diff options
Diffstat (limited to 'tensorflow/g3doc/api_docs/python/functions_and_classes/shard3')
12 files changed, 268 insertions, 145 deletions
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.BetaWithSoftplusAB.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.BetaWithSoftplusAB.md index 35b8d12834..5c082fe602 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.BetaWithSoftplusAB.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.BetaWithSoftplusAB.md @@ -70,7 +70,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.cdf(value, name='cdf')` {#BetaWithSoftplusAB.cdf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.cdf(value, name='cdf', **condition_kwargs)` {#BetaWithSoftplusAB.cdf} Cumulative distribution function. @@ -85,6 +85,7 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -104,7 +105,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.BetaWithSoftplusAB.entropy(name='entropy')` {#BetaWithSoftplusAB.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -168,7 +169,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.log_cdf(value, name='log_cdf')` {#BetaWithSoftplusAB.log_cdf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.log_cdf(value, name='log_cdf', **condition_kwargs)` {#BetaWithSoftplusAB.log_cdf} Log cumulative distribution function. @@ -195,6 +196,7 @@ distribution in `self.a` and `self.b`. `x` is only legal if `0 < x < 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -205,7 +207,7 @@ distribution in `self.a` and `self.b`. `x` is only legal if `0 < x < 1`. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.log_pdf(value, name='log_pdf')` {#BetaWithSoftplusAB.log_pdf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.log_pdf(value, name='log_pdf', **condition_kwargs)` {#BetaWithSoftplusAB.log_pdf} Log probability density function. @@ -214,6 +216,7 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -229,7 +232,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.log_pmf(value, name='log_pmf')` {#BetaWithSoftplusAB.log_pmf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.log_pmf(value, name='log_pmf', **condition_kwargs)` {#BetaWithSoftplusAB.log_pmf} Log probability mass function. @@ -238,6 +241,7 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -253,7 +257,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.log_prob(value, name='log_prob')` {#BetaWithSoftplusAB.log_prob} +#### `tf.contrib.distributions.BetaWithSoftplusAB.log_prob(value, name='log_prob', **condition_kwargs)` {#BetaWithSoftplusAB.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -262,6 +266,7 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -272,7 +277,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.log_survival_function(value, name='log_survival_function')` {#BetaWithSoftplusAB.log_survival_function} +#### `tf.contrib.distributions.BetaWithSoftplusAB.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#BetaWithSoftplusAB.log_survival_function} Log survival function. @@ -292,6 +297,7 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -378,7 +384,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.pdf(value, name='pdf')` {#BetaWithSoftplusAB.pdf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.pdf(value, name='pdf', **condition_kwargs)` {#BetaWithSoftplusAB.pdf} Probability density function. @@ -387,6 +393,7 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -402,7 +409,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.pmf(value, name='pmf')` {#BetaWithSoftplusAB.pmf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.pmf(value, name='pmf', **condition_kwargs)` {#BetaWithSoftplusAB.pmf} Probability mass function. @@ -411,6 +418,7 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -426,7 +434,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.prob(value, name='prob')` {#BetaWithSoftplusAB.prob} +#### `tf.contrib.distributions.BetaWithSoftplusAB.prob(value, name='prob', **condition_kwargs)` {#BetaWithSoftplusAB.prob} Probability density/mass function (depending on `is_continuous`). @@ -443,6 +451,7 @@ distribution in `self.a` and `self.b`. `x` is only legal if `0 < x < 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -453,7 +462,7 @@ distribution in `self.a` and `self.b`. `x` is only legal if `0 < x < 1`. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.sample(sample_shape=(), seed=None, name='sample')` {#BetaWithSoftplusAB.sample} +#### `tf.contrib.distributions.BetaWithSoftplusAB.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#BetaWithSoftplusAB.sample} Generate samples of the specified shape. @@ -466,6 +475,7 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -475,7 +485,7 @@ sample. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.sample_n(n, seed=None, name='sample_n')` {#BetaWithSoftplusAB.sample_n} +#### `tf.contrib.distributions.BetaWithSoftplusAB.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#BetaWithSoftplusAB.sample_n} Generate `n` samples. @@ -486,6 +496,7 @@ Generate `n` samples. observations to sample. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -507,7 +518,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.survival_function(value, name='survival_function')` {#BetaWithSoftplusAB.survival_function} +#### `tf.contrib.distributions.BetaWithSoftplusAB.survival_function(value, name='survival_function', **condition_kwargs)` {#BetaWithSoftplusAB.survival_function} Survival function. @@ -524,6 +535,7 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Binomial.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Binomial.md index 10897cfe66..8a17fef2cf 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Binomial.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Binomial.md @@ -135,7 +135,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Binomial.cdf(value, name='cdf')` {#Binomial.cdf} +#### `tf.contrib.distributions.Binomial.cdf(value, name='cdf', **condition_kwargs)` {#Binomial.cdf} Cumulative distribution function. @@ -150,6 +150,7 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -169,7 +170,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Binomial.entropy(name='entropy')` {#Binomial.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -233,7 +234,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Binomial.log_cdf(value, name='log_cdf')` {#Binomial.log_cdf} +#### `tf.contrib.distributions.Binomial.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Binomial.log_cdf} Log cumulative distribution function. @@ -252,6 +253,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -262,7 +264,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Binomial.log_pdf(value, name='log_pdf')` {#Binomial.log_pdf} +#### `tf.contrib.distributions.Binomial.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Binomial.log_pdf} Log probability density function. @@ -271,6 +273,7 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -286,7 +289,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Binomial.log_pmf(value, name='log_pmf')` {#Binomial.log_pmf} +#### `tf.contrib.distributions.Binomial.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Binomial.log_pmf} Log probability mass function. @@ -295,6 +298,7 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -310,7 +314,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Binomial.log_prob(value, name='log_prob')` {#Binomial.log_prob} +#### `tf.contrib.distributions.Binomial.log_prob(value, name='log_prob', **condition_kwargs)` {#Binomial.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -332,6 +336,7 @@ values. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -342,7 +347,7 @@ values. - - - -#### `tf.contrib.distributions.Binomial.log_survival_function(value, name='log_survival_function')` {#Binomial.log_survival_function} +#### `tf.contrib.distributions.Binomial.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Binomial.log_survival_function} Log survival function. @@ -362,6 +367,7 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -468,7 +474,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Binomial.pdf(value, name='pdf')` {#Binomial.pdf} +#### `tf.contrib.distributions.Binomial.pdf(value, name='pdf', **condition_kwargs)` {#Binomial.pdf} Probability density function. @@ -477,6 +483,7 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -492,7 +499,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Binomial.pmf(value, name='pmf')` {#Binomial.pmf} +#### `tf.contrib.distributions.Binomial.pmf(value, name='pmf', **condition_kwargs)` {#Binomial.pmf} Probability mass function. @@ -501,6 +508,7 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -516,7 +524,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Binomial.prob(value, name='prob')` {#Binomial.prob} +#### `tf.contrib.distributions.Binomial.prob(value, name='prob', **condition_kwargs)` {#Binomial.prob} Probability density/mass function (depending on `is_continuous`). @@ -538,6 +546,7 @@ values. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -548,7 +557,7 @@ values. - - - -#### `tf.contrib.distributions.Binomial.sample(sample_shape=(), seed=None, name='sample')` {#Binomial.sample} +#### `tf.contrib.distributions.Binomial.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Binomial.sample} Generate samples of the specified shape. @@ -561,6 +570,7 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -570,7 +580,7 @@ sample. - - - -#### `tf.contrib.distributions.Binomial.sample_n(n, seed=None, name='sample_n')` {#Binomial.sample_n} +#### `tf.contrib.distributions.Binomial.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Binomial.sample_n} Generate `n` samples. @@ -581,6 +591,7 @@ Generate `n` samples. observations to sample. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -602,7 +613,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Binomial.survival_function(value, name='survival_function')` {#Binomial.survival_function} +#### `tf.contrib.distributions.Binomial.survival_function(value, name='survival_function', **condition_kwargs)` {#Binomial.survival_function} Survival function. @@ -619,6 +630,7 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DirichletMultinomial.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DirichletMultinomial.md index b9838a4c66..d58f0a5654 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DirichletMultinomial.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DirichletMultinomial.md @@ -162,7 +162,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.DirichletMultinomial.cdf(value, name='cdf')` {#DirichletMultinomial.cdf} +#### `tf.contrib.distributions.DirichletMultinomial.cdf(value, name='cdf', **condition_kwargs)` {#DirichletMultinomial.cdf} Cumulative distribution function. @@ -177,6 +177,7 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -196,7 +197,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.DirichletMultinomial.entropy(name='entropy')` {#DirichletMultinomial.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -260,7 +261,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.DirichletMultinomial.log_cdf(value, name='log_cdf')` {#DirichletMultinomial.log_cdf} +#### `tf.contrib.distributions.DirichletMultinomial.log_cdf(value, name='log_cdf', **condition_kwargs)` {#DirichletMultinomial.log_cdf} Log cumulative distribution function. @@ -279,6 +280,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -289,7 +291,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.DirichletMultinomial.log_pdf(value, name='log_pdf')` {#DirichletMultinomial.log_pdf} +#### `tf.contrib.distributions.DirichletMultinomial.log_pdf(value, name='log_pdf', **condition_kwargs)` {#DirichletMultinomial.log_pdf} Log probability density function. @@ -298,6 +300,7 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -313,7 +316,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.DirichletMultinomial.log_pmf(value, name='log_pmf')` {#DirichletMultinomial.log_pmf} +#### `tf.contrib.distributions.DirichletMultinomial.log_pmf(value, name='log_pmf', **condition_kwargs)` {#DirichletMultinomial.log_pmf} Log probability mass function. @@ -322,6 +325,7 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -337,7 +341,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.DirichletMultinomial.log_prob(value, name='log_prob')` {#DirichletMultinomial.log_prob} +#### `tf.contrib.distributions.DirichletMultinomial.log_prob(value, name='log_prob', **condition_kwargs)` {#DirichletMultinomial.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -361,6 +365,7 @@ it sums up to `n` and its components are equal to integer values. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -371,7 +376,7 @@ it sums up to `n` and its components are equal to integer values. - - - -#### `tf.contrib.distributions.DirichletMultinomial.log_survival_function(value, name='log_survival_function')` {#DirichletMultinomial.log_survival_function} +#### `tf.contrib.distributions.DirichletMultinomial.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#DirichletMultinomial.log_survival_function} Log survival function. @@ -391,6 +396,7 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -477,7 +483,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.DirichletMultinomial.pdf(value, name='pdf')` {#DirichletMultinomial.pdf} +#### `tf.contrib.distributions.DirichletMultinomial.pdf(value, name='pdf', **condition_kwargs)` {#DirichletMultinomial.pdf} Probability density function. @@ -486,6 +492,7 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -501,7 +508,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.DirichletMultinomial.pmf(value, name='pmf')` {#DirichletMultinomial.pmf} +#### `tf.contrib.distributions.DirichletMultinomial.pmf(value, name='pmf', **condition_kwargs)` {#DirichletMultinomial.pmf} Probability mass function. @@ -510,6 +517,7 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -525,7 +533,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.DirichletMultinomial.prob(value, name='prob')` {#DirichletMultinomial.prob} +#### `tf.contrib.distributions.DirichletMultinomial.prob(value, name='prob', **condition_kwargs)` {#DirichletMultinomial.prob} Probability density/mass function (depending on `is_continuous`). @@ -549,6 +557,7 @@ it sums up to `n` and its components are equal to integer values. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -559,7 +568,7 @@ it sums up to `n` and its components are equal to integer values. - - - -#### `tf.contrib.distributions.DirichletMultinomial.sample(sample_shape=(), seed=None, name='sample')` {#DirichletMultinomial.sample} +#### `tf.contrib.distributions.DirichletMultinomial.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#DirichletMultinomial.sample} Generate samples of the specified shape. @@ -572,6 +581,7 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -581,7 +591,7 @@ sample. - - - -#### `tf.contrib.distributions.DirichletMultinomial.sample_n(n, seed=None, name='sample_n')` {#DirichletMultinomial.sample_n} +#### `tf.contrib.distributions.DirichletMultinomial.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#DirichletMultinomial.sample_n} Generate `n` samples. @@ -592,6 +602,7 @@ Generate `n` samples. observations to sample. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -613,7 +624,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.DirichletMultinomial.survival_function(value, name='survival_function')` {#DirichletMultinomial.survival_function} +#### `tf.contrib.distributions.DirichletMultinomial.survival_function(value, name='survival_function', **condition_kwargs)` {#DirichletMultinomial.survival_function} Survival function. @@ -630,6 +641,7 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Exponential.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Exponential.md index a8f09489e6..c97978f684 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Exponential.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Exponential.md @@ -85,7 +85,7 @@ Inverse scale parameter. - - - -#### `tf.contrib.distributions.Exponential.cdf(value, name='cdf')` {#Exponential.cdf} +#### `tf.contrib.distributions.Exponential.cdf(value, name='cdf', **condition_kwargs)` {#Exponential.cdf} Cumulative distribution function. @@ -100,6 +100,7 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -119,7 +120,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Exponential.entropy(name='entropy')` {#Exponential.entropy} -Shanon entropy in nats. +Shannon entropy in nats. Additional documentation from `Gamma`: @@ -201,7 +202,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Exponential.log_cdf(value, name='log_cdf')` {#Exponential.log_cdf} +#### `tf.contrib.distributions.Exponential.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Exponential.log_cdf} Log cumulative distribution function. @@ -220,6 +221,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -230,7 +232,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Exponential.log_pdf(value, name='log_pdf')` {#Exponential.log_pdf} +#### `tf.contrib.distributions.Exponential.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Exponential.log_pdf} Log probability density function. @@ -239,6 +241,7 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -254,7 +257,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Exponential.log_pmf(value, name='log_pmf')` {#Exponential.log_pmf} +#### `tf.contrib.distributions.Exponential.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Exponential.log_pmf} Log probability mass function. @@ -263,6 +266,7 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -278,7 +282,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Exponential.log_prob(value, name='log_prob')` {#Exponential.log_prob} +#### `tf.contrib.distributions.Exponential.log_prob(value, name='log_prob', **condition_kwargs)` {#Exponential.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -287,6 +291,7 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -297,7 +302,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Exponential.log_survival_function(value, name='log_survival_function')` {#Exponential.log_survival_function} +#### `tf.contrib.distributions.Exponential.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Exponential.log_survival_function} Log survival function. @@ -317,6 +322,7 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -402,7 +408,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Exponential.pdf(value, name='pdf')` {#Exponential.pdf} +#### `tf.contrib.distributions.Exponential.pdf(value, name='pdf', **condition_kwargs)` {#Exponential.pdf} Probability density function. @@ -411,6 +417,7 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -426,7 +433,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Exponential.pmf(value, name='pmf')` {#Exponential.pmf} +#### `tf.contrib.distributions.Exponential.pmf(value, name='pmf', **condition_kwargs)` {#Exponential.pmf} Probability mass function. @@ -435,6 +442,7 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -450,7 +458,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Exponential.prob(value, name='prob')` {#Exponential.prob} +#### `tf.contrib.distributions.Exponential.prob(value, name='prob', **condition_kwargs)` {#Exponential.prob} Probability density/mass function (depending on `is_continuous`). @@ -459,6 +467,7 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -469,7 +478,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Exponential.sample(sample_shape=(), seed=None, name='sample')` {#Exponential.sample} +#### `tf.contrib.distributions.Exponential.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Exponential.sample} Generate samples of the specified shape. @@ -482,6 +491,7 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -491,7 +501,7 @@ sample. - - - -#### `tf.contrib.distributions.Exponential.sample_n(n, seed=None, name='sample_n')` {#Exponential.sample_n} +#### `tf.contrib.distributions.Exponential.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Exponential.sample_n} Generate `n` samples. @@ -507,6 +517,7 @@ See the documentation for tf.random_gamma for more details. observations to sample. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -528,7 +539,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Exponential.survival_function(value, name='survival_function')` {#Exponential.survival_function} +#### `tf.contrib.distributions.Exponential.survival_function(value, name='survival_function', **condition_kwargs)` {#Exponential.survival_function} Survival function. @@ -545,6 +556,7 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Gamma.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Gamma.md index b63972021a..64a93021e8 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Gamma.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Gamma.md @@ -112,7 +112,7 @@ Inverse scale parameter. - - - -#### `tf.contrib.distributions.Gamma.cdf(value, name='cdf')` {#Gamma.cdf} +#### `tf.contrib.distributions.Gamma.cdf(value, name='cdf', **condition_kwargs)` {#Gamma.cdf} Cumulative distribution function. @@ -127,6 +127,7 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -146,7 +147,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Gamma.entropy(name='entropy')` {#Gamma.entropy} -Shanon entropy in nats. +Shannon entropy in nats. Additional documentation from `Gamma`: @@ -221,7 +222,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Gamma.log_cdf(value, name='log_cdf')` {#Gamma.log_cdf} +#### `tf.contrib.distributions.Gamma.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Gamma.log_cdf} Log cumulative distribution function. @@ -240,6 +241,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -250,7 +252,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Gamma.log_pdf(value, name='log_pdf')` {#Gamma.log_pdf} +#### `tf.contrib.distributions.Gamma.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Gamma.log_pdf} Log probability density function. @@ -259,6 +261,7 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -274,7 +277,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Gamma.log_pmf(value, name='log_pmf')` {#Gamma.log_pmf} +#### `tf.contrib.distributions.Gamma.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Gamma.log_pmf} Log probability mass function. @@ -283,6 +286,7 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -298,7 +302,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Gamma.log_prob(value, name='log_prob')` {#Gamma.log_prob} +#### `tf.contrib.distributions.Gamma.log_prob(value, name='log_prob', **condition_kwargs)` {#Gamma.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -307,6 +311,7 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -317,7 +322,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Gamma.log_survival_function(value, name='log_survival_function')` {#Gamma.log_survival_function} +#### `tf.contrib.distributions.Gamma.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Gamma.log_survival_function} Log survival function. @@ -337,6 +342,7 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -422,7 +428,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Gamma.pdf(value, name='pdf')` {#Gamma.pdf} +#### `tf.contrib.distributions.Gamma.pdf(value, name='pdf', **condition_kwargs)` {#Gamma.pdf} Probability density function. @@ -431,6 +437,7 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -446,7 +453,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Gamma.pmf(value, name='pmf')` {#Gamma.pmf} +#### `tf.contrib.distributions.Gamma.pmf(value, name='pmf', **condition_kwargs)` {#Gamma.pmf} Probability mass function. @@ -455,6 +462,7 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -470,7 +478,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Gamma.prob(value, name='prob')` {#Gamma.prob} +#### `tf.contrib.distributions.Gamma.prob(value, name='prob', **condition_kwargs)` {#Gamma.prob} Probability density/mass function (depending on `is_continuous`). @@ -479,6 +487,7 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -489,7 +498,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Gamma.sample(sample_shape=(), seed=None, name='sample')` {#Gamma.sample} +#### `tf.contrib.distributions.Gamma.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Gamma.sample} Generate samples of the specified shape. @@ -502,6 +511,7 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -511,7 +521,7 @@ sample. - - - -#### `tf.contrib.distributions.Gamma.sample_n(n, seed=None, name='sample_n')` {#Gamma.sample_n} +#### `tf.contrib.distributions.Gamma.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Gamma.sample_n} Generate `n` samples. @@ -527,6 +537,7 @@ See the documentation for tf.random_gamma for more details. observations to sample. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -548,7 +559,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Gamma.survival_function(value, name='survival_function')` {#Gamma.survival_function} +#### `tf.contrib.distributions.Gamma.survival_function(value, name='survival_function', **condition_kwargs)` {#Gamma.survival_function} Survival function. @@ -565,6 +576,7 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.GammaWithSoftplusAlphaBeta.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.GammaWithSoftplusAlphaBeta.md index 4eff4bd5a6..c9b283547a 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.GammaWithSoftplusAlphaBeta.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.GammaWithSoftplusAlphaBeta.md @@ -63,7 +63,7 @@ Inverse scale parameter. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.cdf(value, name='cdf')` {#GammaWithSoftplusAlphaBeta.cdf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.cdf(value, name='cdf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.cdf} Cumulative distribution function. @@ -78,6 +78,7 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -97,7 +98,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.entropy(name='entropy')` {#GammaWithSoftplusAlphaBeta.entropy} -Shanon entropy in nats. +Shannon entropy in nats. Additional documentation from `Gamma`: @@ -172,7 +173,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_cdf(value, name='log_cdf')` {#GammaWithSoftplusAlphaBeta.log_cdf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_cdf(value, name='log_cdf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.log_cdf} Log cumulative distribution function. @@ -191,6 +192,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -201,7 +203,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_pdf(value, name='log_pdf')` {#GammaWithSoftplusAlphaBeta.log_pdf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_pdf(value, name='log_pdf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.log_pdf} Log probability density function. @@ -210,6 +212,7 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -225,7 +228,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf')` {#GammaWithSoftplusAlphaBeta.log_pmf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.log_pmf} Log probability mass function. @@ -234,6 +237,7 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -249,7 +253,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob')` {#GammaWithSoftplusAlphaBeta.log_prob} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -258,6 +262,7 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -268,7 +273,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_survival_function(value, name='log_survival_function')` {#GammaWithSoftplusAlphaBeta.log_survival_function} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.log_survival_function} Log survival function. @@ -288,6 +293,7 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -373,7 +379,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.pdf(value, name='pdf')` {#GammaWithSoftplusAlphaBeta.pdf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.pdf(value, name='pdf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.pdf} Probability density function. @@ -382,6 +388,7 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -397,7 +404,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.pmf(value, name='pmf')` {#GammaWithSoftplusAlphaBeta.pmf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.pmf(value, name='pmf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.pmf} Probability mass function. @@ -406,6 +413,7 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -421,7 +429,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.prob(value, name='prob')` {#GammaWithSoftplusAlphaBeta.prob} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.prob(value, name='prob', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.prob} Probability density/mass function (depending on `is_continuous`). @@ -430,6 +438,7 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -440,7 +449,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample')` {#GammaWithSoftplusAlphaBeta.sample} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.sample} Generate samples of the specified shape. @@ -453,6 +462,7 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -462,7 +472,7 @@ sample. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.sample_n(n, seed=None, name='sample_n')` {#GammaWithSoftplusAlphaBeta.sample_n} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.sample_n} Generate `n` samples. @@ -478,6 +488,7 @@ See the documentation for tf.random_gamma for more details. observations to sample. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -499,7 +510,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.survival_function(value, name='survival_function')` {#GammaWithSoftplusAlphaBeta.survival_function} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.survival_function(value, name='survival_function', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.survival_function} Survival function. @@ -516,6 +527,7 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.InverseGamma.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.InverseGamma.md index 6dbfcfd6f8..a67eba1cfb 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.InverseGamma.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.InverseGamma.md @@ -108,7 +108,7 @@ Scale parameter. - - - -#### `tf.contrib.distributions.InverseGamma.cdf(value, name='cdf')` {#InverseGamma.cdf} +#### `tf.contrib.distributions.InverseGamma.cdf(value, name='cdf', **condition_kwargs)` {#InverseGamma.cdf} Cumulative distribution function. @@ -123,6 +123,7 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -142,7 +143,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.InverseGamma.entropy(name='entropy')` {#InverseGamma.entropy} -Shanon entropy in nats. +Shannon entropy in nats. Additional documentation from `InverseGamma`: @@ -217,7 +218,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.InverseGamma.log_cdf(value, name='log_cdf')` {#InverseGamma.log_cdf} +#### `tf.contrib.distributions.InverseGamma.log_cdf(value, name='log_cdf', **condition_kwargs)` {#InverseGamma.log_cdf} Log cumulative distribution function. @@ -236,6 +237,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -246,7 +248,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.InverseGamma.log_pdf(value, name='log_pdf')` {#InverseGamma.log_pdf} +#### `tf.contrib.distributions.InverseGamma.log_pdf(value, name='log_pdf', **condition_kwargs)` {#InverseGamma.log_pdf} Log probability density function. @@ -255,6 +257,7 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -270,7 +273,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.InverseGamma.log_pmf(value, name='log_pmf')` {#InverseGamma.log_pmf} +#### `tf.contrib.distributions.InverseGamma.log_pmf(value, name='log_pmf', **condition_kwargs)` {#InverseGamma.log_pmf} Log probability mass function. @@ -279,6 +282,7 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -294,7 +298,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.InverseGamma.log_prob(value, name='log_prob')` {#InverseGamma.log_prob} +#### `tf.contrib.distributions.InverseGamma.log_prob(value, name='log_prob', **condition_kwargs)` {#InverseGamma.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -303,6 +307,7 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -313,7 +318,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.InverseGamma.log_survival_function(value, name='log_survival_function')` {#InverseGamma.log_survival_function} +#### `tf.contrib.distributions.InverseGamma.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#InverseGamma.log_survival_function} Log survival function. @@ -333,6 +338,7 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -422,7 +428,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.InverseGamma.pdf(value, name='pdf')` {#InverseGamma.pdf} +#### `tf.contrib.distributions.InverseGamma.pdf(value, name='pdf', **condition_kwargs)` {#InverseGamma.pdf} Probability density function. @@ -431,6 +437,7 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -446,7 +453,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.InverseGamma.pmf(value, name='pmf')` {#InverseGamma.pmf} +#### `tf.contrib.distributions.InverseGamma.pmf(value, name='pmf', **condition_kwargs)` {#InverseGamma.pmf} Probability mass function. @@ -455,6 +462,7 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -470,7 +478,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.InverseGamma.prob(value, name='prob')` {#InverseGamma.prob} +#### `tf.contrib.distributions.InverseGamma.prob(value, name='prob', **condition_kwargs)` {#InverseGamma.prob} Probability density/mass function (depending on `is_continuous`). @@ -479,6 +487,7 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -489,7 +498,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.InverseGamma.sample(sample_shape=(), seed=None, name='sample')` {#InverseGamma.sample} +#### `tf.contrib.distributions.InverseGamma.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#InverseGamma.sample} Generate samples of the specified shape. @@ -502,6 +511,7 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -511,7 +521,7 @@ sample. - - - -#### `tf.contrib.distributions.InverseGamma.sample_n(n, seed=None, name='sample_n')` {#InverseGamma.sample_n} +#### `tf.contrib.distributions.InverseGamma.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#InverseGamma.sample_n} Generate `n` samples. @@ -527,6 +537,7 @@ See the documentation for tf.random_gamma for more details. observations to sample. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -548,7 +559,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.InverseGamma.survival_function(value, name='survival_function')` {#InverseGamma.survival_function} +#### `tf.contrib.distributions.InverseGamma.survival_function(value, name='survival_function', **condition_kwargs)` {#InverseGamma.survival_function} Survival function. @@ -565,6 +576,7 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.md index 296a4ee985..38a1753221 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.md @@ -63,7 +63,7 @@ Scale parameter. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.cdf(value, name='cdf')` {#InverseGammaWithSoftplusAlphaBeta.cdf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.cdf(value, name='cdf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.cdf} Cumulative distribution function. @@ -78,6 +78,7 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -97,7 +98,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.entropy(name='entropy')` {#InverseGammaWithSoftplusAlphaBeta.entropy} -Shanon entropy in nats. +Shannon entropy in nats. Additional documentation from `InverseGamma`: @@ -172,7 +173,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_cdf(value, name='log_cdf')` {#InverseGammaWithSoftplusAlphaBeta.log_cdf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_cdf(value, name='log_cdf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.log_cdf} Log cumulative distribution function. @@ -191,6 +192,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -201,7 +203,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_pdf(value, name='log_pdf')` {#InverseGammaWithSoftplusAlphaBeta.log_pdf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_pdf(value, name='log_pdf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.log_pdf} Log probability density function. @@ -210,6 +212,7 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -225,7 +228,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf')` {#InverseGammaWithSoftplusAlphaBeta.log_pmf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.log_pmf} Log probability mass function. @@ -234,6 +237,7 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -249,7 +253,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob')` {#InverseGammaWithSoftplusAlphaBeta.log_prob} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -258,6 +262,7 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -268,7 +273,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_survival_function(value, name='log_survival_function')` {#InverseGammaWithSoftplusAlphaBeta.log_survival_function} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.log_survival_function} Log survival function. @@ -288,6 +293,7 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -377,7 +383,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pdf(value, name='pdf')` {#InverseGammaWithSoftplusAlphaBeta.pdf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pdf(value, name='pdf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.pdf} Probability density function. @@ -386,6 +392,7 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -401,7 +408,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pmf(value, name='pmf')` {#InverseGammaWithSoftplusAlphaBeta.pmf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pmf(value, name='pmf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.pmf} Probability mass function. @@ -410,6 +417,7 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -425,7 +433,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.prob(value, name='prob')` {#InverseGammaWithSoftplusAlphaBeta.prob} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.prob(value, name='prob', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.prob} Probability density/mass function (depending on `is_continuous`). @@ -434,6 +442,7 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -444,7 +453,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample')` {#InverseGammaWithSoftplusAlphaBeta.sample} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.sample} Generate samples of the specified shape. @@ -457,6 +466,7 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -466,7 +476,7 @@ sample. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.sample_n(n, seed=None, name='sample_n')` {#InverseGammaWithSoftplusAlphaBeta.sample_n} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.sample_n} Generate `n` samples. @@ -482,6 +492,7 @@ See the documentation for tf.random_gamma for more details. observations to sample. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -503,7 +514,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.survival_function(value, name='survival_function')` {#InverseGammaWithSoftplusAlphaBeta.survival_function} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.survival_function(value, name='survival_function', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.survival_function} Survival function. @@ -520,6 +531,7 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Multinomial.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Multinomial.md index 15e6b46e83..82d265b2c2 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Multinomial.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Multinomial.md @@ -145,7 +145,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Multinomial.cdf(value, name='cdf')` {#Multinomial.cdf} +#### `tf.contrib.distributions.Multinomial.cdf(value, name='cdf', **condition_kwargs)` {#Multinomial.cdf} Cumulative distribution function. @@ -160,6 +160,7 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -179,7 +180,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Multinomial.entropy(name='entropy')` {#Multinomial.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -243,7 +244,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Multinomial.log_cdf(value, name='log_cdf')` {#Multinomial.log_cdf} +#### `tf.contrib.distributions.Multinomial.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Multinomial.log_cdf} Log cumulative distribution function. @@ -262,6 +263,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -272,7 +274,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Multinomial.log_pdf(value, name='log_pdf')` {#Multinomial.log_pdf} +#### `tf.contrib.distributions.Multinomial.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Multinomial.log_pdf} Log probability density function. @@ -281,6 +283,7 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -296,7 +299,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Multinomial.log_pmf(value, name='log_pmf')` {#Multinomial.log_pmf} +#### `tf.contrib.distributions.Multinomial.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Multinomial.log_pmf} Log probability mass function. @@ -305,6 +308,7 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -320,7 +324,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Multinomial.log_prob(value, name='log_prob')` {#Multinomial.log_prob} +#### `tf.contrib.distributions.Multinomial.log_prob(value, name='log_prob', **condition_kwargs)` {#Multinomial.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -344,6 +348,7 @@ if it sums up to `n` and its components are equal to integer values. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -354,7 +359,7 @@ if it sums up to `n` and its components are equal to integer values. - - - -#### `tf.contrib.distributions.Multinomial.log_survival_function(value, name='log_survival_function')` {#Multinomial.log_survival_function} +#### `tf.contrib.distributions.Multinomial.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Multinomial.log_survival_function} Log survival function. @@ -374,6 +379,7 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -476,7 +482,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Multinomial.pdf(value, name='pdf')` {#Multinomial.pdf} +#### `tf.contrib.distributions.Multinomial.pdf(value, name='pdf', **condition_kwargs)` {#Multinomial.pdf} Probability density function. @@ -485,6 +491,7 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -500,7 +507,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Multinomial.pmf(value, name='pmf')` {#Multinomial.pmf} +#### `tf.contrib.distributions.Multinomial.pmf(value, name='pmf', **condition_kwargs)` {#Multinomial.pmf} Probability mass function. @@ -509,6 +516,7 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -524,7 +532,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Multinomial.prob(value, name='prob')` {#Multinomial.prob} +#### `tf.contrib.distributions.Multinomial.prob(value, name='prob', **condition_kwargs)` {#Multinomial.prob} Probability density/mass function (depending on `is_continuous`). @@ -548,6 +556,7 @@ if it sums up to `n` and its components are equal to integer values. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -558,7 +567,7 @@ if it sums up to `n` and its components are equal to integer values. - - - -#### `tf.contrib.distributions.Multinomial.sample(sample_shape=(), seed=None, name='sample')` {#Multinomial.sample} +#### `tf.contrib.distributions.Multinomial.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Multinomial.sample} Generate samples of the specified shape. @@ -571,6 +580,7 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -580,7 +590,7 @@ sample. - - - -#### `tf.contrib.distributions.Multinomial.sample_n(n, seed=None, name='sample_n')` {#Multinomial.sample_n} +#### `tf.contrib.distributions.Multinomial.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Multinomial.sample_n} Generate `n` samples. @@ -591,6 +601,7 @@ Generate `n` samples. observations to sample. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -612,7 +623,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Multinomial.survival_function(value, name='survival_function')` {#Multinomial.survival_function} +#### `tf.contrib.distributions.Multinomial.survival_function(value, name='survival_function', **condition_kwargs)` {#Multinomial.survival_function} Survival function. @@ -629,6 +640,7 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.md index 51c22b9e10..191e6e9a28 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.md @@ -145,7 +145,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.cdf(value, name='cdf')` {#MultivariateNormalDiagPlusVDVT.cdf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.cdf(value, name='cdf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.cdf} Cumulative distribution function. @@ -160,6 +160,7 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -179,7 +180,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.entropy(name='entropy')` {#MultivariateNormalDiagPlusVDVT.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -243,7 +244,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_cdf(value, name='log_cdf')` {#MultivariateNormalDiagPlusVDVT.log_cdf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_cdf(value, name='log_cdf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.log_cdf} Log cumulative distribution function. @@ -262,6 +263,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -272,7 +274,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pdf(value, name='log_pdf')` {#MultivariateNormalDiagPlusVDVT.log_pdf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pdf(value, name='log_pdf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.log_pdf} Log probability density function. @@ -281,6 +283,7 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -296,7 +299,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pmf(value, name='log_pmf')` {#MultivariateNormalDiagPlusVDVT.log_pmf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pmf(value, name='log_pmf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.log_pmf} Log probability mass function. @@ -305,6 +308,7 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -320,7 +324,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_prob(value, name='log_prob')` {#MultivariateNormalDiagPlusVDVT.log_prob} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_prob(value, name='log_prob', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -345,6 +349,7 @@ or * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -362,7 +367,7 @@ Log of determinant of covariance matrix. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_survival_function(value, name='log_survival_function')` {#MultivariateNormalDiagPlusVDVT.log_survival_function} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.log_survival_function} Log survival function. @@ -382,6 +387,7 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -468,7 +474,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pdf(value, name='pdf')` {#MultivariateNormalDiagPlusVDVT.pdf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pdf(value, name='pdf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.pdf} Probability density function. @@ -477,6 +483,7 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -492,7 +499,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pmf(value, name='pmf')` {#MultivariateNormalDiagPlusVDVT.pmf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pmf(value, name='pmf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.pmf} Probability mass function. @@ -501,6 +508,7 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -516,7 +524,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.prob(value, name='prob')` {#MultivariateNormalDiagPlusVDVT.prob} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.prob(value, name='prob', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.prob} Probability density/mass function (depending on `is_continuous`). @@ -541,6 +549,7 @@ or * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -551,7 +560,7 @@ or - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalDiagPlusVDVT.sample} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.sample} Generate samples of the specified shape. @@ -564,6 +573,7 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -573,7 +583,7 @@ sample. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sample_n(n, seed=None, name='sample_n')` {#MultivariateNormalDiagPlusVDVT.sample_n} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.sample_n} Generate `n` samples. @@ -584,6 +594,7 @@ Generate `n` samples. observations to sample. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -619,7 +630,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.survival_function(value, name='survival_function')` {#MultivariateNormalDiagPlusVDVT.survival_function} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.survival_function(value, name='survival_function', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.survival_function} Survival function. @@ -636,6 +647,7 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.bijector.ScaleAndShift.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.bijector.ScaleAndShift.md index 7f1246b964..4e1eda55fb 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.bijector.ScaleAndShift.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.bijector.ScaleAndShift.md @@ -54,7 +54,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.ScaleAndShift.forward(x, name='forward')` {#ScaleAndShift.forward} +#### `tf.contrib.distributions.bijector.ScaleAndShift.forward(x, name='forward', **condition_kwargs)` {#ScaleAndShift.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -63,6 +63,7 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). * <b>`x`</b>: `Tensor`. The input to the "forward" evaluation. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -78,15 +79,16 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). - - - -#### `tf.contrib.distributions.bijector.ScaleAndShift.inverse(x, name='inverse')` {#ScaleAndShift.inverse} +#### `tf.contrib.distributions.bijector.ScaleAndShift.inverse(y, name='inverse', **condition_kwargs)` {#ScaleAndShift.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" evaluation. +* <b>`y`</b>: `Tensor`. The input to the "inverse" evaluation. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -95,7 +97,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse` nor `_inverse_and_inverse_log_det_jacobian` are implemented. @@ -103,7 +105,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.ScaleAndShift.inverse_and_inverse_log_det_jacobian(x, name='inverse_and_inverse_log_det_jacobian')` {#ScaleAndShift.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.ScaleAndShift.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#ScaleAndShift.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -115,8 +117,9 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. +* <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -125,7 +128,7 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse_and_inverse_log_det_jacobian` nor {`_inverse`, `_inverse_log_det_jacobian`} are implemented. @@ -133,20 +136,20 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. - - - -#### `tf.contrib.distributions.bijector.ScaleAndShift.inverse_log_det_jacobian(x, name='inverse_log_det_jacobian')` {#ScaleAndShift.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.ScaleAndShift.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#ScaleAndShift.inverse_log_det_jacobian} -Returns the (log o det o Jacobian o inverse)(x). +Returns the (log o det o Jacobian o inverse)(y). -Mathematically, returns: log(det(dY/dX g^{-1}))(Y). +Mathematically, returns: `log(det(dX/dY))(Y)`. (Recall that: `X=g^{-1}(Y)`.) -Note that forward_log_det_jacobian is the negative of this function. (See -is_constant_jacobian for related proof.) +Note that `forward_log_det_jacobian` is the negative of this function. ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. +* <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. +* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -155,7 +158,7 @@ is_constant_jacobian for related proof.) ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse_log_det_jacobian` nor `_inverse_and_inverse_log_det_jacobian` are implemented. diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.Estimator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.Estimator.md index 99899f1421..64c16118ca 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.Estimator.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.Estimator.md @@ -105,7 +105,7 @@ The signature of the input_fn accepted by export is changing to be consistent wi string key to `Tensor` and targets is a `Tensor` that's currently not used (and so can be `None`). input_feature_key: Only used if `use_deprecated_input_fn` is false. String - key into the features dict returned by `input_fn` that corresponds toa + key into the features dict returned by `input_fn` that corresponds to a the raw `Example` strings `Tensor` that the exported model will take as input. Can only be `None` if you're using a custom `signature_fn` that does not use the first arg (examples). |