!5789 Add private interface specification in distribution docs

Merge pull request !5789 from XunDeng/pp_issue_branch

mindspore/nn/probability/distribution/bernoulli.py

@@ -33,7 +33,7 @@ class Bernoulli(Distribution):
 
     Note:
         probs should be proper probabilities (0 < p < 1).
-        Dist_spec_args is probs.
+        dist_spec_args is probs.
 
     Examples:
         >>> # To initialize a Bernoulli distribution of prob 0.5
@@ -57,32 +57,50 @@ class Bernoulli(Distribution):
         >>>     # All the following calls in construct are valid
         >>>     def construct(self, value, probs_b, probs_a):
         >>>
+        >>>         # Private interfaces of probability functions corresponding to public interfaces, including
+        >>>         # 'prob', 'log_prob', 'cdf', 'log_cdf', 'survival_function', 'log_survival', have the form:
+        >>>         # Args:
+        >>>         #     value (Tensor): value to be evaluated.
+        >>>         #     probs1 (Tensor): probability of success. Default: self.probs.
+        >>>
+        >>>         # Example of prob.
         >>>         # 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 during function calls
         >>>         ans = self.b2.prob(value, probs_a)
         >>>
-        >>>         # Functions 'sd', 'var', 'entropy' have the same usage as 'mean'
-        >>>         # Will return 0.5
-        >>>         ans = self.b1.mean()
-        >>>         # Will return probs_b
-        >>>         ans = self.b1.mean(probs_b)
         >>>
+        >>>         # Functions 'sd', 'var', 'entropy' have the same args.
+        >>>         # Args:
+        >>>         #     probs1 (Tensor): probability of success. Default: self.probs.
+        >>>
+        >>>         # Example of mean. sd, var have similar usage.
+        >>>         ans = self.b1.mean() # return 0.5
+        >>>         ans = self.b1.mean(probs_b) # return probs_b
         >>>         # probs must be passed in during function calls
         >>>         ans = self.b2.mean(probs_a)
         >>>
-        >>>         # Usage of 'kl_loss' and 'cross_entropy' are similar
+        >>>
+        >>>         # Interfaces of 'kl_loss' and 'cross_entropy' are similar:
+        >>>         # Args:
+        >>>         #     dist (str): name of the distribution. Only 'Bernoulli' is supported.
+        >>>         #     probs1_b (Tensor): probability of success of distribution b.
+        >>>         #     probs1_a (Tensor): probability of success of distribution a. Default: self.probs.
+        >>>
+        >>>         # Example of kl_loss (cross_entropy is 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
+        >>>         # Additional probs_a must be passed in
         >>>         ans = self.b2.kl_loss('Bernoulli', probs_b, probs_a)
         >>>
-        >>>         # Sample
+        >>>
+        >>>         # sample
+        >>>         # Args:
+        >>>         #     shape (tuple): shape of the sample. Default: ()
+        >>>         #     probs1 (Tensor): probability of success. Default: self.probs.
         >>>         ans = self.b1.sample()
         >>>         ans = self.b1.sample((2,3))
         >>>         ans = self.b1.sample((2,3), probs_b)

mindspore/nn/probability/distribution/exponential.py

@@ -34,7 +34,8 @@ class Exponential(Distribution):
 
     Note:
         rate should be strictly greater than 0.
-        Dist_spec_args is rate.
+        dist_spec_args is rate.
+        dtype should be float type because Exponential distributions are continuous.
 
     Examples:
         >>> # To initialize an Exponential distribution of rate 0.5
@@ -58,32 +59,50 @@ class Exponential(Distribution):
         >>>     # All the following calls in construct are valid
         >>>     def construct(self, value, rate_b, rate_a):
         >>>
+        >>>         # Private interfaces of probability functions corresponding to public interfaces, including
+        >>>         # 'prob', 'log_prob', 'cdf', 'log_cdf', 'survival_function', 'log_survival', have the form:
+        >>>         # Args:
+        >>>         #     value (Tensor): value to be evaluated.
+        >>>         #     rate (Tensor): rate of the distribution. Default: self.rate.
+        >>>
+        >>>         # Example of prob.
         >>>         # 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 during function calls
         >>>         ans = self.e2.prob(value, rate_a)
         >>>
-        >>>         # Functions 'sd', 'var', 'entropy' have the same usage as'mean'
-        >>>         # Will return 2
-        >>>         ans = self.e1.mean()
-        >>>         # Will return 1 / rate_b
-        >>>         ans = self.e1.mean(rate_b)
         >>>
+        >>>         # Functions 'sd', 'var', 'entropy' have the same args.
+        >>>         # Args:
+        >>>         #     rate (Tensor): rate of the distribution. Default: self.rate.
+        >>>
+        >>>         # Example of mean. sd, var have similar usage.
+        >>>         ans = self.e1.mean() # return 2
+        >>>         ans = self.e1.mean(rate_b) # return 1 / rate_b
         >>>         # Rate must be passed in during function calls
         >>>         ans = self.e2.mean(rate_a)
         >>>
-        >>>         # Usage of 'kl_loss' and 'cross_entropy' are similar
+        >>>
+        >>>         # Interfaces of 'kl_loss' and 'cross_entropy' are similar:
+        >>>         # Args:
+        >>>         #     dist (str): name of the distribution. Only 'Exponential' is supported.
+        >>>         #     rate_b (Tensor): rate of distribution b.
+        >>>         #     rate_a (Tensor): rate of distribution a. Default: self.rate.
+        >>>
+        >>>         # Example of kl_loss (cross_entropy is 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
         >>>         ans = self.e2.kl_loss('Exponential', rate_b, rate_a)
         >>>
-        >>>         # Sample
+        >>>
+        >>>         # sample
+        >>>         # Args:
+        >>>         #     shape (tuple): shape of the sample. Default: ()
+        >>>         #     probs1 (Tensor): rate of distribution. Default: self.rate.
         >>>         ans = self.e1.sample()
         >>>         ans = self.e1.sample((2,3))
         >>>         ans = self.e1.sample((2,3), rate_b)

mindspore/nn/probability/distribution/geometric.py

@@ -36,7 +36,7 @@ class Geometric(Distribution):
 
     Note:
         probs should be proper probabilities (0 < p < 1).
-        Dist_spec_args is probs.
+        dist_spec_args is probs.
 
     Examples:
         >>> # To initialize a Geometric distribution of prob 0.5
@@ -60,32 +60,50 @@ class Geometric(Distribution):
         >>>     # Tthe following calls are valid in construct
         >>>     def construct(self, value, probs_b, probs_a):
         >>>
+        >>>         # Private interfaces of probability functions corresponding to public interfaces, including
+        >>>         # 'prob', 'log_prob', 'cdf', 'log_cdf', 'survival_function', 'log_survival', have the form:
+        >>>         # Args:
+        >>>         #     value (Tensor): value to be evaluated.
+        >>>         #     probs1 (Tensor): probability of success of a Bernoulli trail. Default: self.probs.
+        >>>
+        >>>         # Example of prob.
         >>>         # 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 during function calls
         >>>         ans = self.g2.prob(value, probs_a)
         >>>
-        >>>         # Functions 'sd', 'var', 'entropy' have the same usage as 'mean'
-        >>>         # Will return 1.0
-        >>>         ans = self.g1.mean()
-        >>>         # Another possible usage
-        >>>         ans = self.g1.mean(probs_b)
         >>>
+        >>>         # Functions 'sd', 'var', 'entropy' have the same args.
+        >>>         # Args:
+        >>>         #     probs1 (Tensor): probability of success of a Bernoulli trail. Default: self.probs.
+        >>>
+        >>>         # Example of mean. sd, var have similar usage.
+        >>>         ans = self.g1.mean() # return 1.0
+        >>>         ans = self.g1.mean(probs_b)
         >>>         # Probs must be passed in during function calls
         >>>         ans = self.g2.mean(probs_a)
         >>>
-        >>>         # Usage of 'kl_loss' and 'cross_entropy' are similar
+        >>>
+        >>>         # Interfaces of 'kl_loss' and 'cross_entropy' are similar:
+        >>>         # Args:
+        >>>         #     dist (str): name of the distribution. Only 'Geometric' is supported.
+        >>>         #     probs1_b (Tensor): probability of success of a Bernoulli trail of distribution b.
+        >>>         #     probs1_a (Tensor): probability of success of a Bernoulli trail of distribution a. Default: self.probs.
+        >>>
+        >>>         # Example of kl_loss (cross_entropy is 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
         >>>         ans = self.g2.kl_loss('Geometric', probs_b, probs_a)
         >>>
-        >>>         # Sample
+        >>>
+        >>>         # sample
+        >>>         # Args:
+        >>>         #     shape (tuple): shape of the sample. Default: ()
+        >>>         #     probs1 (Tensor): probability of success of a Bernoulli trail. Default: self.probs.
         >>>         ans = self.g1.sample()
         >>>         ans = self.g1.sample((2,3))
         >>>         ans = self.g1.sample((2,3), probs_b)

mindspore/nn/probability/distribution/normal.py

@@ -35,7 +35,8 @@ class Normal(Distribution):
 
     Note:
         Standard deviation should be greater than zero.
-        Dist_spec_args are mean and sd.
+        dist_spec_args are mean and sd.
+        dtype should be float type because Normal distributions are continuous.
 
     Examples:
         >>> # To initialize a Normal distribution of mean 3.0 and standard deviation 4.0
@@ -59,32 +60,54 @@ class Normal(Distribution):
         >>>     # The following calls are valid in construct
         >>>     def construct(self, value, mean_b, sd_b, mean_a, sd_a):
         >>>
+        >>>         # Private interfaces of probability functions corresponding to public interfaces, including
+        >>>         # 'prob', 'log_prob', 'cdf', 'log_cdf', 'survival_function', 'log_survival', have the form:
+        >>>         # Args:
+        >>>         #     value (Tensor): value to be evaluated.
+        >>>         #     mean (Tensor): mean of distribution. Default: self._mean_value.
+        >>>         #     sd (Tensor): standard deviation of distribution. Default: self._sd_value.
+        >>>
+        >>>         # Example of prob.
         >>>         # 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 during function calls
         >>>         ans = self.n2.prob(value, mean_a, sd_a)
         >>>
-        >>>         # Functions 'sd', 'var', 'entropy' have the same usage as '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 during function calls
+        >>>         # Functions 'sd', 'var', 'entropy' have the same args.
+        >>>         # Args:
+        >>>         #     mean (Tensor): mean of distribution. Default: self._mean_value.
+        >>>         #     sd (Tensor): standard deviation of distribution. Default: self._sd_value.
+        >>>
+        >>>         # Example of mean. sd, var have similar usage.
+        >>>         ans = self.n1.mean() # return 0.0
+        >>>         ans = self.n1.mean(mean_b, sd_b) # return mean_b
+        >>>         # mean and sd must be passed in during function calls
         >>>         ans = self.n2.mean(mean_a, sd_a)
         >>>
-        >>>         # Usage of 'kl_loss' and 'cross_entropy' are similar
+        >>>
+        >>>         # Interfaces of 'kl_loss' and 'cross_entropy' are similar:
+        >>>         # Args:
+        >>>         #     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.
+        >>>
+        >>>         # Example of kl_loss (cross_entropy is 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
+        >>>         # Additional mean and sd must be passed in
         >>>         ans = self.n2.kl_loss('Normal', mean_b, sd_b, mean_a, sd_a)
         >>>
-        >>>         # Sample
+        >>>         # sample
+        >>>         # Args:
+        >>>         #     shape (tuple): shape of the sample. Default: ()
+        >>>         #     mean (Tensor): mean of distribution. Default: self._mean_value.
+        >>>         #     sd (Tensor): standard deviation of distribution. Default: self._sd_value.
         >>>         ans = self.n1.sample()
         >>>         ans = self.n1.sample((2,3))
         >>>         ans = self.n1.sample((2,3), mean_b, sd_b)

mindspore/nn/probability/distribution/uniform.py

@@ -34,7 +34,8 @@ class Uniform(Distribution):
 
     Note:
         low should be stricly less than high.
-        Dist_spec_args are high and low.
+        dist_spec_args are high and low.
+        dtype should be float type because Uniform distributions are continuous.
 
     Examples:
         >>> # To initialize a Uniform distribution of mean 3.0 and standard deviation 4.0
@@ -58,32 +59,54 @@ class Uniform(Distribution):
         >>>     # All the following calls in construct are valid
         >>>     def construct(self, value, low_b, high_b, low_a, high_a):
         >>>
+        >>>         # Private interfaces of probability functions corresponding to public interfaces, including
+        >>>         # 'prob', 'log_prob', 'cdf', 'log_cdf', 'survival_function', 'log_survival', have the form:
+        >>>         # Args:
+        >>>         #     value (Tensor): value to be evaluated.
+        >>>         #     low (Tensor): lower bound of distribution. Default: self.low.
+        >>>         #     high (Tensor): higher bound of distribution. Default: self.high.
+        >>>
+        >>>         # Example of prob.
         >>>         # 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 during function calls
         >>>         ans = self.u2.prob(value, low_a, high_a)
         >>>
-        >>>         # Functions 'sd', 'var', 'entropy' have the same usage as 'mean'
-        >>>         # Will return 0.5
-        >>>         ans = self.u1.mean()
-        >>>         # Will return (low_b + high_b) / 2
-        >>>         ans = self.u1.mean(low_b, high_b)
         >>>
+        >>>         # Functions 'sd', 'var', 'entropy' have the same args.
+        >>>         # Args:
+        >>>         #     low (Tensor): lower bound of distribution. Default: self.low.
+        >>>         #     high (Tensor): higher bound of distribution. Default: self.high.
+        >>>
+        >>>         # Example of mean. sd, var have similar usage.
+        >>>         ans = self.u1.mean() # return 0.5
+        >>>         ans = self.u1.mean(low_b, high_b) # return (low_b + high_b) / 2
         >>>         # High and low must be passed in during function calls
         >>>         ans = self.u2.mean(low_a, high_a)
         >>>
-        >>>         # Usage of 'kl_loss' and 'cross_entropy' are similar
+        >>>         # Interfaces of 'kl_loss' and 'cross_entropy' are similar:
+        >>>         # Args:
+        >>>         #     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.
+        >>>
+        >>>         # Example of kl_loss (cross_entropy is 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
+        >>>         # Additional high and low must be passed in
         >>>         ans = self.u2.kl_loss('Uniform', low_b, high_b, low_a, high_a)
         >>>
-        >>>         # Sample
+        >>>
+        >>>         # sample
+        >>>         # Args:
+        >>>         #     shape (tuple): shape of the sample. Default: ()
+        >>>         #     low (Tensor): lower bound of distribution. Default: self.low.
+        >>>         #     high (Tensor): higher bound of distribution. Default: self.high.
         >>>         ans = self.u1.sample()
         >>>         ans = self.u1.sample((2,3))
         >>>         ans = self.u1.sample((2,3), low_b, high_b)