype.float32,
name="Uniform"):
"""
Constructor of Uniform distribution.
"""
param = dict(locals())
super(Uniform, self).__init__(dtype, name, param)
if low is not None and high is not None:
self._low = convert_to_batch(low, self._broadcast_shape, dtype)
self._high = convert_to_batch(high, self._broadcast_shape, dtype)
check_greater(self.low, self.high, "low value", "high value")
else:
self._low = low
self._high = high
# ops needed for the class
self.const = P.ScalarToArray()
self.dtypeop = P.DType()
self.exp = P.Exp()
self.fill = P.Fill()
self.less = P.Less()
self.lessequal = P.LessEqual()
self.log = P.Log()
self.logicaland = P.LogicalAnd()
self.select = P.Select()
self.shape = P.Shape()
self.sq = P.Square()
self.sqrt = P.Sqrt()
self.uniform = P.UniformReal(seed=seed)
self.zeroslike = P.ZerosLike()
def extend_repr(self):
if self.is_scalar_batch:
str_info = f'low = {self.low}, high = {self.high}'
else:
str_info = f'batch_shape = {self._broadcast_shape}'
return str_info
@property
def low(self):
"""
Return lower bound of the distribution.
"""
return self._low
@property
def high(self):
"""
Return upper bound of the distribution.
"""
return self._high
def _range(self, name='range', low=None, high=None):
r"""
Return the range of the distribution.
.. math::
range(U) = high -low
"""
if name == 'range':
low = self.low if low is None else low
high = self.high if high is None else high
return high - low
return None
def _mean(self, name='mean', low=None, high=None):
r"""
.. math::
MEAN(U) = \fract{low + high}{2}.
"""
if name == 'mean':
low = self.low if low is None else low
high = self.high if high is None else high
return (low + high) / 2.
return None
def _var(self, name='var', low=None, high=None):
r"""
.. math::
VAR(U) = \fract{(high -low) ^ 2}{12}.
"""
if name in self._variance_functions:
low = self.low if low is None else low
high = self.high if high is None else high
return self.sq(high - low) / 12.0
return None
def _entropy(self, name='entropy', low=None, high=None):
r"""
.. math::
H(U) = \log(high - low).
"""
if name == 'entropy':
low = self.low if low is None else low
high = self.high if high is None else high
return self.log(high - low)
return None
def _cross_entropy(self, name, dist, low_b, high_b, low_a=None, high_a=None):
"""
Evaluate cross_entropy between Uniform distributoins.
Args:
name (str): name of the funtion.
dist (str): type of the distributions. Should be "Uniform" in this case.
low_b (Tensor): lower bound of distribution b.
high_b (Tensor): upper bound of distribution b.
low_a (Tensor): lower bound of distribution a. Default: self.low.
high_a (Tensor): upper bound of distribution a. Default: self.high.
"""
if name == 'cross_entropy' and dist == 'Uniform':
return self._entropy(low=low_a, high=high_a) + self._kl_loss(name, dist, low_b, high_b, low_a, high_a)
return None
def _prob(self, name, value, low=None, high=None):
r"""
pdf of Uniform distribution.
Args:
name (str): name of the function.
value (Tensor): value to be evaluated.
low (Tensor): lower bound of the distribution. Default: self.low.
high (Tensor): upper bound of the distribution. Default: self.high.
.. math::
pdf(x) = 0 if x < low;
pdf(x) = \fract{1.0}{high -low} if low <= x <= high;
pdf(x) = 0 if x > high;
"""
if name in self._prob_functions:
low = self.low if low is None else low
high = self.high if high is None else high
ones = self.fill(self.dtype, self.shape(value), 1.0)
prob = ones / (high - low)
broadcast_shape = self.shape(prob)
zeros = self.fill(self.dtypeop(prob), broadcast_shape, 0.0)
comp_lo = self.less(value, low)
comp_hi = self.lessequal(value, high)
less_than_low = self.select(comp_lo, zeros, prob)
return self.select(comp_hi, less_than_low, zeros)
return None
def _kl_loss(self, name, dist, low_b, high_b, low_a=None, high_a=None):
"""
Evaluate uniform-uniform kl divergence, i.e. KL(a||b).
Args:
name (str): name of the funtion.
dist (str): type of the distributions. Should be "Uniform" in this case.
low_b (Tensor): lower bound of distribution b.
high_b (Tensor): upper bound of distribution b.
low_a (Tensor): lower bound of distribution a. Default: self.low.
high_a (Tensor): upper bound of distribution a. Default: self.high.
"""
if name in self._divergence_functions and dist == 'Uniform':
low_a = self.low if low_a is None else low_a
high_a = self.high if high_a is None else high_a
kl = self.log(high_b - low_b) / self.log(high_a - low_a)
comp = self.logicaland(self.lessequal(low_b, low_a), self.lessequal(high_a, high_b))
return self.select(comp, kl, self.log(self.zeroslike(kl)))
return None
def _cdf(self, name, value, low=None, high=None):
r"""
cdf of Uniform distribution.
Args:
name (str): name of the function.
value (Tensor): value to be evaluated.
low (Tensor): lower bound of the distribution. Default: self.low.
high (Tensor): upper bound of the distribution. Default: self.high.
.. math::
cdf(x) = 0 if x < low;
cdf(x) = \fract{x - low}{high -low} if low <= x <= high;
cdf(x) = 1 if x > high;
"""
if name in self._cdf_survival_functions:
low = self.low if low is None else low
high = self.high if high is None else high
prob = (value - low) / (high - low)
broadcast_shape = self.shape(prob)
zeros = self.fill(self.dtypeop(prob), broadcast_shape, 0.0)
ones = self.fill(self.dtypeop(prob), broadcast_shape, 1.0)
comp_lo = self.less(value, low)
comp_hi = self.less(value, high)
less_than_low = self.select(comp_lo, zeros, prob)
return self.select(comp_hi, less_than_low, ones)
return None
def _sample(self, name, shape=(), low=None, high=None):
"""
Sampling.
Args:
name (str): name of the function. Should always be 'sample' when passed in from construct.
shape (tuple): shape of the sample. Default: ().
low (Tensor): lower bound of the distribution. Default: self.low.
high (Tensor): upper bound of the distribution. Default: self.high.
Returns:
Tensor, shape is shape + batch_shape.
"""
if name == 'sample':
low = self.low if low is None else low
high = self.high if high is None else high
broadcast_shape = self.shape(low + high)
l_zero = self.const(0.0)
h_one = self.const(1.0)
sample_uniform = self.uniform(shape + broadcast_shape, l_zero, h_one)
sample = (high - low) * sample_uniform + low
return sample
return None

282
tests/st/ops/ascend/test_distribution/test_bernoulli.py
View File

@ -23,91 +23,67 @@ from mindspore import dtype
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
class Net(nn.Cell):
class Prob(nn.Cell):
"""
Test class: probability of bernoulli distribution.
Test class: probability of Bernoulli distribution.
"""
def __init__(self):
super(Net, self).__init__()
super(Prob, self).__init__()
self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
return self.b('prob', x_)
class Net1(nn.Cell):
"""
Test class: log probability of bernoulli distribution.
"""
def __init__(self):
super(Net1, self).__init__()
self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
return self.b('log_prob', x_)
class Net2(nn.Cell):
"""
Test class: kl_loss between bernoulli distributions.
"""
def __init__(self):
super(Net2, self).__init__()
self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
return self.b('kl_loss', 'Bernoulli', x_)
class Net3(nn.Cell):
"""
Test class: mean/sd of bernoulli distribution.
"""
def __init__(self):
super(Net3, self).__init__()
self.b = nn.Bernoulli([0.5, 0.5], dtype=dtype.int32)
@ms_function
def construct(self):
return self.b('mean'), self.b('sd')
class Net4(nn.Cell):
"""
Test class: log probability of bernoulli distribution.
"""
def __init__(self, shape, seed=0):
super(Net4, self).__init__()
self.b = nn.Bernoulli([0.7, 0.5], seed=seed, dtype=dtype.int32)
self.shape = shape
@ms_function
def construct(self, probs=None):
return self.b('sample', self.shape, probs)
def test_pmf():
"""
Test pmf.
"""
bernoulli_benchmark = stats.bernoulli(0.7)
expect_pmf = bernoulli_benchmark.pmf([0, 1, 0, 1, 1]).astype(np.float32)
pdf = Net()
pmf = Prob()
x_ = Tensor(np.array([0, 1, 0, 1, 1]).astype(np.int32), dtype=dtype.float32)
output = pdf(x_)
output = pmf(x_)
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_pmf) < tol).all()
class LogProb(nn.Cell):
"""
Test class: log probability of Bernoulli distribution.
"""
def __init__(self):
super(LogProb, self).__init__()
self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
return self.b('log_prob', x_)
def test_log_likelihood():
"""
Test log_pmf.
"""
bernoulli_benchmark = stats.bernoulli(0.7)
expect_logpmf = bernoulli_benchmark.logpmf([0, 1, 0, 1, 1]).astype(np.float32)
logprob = Net1()
logprob = LogProb()
x_ = Tensor(np.array([0, 1, 0, 1, 1]).astype(np.int32), dtype=dtype.float32)
output = logprob(x_)
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_logpmf) < tol).all()
class KL(nn.Cell):
"""
Test class: kl_loss between Bernoulli distributions.
"""
def __init__(self):
super(KL, self).__init__()
self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
return self.b('kl_loss', 'Bernoulli', x_)
def test_kl_loss():
"""
Test kl_loss.
@ -117,31 +93,203 @@ def test_kl_loss():
probs0_a = 1 - probs1_a
probs0_b = 1 - probs1_b
expect_kl_loss = probs1_a * np.log(probs1_a / probs1_b) + probs0_a * np.log(probs0_a / probs0_b)
kl_loss = Net2()
kl_loss = KL()
output = kl_loss(Tensor([probs1_b], dtype=dtype.float32))
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_kl_loss) < tol).all()
class Basics(nn.Cell):
"""
Test class: mean/sd/mode of Bernoulli distribution.
"""
def __init__(self):
super(Basics, self).__init__()
self.b = nn.Bernoulli([0.3, 0.5, 0.7], dtype=dtype.int32)
@ms_function
def construct(self):
return self.b('mean'), self.b('sd'), self.b('mode')
def test_basics():
"""
Test mean/standard deviation and probs.
Test mean/standard deviation/mode.
"""
basics = Net3()
mean, sd = basics()
expect_mean = [0.5, 0.5]
assert (mean.asnumpy() == expect_mean).all()
assert (sd.asnumpy() == expect_mean).all()
b = nn.Bernoulli([0.7, 0.5], dtype=dtype.int32)
probs = b.probs()
expect_probs = [0.7, 0.5]
basics = Basics()
mean, sd, mode = basics()
expect_mean = [0.3, 0.5, 0.7]
expect_sd = np.sqrt(np.multiply([0.7, 0.5, 0.3], [0.3, 0.5, 0.7]))
expect_mode = [0.0, 0.0, 1.0]
tol = 1e-6
assert (np.abs(probs.asnumpy() - expect_probs) < tol).all()
assert (np.abs(mean.asnumpy() - expect_mean) < tol).all()
assert (np.abs(sd.asnumpy() - expect_sd) < tol).all()
assert (np.abs(mode.asnumpy() - expect_mode) < tol).all()
class Sampling(nn.Cell):
"""
Test class: log probability of Bernoulli distribution.
"""
def __init__(self, shape, seed=0):
super(Sampling, self).__init__()
self.b = nn.Bernoulli([0.7, 0.5], seed=seed, dtype=dtype.int32)
self.shape = shape
@ms_function
def construct(self, probs=None):
return self.b('sample', self.shape, probs)
def test_sample():
"""
Test sample.
"""
shape = (2, 3)
sample = Net4(shape)
sample = Sampling(shape)
output = sample()
assert output.shape == (2, 3, 2)
class CDF(nn.Cell):
"""
Test class: cdf of bernoulli distributions.
"""
def __init__(self):
super(CDF, self).__init__()
self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
return self.b('cdf', x_)
def test_cdf():
"""
Test cdf.
"""
bernoulli_benchmark = stats.bernoulli(0.7)
expect_cdf = bernoulli_benchmark.cdf([0, 0, 1, 0, 1]).astype(np.float32)
x_ = Tensor(np.array([0, 0, 1, 0, 1]).astype(np.int32), dtype=dtype.float32)
cdf = CDF()
output = cdf(x_)
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_cdf) < tol).all()
class LogCDF(nn.Cell):
"""
Test class: log cdf of bernoulli distributions.
"""
def __init__(self):
super(LogCDF, self).__init__()
self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
return self.b('log_cdf', x_)
def test_logcdf():
"""
Test log_cdf.
"""
bernoulli_benchmark = stats.bernoulli(0.7)
expect_logcdf = bernoulli_benchmark.logcdf([0, 0, 1, 0, 1]).astype(np.float32)
x_ = Tensor(np.array([0, 0, 1, 0, 1]).astype(np.int32), dtype=dtype.float32)
logcdf = LogCDF()
output = logcdf(x_)
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_logcdf) < tol).all()
class SF(nn.Cell):
"""
Test class: survival function of Bernoulli distributions.
"""
def __init__(self):
super(SF, self).__init__()
self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
return self.b('survival_function', x_)
def test_survival():
"""
Test survival funciton.
"""
bernoulli_benchmark = stats.bernoulli(0.7)
expect_survival = bernoulli_benchmark.sf([0, 1, 1, 0, 0]).astype(np.float32)
x_ = Tensor(np.array([0, 1, 1, 0, 0]).astype(np.int32), dtype=dtype.float32)
sf = SF()
output = sf(x_)
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_survival) < tol).all()
class LogSF(nn.Cell):
"""
Test class: log survival function of Bernoulli distributions.
"""
def __init__(self):
super(LogSF, self).__init__()
self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
return self.b('log_survival', x_)
def test_log_survival():
"""
Test log survival funciton.
"""
bernoulli_benchmark = stats.bernoulli(0.7)
expect_logsurvival = bernoulli_benchmark.logsf([-1, 0.9, 0, 0, 0]).astype(np.float32)
x_ = Tensor(np.array([-1, 0.9, 0, 0, 0]).astype(np.float32), dtype=dtype.float32)
log_sf = LogSF()
output = log_sf(x_)
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_logsurvival) < tol).all()
class EntropyH(nn.Cell):
"""
Test class: entropy of Bernoulli distributions.
"""
def __init__(self):
super(EntropyH, self).__init__()
self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
@ms_function
def construct(self):
return self.b('entropy')
def test_entropy():
"""
Test entropy.
"""
bernoulli_benchmark = stats.bernoulli(0.7)
expect_entropy = bernoulli_benchmark.entropy().astype(np.float32)
entropy = EntropyH()
output = entropy()
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_entropy) < tol).all()
class CrossEntropy(nn.Cell):
"""
Test class: cross entropy between bernoulli distributions.
"""
def __init__(self):
super(CrossEntropy, self).__init__()
self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
entropy = self.b('entropy')
kl_loss = self.b('kl_loss', 'Bernoulli', x_)
h_sum_kl = entropy + kl_loss
cross_entropy = self.b('cross_entropy', 'Bernoulli', x_)
return h_sum_kl - cross_entropy
def test_cross_entropy():
"""
Test cross_entropy.
"""
cross_entropy = CrossEntropy()
prob = Tensor([0.3], dtype=dtype.float32)
diff = cross_entropy(prob)
tol = 1e-6
assert (np.abs(diff.asnumpy() - np.zeros(diff.shape)) < tol).all()

291
tests/st/ops/ascend/test_distribution/test_exponential.py Normal file
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@ -0,0 +1,291 @@
# Copyright 2019 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.
# ============================================================================
"""test cases for exponential distribution"""
import numpy as np
from scipy import stats
import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common.api import ms_function
from mindspore import dtype
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
class Prob(nn.Cell):
"""
Test class: probability of Exponential distribution.
"""
def __init__(self):
super(Prob, self).__init__()
self.e = nn.Exponential([[1.0], [0.5]], dtype=dtype.float32)
@ms_function
def construct(self, x_):
return self.e('prob', x_)
def test_pdf():
"""
Test pdf.
"""
expon_benchmark = stats.expon(scale=[[1.0], [2.0]])
expect_pdf = expon_benchmark.pdf([-1.0, 0.0, 1.0]).astype(np.float32)
pdf = Prob()
x_ = Tensor(np.array([-1.0, 0.0, 1.0]).astype(np.float32), dtype=dtype.float32)
output = pdf(x_)
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_pdf) < tol).all()
class LogProb(nn.Cell):
"""
Test class: log probability of Exponential distribution.
"""
def __init__(self):
super(LogProb, self).__init__()
self.e = nn.Exponential([[1.0], [0.5]], dtype=dtype.float32)
@ms_function
def construct(self, x_):
return self.e('log_prob', x_)
def test_log_likelihood():
"""
Test log_pdf.
"""
expon_benchmark = stats.expon(scale=[[1.0], [2.0]])
expect_logpdf = expon_benchmark.logpdf([0.5, 1.0, 2.0]).astype(np.float32)
logprob = LogProb()
x_ = Tensor(np.array([0.5, 1.0, 2.0]).astype(np.float32), dtype=dtype.float32)
output = logprob(x_)
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_logpdf) < tol).all()
class KL(nn.Cell):
"""
Test class: kl_loss between Exponential distributions.
"""
def __init__(self):
super(KL, self).__init__()
self.e = nn.Exponential([1.5], dtype=dtype.float32)
@ms_function
def construct(self, x_):
return self.e('kl_loss', 'Exponential', x_)
def test_kl_loss():
"""
Test kl_loss.
"""
rate_a = 1.5
rate_b = np.array([0.5, 2.0]).astype(np.float32)
expect_kl_loss = np.log(rate_a) - np.log(rate_b) + rate_b / rate_a - 1.0
kl = KL()
output = kl(Tensor(rate_b, dtype=dtype.float32))
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_kl_loss) < tol).all()
class Basics(nn.Cell):
"""
Test class: mean/sd/mode of Exponential distribution.
"""
def __init__(self):
super(Basics, self).__init__()
self.e = nn.Exponential([0.5], dtype=dtype.float32)
@ms_function
def construct(self):
return self.e('mean'), self.e('sd'), self.e('mode')
def test_basics():
"""
Test mean/standard/mode deviation.
"""
basics = Basics()
mean, sd, mode = basics()
expect_mean = 2.
expect_sd = 2.
expect_mode = 0.
tol = 1e-6
assert (np.abs(mean.asnumpy() - expect_mean) < tol).all()
assert (np.abs(sd.asnumpy() - expect_sd) < tol).all()
assert (np.abs(mode.asnumpy() - expect_mode) < tol).all()
class Sampling(nn.Cell):
"""
Test class: sample of Exponential distribution.
"""
def __init__(self, shape, seed=0):
super(Sampling, self).__init__()
self.e = nn.Exponential([[1.0], [0.5]], seed=seed, dtype=dtype.float32)
self.shape = shape
@ms_function
def construct(self, rate=None):
return self.e('sample', self.shape, rate)
def test_sample():
"""
Test sample.
"""
shape = (2, 3)
seed = 10
rate = Tensor([1.0, 2.0, 3.0], dtype=dtype.float32)
sample = Sampling(shape, seed=seed)
output = sample(rate)
assert output.shape == (2, 3, 3)
class CDF(nn.Cell):
"""
Test class: cdf of Exponential distribution.
"""
def __init__(self):
super(CDF, self).__init__()
self.e = nn.Exponential([[1.0], [0.5]], dtype=dtype.float32)
@ms_function
def construct(self, x_):
return self.e('cdf', x_)
def test_cdf():
"""
Test cdf.
"""
expon_benchmark = stats.expon(scale=[[1.0], [2.0]])
expect_cdf = expon_benchmark.cdf([-1.0, 0.0, 1.0]).astype(np.float32)
cdf = CDF()
x_ = Tensor(np.array([-1.0, 0.0, 1.0]).astype(np.float32), dtype=dtype.float32)
output = cdf(x_)
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_cdf) < tol).all()
class LogCDF(nn.Cell):
"""
Test class: log_cdf of Exponential distribution.
"""
def __init__(self):
super(LogCDF, self).__init__()
self.e = nn.Exponential([[1.0], [0.5]], dtype=dtype.float32)
@ms_function
def construct(self, x_):
return self.e('log_cdf', x_)
def test_log_cdf():
"""
Test log_cdf.
"""
expon_benchmark = stats.expon(scale=[[1.0], [2.0]])
expect_logcdf = expon_benchmark.logcdf([0.5, 1.0, 2.5]).astype(np.float32)
logcdf = LogCDF()
x_ = Tensor(np.array([0.5, 1.0, 2.5]).astype(np.float32), dtype=dtype.float32)
output = logcdf(x_)
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_logcdf) < tol).all()
class SF(nn.Cell):
"""
Test class: survival function of Exponential distribution.
"""
def __init__(self):
super(SF, self).__init__()
self.e = nn.Exponential([[1.0], [0.5]], dtype=dtype.float32)
@ms_function
def construct(self, x_):
return self.e('survival_function', x_)
def test_survival():
"""
Test survival function.
"""
expon_benchmark = stats.expon(scale=[[1.0], [2.0]])
expect_survival = expon_benchmark.sf([-1.0, 0.0, 1.0]).astype(np.float32)
survival = SF()
x_ = Tensor(np.array([-1.0, 0.0, 1.0]).astype(np.float32), dtype=dtype.float32)
output = survival(x_)
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_survival) < tol).all()
class LogSF(nn.Cell):
"""
Test class: log survival function of Exponential distribution.
"""
def __init__(self):
super(LogSF, self).__init__()
self.e = nn.Exponential([[1.0], [0.5]], dtype=dtype.float32)
@ms_function
def construct(self, x_):
return self.e('log_survival', x_)
def test_log_survival():
"""
Test log survival function.
"""
expon_benchmark = stats.expon(scale=[[1.0], [2.0]])
expect_logsurvival = expon_benchmark.logsf([-1.0, 0.0, 1.0]).astype(np.float32)
logsurvival = LogSF()
x_ = Tensor(np.array([-1.0, 0.0, 1.0]).astype(np.float32), dtype=dtype.float32)
output = logsurvival(x_)
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_logsurvival) < tol).all()
class EntropyH(nn.Cell):
"""
Test class: entropy of Exponential distribution.
"""
def __init__(self):
super(EntropyH, self).__init__()
self.e = nn.Exponential([[1.0], [0.5]], dtype=dtype.float32)
@ms_function
def construct(self):
return self.e('entropy')
def test_entropy():
"""
Test entropy.
"""
expon_benchmark = stats.expon(scale=[[1.0], [2.0]])
expect_entropy = expon_benchmark.entropy().astype(np.float32)
entropy = EntropyH()
output = entropy()
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_entropy) < tol).all()
class CrossEntropy(nn.Cell):
"""
Test class: cross entropy between Exponential distribution.
"""
def __init__(self):
super(CrossEntropy, self).__init__()
self.e = nn.Exponential([1.0], dtype=dtype.float32)
@ms_function
def construct(self, x_):
entropy = self.e('entropy')
kl_loss = self.e('kl_loss', 'Exponential', x_)
h_sum_kl = entropy + kl_loss
cross_entropy = self.e('cross_entropy', 'Exponential', x_)
return h_sum_kl - cross_entropy
def test_cross_entropy():
"""
Test cross_entropy.
"""
cross_entropy = CrossEntropy()
rate = Tensor([0.5], dtype=dtype.float32)
diff = cross_entropy(rate)
tol = 1e-6
assert (np.abs(diff.asnumpy() - np.zeros(diff.shape)) < tol).all()

291
tests/st/ops/ascend/test_distribution/test_geometric.py Normal file
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@ -0,0 +1,291 @@
# Copyright 2019 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.
# ============================================================================
"""test cases for Geometric distribution"""
import numpy as np
from scipy import stats
import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common.api import ms_function
from mindspore import dtype
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
class Prob(nn.Cell):
"""
Test class: probability of Geometric distribution.
"""
def __init__(self):
super(Prob, self).__init__()
self.g = nn.Geometric(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
return self.g('prob', x_)
def test_pmf():
"""
Test pmf.
"""
geom_benchmark = stats.geom(0.7)
expect_pmf = geom_benchmark.pmf([0, 1, 2, 3, 4]).astype(np.float32)
pdf = Prob()
x_ = Tensor(np.array([-1, 0, 1, 2, 3]).astype(np.float32), dtype=dtype.float32)
output = pdf(x_)
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_pmf) < tol).all()
class LogProb(nn.Cell):
"""
Test class: log probability of Geometric distribution.
"""
def __init__(self):
super(LogProb, self).__init__()
self.g = nn.Geometric(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
return self.g('log_prob', x_)
def test_log_likelihood():
"""
Test log_pmf.
"""
geom_benchmark = stats.geom(0.7)
expect_logpmf = geom_benchmark.logpmf([1, 2, 3, 4, 5]).astype(np.float32)
logprob = LogProb()
x_ = Tensor(np.array([0, 1, 2, 3, 4]).astype(np.int32), dtype=dtype.float32)
output = logprob(x_)
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_logpmf) < tol).all()
class KL(nn.Cell):
"""
Test class: kl_loss between Geometric distributions.
"""
def __init__(self):
super(KL, self).__init__()
self.g = nn.Geometric(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
return self.g('kl_loss', 'Geometric', x_)
def test_kl_loss():
"""
Test kl_loss.
"""
probs1_a = 0.7
probs1_b = 0.5
probs0_a = 1 - probs1_a
probs0_b = 1 - probs1_b
expect_kl_loss = np.log(probs1_a / probs1_b) + (probs0_a / probs1_a) * np.log(probs0_a / probs0_b)
kl_loss = KL()
output = kl_loss(Tensor([probs1_b], dtype=dtype.float32))
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_kl_loss) < tol).all()
class Basics(nn.Cell):
"""
Test class: mean/sd/mode of Geometric distribution.
"""
def __init__(self):
super(Basics, self).__init__()
self.g = nn.Geometric([0.5, 0.5], dtype=dtype.int32)
@ms_function
def construct(self):
return self.g('mean'), self.g('sd'), self.g('mode')
def test_basics():
"""
Test mean/standard deviation/mode.
"""
basics = Basics()
mean, sd, mode = basics()
expect_mean = [1.0, 1.0]
expect_sd = np.sqrt(np.array([0.5, 0.5]) / np.square(np.array([0.5, 0.5])))
expect_mode = [0.0, 0.0]
tol = 1e-6
assert (np.abs(mean.asnumpy()- expect_mean) < tol).all()
assert (np.abs(sd.asnumpy() - expect_sd) < tol).all()
assert (np.abs(mode.asnumpy() - expect_mode) < tol).all()
class Sampling(nn.Cell):
"""
Test class: log probability of bernoulli distribution.
"""
def __init__(self, shape, seed=0):
super(Sampling, self).__init__()
self.g = nn.Geometric([0.7, 0.5], seed=seed, dtype=dtype.int32)
self.shape = shape
@ms_function
def construct(self, probs=None):
return self.g('sample', self.shape, probs)
def test_sample():
"""
Test sample.
"""
shape = (2, 3)
sample = Sampling(shape)
output = sample()
assert output.shape == (2, 3, 2)
class CDF(nn.Cell):
"""
Test class: cdf of Geometric distribution.
"""
def __init__(self):
super(CDF, self).__init__()
self.g = nn.Geometric(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
return self.g('cdf', x_)
def test_cdf():
"""
Test cdf.
"""
geom_benchmark = stats.geom(0.7)
expect_cdf = geom_benchmark.cdf([0, 1, 2, 3, 4]).astype(np.float32)
x_ = Tensor(np.array([-1, 0, 1, 2, 3]).astype(np.int32), dtype=dtype.float32)
cdf = CDF()
output = cdf(x_)
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_cdf) < tol).all()
class LogCDF(nn.Cell):
"""
Test class: log cdf of Geometric distribution.
"""
def __init__(self):
super(LogCDF, self).__init__()
self.g = nn.Geometric(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
return self.g('log_cdf', x_)
def test_logcdf():
"""
Test log_cdf.
"""
geom_benchmark = stats.geom(0.7)
expect_logcdf = geom_benchmark.logcdf([1, 2, 3, 4, 5]).astype(np.float32)
x_ = Tensor(np.array([0, 1, 2, 3, 4]).astype(np.int32), dtype=dtype.float32)
logcdf = LogCDF()
output = logcdf(x_)
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_logcdf) < tol).all()
class SF(nn.Cell):
"""
Test class: survial funciton of Geometric distribution.
"""
def __init__(self):
super(SF, self).__init__()
self.g = nn.Geometric(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
return self.g('survival_function', x_)
def test_survival():
"""
Test survival function.
"""
geom_benchmark = stats.geom(0.7)
expect_survival = geom_benchmark.sf([0, 1, 2, 3, 4]).astype(np.float32)
x_ = Tensor(np.array([-1, 0, 1, 2, 3]).astype(np.int32), dtype=dtype.float32)
sf = SF()
output = sf(x_)
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_survival) < tol).all()
class LogSF(nn.Cell):
"""
Test class: log survial funciton of Geometric distribution.
"""
def __init__(self):
super(LogSF, self).__init__()
self.g = nn.Geometric(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
return self.g('log_survival', x_)
def test_log_survival():
"""
Test log_survival function.
"""
geom_benchmark = stats.geom(0.7)
expect_logsurvival = geom_benchmark.logsf([0, 1, 2, 3, 4]).astype(np.float32)
x_ = Tensor(np.array([-1, 0, 1, 2, 3]).astype(np.float32), dtype=dtype.float32)
log_sf = LogSF()
output = log_sf(x_)
tol = 5e-6
assert (np.abs(output.asnumpy() - expect_logsurvival) < tol).all()
class EntropyH(nn.Cell):
"""
Test class: entropy of Geometric distribution.
"""
def __init__(self):
super(EntropyH, self).__init__()
self.g = nn.Geometric(0.7, dtype=dtype.int32)
@ms_function
def construct(self):
return self.g('entropy')
def test_entropy():
"""
Test entropy.
"""
geom_benchmark = stats.geom(0.7)
expect_entropy = geom_benchmark.entropy().astype(np.float32)
entropy = EntropyH()
output = entropy()
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_entropy) < tol).all()
class CrossEntropy(nn.Cell):
"""
Test class: cross entropy between Geometric distributions.
"""
def __init__(self):
super(CrossEntropy, self).__init__()
self.g = nn.Geometric(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
entropy = self.g('entropy')
kl_loss = self.g('kl_loss', 'Geometric', x_)
h_sum_kl = entropy + kl_loss
ans = self.g('cross_entropy', 'Geometric', x_)
return h_sum_kl - ans
def test_cross_entropy():
"""
Test cross_entropy.
"""
cross_entropy = CrossEntropy()
prob = Tensor([0.5], dtype=dtype.float32)
diff = cross_entropy(prob)
tol = 1e-6
assert (np.abs(diff.asnumpy() - np.zeros(diff.shape)) < tol).all()

264
tests/st/ops/ascend/test_distribution/test_normal.py
View File

@ -23,89 +23,66 @@ from mindspore import dtype
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
class Net(nn.Cell):
class Prob(nn.Cell):
"""
Test class: probability of normal distribution.
Test class: probability of Normal distribution.
"""
def __init__(self):
super(Net, self).__init__()
super(Prob, self).__init__()
self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
@ms_function
def construct(self, x_):
return self.n('prob', x_)
class Net1(nn.Cell):
"""
Test class: log probability of normal distribution.
"""
def __init__(self):
super(Net1, self).__init__()
self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
@ms_function
def construct(self, x_):
return self.n('log_prob', x_)
class Net2(nn.Cell):
"""
Test class: kl_loss of normal distribution.
"""
def __init__(self):
super(Net2, self).__init__()
self.n = nn.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
@ms_function
def construct(self, x_, y_):
return self.n('kl_loss', 'Normal', x_, y_)
class Net3(nn.Cell):
"""
Test class: mean/sd of normal distribution.
"""
def __init__(self):
super(Net3, self).__init__()
self.n = nn.Normal(np.array([3.0]), np.array([2.0, 4.0]), dtype=dtype.float32)
@ms_function
def construct(self):
return self.n('mean'), self.n('sd')
class Net4(nn.Cell):
"""
Test class: mean/sd of normal distribution.
"""
def __init__(self, shape, seed=0):
super(Net4, self).__init__()
self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), seed=seed, dtype=dtype.float32)
self.shape = shape
@ms_function
def construct(self, mean=None, sd=None):
return self.n('sample', self.shape, mean, sd)
def test_pdf():
"""
Test pdf.
"""
norm_benchmark = stats.norm(np.array([3.0]), np.array([[2.0], [4.0]]))
expect_pdf = norm_benchmark.pdf([1.0, 2.0]).astype(np.float32)
pdf = Net()
pdf = Prob()
output = pdf(Tensor([1.0, 2.0], dtype=dtype.float32))
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_pdf) < tol).all()
class LogProb(nn.Cell):
"""
Test class: log probability of Normal distribution.
"""
def __init__(self):
super(LogProb, self).__init__()
self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
@ms_function
def construct(self, x_):
return self.n('log_prob', x_)
def test_log_likelihood():
"""
Test log_pdf.
"""
norm_benchmark = stats.norm(np.array([3.0]), np.array([[2.0], [4.0]]))
expect_logpdf = norm_benchmark.logpdf([1.0, 2.0]).astype(np.float32)
logprob = Net1()
logprob = LogProb()
output = logprob(Tensor([1.0, 2.0], dtype=dtype.float32))
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_logpdf) < tol).all()
class KL(nn.Cell):
"""
Test class: kl_loss of Normal distribution.
"""
def __init__(self):
super(KL, self).__init__()
self.n = nn.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
@ms_function
def construct(self, x_, y_):
return self.n('kl_loss', 'Normal', x_, y_)
def test_kl_loss():
"""
Test kl_loss.
@ -120,25 +97,51 @@ def test_kl_loss():
squared_diff = np.square(mean_a / sd_b - mean_b / sd_b)
expect_kl_loss = 0.5 * squared_diff + 0.5 * np.expm1(2 * diff_log_scale) - diff_log_scale
kl_loss = Net2()
kl_loss = KL()
mean = Tensor(mean_b, dtype=dtype.float32)
sd = Tensor(sd_b, dtype=dtype.float32)
output = kl_loss(mean, sd)
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_kl_loss) < tol).all()
class Basics(nn.Cell):
"""
Test class: mean/sd/mode of Normal distribution.
"""
def __init__(self):
super(Basics, self).__init__()
self.n = nn.Normal(np.array([3.0]), np.array([2.0, 4.0]), dtype=dtype.float32)
@ms_function
def construct(self):
return self.n('mean'), self.n('sd'), self.n('mode')
def test_basics():
"""
Test mean/standard deviation.
Test mean/standard deviation/mode.
"""
basics = Net3()
mean, sd = basics()
basics = Basics()
mean, sd, mode = basics()
expect_mean = [3.0, 3.0]
expect_sd = [2.0, 4.0]
tol = 1e-6
assert (np.abs(mean.asnumpy() - expect_mean) < tol).all()
assert (np.abs(mode.asnumpy() - expect_mean) < tol).all()
assert (np.abs(sd.asnumpy() - expect_sd) < tol).all()
class Sampling(nn.Cell):
"""
Test class: sample of Normal distribution.
"""
def __init__(self, shape, seed=0):
super(Sampling, self).__init__()
self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), seed=seed, dtype=dtype.float32)
self.shape = shape
@ms_function
def construct(self, mean=None, sd=None):
return self.n('sample', self.shape, mean, sd)
def test_sample():
"""
Test sample.
@ -147,6 +150,149 @@ def test_sample():
seed = 10
mean = Tensor([2.0], dtype=dtype.float32)
sd = Tensor([2.0, 2.0, 2.0], dtype=dtype.float32)
sample = Net4(shape, seed=seed)
sample = Sampling(shape, seed=seed)
output = sample(mean, sd)
assert output.shape == (2, 3, 3)
class CDF(nn.Cell):
"""
Test class: cdf of Normal distribution.
"""
def __init__(self):
super(CDF, self).__init__()
self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
@ms_function
def construct(self, x_):
return self.n('cdf', x_)
def test_cdf():
"""
Test cdf.
"""
norm_benchmark = stats.norm(np.array([3.0]), np.array([[2.0], [4.0]]))
expect_cdf = norm_benchmark.cdf([1.0, 2.0]).astype(np.float32)
cdf = CDF()
output = cdf(Tensor([1.0, 2.0], dtype=dtype.float32))
tol = 2e-5
assert (np.abs(output.asnumpy() - expect_cdf) < tol).all()
class LogCDF(nn.Cell):
"""
Test class: log_cdf of Mormal distribution.
"""
def __init__(self):
super(LogCDF, self).__init__()
self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
@ms_function
def construct(self, x_):
return self.n('log_cdf', x_)
def test_log_cdf():
"""
Test log cdf.
"""
norm_benchmark = stats.norm(np.array([3.0]), np.array([[2.0], [4.0]]))
expect_logcdf = norm_benchmark.logcdf([1.0, 2.0]).astype(np.float32)
logcdf = LogCDF()
output = logcdf(Tensor([1.0, 2.0], dtype=dtype.float32))
tol = 5e-5
assert (np.abs(output.asnumpy() - expect_logcdf) < tol).all()
class SF(nn.Cell):
"""
Test class: survival function of Normal distribution.
"""
def __init__(self):
super(SF, self).__init__()
self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
@ms_function
def construct(self, x_):
return self.n('survival_function', x_)
def test_survival():
"""
Test log_survival.
"""
norm_benchmark = stats.norm(np.array([3.0]), np.array([[2.0], [4.0]]))
expect_survival = norm_benchmark.sf([1.0, 2.0]).astype(np.float32)
survival_function = SF()
output = survival_function(Tensor([1.0, 2.0], dtype=dtype.float32))
tol = 2e-5
assert (np.abs(output.asnumpy() - expect_survival) < tol).all()
class LogSF(nn.Cell):
"""
Test class: log survival function of Normal distribution.
"""
def __init__(self):
super(LogSF, self).__init__()
self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
@ms_function
def construct(self, x_):
return self.n('log_survival', x_)
def test_log_survival():
"""
Test log_survival.
"""
norm_benchmark = stats.norm(np.array([3.0]), np.array([[2.0], [4.0]]))
expect_log_survival = norm_benchmark.logsf([1.0, 2.0]).astype(np.float32)
log_survival = LogSF()
output = log_survival(Tensor([1.0, 2.0], dtype=dtype.float32))
tol = 2e-5
assert (np.abs(output.asnumpy() - expect_log_survival) < tol).all()
class EntropyH(nn.Cell):
"""
Test class: entropy of Normal distribution.
"""
def __init__(self):
super(EntropyH, self).__init__()
self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
@ms_function
def construct(self):
return self.n('entropy')
def test_entropy():
"""
Test entropy.
"""
norm_benchmark = stats.norm(np.array([3.0]), np.array([[2.0], [4.0]]))
expect_entropy = norm_benchmark.entropy().astype(np.float32)
entropy = EntropyH()
output = entropy()
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_entropy) < tol).all()
class CrossEntropy(nn.Cell):
"""
Test class: cross entropy between Normal distributions.
"""
def __init__(self):
super(CrossEntropy, self).__init__()
self.n = nn.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
@ms_function
def construct(self, x_, y_):
entropy = self.n('entropy')
kl_loss = self.n('kl_loss', 'Normal', x_, y_)
h_sum_kl = entropy + kl_loss
cross_entropy = self.n('cross_entropy', 'Normal', x_, y_)
return h_sum_kl - cross_entropy
def test_cross_entropy():
"""
Test cross_entropy.
"""
cross_entropy = CrossEntropy()
mean = Tensor([1.0], dtype=dtype.float32)
sd = Tensor([1.0], dtype=dtype.float32)
diff = cross_entropy(mean, sd)
tol = 1e-6
assert (np.abs(diff.asnumpy() - np.zeros(diff.shape)) < tol).all()

293
tests/st/ops/ascend/test_distribution/test_uniform.py Normal file
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@ -0,0 +1,293 @@
# Copyright 2019 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.
# ============================================================================
"""test cases for uniform distribution"""
import numpy as np
from scipy import stats
import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common.api import ms_function
from mindspore import dtype
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
class Prob(nn.Cell):
"""
Test class: probability of Uniform distribution.
"""
def __init__(self):
super(Prob, self).__init__()
self.u = nn.Uniform([0.0], [[1.0], [2.0]], dtype=dtype.float32)
@ms_function
def construct(self, x_):
return self.u('prob', x_)
def test_pdf():
"""
Test pdf.
"""
uniform_benchmark = stats.uniform([0.0], [[1.0], [2.0]])
expect_pdf = uniform_benchmark.pdf([-1.0, 0.0, 0.5, 1.0, 1.5, 3.0]).astype(np.float32)
pdf = Prob()
x_ = Tensor(np.array([-1.0, 0.0, 0.5, 1.0, 1.5, 3.0]).astype(np.float32), dtype=dtype.float32)
output = pdf(x_)
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_pdf) < tol).all()
class LogProb(nn.Cell):
"""
Test class: log probability of Uniform distribution.
"""
def __init__(self):
super(LogProb, self).__init__()
self.u = nn.Uniform([0.0], [[1.0], [2.0]], dtype=dtype.float32)
@ms_function
def construct(self, x_):
return self.u('log_prob', x_)
def test_log_likelihood():
"""
Test log_pdf.
"""
uniform_benchmark = stats.uniform([0.0], [[1.0], [2.0]])
expect_logpdf = uniform_benchmark.logpdf([0.5]).astype(np.float32)
logprob = LogProb()
x_ = Tensor(np.array([0.5]).astype(np.float32), dtype=dtype.float32)
output = logprob(x_)
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_logpdf) < tol).all()
class KL(nn.Cell):
"""
Test class: kl_loss between Uniform distributions.
"""
def __init__(self):
super(KL, self).__init__()
self.u = nn.Uniform([0.0], [1.5], dtype=dtype.float32)
@ms_function
def construct(self, x_, y_):
return self.u('kl_loss', 'Uniform', x_, y_)
def test_kl_loss():
"""
Test kl_loss.
"""
low_a = 0.0
high_a = 1.5
low_b = -1.0
high_b = 2.0
expect_kl_loss = np.log(high_b - low_b) / np.log(high_a - low_a)
kl = KL()
output = kl(Tensor(low_b, dtype=dtype.float32), Tensor(high_b, dtype=dtype.float32))
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_kl_loss) < tol).all()
class Basics(nn.Cell):
"""
Test class: mean/sd of Uniform distribution.
"""
def __init__(self):
super(Basics, self).__init__()
self.u = nn.Uniform([0.0], [3.0], dtype=dtype.float32)
@ms_function
def construct(self):
return self.u('mean'), self.u('sd')
def test_basics():
"""
Test mean/standard deviation.
"""
basics = Basics()
mean, sd = basics()
expect_mean = [1.5]
expect_sd = np.sqrt([0.75])
tol = 1e-6
assert (np.abs(mean.asnumpy() - expect_mean) < tol).all()
assert (np.abs(sd.asnumpy() - expect_sd) < tol).all()
class Sampling(nn.Cell):
"""
Test class: sample of Uniform distribution.
"""
def __init__(self, shape, seed=0):
super(Sampling, self).__init__()
self.u = nn.Uniform([0.0], [[1.0], [2.0]], seed=seed, dtype=dtype.float32)
self.shape = shape
@ms_function
def construct(self, low=None, high=None):
return self.u('sample', self.shape, low, high)
def test_sample():
"""
Test sample.
"""
shape = (2, 3)
seed = 10
low = Tensor([1.0], dtype=dtype.float32)
high = Tensor([2.0, 3.0, 4.0], dtype=dtype.float32)
sample = Sampling(shape, seed=seed)
output = sample(low, high)
assert output.shape == (2, 3, 3)
class CDF(nn.Cell):
"""
Test class: cdf of Uniform distribution.
"""
def __init__(self):
super(CDF, self).__init__()
self.u = nn.Uniform([0.0], [1.0], dtype=dtype.float32)
@ms_function
def construct(self, x_):
return self.u('cdf', x_)
def test_cdf():
"""
Test cdf.
"""
uniform_benchmark = stats.uniform([0.0], [1.0])
expect_cdf = uniform_benchmark.cdf([-1.0, 0.5, 1.0, 2.0]).astype(np.float32)
cdf = CDF()
x_ = Tensor(np.array([-1.0, 0.5, 1.0, 2.0]).astype(np.float32), dtype=dtype.float32)
output = cdf(x_)
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_cdf) < tol).all()
class LogCDF(nn.Cell):
"""
Test class: log_cdf of Uniform distribution.
"""
def __init__(self):
super(LogCDF, self).__init__()
self.u = nn.Uniform([0.0], [1.0], dtype=dtype.float32)
@ms_function
def construct(self, x_):
return self.u('log_cdf', x_)
class SF(nn.Cell):
"""
Test class: survival function of Uniform distribution.
"""
def __init__(self):
super(SF, self).__init__()
self.u = nn.Uniform([0.0], [1.0], dtype=dtype.float32)
@ms_function
def construct(self, x_):
return self.u('survival_function', x_)
class LogSF(nn.Cell):
"""
Test class: log survival function of Uniform distribution.
"""
def __init__(self):
super(LogSF, self).__init__()
self.u = nn.Uniform([0.0], [1.0], dtype=dtype.float32)
@ms_function
def construct(self, x_):
return self.u('log_survival', x_)
class EntropyH(nn.Cell):
"""
Test class: entropy of Uniform distribution.
"""
def __init__(self):
super(EntropyH, self).__init__()
self.u = nn.Uniform([0.0], [1.0, 2.0], dtype=dtype.float32)
@ms_function
def construct(self):
return self.u('entropy')
def test_entropy():
"""
Test entropy.
"""
uniform_benchmark = stats.uniform([0.0], [1.0, 2.0])
expect_entropy = uniform_benchmark.entropy().astype(np.float32)
entropy = EntropyH()
output = entropy()
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_entropy) < tol).all()
class CrossEntropy(nn.Cell):
"""
Test class: cross_entropy between Uniform distributions.
"""
def __init__(self):
super(CrossEntropy, self).__init__()
self.u = nn.Uniform([0.0], [1.5], dtype=dtype.float32)
@ms_function
def construct(self, x_, y_):
entropy = self.u('entropy')
kl_loss = self.u('kl_loss', 'Uniform', x_, y_)
h_sum_kl = entropy + kl_loss
cross_entropy = self.u('cross_entropy', 'Uniform', x_, y_)
return h_sum_kl - cross_entropy
def test_log_cdf():
"""
Test log_cdf.
"""
uniform_benchmark = stats.uniform([0.0], [1.0])
expect_logcdf = uniform_benchmark.logcdf([0.5, 0.8, 2.0]).astype(np.float32)
logcdf = LogCDF()
x_ = Tensor(np.array([0.5, 0.8, 2.0]).astype(np.float32), dtype=dtype.float32)
output = logcdf(x_)
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_logcdf) < tol).all()
def test_survival():
"""
Test survival function.
"""
uniform_benchmark = stats.uniform([0.0], [1.0])
expect_survival = uniform_benchmark.sf([-1.0, 0.5, 1.0, 2.0]).astype(np.float32)
survival = SF()
x_ = Tensor(np.array([-1.0, 0.5, 1.0, 2.0]).astype(np.float32), dtype=dtype.float32)
output = survival(x_)
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_survival) < tol).all()
def test_log_survival():
"""
Test log survival function.
"""
uniform_benchmark = stats.uniform([0.0], [1.0])
expect_logsurvival = uniform_benchmark.logsf([0.5, 0.8, -2.0]).astype(np.float32)
logsurvival = LogSF()
x_ = Tensor(np.array([0.5, 0.8, -2.0]).astype(np.float32), dtype=dtype.float32)
output = logsurvival(x_)
tol = 1e-6
assert (np.abs(output.asnumpy() - expect_logsurvival) < tol).all()
def test_cross_entropy():
"""
Test cross_entropy.
"""
cross_entropy = CrossEntropy()
low_b = -1.0
high_b = 2.0
diff = cross_entropy(Tensor(low_b, dtype=dtype.float32), Tensor(high_b, dtype=dtype.float32))
tol = 1e-6
assert (np.abs(diff.asnumpy() - np.zeros(diff.shape)) < tol).all()

165
tests/ut/python/nn/distribution/test_bernoulli.py Normal file
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@ -0,0 +1,165 @@
# 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.
# ============================================================================
"""
Test nn.Distribution.Bernoulli.
"""
import pytest
import mindspore.nn as nn
from mindspore import dtype
from mindspore import Tensor
def test_arguments():
"""
Args passing during initialization.
"""
b = nn.Bernoulli()
assert isinstance(b, nn.Distribution)
b = nn.Bernoulli([0.0, 0.3, 0.5, 1.0], dtype=dtype.int32)
assert isinstance(b, nn.Distribution)
def test_prob():
"""
Invalid probability.
"""
with pytest.raises(ValueError):
nn.Bernoulli([-0.1], dtype=dtype.int32)
with pytest.raises(ValueError):
nn.Bernoulli([1.1], dtype=dtype.int32)
class BernoulliProb(nn.Cell):
"""
Bernoulli distribution: initialize with probs.
"""
def __init__(self):
super(BernoulliProb, self).__init__()
self.b = nn.Bernoulli(0.5, dtype=dtype.int32)
def construct(self, value):
prob = self.b('prob', value)
log_prob = self.b('log_prob', value)
cdf = self.b('cdf', value)
log_cdf = self.b('log_cdf', value)
sf = self.b('survival_function', value)
log_sf = self.b('log_survival', value)
return prob + log_prob + cdf + log_cdf + sf + log_sf
def test_bernoulli_prob():
"""
Test probability functions: passing value through construct.
"""
net = BernoulliProb()
value = Tensor([0, 0, 0, 0, 0], dtype=dtype.float32)
ans = net(value)
assert isinstance(ans, Tensor)
class BernoulliProb1(nn.Cell):
"""
Bernoulli distribution: initialize without probs.
"""
def __init__(self):
super(BernoulliProb1, self).__init__()
self.b = nn.Bernoulli(dtype=dtype.int32)
def construct(self, value, probs):
prob = self.b('prob', value, probs)
log_prob = self.b('log_prob', value, probs)
cdf = self.b('cdf', value, probs)
log_cdf = self.b('log_cdf', value, probs)
sf = self.b('survival_function', value, probs)
log_sf = self.b('log_survival', value, probs)
return prob + log_prob + cdf + log_cdf + sf + log_sf
def test_bernoulli_prob1():
"""
Test probability functions: passing value/probs through construct.
"""
net = BernoulliProb1()
value = Tensor([0, 0, 0, 0, 0], dtype=dtype.float32)
probs = Tensor([0.5], dtype=dtype.float32)
ans = net(value, probs)
assert isinstance(ans, Tensor)
class BernoulliKl(nn.Cell):
"""
Test class: kl_loss between Bernoulli distributions.
"""
def __init__(self):
super(BernoulliKl, self).__init__()
self.b1 = nn.Bernoulli(0.7, dtype=dtype.int32)
self.b2 = nn.Bernoulli(dtype=dtype.int32)
def construct(self, probs_b, probs_a):
kl1 = self.b1('kl_loss', 'Bernoulli', probs_b)
kl2 = self.b2('kl_loss', 'Bernoulli', probs_b, probs_a)
return kl1 + kl2
def test_kl():
"""
Test kl_loss function.
"""
ber_net = BernoulliKl()
probs_b = Tensor([0.3], dtype=dtype.float32)
probs_a = Tensor([0.7], dtype=dtype.float32)
ans = ber_net(probs_b, probs_a)
assert isinstance(ans, Tensor)
class BernoulliCrossEntropy(nn.Cell):
"""
Test class: cross_entropy of Bernoulli distribution.
"""
def __init__(self):
super(BernoulliCrossEntropy, self).__init__()
self.b1 = nn.Bernoulli(0.7, dtype=dtype.int32)
self.b2 = nn.Bernoulli(dtype=dtype.int32)
def construct(self, probs_b, probs_a):
h1 = self.b1('cross_entropy', 'Bernoulli', probs_b)
h2 = self.b2('cross_entropy', 'Bernoulli', probs_b, probs_a)
return h1 + h2
def test_cross_entropy():
"""
Test cross_entropy between Bernoulli distributions.
"""
net = BernoulliCrossEntropy()
probs_b = Tensor([0.3], dtype=dtype.float32)
probs_a = Tensor([0.7], dtype=dtype.float32)
ans = net(probs_b, probs_a)
assert isinstance(ans, Tensor)
class BernoulliBasics(nn.Cell):
"""
Test class: basic mean/sd/var/mode/entropy function.
"""
def __init__(self):
super(BernoulliBasics, self).__init__()
self.b = nn.Bernoulli([0.3, 0.5], dtype=dtype.int32)
def construct(self):
mean = self.b('mean')
sd = self.b('sd')
var = self.b('var')
mode = self.b('mode')
entropy = self.b('entropy')
return mean + sd + var + mode + entropy
def test_bascis():
"""
Test mean/sd/var/mode/entropy functionality of Bernoulli distribution.
"""
net = BernoulliBasics()
ans = net()
assert isinstance(ans, Tensor)

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tests/ut/python/nn/distribution/test_exponential.py Normal file
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# 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.
# ============================================================================
"""
Test nn.Distribution.Exponential.
"""
import pytest
import mindspore.nn as nn
from mindspore import dtype
from mindspore import Tensor
def test_arguments():
"""
Args passing during initialization.
"""
e = nn.Exponential()
assert isinstance(e, nn.Distribution)
e = nn.Exponential([0.1, 0.3, 0.5, 1.0], dtype=dtype.float32)
assert isinstance(e, nn.Distribution)
def test_rate():
"""
Invalid rate.
"""
with pytest.raises(ValueError):
nn.Exponential([-0.1], dtype=dtype.float32)
with pytest.raises(ValueError):
nn.Exponential([0.0], dtype=dtype.float32)
class ExponentialProb(nn.Cell):
"""
Exponential distribution: initialize with rate.
"""
def __init__(self):
super(ExponentialProb, self).__init__()
self.e = nn.Exponential(0.5, dtype=dtype.float32)
def construct(self, value):
prob = self.e('prob', value)
log_prob = self.e('log_prob', value)
cdf = self.e('cdf', value)
log_cdf = self.e('log_cdf', value)
sf = self.e('survival_function', value)
log_sf = self.e('log_survival', value)
return prob + log_prob + cdf + log_cdf + sf + log_sf
def test_exponential_prob():
"""
Test probability functions: passing value through construct.
"""
net = ExponentialProb()
value = Tensor([0.2, 0.3, 5.0, 2, 3.9], dtype=dtype.float32)
ans = net(value)
assert isinstance(ans, Tensor)
class ExponentialProb1(nn.Cell):
"""
Exponential distribution: initialize without rate.
"""
def __init__(self):
super(ExponentialProb1, self).__init__()
self.e = nn.Exponential(dtype=dtype.float32)
def construct(self, value, rate):
prob = self.e('prob', value, rate)
log_prob = self.e('log_prob', value, rate)
cdf = self.e('cdf', value, rate)
log_cdf = self.e('log_cdf', value, rate)
sf = self.e('survival_function', value, rate)
log_sf = self.e('log_survival', value, rate)
return prob + log_prob + cdf + log_cdf + sf + log_sf
def test_exponential_prob1():
"""
Test probability functions: passing value/rate through construct.
"""
net = ExponentialProb1()
value = Tensor([0.2, 0.9, 1, 2, 3], dtype=dtype.float32)
rate = Tensor([0.5], dtype=dtype.float32)
ans = net(value, rate)
assert isinstance(ans, Tensor)
class ExponentialKl(nn.Cell):
"""
Test class: kl_loss between Exponential distributions.
"""
def __init__(self):
super(ExponentialKl, self).__init__()
self.e1 = nn.Exponential(0.7, dtype=dtype.float32)
self.e2 = nn.Exponential(dtype=dtype.float32)
def construct(self, rate_b, rate_a):
kl1 = self.e1('kl_loss', 'Exponential', rate_b)
kl2 = self.e2('kl_loss', 'Exponential', rate_b, rate_a)
return kl1 + kl2
def test_kl():
"""
Test kl_loss function.
"""
net = ExponentialKl()
rate_b = Tensor([0.3], dtype=dtype.float32)
rate_a = Tensor([0.7], dtype=dtype.float32)
ans = net(rate_b, rate_a)
assert isinstance(ans, Tensor)
class ExponentialCrossEntropy(nn.Cell):
"""
Test class: cross_entropy of Exponential distribution.
"""
def __init__(self):
super(ExponentialCrossEntropy, self).__init__()
self.e1 = nn.Exponential(0.3, dtype=dtype.float32)
self.e2 = nn.Exponential(dtype=dtype.float32)
def construct(self, rate_b, rate_a):
h1 = self.e1('cross_entropy', 'Exponential', rate_b)
h2 = self.e2('cross_entropy', 'Exponential', rate_b, rate_a)
return h1 + h2
def test_cross_entropy():
"""
Test cross_entropy between Exponential distributions.
"""
net = ExponentialCrossEntropy()
rate_b = Tensor([0.3], dtype=dtype.float32)
rate_a = Tensor([0.7], dtype=dtype.float32)
ans = net(rate_b, rate_a)
assert isinstance(ans, Tensor)
class ExponentialBasics(nn.Cell):
"""
Test class: basic mean/sd/mode/entropy function.
"""
def __init__(self):
super(ExponentialBasics, self).__init__()
self.e = nn.Exponential([0.3, 0.5], dtype=dtype.float32)
def construct(self):
mean = self.e('mean')
sd = self.e('sd')
var = self.e('var')
mode = self.e('mode')
entropy = self.e('entropy')
return mean + sd + var + mode + entropy
def test_bascis():
"""
Test mean/sd/var/mode/entropy functionality of Exponential distribution.
"""
net = ExponentialBasics()
ans = net()
assert isinstance(ans, Tensor)

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tests/ut/python/nn/distribution/test_geometric.py Normal file
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# 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.
# ============================================================================
"""
Test nn.Distribution.Geometric.
"""
import pytest
import mindspore.nn as nn
from mindspore import dtype
from mindspore import Tensor
def test_arguments():
"""
Args passing during initialization.
"""
g = nn.Geometric()
assert isinstance(g, nn.Distribution)
g = nn.Geometric([0.0, 0.3, 0.5, 1.0], dtype=dtype.int32)
assert isinstance(g, nn.Distribution)
def test_prob():
"""
Invalid probability.
"""
with pytest.raises(ValueError):
nn.Geometric([-0.1], dtype=dtype.int32)
with pytest.raises(ValueError):
nn.Geometric([1.1], dtype=dtype.int32)
class GeometricProb(nn.Cell):
"""
Geometric distribution: initialize with probs.
"""
def __init__(self):
super(GeometricProb, self).__init__()
self.g = nn.Geometric(0.5, dtype=dtype.int32)
def construct(self, value):
prob = self.g('prob', value)
log_prob = self.g('log_prob', value)
cdf = self.g('cdf', value)
log_cdf = self.g('log_cdf', value)
sf = self.g('survival_function', value)
log_sf = self.g('log_survival', value)
return prob + log_prob + cdf + log_cdf + sf + log_sf
def test_geometric_prob():
"""
Test probability functions: passing value through construct.
"""
net = GeometricProb()
value = Tensor([3, 4, 5, 6, 7], dtype=dtype.float32)
ans = net(value)
assert isinstance(ans, Tensor)
class GeometricProb1(nn.Cell):
"""
Geometric distribution: initialize without probs.
"""
def __init__(self):
super(GeometricProb1, self).__init__()
self.g = nn.Geometric(dtype=dtype.int32)
def construct(self, value, probs):
prob = self.g('prob', value, probs)
log_prob = self.g('log_prob', value, probs)
cdf = self.g('cdf', value, probs)
log_cdf = self.g('log_cdf', value, probs)
sf = self.g('survival_function', value, probs)
log_sf = self.g('log_survival', value, probs)
return prob + log_prob + cdf + log_cdf + sf + log_sf
def test_geometric_prob1():
"""
Test probability functions: passing value/probs through construct.
"""
net = GeometricProb1()
value = Tensor([3, 4, 5, 6, 7], dtype=dtype.float32)
probs = Tensor([0.5], dtype=dtype.float32)
ans = net(value, probs)
assert isinstance(ans, Tensor)
class GeometricKl(nn.Cell):
"""
Test class: kl_loss between Geometric distributions.
"""
def __init__(self):
super(GeometricKl, self).__init__()
self.g1 = nn.Geometric(0.7, dtype=dtype.int32)
self.g2 = nn.Geometric(dtype=dtype.int32)
def construct(self, probs_b, probs_a):
kl1 = self.g1('kl_loss', 'Geometric', probs_b)
kl2 = self.g2('kl_loss', 'Geometric', probs_b, probs_a)
return kl1 + kl2
def test_kl():
"""
Test kl_loss function.
"""
ber_net = GeometricKl()
probs_b = Tensor([0.3], dtype=dtype.float32)
probs_a = Tensor([0.7], dtype=dtype.float32)
ans = ber_net(probs_b, probs_a)
assert isinstance(ans, Tensor)
class GeometricCrossEntropy(nn.Cell):
"""
Test class: cross_entropy of Geometric distribution.
"""
def __init__(self):
super(GeometricCrossEntropy, self).__init__()
self.g1 = nn.Geometric(0.3, dtype=dtype.int32)
self.g2 = nn.Geometric(dtype=dtype.int32)
def construct(self, probs_b, probs_a):
h1 = self.g1('cross_entropy', 'Geometric', probs_b)
h2 = self.g2('cross_entropy', 'Geometric', probs_b, probs_a)
return h1 + h2
def test_cross_entropy():
"""
Test cross_entropy between Geometric distributions.
"""
net = GeometricCrossEntropy()
probs_b = Tensor([0.3], dtype=dtype.float32)
probs_a = Tensor([0.7], dtype=dtype.float32)
ans = net(probs_b, probs_a)
assert isinstance(ans, Tensor)
class GeometricBasics(nn.Cell):
"""
Test class: basic mean/sd/mode/entropy function.
"""
def __init__(self):
super(GeometricBasics, self).__init__()
self.g = nn.Geometric([0.3, 0.5], dtype=dtype.int32)
def construct(self):
mean = self.g('mean')
sd = self.g('sd')
var = self.g('var')
mode = self.g('mode')
entropy = self.g('entropy')
return mean + sd + var + mode + entropy
def test_bascis():
"""
Test mean/sd/mode/entropy functionality of Geometric distribution.
"""
net = GeometricBasics()
ans = net()
assert isinstance(ans, Tensor)

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tests/ut/python/nn/distribution/test_normal.py Normal file
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# 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.
# ============================================================================
"""
Test nn.Distribution.Normal.
"""
import numpy as np
import pytest
import mindspore.nn as nn
from mindspore import dtype
from mindspore import Tensor
def test_normal_shape_errpr():
"""
Invalid shapes.
"""
with pytest.raises(ValueError):
nn.Normal([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)
def test_arguments():
"""
args passing during initialization.
"""
n = nn.Normal()
assert isinstance(n, nn.Distribution)
n = nn.Normal([3.0], [4.0], dtype=dtype.float32)
assert isinstance(n, nn.Distribution)
class NormalProb(nn.Cell):
"""
Normal distribution: initialize with mean/sd.
"""
def __init__(self):
super(NormalProb, self).__init__()
self.normal = nn.Normal(3.0, 4.0, dtype=dtype.float32)
def construct(self, value):
prob = self.normal('prob', value)
log_prob = self.normal('log_prob', value)
cdf = self.normal('cdf', value)
log_cdf = self.normal('log_cdf', value)
sf = self.normal('survival_function', value)
log_sf = self.normal('log_survival', value)
return prob + log_prob + cdf + log_cdf + sf + log_sf
def test_normal_prob():
"""
Test probability functions: passing value through construct.
"""
net = NormalProb()
value = Tensor([0.5, 1.0], dtype=dtype.float32)
ans = net(value)
assert isinstance(ans, Tensor)
class NormalProb1(nn.Cell):
"""
Normal distribution: initialize without mean/sd.
"""
def __init__(self):
super(NormalProb1, self).__init__()
self.normal = nn.Normal()
def construct(self, value, mean, sd):
prob = self.normal('prob', value, mean, sd)
log_prob = self.normal('log_prob', value, mean, sd)
cdf = self.normal('cdf', value, mean, sd)
log_cdf = self.normal('log_cdf', value, mean, sd)
sf = self.normal('survival_function', value, mean, sd)
log_sf = self.normal('log_survival', value, mean, sd)
return prob + log_prob + cdf + log_cdf + sf + log_sf
def test_normal_prob1():
"""
Test probability functions: passing mean/sd, value through construct.
"""
net = NormalProb1()
value = Tensor([0.5, 1.0], dtype=dtype.float32)
mean = Tensor([0.0], dtype=dtype.float32)
sd = Tensor([1.0], dtype=dtype.float32)
ans = net(value, mean, sd)
assert isinstance(ans, Tensor)
class NormalKl(nn.Cell):
"""
Test class: kl_loss of Normal distribution.
"""
def __init__(self):
super(NormalKl, self).__init__()
self.n1 = nn.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
self.n2 = nn.Normal(dtype=dtype.float32)
def construct(self, mean_b, sd_b, mean_a, sd_a):
kl1 = self.n1('kl_loss', 'Normal', mean_b, sd_b)
kl2 = self.n2('kl_loss', 'Normal', mean_b, sd_b, mean_a, sd_a)
return kl1 + kl2
def test_kl():
"""
Test kl_loss.
"""
net = NormalKl()
mean_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
sd_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
mean_a = Tensor(np.array([2.0]).astype(np.float32), dtype=dtype.float32)
sd_a = Tensor(np.array([3.0]).astype(np.float32), dtype=dtype.float32)
ans = net(mean_b, sd_b, mean_a, sd_a)
assert isinstance(ans, Tensor)
class NormalCrossEntropy(nn.Cell):
"""
Test class: cross_entropy of Normal distribution.
"""
def __init__(self):
super(NormalCrossEntropy, self).__init__()
self.n1 = nn.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
self.n2 = nn.Normal(dtype=dtype.float32)
def construct(self, mean_b, sd_b, mean_a, sd_a):
h1 = self.n1('cross_entropy', 'Normal', mean_b, sd_b)
h2 = self.n2('cross_entropy', 'Normal', mean_b, sd_b, mean_a, sd_a)
return h1 + h2
def test_cross_entropy():
"""
Test cross entropy between Normal distributions.
"""
net = NormalCrossEntropy()
mean_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
sd_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
mean_a = Tensor(np.array([2.0]).astype(np.float32), dtype=dtype.float32)
sd_a = Tensor(np.array([3.0]).astype(np.float32), dtype=dtype.float32)
ans = net(mean_b, sd_b, mean_a, sd_a)
assert isinstance(ans, Tensor)
class NormalBasics(nn.Cell):
"""
Test class: basic mean/sd function.
"""
def __init__(self):
super(NormalBasics, self).__init__()
self.n = nn.Normal(3.0, 4.0, dtype=dtype.float32)
def construct(self):
mean = self.n('mean')
sd = self.n('sd')
mode = self.n('mode')
entropy = self.n('entropy')
return mean + sd + mode + entropy
def test_bascis():
"""
Test mean/sd/mode/entropy functionality of Normal.
"""
net = NormalBasics()
ans = net()
assert isinstance(ans, Tensor)

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# 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.
# ============================================================================
"""
Test nn.Distribution.Uniform.
"""
import numpy as np
import pytest
import mindspore.nn as nn
from mindspore import dtype
from mindspore import Tensor
def test_uniform_shape_errpr():
"""
Invalid shapes.
"""
with pytest.raises(ValueError):
nn.Uniform([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)
def test_arguments():
"""
Args passing during initialization.
"""
u = nn.Uniform()
assert isinstance(u, nn.Distribution)
u = nn.Uniform([3.0], [4.0], dtype=dtype.float32)
assert isinstance(u, nn.Distribution)
def test_invalid_range():
"""
Test range of uniform distribution.
"""
with pytest.raises(ValueError):
nn.Uniform(0.0, 0.0, dtype=dtype.float32)
with pytest.raises(ValueError):
nn.Uniform(1.0, 0.0, dtype=dtype.float32)
class UniformProb(nn.Cell):
"""
Uniform distribution: initialize with low/high.
"""
def __init__(self):
super(UniformProb, self).__init__()
self.u = nn.Uniform(3.0, 4.0, dtype=dtype.float32)
def construct(self, value):
prob = self.u('prob', value)
log_prob = self.u('log_prob', value)
cdf = self.u('cdf', value)
log_cdf = self.u('log_cdf', value)
sf = self.u('survival_function', value)
log_sf = self.u('log_survival', value)
return prob + log_prob + cdf + log_cdf + sf + log_sf
def test_uniform_prob():
"""
Test probability functions: passing value through construct.
"""
net = UniformProb()
value = Tensor([3.1, 3.2, 3.3, 3.4], dtype=dtype.float32)
ans = net(value)
assert isinstance(ans, Tensor)
class UniformProb1(nn.Cell):
"""
Uniform distribution: initialize without low/high.
"""
def __init__(self):
super(UniformProb1, self).__init__()
self.u = nn.Uniform(dtype=dtype.float32)
def construct(self, value, low, high):
prob = self.u('prob', value, low, high)
log_prob = self.u('log_prob', value, low, high)
cdf = self.u('cdf', value, low, high)
log_cdf = self.u('log_cdf', value, low, high)
sf = self.u('survival_function', value, low, high)
log_sf = self.u('log_survival', value, low, high)
return prob + log_prob + cdf + log_cdf + sf + log_sf
def test_uniform_prob1():
"""
Test probability functions: passing low/high, value through construct.
"""
net = UniformProb1()
value = Tensor([0.1, 0.2, 0.3, 0.9], dtype=dtype.float32)
low = Tensor([0.0], dtype=dtype.float32)
high = Tensor([1.0], dtype=dtype.float32)
ans = net(value, low, high)
assert isinstance(ans, Tensor)
class UniformKl(nn.Cell):
"""
Test class: kl_loss of Uniform distribution.
"""
def __init__(self):
super(UniformKl, self).__init__()
self.u1 = nn.Uniform(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
self.u2 = nn.Uniform(dtype=dtype.float32)
def construct(self, low_b, high_b, low_a, high_a):
kl1 = self.u1('kl_loss', 'Uniform', low_b, high_b)
kl2 = self.u2('kl_loss', 'Uniform', low_b, high_b, low_a, high_a)
return kl1 + kl2
def test_kl():
"""
Test kl_loss.
"""
net = UniformKl()
low_b = Tensor(np.array([0.0]).astype(np.float32), dtype=dtype.float32)
high_b = Tensor(np.array([5.0]).astype(np.float32), dtype=dtype.float32)
low_a = Tensor(np.array([2.0]).astype(np.float32), dtype=dtype.float32)
high_a = Tensor(np.array([3.0]).astype(np.float32), dtype=dtype.float32)
ans = net(low_b, high_b, low_a, high_a)
assert isinstance(ans, Tensor)
class UniformCrossEntropy(nn.Cell):
"""
Test class: cross_entropy of Uniform distribution.
"""
def __init__(self):
super(UniformCrossEntropy, self).__init__()
self.u1 = nn.Uniform(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
self.u2 = nn.Uniform(dtype=dtype.float32)
def construct(self, low_b, high_b, low_a, high_a):
h1 = self.u1('cross_entropy', 'Uniform', low_b, high_b)
h2 = self.u2('cross_entropy', 'Uniform', low_b, high_b, low_a, high_a)
return h1 + h2
def test_cross_entropy():
"""
Test cross_entropy between Unifrom distributions.
"""
net = UniformCrossEntropy()
low_b = Tensor(np.array([0.0]).astype(np.float32), dtype=dtype.float32)
high_b = Tensor(np.array([5.0]).astype(np.float32), dtype=dtype.float32)
low_a = Tensor(np.array([2.0]).astype(np.float32), dtype=dtype.float32)
high_a = Tensor(np.array([3.0]).astype(np.float32), dtype=dtype.float32)
ans = net(low_b, high_b, low_a, high_a)
assert isinstance(ans, Tensor)
class UniformBasics(nn.Cell):
"""
Test class: basic mean/sd/var/mode/entropy function.
"""
def __init__(self):
super(UniformBasics, self).__init__()
self.u = nn.Uniform(3.0, 4.0, dtype=dtype.float32)
def construct(self):
mean = self.u('mean')
sd = self.u('sd')
var = self.u('var')
entropy = self.u('entropy')
return mean + sd + var + entropy
def test_bascis():
"""
Test mean/sd/var/mode/entropy functionality of Uniform.
"""
net = UniformBasics()
ans = net()
assert isinstance(ans, Tensor)

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tests/ut/python/nn/test_distribution.py
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@ -1,369 +0,0 @@
# 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.
# ============================================================================
"""
Test nn.Distribution.
Including Normal Distribution and Bernoulli Distribution.
"""
import pytest
import numpy as np
import mindspore.nn as nn
from mindspore import dtype
from mindspore import Tensor
def test_normal_shape_errpr():
"""
Invalid shapes.
"""
with pytest.raises(ValueError):
nn.Normal([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)
def test_no_arguments():
"""
No args passed in during initialization.
"""
n = nn.Normal()
assert isinstance(n, nn.Distribution)
b = nn.Bernoulli()
assert isinstance(b, nn.Distribution)
def test_with_arguments():
"""
Args passed in during initialization.
"""
n = nn.Normal([3.0], [4.0], dtype=dtype.float32)
assert isinstance(n, nn.Distribution)
b = nn.Bernoulli([0.3, 0.5], dtype=dtype.int32)
assert isinstance(b, nn.Distribution)
class NormalProb(nn.Cell):
"""
Normal distribution: initialize with mean/sd.
"""
def __init__(self):
super(NormalProb, self).__init__()
self.normal = nn.Normal(3.0, 4.0, dtype=dtype.float32)
def construct(self, value):
x = self.normal('prob', value)
y = self.normal('log_prob', value)
return x, y
def test_normal_prob():
"""
Test pdf/log_pdf: passing value through construct.
"""
net = NormalProb()
value = Tensor([0.5, 1.0], dtype=dtype.float32)
pdf, log_pdf = net(value)
assert isinstance(pdf, Tensor)
assert isinstance(log_pdf, Tensor)
class NormalProb1(nn.Cell):
"""
Normal distribution: initialize without mean/sd.
"""
def __init__(self):
super(NormalProb1, self).__init__()
self.normal = nn.Normal()
def construct(self, value, mean, sd):
x = self.normal('prob', value, mean, sd)
y = self.normal('log_prob', value, mean, sd)
return x, y
def test_normal_prob1():
"""
Test pdf/logpdf: passing mean/sd, value through construct.
"""
net = NormalProb1()
value = Tensor([0.5, 1.0], dtype=dtype.float32)
mean = Tensor([0.0], dtype=dtype.float32)
sd = Tensor([1.0], dtype=dtype.float32)
pdf, log_pdf = net(value, mean, sd)
assert isinstance(pdf, Tensor)
assert isinstance(log_pdf, Tensor)
class NormalProb2(nn.Cell):
"""
Normal distribution: initialize with mean/sd.
"""
def __init__(self):
super(NormalProb2, self).__init__()
self.normal = nn.Normal(3.0, 4.0, dtype=dtype.float32)
def construct(self, value, mean, sd):
x = self.normal('prob', value, mean, sd)
y = self.normal('log_prob', value, mean, sd)
return x, y
def test_normal_prob2():
"""
Test pdf/log_pdf: passing mean/sd through construct.
Overwrite original mean/sd.
"""
net = NormalProb2()
value = Tensor([0.5, 1.0], dtype=dtype.float32)
mean = Tensor([0.0], dtype=dtype.float32)
sd = Tensor([1.0], dtype=dtype.float32)
pdf, log_pdf = net(value, mean, sd)
assert isinstance(pdf, Tensor)
assert isinstance(log_pdf, Tensor)
class BernoulliProb(nn.Cell):
"""
Bernoulli distribution: initialize with probs.
"""
def __init__(self):
super(BernoulliProb, self).__init__()
self.bernoulli = nn.Bernoulli(0.5, dtype=dtype.int32)
def construct(self, value):
return self.bernoulli('prob', value)
class BernoulliLogProb(nn.Cell):
"""
Bernoulli distribution: initialize with probs.
"""
def __init__(self):
super(BernoulliLogProb, self).__init__()
self.bernoulli = nn.Bernoulli(0.5, dtype=dtype.int32)
def construct(self, value):
return self.bernoulli('log_prob', value)
def test_bernoulli_prob():
"""
Test pmf/log_pmf: passing value through construct.
"""
net = BernoulliProb()
value = Tensor([1, 0, 1, 0, 1], dtype=dtype.float32)
pmf = net(value)
assert isinstance(pmf, Tensor)
def test_bernoulli_log_prob():
"""
Test pmf/log_pmf: passing value through construct.
"""
net = BernoulliLogProb()
value = Tensor([1, 0, 1, 0, 1], dtype=dtype.float32)
log_pmf = net(value)
assert isinstance(log_pmf, Tensor)
class BernoulliProb1(nn.Cell):
"""
Bernoulli distribution: initialize without probs.
"""
def __init__(self):
super(BernoulliProb1, self).__init__()
self.bernoulli = nn.Bernoulli()
def construct(self, value, probs):
return self.bernoulli('prob', value, probs)
class BernoulliLogProb1(nn.Cell):
"""
Bernoulli distribution: initialize without probs.
"""
def __init__(self):
super(BernoulliLogProb1, self).__init__()
self.bernoulli = nn.Bernoulli()
def construct(self, value, probs):
return self.bernoulli('log_prob', value, probs)
def test_bernoulli_prob1():
"""
Test pmf/log_pmf: passing probs through construct.
"""
net = BernoulliProb1()
value = Tensor([1, 0, 1, 0, 1], dtype=dtype.float32)
probs = Tensor([0.3], dtype=dtype.float32)
pmf = net(value, probs)
assert isinstance(pmf, Tensor)
def test_bernoulli_log_prob1():
"""
Test pmf/log_pmf: passing probs through construct.
"""
net = BernoulliLogProb1()
value = Tensor([1, 0, 1, 0, 1], dtype=dtype.float32)
probs = Tensor([0.3], dtype=dtype.float32)
log_pmf = net(value, probs)
assert isinstance(log_pmf, Tensor)
class BernoulliProb2(nn.Cell):
"""
Bernoulli distribution: initialize with probs.
"""
def __init__(self):
super(BernoulliProb2, self).__init__()
self.bernoulli = nn.Bernoulli(0.5)
def construct(self, value, probs):
return self.bernoulli('prob', value, probs)
class BernoulliLogProb2(nn.Cell):
"""
Bernoulli distribution: initialize with probs.
"""
def __init__(self):
super(BernoulliLogProb2, self).__init__()
self.bernoulli = nn.Bernoulli(0.5)
def construct(self, value, probs):
return self.bernoulli('log_prob', value, probs)
def test_bernoulli_prob2():
"""
Test pmf/log_pmf: passing probs/value through construct.
Overwrite original probs.
"""
net = BernoulliProb2()
value = Tensor([1, 0, 1, 0, 1], dtype=dtype.float32)
probs = Tensor([0.3], dtype=dtype.float32)
pmf = net(value, probs)
assert isinstance(pmf, Tensor)
def test_bernoulli_log_prob2():
"""
Test pmf/log_pmf: passing probs/value through construct.
Overwrite original probs.
"""
net = BernoulliLogProb2()
value = Tensor([1, 0, 1, 0, 1], dtype=dtype.float32)
probs = Tensor([0.3], dtype=dtype.float32)
log_pmf = net(value, probs)
assert isinstance(log_pmf, Tensor)
class NormalKl(nn.Cell):
"""
Test class: kl_loss of Normal distribution.
"""
def __init__(self):
super(NormalKl, self).__init__()
self.n = nn.Normal(Tensor([3.0]), Tensor([4.0]), dtype=dtype.float32)
def construct(self, x_, y_):
return self.n('kl_loss', 'Normal', x_, y_)
class BernoulliKl(nn.Cell):
"""
Test class: kl_loss between Bernoulli distributions.
"""
def __init__(self):
super(BernoulliKl, self).__init__()
self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
def construct(self, x_):
return self.b('kl_loss', 'Bernoulli', x_)
def test_kl():
"""
Test kl_loss function.
"""
nor_net = NormalKl()
mean_b = np.array([1.0]).astype(np.float32)
sd_b = np.array([1.0]).astype(np.float32)
mean = Tensor(mean_b, dtype=dtype.float32)
sd = Tensor(sd_b, dtype=dtype.float32)
loss = nor_net(mean, sd)
assert isinstance(loss, Tensor)
ber_net = BernoulliKl()
probs_b = Tensor([0.3], dtype=dtype.float32)
loss = ber_net(probs_b)
assert isinstance(loss, Tensor)
class NormalKlNoArgs(nn.Cell):
"""
Test class: kl_loss of Normal distribution.
No args during initialization.
"""
def __init__(self):
super(NormalKlNoArgs, self).__init__()
self.n = nn.Normal(dtype=dtype.float32)
def construct(self, x_, y_, w_, v_):
return self.n('kl_loss', 'Normal', x_, y_, w_, v_)
class BernoulliKlNoArgs(nn.Cell):
"""
Test class: kl_loss between Bernoulli distributions.
No args during initialization.
"""
def __init__(self):
super(BernoulliKlNoArgs, self).__init__()
self.b = nn.Bernoulli(dtype=dtype.int32)
def construct(self, x_, y_):
return self.b('kl_loss', 'Bernoulli', x_, y_)
def test_kl_no_args():
"""
Test kl_loss function.
"""
nor_net = NormalKlNoArgs()
mean_b = np.array([1.0]).astype(np.float32)
sd_b = np.array([1.0]).astype(np.float32)
mean_a = np.array([2.0]).astype(np.float32)
sd_a = np.array([3.0]).astype(np.float32)
mean_b = Tensor(mean_b, dtype=dtype.float32)
sd_b = Tensor(sd_b, dtype=dtype.float32)
mean_a = Tensor(mean_a, dtype=dtype.float32)
sd_a = Tensor(sd_a, dtype=dtype.float32)
loss = nor_net(mean_b, sd_b, mean_a, sd_a)
assert isinstance(loss, Tensor)
ber_net = BernoulliKlNoArgs()
probs_b = Tensor([0.3], dtype=dtype.float32)
probs_a = Tensor([0.7], dtype=dtype.float32)
loss = ber_net(probs_b, probs_a)
assert isinstance(loss, Tensor)
class NormalBernoulli(nn.Cell):
"""
Test class: basic mean/sd function.
"""
def __init__(self):
super(NormalBernoulli, self).__init__()
self.n = nn.Normal(3.0, 4.0, dtype=dtype.float32)
self.b = nn.Bernoulli(0.5, dtype=dtype.int32)
def construct(self):
normal_mean = self.n('mean')
normal_sd = self.n('sd')
bernoulli_mean = self.b('mean')
bernoulli_sd = self.b('sd')
return normal_mean, normal_sd, bernoulli_mean, bernoulli_sd
def test_bascis():
"""
Test mean/sd functionality of Normal and Bernoulli.
"""
net = NormalBernoulli()
normal_mean, normal_sd, bernoulli_mean, bernoulli_sd = net()
assert isinstance(normal_mean, Tensor)
assert isinstance(normal_sd, Tensor)
assert isinstance(bernoulli_mean, Tensor)
assert isinstance(bernoulli_sd, Tensor)