!11408 Update PP example

From: @zichun_ye
Reviewed-by: @wang_zi_dong,@sunnybeike
Signed-off-by:

mindspore/nn/probability/bijector/bijector.py

@@ -41,7 +41,7 @@ class Bijector(Cell):
     Note:
         `dtype` of bijector represents the type of the distributions that the bijector could operate on.
         When `dtype` is None, there is no enforcement on the type of input value except that the input value
-        has to be float type. During initilization, when `dtype` is None, there is no enforcement on the dtype
+        has to be float type. During initialization, when `dtype` is None, there is no enforcement on the dtype
         of the parameters. All parameters should have the same float type, otherwise a TypeError will be raised.
         Specifically, the parameter type will follow the dtype of the input value, i.e. parameters of the bijector
         will be casted into the same type as input value when `dtype`is None.
@@ -65,7 +65,8 @@ class Bijector(Cell):
             'is_constant_jacobian', is_constant_jacobian, [bool], name)
         validator.check_value_type('is_injective', is_injective, [bool], name)
         if dtype is not None:
-            validator.check_type_name("dtype", dtype, mstype.float_type, type(self).__name__)
+            validator.check_type_name(
+                "dtype", dtype, mstype.float_type, type(self).__name__)
         self._name = name
         self._dtype = dtype
         self._parameters = {}
@@ -76,7 +77,7 @@ class Bijector(Cell):
             if not(k == 'self' or k.startswith('_')):
                 self._parameters[k] = param[k]
 
-        # if no bijector is used as an argument during initilization
+        # if no bijector is used as an argument during initialization
         if 'bijector' not in param.keys():
             self._batch_shape = self._calc_batch_shape()
             self._is_scalar_batch = self._check_is_scalar_batch()
@@ -141,7 +142,8 @@ class Bijector(Cell):
 
     def _shape_mapping(self, shape):
         shape_tensor = self.fill_base(self.parameter_type, shape, 0.0)
-        dist_shape_tensor = self.fill_base(self.parameter_type, self.batch_shape, 0.0)
+        dist_shape_tensor = self.fill_base(
+            self.parameter_type, self.batch_shape, 0.0)
         return (shape_tensor + dist_shape_tensor).shape
 
     def shape_mapping(self, shape):
@@ -166,12 +168,15 @@ class Bijector(Cell):
             if self.common_dtype is None:
                 self.common_dtype = value_t.dtype
             elif value_t.dtype != self.common_dtype:
-                raise TypeError(f"{name} should have the same dtype as other arguments.")
+                raise TypeError(
+                    f"{name} should have the same dtype as other arguments.")
             # check if the parameters are casted into float-type tensors
-            validator.check_type_name(f"dtype of {name}", value_t.dtype, mstype.float_type, type(self).__name__)
+            validator.check_type_name(
+                f"dtype of {name}", value_t.dtype, mstype.float_type, type(self).__name__)
         # check if the dtype of the input_parameter agrees with the bijector's dtype
         elif value_t.dtype != self.dtype:
-            raise TypeError(f"{name} should have the same dtype as the bijector's dtype.")
+            raise TypeError(
+                f"{name} should have the same dtype as the bijector's dtype.")
         self.default_parameters += [value,]
         self.parameter_names += [name,]
         return value_t

mindspore/nn/probability/bijector/invert.py

@@ -33,24 +33,17 @@ class Invert(Bijector):
         >>> import mindspore.nn as nn
         >>> import mindspore.nn.probability.bijector as msb
         >>> from mindspore import Tensor
-        >>> import mindspore.context as context
-        >>> context.set_context(mode=1)
-        >>>
-        >>> # To initialize an inverse Exp bijector.
-        >>> inv_exp = msb.Invert(msb.Exp())
-        >>> value = Tensor([1, 2, 3], dtype=mindspore.float32)
-        >>> ans1 = inv_exp.forward(value)
-        >>> print(ans1.shape)
-        (3,)
-        >>> ans2 = inv_exp.inverse(value)
-        >>> print(ans2.shape)
-        (3,)
-        >>> ans3 = inv_exp.forward_log_jacobian(value)
-        >>> print(ans3.shape)
-        (3,)
-        >>> ans4 = inv_exp.inverse_log_jacobian(value)
-        >>> print(ans4.shape)
-        (3,)
+        >>> class Net(nn.Cell):
+        ...     def __init__(self):
+        ...         super(Net, self).__init__()
+        ...         self.origin = msb.ScalarAffine(scale=2.0, shift=1.0)
+        ...         self.invert = msb.Invert(self.origin)
+        ...
+        ...     def construct(self, x_):
+        ...         return self.invert.forward(x_)
+        >>> forward = Net()
+        >>> x = np.array([2.0, 3.0, 4.0, 5.0]).astype(np.float32)
+        >>> ans = forward(Tensor(x, dtype=dtype.float32))
     """
 
     def __init__(self,

mindspore/nn/probability/distribution/bernoulli.py

@@ -84,6 +84,7 @@ class Bernoulli(Distribution):
         (3,)
         >>> # `probs` must be passed in during function calls.
         >>> ans = b2.mean(probs_a)
+        >>> print(ans.shape)
         (1,)
         >>> print(ans.shape)
         >>> # Interfaces of `kl_loss` and `cross_entropy` are the same as follows:

mindspore/nn/probability/distribution/categorical.py

@@ -105,22 +105,6 @@ class Categorical(Distribution):
         >>> ans = ca2.kl_loss('Categorical', probs_b, probs_a)
         >>> print(ans.shape)
         ()
-        >>> # Examples of `sample`.
-        >>> # Args:
-        >>> #     shape (tuple): the shape of the sample. Default: ().
-        >>> #     probs (Tensor): event probabilities. Default: self.probs.
-        >>> ans = ca1.sample()
-        >>> print(ans.shape)
-        ()
-        >>> ans = ca1.sample((2,3))
-        >>> print(ans.shape)
-        (2, 3)
-        >>> ans = ca1.sample((2,3), probs_b)
-        >>> print(ans.shape)
-        (2, 3)
-        >>> ans = ca2.sample((2,3), probs_a)
-        >>> print(ans.shape)
-        (2, 3)
     """
 
     def __init__(self,

mindspore/nn/probability/distribution/distribution.py

@@ -18,7 +18,7 @@ from mindspore.ops import operations as P
 from mindspore.nn.cell import Cell
 from mindspore._checkparam import Validator as validator
 from ._utils.utils import raise_none_error, cast_to_tensor, set_param_type, cast_type_for_device,\
-                          raise_not_implemented_util
+    raise_not_implemented_util
 from ._utils.utils import CheckTuple, CheckTensor
 from ._utils.custom_ops import broadcast_to, exp_generic, log_generic
 
@@ -77,7 +77,8 @@ class Distribution(Cell):
 
         # if not a transformed distribution, set the following attribute
         if 'distribution' not in self.parameters.keys():
-            self.parameter_type = set_param_type(self.parameters['param_dict'], dtype)
+            self.parameter_type = set_param_type(
+                self.parameters['param_dict'], dtype)
             self._batch_shape = self._calc_batch_shape()
             self._is_scalar_batch = self._check_is_scalar_batch()
             self._broadcast_shape = self._batch_shape
@@ -152,7 +153,8 @@ class Distribution(Cell):
             self.default_parameters = []
             self.parameter_names = []
         # cast value to a tensor if it is not None
-        value_t = None if value is None else cast_to_tensor(value, self.parameter_type)
+        value_t = None if value is None else cast_to_tensor(
+            value, self.parameter_type)
         self.default_parameters += [value_t,]
         self.parameter_names += [name,]
         return value_t
@@ -180,10 +182,12 @@ class Distribution(Cell):
             if broadcast_shape is None:
                 broadcast_shape = self.shape_base(arg)
                 common_dtype = self.dtype_base(arg)
-                broadcast_shape_tensor = self.fill_base(common_dtype, broadcast_shape, 1.0)
+                broadcast_shape_tensor = self.fill_base(
+                    common_dtype, broadcast_shape, 1.0)
             else:
                 broadcast_shape = self.shape_base(arg + broadcast_shape_tensor)
-                broadcast_shape_tensor = self.fill_base(common_dtype, broadcast_shape, 1.0)
+                broadcast_shape_tensor = self.fill_base(
+                    common_dtype, broadcast_shape, 1.0)
                 arg = self.broadcast(arg, broadcast_shape_tensor)
                 # check if the arguments have the same dtype
                 self.sametypeshape_base(arg, broadcast_shape_tensor)
@@ -240,7 +244,7 @@ class Distribution(Cell):
 
     def _set_prob(self):
         """
-        Set probability funtion based on the availability of `_prob` and `_log_likehood`.
+        Set probability function based on the availability of `_prob` and `_log_likehood`.
         """
         if hasattr(self, '_prob'):
             self._call_prob = self._prob
@@ -303,9 +307,10 @@ class Distribution(Cell):
         Set survival function based on the availability of _survival function and `_log_survival`
         and `_call_cdf`.
         """
-        if not (hasattr(self, '_survival_function') or hasattr(self, '_log_survival') or \
+        if not (hasattr(self, '_survival_function') or hasattr(self, '_log_survival') or
                 hasattr(self, '_cdf') or hasattr(self, '_log_cdf')):
-            self._call_survival = self._raise_not_implemented_error('survival_function')
+            self._call_survival = self._raise_not_implemented_error(
+                'survival_function')
         elif hasattr(self, '_survival_function'):
             self._call_survival = self._survival_function
         elif hasattr(self, '_log_survival'):
@@ -317,7 +322,7 @@ class Distribution(Cell):
         """
         Set log cdf based on the availability of `_log_cdf` and `_call_cdf`.
         """
-        if not (hasattr(self, '_log_cdf') or hasattr(self, '_cdf') or \
+        if not (hasattr(self, '_log_cdf') or hasattr(self, '_cdf') or
                 hasattr(self, '_survival_function') or hasattr(self, '_log_survival')):
             self._call_log_cdf = self._raise_not_implemented_error('log_cdf')
         elif hasattr(self, '_log_cdf'):
@@ -329,9 +334,10 @@ class Distribution(Cell):
         """
         Set log survival based on the availability of `_log_survival` and `_call_survival`.
         """
-        if not (hasattr(self, '_log_survival') or hasattr(self, '_survival_function') or \
+        if not (hasattr(self, '_log_survival') or hasattr(self, '_survival_function') or
                 hasattr(self, '_log_cdf') or hasattr(self, '_cdf')):
-            self._call_log_survival = self._raise_not_implemented_error('log_cdf')
+            self._call_log_survival = self._raise_not_implemented_error(
+                'log_cdf')
         elif hasattr(self, '_log_survival'):
             self._call_log_survival = self._log_survival
         elif hasattr(self, '_call_survival'):
@@ -344,7 +350,8 @@ class Distribution(Cell):
         if hasattr(self, '_cross_entropy'):
             self._call_cross_entropy = self._cross_entropy
         else:
-            self._call_cross_entropy = self._raise_not_implemented_error('cross_entropy')
+            self._call_cross_entropy = self._raise_not_implemented_error(
+                'cross_entropy')
 
     def _get_dist_args(self, *args, **kwargs):
         return raise_not_implemented_util('get_dist_args', self.name, *args, **kwargs)
@@ -375,6 +382,7 @@ class Distribution(Cell):
 
     def _raise_not_implemented_error(self, func_name):
         name = self.name
+
         def raise_error(*args, **kwargs):
             return raise_not_implemented_util(func_name, name, *args, **kwargs)
         return raise_error

mindspore/nn/probability/distribution/gumbel.py

@@ -46,48 +46,19 @@ class Gumbel(TransformedDistribution):
 
     Examples:
         >>> import mindspore
-        >>> import mindspore.context as context
         >>> import mindspore.nn as nn
         >>> import mindspore.nn.probability.distribution as msd
         >>> from mindspore import Tensor
-        >>> context.set_context(mode=1)
-        >>> # To initialize a Gumbel distribution of `loc` 3.0 and `scale` 4.0.
-        >>> gumbel = msd.Gumbel(3.0, 4.0, dtype=mindspore.float32)
-        >>> # Private interfaces of probability functions corresponding to public interfaces, including
-        >>> # `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival`, have the same
-        >>> # arguments as follows.
-        >>> # Args:
-        >>> #     value (Tensor): the value to be evaluated.
-        >>> # Examples of `prob`.
-        >>> # Similar calls can be made to other probability functions
-        >>> # by replacing 'prob' by the name of the function.
-        >>> value = Tensor([1.0, 2.0, 3.0], dtype=mindspore.float32)
-        >>> ans = gumbel.prob(value)
-        >>> print(ans.shape)
-        (3,)
-        >>> # Functions `mean`, `mode`, sd`, `var`, and `entropy` do not take in any argument.
-        >>> ans = gumbel.mean()
-        >>> print(ans.shape)
-        ()
-        >>> # Interfaces of 'kl_loss' and 'cross_entropy' are the same:
-        >>> # Args:
-        >>> #     dist (str): the type of the distributions. Only "Gumbel" is supported.
-        >>> #     loc_b (Tensor): the loc of distribution b.
-        >>> #     scale_b (Tensor): the scale distribution b.
-        >>> # Examples of `kl_loss`. `cross_entropy` is similar.
-        >>> loc_b = Tensor([1.0], dtype=mindspore.float32)
-        >>> scale_b = Tensor([1.0, 1.5, 2.0], dtype=mindspore.float32)
-        >>> ans = gumbel.kl_loss('Gumbel', loc_b, scale_b)
-        >>> print(ans.shape)
-        (3,)
-        >>> # Examples of `sample`.
-        >>> # Args:
-        >>> #     shape (tuple): the shape of the sample. Default: ()
-        >>> ans = gumbel.sample()
-        >>> print(ans.shape)
-        ()
-        >>> ans = gumbel.sample((2,3))
-        >>> print(ans.shape)
+        >>> class Prob(nn.Cell):
+        ...     def __init__(self):
+        ...         super(Prob, self).__init__()
+        ...         self.gum = msd.Gumbel(np.array([0.0]), np.array([[1.0], [2.0]]), dtype=dtype.float32)
+        ...
+        ...     def construct(self, x_):
+        ...         return self.gum.prob(x_)
+        >>> value = np.array([1.0, 2.0]).astype(np.float32)
+        >>> pdf = Prob()
+        >>> output = pdf(Tensor(value, dtype=dtype.float32))
     """
 
     def __init__(self,

mindspore/nn/probability/distribution/log_normal.py

@@ -45,103 +45,17 @@ class LogNormal(msd.TransformedDistribution):
 
     Examples:
         >>> import mindspore
-        >>> import mindspore.context as context
         >>> import mindspore.nn as nn
         >>> import mindspore.nn.probability.distribution as msd
         >>> from mindspore import Tensor
-        >>> context.set_context(mode=1)
-        >>> # To initialize a LogNormal distribution of `loc` 3.0 and `scale` 4.0.
-        >>> n1 = msd.LogNormal(3.0, 4.0, dtype=mindspore.float32)
-        >>> # A LogNormal distribution can be initialized without arguments.
-        >>> # In this case, `loc` and `scale` must be passed in during function calls.
-        >>> n2 = msd.LogNormal(dtype=mindspore.float32)
-        >>>
-        >>> # Here are some tensors used below for testing
-        >>> value = Tensor([1.0, 2.0, 3.0], dtype=mindspore.float32)
-        >>> loc_a = Tensor([2.0], dtype=mindspore.float32)
-        >>> scale_a = Tensor([2.0, 2.0, 2.0], dtype=mindspore.float32)
-        >>> loc_b = Tensor([1.0], dtype=mindspore.float32)
-        >>> scale_b = Tensor([1.0, 1.5, 2.0], dtype=mindspore.float32)
-        >>>
-        >>> # Private interfaces of probability functions corresponding to public interfaces, including
-        >>> # `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival`, have the same
-        >>> # arguments as follows.
-        >>> # Args:
-        >>> #     value (Tensor): the value to be evaluated.
-        >>> #     loc (Tensor): the loc of distribution. Default: None. If `loc` is passed in as None,
-        >>> #         the mean of the underlying Normal distribution will be used.
-        >>> #     scale (Tensor): the scale of distribution. Default: None. If `scale` is passed in as None,
-        >>> #         the standard deviation of the underlying Normal distribution will be used.
-        >>> # Examples of `prob`.
-        >>> # Similar calls can be made to other probability functions
-        >>> # by replacing 'prob' by the name of the function.
-        >>> ans = n1.prob(value)
-        >>> print(ans.shape)
-        (3,)
-        >>> # Evaluate with respect to distribution b.
-        >>> ans = n1.prob(value, loc_b, scale_b)
-        >>> print(ans.shape)
-        (3,)
-        >>> # `loc` and `scale` must be passed in during function calls since they were not passed in construct.
-        >>> ans = n2.prob(value, loc_a, scale_a)
-        >>> print(ans.shape)
-        (3,)
-        >>> # Functions `mean`, `sd`, `var`, and `entropy` have the same arguments.
-        >>> # Args:
-        >>> #     loc (Tensor): the loc of distribution. Default: None. If `loc` is passed in as None,
-        >>> #         the mean of the underlying Normal distribution will be used.
-        >>> #     scale (Tensor): the scale of distribution. Default: None. If `scale` is passed in as None,
-        >>> #         the standard deviation of the underlying Normal distribution will be used.
-        >>> # Example of `mean`. `sd`, `var`, and `entropy` are similar.
-        >>> ans = n1.mean()
-        >>> print(ans.shape)
-        ()
-        >>> ans = n1.mean(loc_b, scale_b)
-        >>> print(ans.shape)
-        (3,)
-        >>> # `loc` and `scale` must be passed in during function calls since they were not passed in construct.
-        >>> ans = n2.mean(loc_a, scale_a)
-        >>> print(ans.shape)
-        (3,)
-        >>> # Interfaces of 'kl_loss' and 'cross_entropy' are the same:
-        >>> # Args:
-        >>> #     dist (str): the type of the distributions. Only "Normal" is supported.
-        >>> #     loc_b (Tensor): the loc of distribution b.
-        >>> #     scale_b (Tensor): the scale distribution b.
-        >>> #     loc_a (Tensor): the loc of distribution a. Default: None. If `loc` is passed in as None,
-        >>> #         the mean of the underlying Normal distribution will be used.
-        >>> #     scale_a (Tensor): the scale distribution a. Default: None. If `scale` is passed in as None,
-        >>> #         the standard deviation of the underlying Normal distribution will be used.
-        >>> # Examples of `kl_loss`. `cross_entropy` is similar.
-        >>> ans = n1.kl_loss('LogNormal', loc_b, scale_b)
-        >>> print(ans.shape)
-        (3,)
-        >>> ans = n1.kl_loss('LogNormal', loc_b, scale_b, loc_a, scale_a)
-        >>> print(ans.shape)
-        (3,)
-        >>> # Additional `loc` and `scale` must be passed in since they were not passed in construct.
-        >>> ans = n2.kl_loss('LogNormal', loc_b, scale_b, loc_a, scale_a)
-        >>> print(ans.shape)
-        (3,)
-        >>> # Examples of `sample`.
-        >>> # Args:
-        >>> #     shape (tuple): the shape of the sample. Default: ()
-        >>> #     loc (Tensor): the loc of the distribution. Default: None. If `loc` is passed in as None,
-        >>> #         the mean of the underlying Normal distribution will be used.
-        >>> #     scale (Tensor): the scale of the distribution. Default: None. If `scale` is passed in as None,
-        >>> #         the standard deviation of the underlying Normal distribution will be used.
-        >>> ans = n1.sample()
-        >>> print(ans.shape)
-        ()
-        >>> ans = n1.sample((2,3))
-        >>> print(ans.shape)
-        (2, 3)
-        >>> ans = n1.sample((2,3), loc_b, scale_b)
-        >>> print(ans.shape)
-        (2, 3, 3)
-        >>> ans = n2.sample((2,3), loc_a, scale_a)
-        >>> print(ans.shape)
-        (2, 3, 3)
+        ...     class Prob(nn.Cell):
+        ...         def __init__(self):
+        ...             super(Prob, self).__init__()
+        ...             self.ln = msd.LogNormal(np.array([0.3]), np.array([[0.2], [0.4]]), dtype=dtype.float32)
+        ...         def construct(self, x_):
+        ...             return self.ln.prob(x_)
+        >>> pdf = Prob()
+        >>> output = pdf(Tensor([1.0, 2.0], dtype=dtype.float32))
     """
 
     def __init__(self,

mindspore/nn/probability/distribution/poisson.py

@@ -89,7 +89,7 @@ class Poisson(Distribution):
         >>> # `rate` must be passed in during function calls.
         >>> ans = p2.mean(rate_a)
         >>> print(ans.shape)
-        (1,)
+        ()
         >>> # Examples of `sample`.
         >>> # Args:
         >>> #     shape (tuple): the shape of the sample. Default: ()

mindspore/nn/probability/distribution/transformed_distribution.py

@@ -53,25 +53,28 @@ class TransformedDistribution(Distribution):
 
     Examples:
         >>> import mindspore
-        >>> import mindspore.context as context
         >>> import mindspore.nn as nn
         >>> import mindspore.nn.probability.distribution as msd
         >>> import mindspore.nn.probability.bijector as msb
         >>> from mindspore import Tensor
-        >>> context.set_context(mode=1)
-        >>>
-        >>> # To initialize a transformed distribution
-        >>> # using a Normal distribution as the base distribution,
-        >>> # and an Exp bijector as the bijector function.
-        >>> trans_dist = msd.TransformedDistribution(msb.Exp(), msd.Normal(0.0, 1.0))
-        >>>
-        >>> value = Tensor([1.0, 2.0, 3.0], dtype=mindspore.float32)
-        >>> prob = trans_dist.prob(value)
-        >>> print(prob.shape)
-        (3,)
-        >>> sample = trans_dist.sample(shape=(2, 3))
-        >>> print(sample.shape)
-        (2, 3)
+        >>> class Net(nn.Cell):
+        ...     def __init__(self, shape, dtype=dtype.float32, seed=0, name='transformed_distribution'):
+        ...         super(Net, self).__init__()
+        ...         # create TransformedDistribution distribution
+        ...         self.exp = msb.Exp()
+        ...         self.normal = msd.Normal(0.0, 1.0, dtype=dtype)
+        ...         self.lognormal = msd.TransformedDistribution(self.exp, self.normal, seed=seed, name=name)
+        ...         self.shape = shape
+        ...
+        ...     def construct(self, value):
+        ...         cdf = self.lognormal.cdf(value)
+        ...         sample = self.lognormal.sample(self.shape)
+        ...         return cdf, sample
+        >>> shape = (2, 3)
+        >>> net = Net(shape=shape, name="LogNormal")
+        >>> x = np.array([2.0, 3.0, 4.0, 5.0]).astype(np.float32)
+        >>> tx = Tensor(x, dtype=dtype.float32)
+        >>> cdf, sample = net(tx)
     """
 
     def __init__(self,

mindspore/nn/probability/distribution/uniform.py

@@ -38,7 +38,7 @@ class Uniform(Distribution):
         ``Ascend`` ``GPU``
 
     Note:
-        `low` must be stricly less than `high`.
+        `low` must be strictly less than `high`.
         `dist_spec_args` are `high` and `low`.
         `dtype` must be float type because Uniform distributions are continuous.
 
@@ -143,7 +143,8 @@ class Uniform(Distribution):
         param = dict(locals())
         param['param_dict'] = {'low': low, 'high': high}
         valid_dtype = mstype.float_type
-        Validator.check_type_name("dtype", dtype, valid_dtype, type(self).__name__)
+        Validator.check_type_name(
+            "dtype", dtype, valid_dtype, type(self).__name__)
         super(Uniform, self).__init__(seed, dtype, name, param)
 
         self._low = self._add_parameter(low, 'low')
@@ -151,7 +152,6 @@ class Uniform(Distribution):
         if self.low is not None and self.high is not None:
             check_greater(self.low, self.high, 'low', 'high')
 
-
         # ops needed for the class
         self.exp = exp_generic
         self.log = log_generic

mindspore/nn/probability/zhusuan/framework/bn.py

@@ -20,11 +20,13 @@ import mindspore.nn.probability.distribution as msd
 from mindspore.common import dtype as mstype
 from mindspore.ops import operations as P
 
+
 class BayesianNet(nn.Cell):
     """
     We currently support 3 types of variables: x = observation, z = latent, y = condition.
-    A Bayeisian Network models a generative process for certain varaiables: p(x,z|y) or p(z|x,y) or p(x|z,y)
+    A Bayeisian Network models a generative process for certain variables: p(x,z|y) or p(z|x,y) or p(x|z,y)
     """
+
     def __init__(self):
         super().__init__()
         self.normal_dist = msd.Normal(dtype=mstype.float32)
@@ -49,14 +51,16 @@ class BayesianNet(nn.Cell):
 
         if observation is None:
             if reparameterize:
-                epsilon = self.normal_dist('sample', shape, self.zeros(mean.shape), self.ones(std.shape))
+                epsilon = self.normal_dist('sample', shape, self.zeros(
+                    mean.shape), self.ones(std.shape))
                 sample = mean + std * epsilon
             else:
                 sample = self.normal_dist('sample', shape, mean, std)
         else:
             sample = observation
 
-        log_prob = self.reduce_sum(self.normal_dist('log_prob', sample, mean, std), 1)
+        log_prob = self.reduce_sum(self.normal_dist(
+            'log_prob', sample, mean, std), 1)
         return sample, log_prob
 
     def Bernoulli(self,
@@ -77,7 +81,8 @@ class BayesianNet(nn.Cell):
         else:
             sample = observation
 
-        log_prob = self.reduce_sum(self.bernoulli_dist('log_prob', sample, probs), 1)
+        log_prob = self.reduce_sum(
+            self.bernoulli_dist('log_prob', sample, probs), 1)
         return sample, log_prob
 
     def construct(self, *inputs, **kwargs):

tests/st/probability/distribution/test_categorical.py

@@ -24,10 +24,12 @@ from mindspore import dtype
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
+
 class Prob(nn.Cell):
     """
     Test class: probability of categorical distribution.
     """
+
     def __init__(self):
         super(Prob, self).__init__()
         self.c = msd.Categorical([0.7, 0.3], dtype=dtype.int32)
@@ -35,13 +37,15 @@ class Prob(nn.Cell):
     def construct(self, x_):
         return self.c.prob(x_)
 
+
 def test_pmf():
     """
     Test pmf.
     """
     expect_pmf = [0.7, 0.3, 0.7, 0.3, 0.3]
     pmf = Prob()
-    x_ = Tensor(np.array([0, 1, 0, 1, 1]).astype(np.int32), dtype=dtype.float32)
+    x_ = Tensor(np.array([0, 1, 0, 1, 1]).astype(
+        np.int32), dtype=dtype.float32)
     output = pmf(x_)
     tol = 1e-6
     assert (np.abs(output.asnumpy() - expect_pmf) < tol).all()
@@ -51,6 +55,7 @@ class LogProb(nn.Cell):
     """
     Test class: log probability of categorical distribution.
     """
+
     def __init__(self):
         super(LogProb, self).__init__()
         self.c = msd.Categorical([0.7, 0.3], dtype=dtype.int32)
@@ -58,21 +63,25 @@ class LogProb(nn.Cell):
     def construct(self, x_):
         return self.c.log_prob(x_)
 
+
 def test_log_likelihood():
     """
     Test log_pmf.
     """
     expect_logpmf = np.log([0.7, 0.3, 0.7, 0.3, 0.3])
     logprob = LogProb()
-    x_ = Tensor(np.array([0, 1, 0, 1, 1]).astype(np.int32), dtype=dtype.float32)
+    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 categorical distributions.
     """
+
     def __init__(self):
         super(KL, self).__init__()
         self.c = msd.Categorical([0.7, 0.3], dtype=dtype.int32)
@@ -80,6 +89,7 @@ class KL(nn.Cell):
     def construct(self, x_):
         return self.c.kl_loss('Categorical', x_)
 
+
 def test_kl_loss():
     """
     Test kl_loss.
@@ -89,10 +99,12 @@ def test_kl_loss():
     tol = 1e-6
     assert (np.abs(output.asnumpy()) < tol).all()
 
+
 class Sampling(nn.Cell):
     """
     Test class: sampling of categorical distribution.
     """
+
     def __init__(self):
         super(Sampling, self).__init__()
         self.c = msd.Categorical([0.2, 0.1, 0.7], dtype=dtype.int32)
@@ -101,6 +113,7 @@ class Sampling(nn.Cell):
     def construct(self):
         return self.c.sample(self.shape)
 
+
 def test_sample():
     """
     Test sample.
@@ -109,10 +122,12 @@ def test_sample():
         sample = Sampling()
         sample()
 
+
 class Basics(nn.Cell):
     """
     Test class: mean/var/mode of categorical distribution.
     """
+
     def __init__(self):
         super(Basics, self).__init__()
         self.c = msd.Categorical([0.2, 0.1, 0.7], dtype=dtype.int32)
@@ -120,6 +135,7 @@ class Basics(nn.Cell):
     def construct(self):
         return self.c.mean(), self.c.var(), self.c.mode()
 
+
 def test_basics():
     """
     Test mean/variance/mode.
@@ -139,6 +155,7 @@ class CDF(nn.Cell):
     """
     Test class: cdf of categorical distributions.
     """
+
     def __init__(self):
         super(CDF, self).__init__()
         self.c = msd.Categorical([0.7, 0.3], dtype=dtype.int32)
@@ -146,21 +163,25 @@ class CDF(nn.Cell):
     def construct(self, x_):
         return self.c.cdf(x_)
 
+
 def test_cdf():
     """
     Test cdf.
     """
     expect_cdf = [0.7, 0.7, 1, 0.7, 1]
-    x_ = Tensor(np.array([0, 0, 1, 0, 1]).astype(np.int32), dtype=dtype.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 categorical distributions.
     """
+
     def __init__(self):
         super(LogCDF, self).__init__()
         self.c = msd.Categorical([0.7, 0.3], dtype=dtype.int32)
@@ -168,12 +189,14 @@ class LogCDF(nn.Cell):
     def construct(self, x_):
         return self.c.log_cdf(x_)
 
+
 def test_logcdf():
     """
     Test log_cdf.
     """
     expect_logcdf = np.log([0.7, 0.7, 1, 0.7, 1])
-    x_ = Tensor(np.array([0, 0, 1, 0, 1]).astype(np.int32), dtype=dtype.float32)
+    x_ = Tensor(np.array([0, 0, 1, 0, 1]).astype(
+        np.int32), dtype=dtype.float32)
     logcdf = LogCDF()
     output = logcdf(x_)
     tol = 1e-6
@@ -184,6 +207,7 @@ class SF(nn.Cell):
     """
     Test class: survival function of categorical distributions.
     """
+
     def __init__(self):
         super(SF, self).__init__()
         self.c = msd.Categorical([0.7, 0.3], dtype=dtype.int32)
@@ -191,12 +215,14 @@ class SF(nn.Cell):
     def construct(self, x_):
         return self.c.survival_function(x_)
 
+
 def test_survival():
     """
-    Test survival funciton.
+    Test survival function.
     """
     expect_survival = [0.3, 0., 0., 0.3, 0.3]
-    x_ = Tensor(np.array([0, 1, 1, 0, 0]).astype(np.int32), dtype=dtype.float32)
+    x_ = Tensor(np.array([0, 1, 1, 0, 0]).astype(
+        np.int32), dtype=dtype.float32)
     sf = SF()
     output = sf(x_)
     tol = 1e-6
@@ -207,6 +233,7 @@ class LogSF(nn.Cell):
     """
     Test class: log survival function of categorical distributions.
     """
+
     def __init__(self):
         super(LogSF, self).__init__()
         self.c = msd.Categorical([0.7, 0.3], dtype=dtype.int32)
@@ -214,21 +241,25 @@ class LogSF(nn.Cell):
     def construct(self, x_):
         return self.c.log_survival(x_)
 
+
 def test_log_survival():
     """
-    Test log survival funciton.
+    Test log survival function.
     """
     expect_logsurvival = np.log([1., 0.3, 0.3, 0.3, 0.3])
-    x_ = Tensor(np.array([-2, 0, 0, 0.5, 0.5]).astype(np.float32), dtype=dtype.float32)
+    x_ = Tensor(np.array([-2, 0, 0, 0.5, 0.5]
+                         ).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 categorical distributions.
     """
+
     def __init__(self):
         super(EntropyH, self).__init__()
         self.c = msd.Categorical([0.7, 0.3], dtype=dtype.int32)
@@ -236,6 +267,7 @@ class EntropyH(nn.Cell):
     def construct(self):
         return self.c.entropy()
 
+
 def test_entropy():
     """
     Test entropy.
@@ -247,10 +279,12 @@ def test_entropy():
     tol = 1e-6
     assert (np.abs(output.asnumpy() - expect_entropy) < tol).all()
 
+
 class CrossEntropy(nn.Cell):
     """
     Test class: cross entropy between categorical distributions.
     """
+
     def __init__(self):
         super(CrossEntropy, self).__init__()
         self.c = msd.Categorical([0.7, 0.3], dtype=dtype.int32)
@@ -262,6 +296,7 @@ class CrossEntropy(nn.Cell):
         cross_entropy = self.c.cross_entropy('Categorical', x_)
         return h_sum_kl - cross_entropy
 
+
 def test_cross_entropy():
     """
     Test cross_entropy.

tests/st/probability/zhusuan/vae/utils.py

@@ -43,20 +43,29 @@ def create_dataset(data_path, batch_size=32, repeat_size=1,
     shift = 0.0
 
     # define map operations
-    resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR)  # resize images to (32, 32)
+    # resize images to (32, 32)
+    resize_op = CV.Resize((resize_height, resize_width),
+                          interpolation=Inter.LINEAR)
     rescale_op = CV.Rescale(rescale, shift)  # rescale images
-    hwc2chw_op = CV.HWC2CHW()  # change shape from (height, width, channel) to (channel, height, width) to fit network.
-    type_cast_op = C.TypeCast(mstype.int32)  # change data type of label to int32 to fit network
+    # change shape from (height, width, channel) to (channel, height, width) to fit network.
+    hwc2chw_op = CV.HWC2CHW()
+    # change data type of label to int32 to fit network
+    type_cast_op = C.TypeCast(mstype.int32)
 
     # apply map operations on images
-    mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=num_parallel_workers)
-    mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers)
-    mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers)
-    mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers)
+    mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op,
+                            num_parallel_workers=num_parallel_workers)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op,
+                            num_parallel_workers=num_parallel_workers)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op,
+                            num_parallel_workers=num_parallel_workers)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op,
+                            num_parallel_workers=num_parallel_workers)
 
     # apply DatasetOps
     buffer_size = 10000
-    mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size)  # 10000 as in LeNet train script
+    # 10000 as in LeNet train script
+    mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size)
     mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
     mnist_ds = mnist_ds.repeat(repeat_size)
 
@@ -68,7 +77,7 @@ def save_img(data, name, size=32, num=32):
     Visualize data and save to target files
     Args:
         data: nparray of size (num, size, size)
-        name: ouput file name
+        name: output file name
         size: image size
         num: number of images
     """

tests/ut/python/nn/probability/distribution/test_categorical.py

@@ -62,15 +62,17 @@ def test_prob():
     with pytest.raises(ValueError):
         msd.Categorical([1.0], dtype=dtype.int32)
 
+
 def test_categorical_sum():
     """
-    Invaild probabilities.
+    Invalid probabilities.
     """
     with pytest.raises(ValueError):
         msd.Categorical([[0.1, 0.2], [0.4, 0.6]], dtype=dtype.int32)
     with pytest.raises(ValueError):
         msd.Categorical([[0.5, 0.7], [0.6, 0.6]], dtype=dtype.int32)
 
+
 def rank():
     """
     Rank dimenshion less than 1.
@@ -80,7 +82,9 @@ def rank():
     with pytest.raises(ValueError):
         msd.Categorical(np.array(0.3).astype(np.float32), dtype=dtype.int32)
     with pytest.raises(ValueError):
-        msd.Categorical(Tensor(np.array(0.3).astype(np.float32)), dtype=dtype.int32)
+        msd.Categorical(
+            Tensor(np.array(0.3).astype(np.float32)), dtype=dtype.int32)
+
 
 class CategoricalProb(nn.Cell):
     """
@@ -211,6 +215,7 @@ class CategoricalConstruct(nn.Cell):
         prob2 = self.c1('prob', value, probs)
         return prob + prob1 + prob2
 
+
 def test_categorical_construct():
     """
     Test probability function going through construct.
@@ -235,7 +240,7 @@ class CategoricalBasics(nn.Cell):
     def construct(self, probs):
         basics1 = self.c.mean() + self.c.var() + self.c.mode() + self.c.entropy()
         basics2 = self.c1.mean(probs) + self.c1.var(probs) +\
-                  self.c1.mode(probs) + self.c1.entropy(probs)
+            self.c1.mode(probs) + self.c1.entropy(probs)
         return basics1 + basics2
 
 

tests/ut/python/nn/probability/distribution/test_distribution.py

@@ -29,19 +29,23 @@ func_name_list = ['prob', 'log_prob', 'cdf', 'log_cdf',
                   'entropy', 'kl_loss', 'cross_entropy',
                   'sample']
 
+
 class MyExponential(msd.Distribution):
     """
-    Test distirbution class: no function is implemented.
+    Test distribution class: no function is implemented.
     """
+
     def __init__(self, rate=None, seed=None, dtype=mstype.float32, name="MyExponential"):
         param = dict(locals())
         param['param_dict'] = {'rate': rate}
         super(MyExponential, self).__init__(seed, dtype, name, param)
 
+
 class Net(nn.Cell):
     """
     Test Net: function called through construct.
     """
+
     def __init__(self, func_name):
         super(Net, self).__init__()
         self.dist = MyExponential()
@@ -61,6 +65,7 @@ def test_raise_not_implemented_error_construct():
             net = Net(func_name)
             net(value)
 
+
 def test_raise_not_implemented_error_construct_graph_mode():
     """
     test raise not implemented error in graph mode.
@@ -72,10 +77,12 @@ def test_raise_not_implemented_error_construct_graph_mode():
             net = Net(func_name)
             net(value)
 
+
 class Net1(nn.Cell):
     """
     Test Net: function called directly.
     """
+
     def __init__(self, func_name):
         super(Net1, self).__init__()
         self.dist = MyExponential()
@@ -84,6 +91,7 @@ class Net1(nn.Cell):
     def construct(self, *args, **kwargs):
         return self.func(*args, **kwargs)
 
+
 def test_raise_not_implemented_error():
     """
     test raise not implemented error in pynative mode.
@@ -94,6 +102,7 @@ def test_raise_not_implemented_error():
             net = Net1(func_name)
             net(value)
 
+
 def test_raise_not_implemented_error_graph_mode():
     """
     test raise not implemented error in graph mode.

tests/ut/python/nn/probability/distribution/test_uniform.py

@@ -23,6 +23,7 @@ import mindspore.nn.probability.distribution as msd
 from mindspore import dtype
 from mindspore import Tensor
 
+
 def test_uniform_shape_errpr():
     """
     Invalid shapes.
@@ -30,18 +31,22 @@ def test_uniform_shape_errpr():
     with pytest.raises(ValueError):
         msd.Uniform([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)
 
+
 def test_type():
     with pytest.raises(TypeError):
         msd.Uniform(0., 1., dtype=dtype.int32)
 
+
 def test_name():
     with pytest.raises(TypeError):
         msd.Uniform(0., 1., name=1.0)
 
+
 def test_seed():
     with pytest.raises(TypeError):
         msd.Uniform(0., 1., seed='seed')
 
+
 def test_arguments():
     """
     Args passing during initialization.
@@ -66,6 +71,7 @@ class UniformProb(nn.Cell):
     """
     Uniform distribution: initialize with low/high.
     """
+
     def __init__(self):
         super(UniformProb, self).__init__()
         self.u = msd.Uniform(3.0, 4.0, dtype=dtype.float32)
@@ -79,6 +85,7 @@ class UniformProb(nn.Cell):
         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.
@@ -88,10 +95,12 @@ def test_uniform_prob():
     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 = msd.Uniform(dtype=dtype.float32)
@@ -105,6 +114,7 @@ class UniformProb1(nn.Cell):
         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.
@@ -116,13 +126,16 @@ def test_uniform_prob1():
     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 = msd.Uniform(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
+        self.u1 = msd.Uniform(
+            np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
         self.u2 = msd.Uniform(dtype=dtype.float32)
 
     def construct(self, low_b, high_b, low_a, high_a):
@@ -130,6 +143,7 @@ class UniformKl(nn.Cell):
         kl2 = self.u2.kl_loss('Uniform', low_b, high_b, low_a, high_a)
         return kl1 + kl2
 
+
 def test_kl():
     """
     Test kl_loss.
@@ -142,13 +156,16 @@ def test_kl():
     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 = msd.Uniform(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
+        self.u1 = msd.Uniform(
+            np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
         self.u2 = msd.Uniform(dtype=dtype.float32)
 
     def construct(self, low_b, high_b, low_a, high_a):
@@ -156,9 +173,10 @@ class UniformCrossEntropy(nn.Cell):
         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.
+    Test cross_entropy between Uniform distributions.
     """
     net = UniformCrossEntropy()
     low_b = Tensor(np.array([0.0]).astype(np.float32), dtype=dtype.float32)
@@ -168,10 +186,12 @@ def test_cross_entropy():
     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 = msd.Uniform(3.0, 4.0, dtype=dtype.float32)
@@ -183,6 +203,7 @@ class UniformBasics(nn.Cell):
         entropy = self.u.entropy()
         return mean + sd + var + entropy
 
+
 def test_bascis():
     """
     Test mean/sd/var/mode/entropy functionality of Uniform.
@@ -194,8 +215,9 @@ def test_bascis():
 
 class UniConstruct(nn.Cell):
     """
-    Unifrom distribution: going through construct.
+    Uniform distribution: going through construct.
     """
+
     def __init__(self):
         super(UniConstruct, self).__init__()
         self.u = msd.Uniform(-4.0, 4.0)
@@ -207,6 +229,7 @@ class UniConstruct(nn.Cell):
         prob2 = self.u1('prob', value, low, high)
         return prob + prob1 + prob2
 
+
 def test_uniform_construct():
     """
     Test probability function going through construct.