diff options
| author |  A. Unique TensorFlower <gardener@tensorflow.org> | 2017-01-30 15:49:27 -0800 |
|---|---|---|
| committer | TensorFlower Gardener <gardener@tensorflow.org> | 2017-01-30 16:06:39 -0800 |
| commit | 3b46e2049357c1e11c0f1e1938e22d25f276b45e (patch) | |
| tree | 18f21a3082da4b1fc8228779b1e1764f99bb0f3f | |
| parent | 37e60084c80faa792ec583d4690c71e418f340a5 (diff) | |
Update generated Python Op docs.
Change: 146049950
44 files changed, 74 insertions, 7386 deletions
diff --git a/tensorflow/g3doc/api_docs/python/contrib.distributions.bijector.md b/tensorflow/g3doc/api_docs/python/contrib.distributions.bijector.md index c7d097fda5..f03c54bbec 100644 --- a/tensorflow/g3doc/api_docs/python/contrib.distributions.bijector.md +++ b/tensorflow/g3doc/api_docs/python/contrib.distributions.bijector.md @@ -834,9 +834,9 @@ Example Use: - Computing a log-likelihood: ```python - def transformed_log_pdf(bijector, log_pdf, x): + def transformed_log_prob(bijector, log_prob, x): return (bijector.inverse_log_det_jacobian(x) + - log_pdf(bijector.inverse(x))) + log_prob(bijector.inverse(x))) ``` - Transforming a random outcome: diff --git a/tensorflow/g3doc/api_docs/python/contrib.distributions.md b/tensorflow/g3doc/api_docs/python/contrib.distributions.md index a560fd8b93..e479598bec 100644 --- a/tensorflow/g3doc/api_docs/python/contrib.distributions.md +++ b/tensorflow/g3doc/api_docs/python/contrib.distributions.md @@ -482,54 +482,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Distribution.log_pdf(value, name='log_pdf')` {#Distribution.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Distribution.log_pmf(value, name='log_pmf')` {#Distribution.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Distribution.log_prob(value, name='log_prob')` {#Distribution.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -660,54 +612,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Distribution.pdf(value, name='pdf')` {#Distribution.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Distribution.pmf(value, name='pmf')` {#Distribution.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Distribution.prob(value, name='prob')` {#Distribution.prob} Probability density/mass function (depending on `is_continuous`). @@ -1217,54 +1121,6 @@ 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} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Binomial.log_pmf(value, name='log_pmf')` {#Binomial.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Binomial.log_prob(value, name='log_prob')` {#Binomial.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -1422,54 +1278,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Binomial.pdf(value, name='pdf')` {#Binomial.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Binomial.pmf(value, name='pmf')` {#Binomial.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Binomial.prob(value, name='prob')` {#Binomial.prob} Probability density/mass function (depending on `is_continuous`). @@ -1945,54 +1753,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Bernoulli.log_pdf(value, name='log_pdf')` {#Bernoulli.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Bernoulli.log_pmf(value, name='log_pmf')` {#Bernoulli.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Bernoulli.log_prob(value, name='log_prob')` {#Bernoulli.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -2134,54 +1894,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Bernoulli.pdf(value, name='pdf')` {#Bernoulli.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Bernoulli.pmf(value, name='pmf')` {#Bernoulli.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Bernoulli.prob(value, name='prob')` {#Bernoulli.prob} Probability density/mass function (depending on `is_continuous`). @@ -2607,54 +2319,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.log_pdf(value, name='log_pdf')` {#BernoulliWithSigmoidProbs.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.log_pmf(value, name='log_pmf')` {#BernoulliWithSigmoidProbs.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.BernoulliWithSigmoidProbs.log_prob(value, name='log_prob')` {#BernoulliWithSigmoidProbs.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -2796,54 +2460,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.pdf(value, name='pdf')` {#BernoulliWithSigmoidProbs.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.pmf(value, name='pmf')` {#BernoulliWithSigmoidProbs.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.BernoulliWithSigmoidProbs.prob(value, name='prob')` {#BernoulliWithSigmoidProbs.prob} Probability density/mass function (depending on `is_continuous`). @@ -3017,7 +2633,7 @@ is the beta function. This class provides methods to create indexed batches of Beta distributions. One entry of the broadcasted shape represents of `a` and `b` represents one single Beta distribution. -When calling distribution functions (e.g. `dist.pdf(x)`), `a`, `b` +When calling distribution functions (e.g. `dist.prob(x)`), `a`, `b` and `x` are broadcast to the same shape (if possible). Every entry in a/b/x corresponds to a single Beta distribution. @@ -3035,15 +2651,15 @@ dist = Beta(a, b) ```python # x same shape as a. x = [.2, .3, .7] -dist.pdf(x) # Shape [3] +dist.prob(x) # Shape [3] # a/b will be broadcast to [[1, 2, 3], [1, 2, 3]] to match x. x = [[.1, .4, .5], [.2, .3, .5]] -dist.pdf(x) # Shape [2, 3] +dist.prob(x) # Shape [2, 3] # a/b will be broadcast to shape [5, 7, 3] to match x. x = [[...]] # Shape [5, 7, 3] -dist.pdf(x) # Shape [5, 7, 3] +dist.prob(x) # Shape [5, 7, 3] ``` Creates a 2-batch of 3-class distributions. @@ -3055,7 +2671,7 @@ dist = Beta(a, b) # x will be broadcast to [[.2, .3, .9], [.2, .3, .9]] to match a/b. x = [.2, .3, .9] -dist.pdf(x) # Shape [2] +dist.prob(x) # Shape [2] ``` - - - @@ -3387,54 +3003,6 @@ distribution in `self.a` and `self.b`. `x` is only legal if `0 < x < 1`. - - - -#### `tf.contrib.distributions.Beta.log_pdf(value, name='log_pdf')` {#Beta.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Beta.log_pmf(value, name='log_pmf')` {#Beta.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Beta.log_prob(value, name='log_prob')` {#Beta.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -3572,54 +3140,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Beta.pdf(value, name='pdf')` {#Beta.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Beta.pmf(value, name='pmf')` {#Beta.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Beta.prob(value, name='prob')` {#Beta.prob} Probability density/mass function (depending on `is_continuous`). @@ -4075,54 +3595,6 @@ 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} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.log_pmf(value, name='log_pmf')` {#BetaWithSoftplusAB.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.BetaWithSoftplusAB.log_prob(value, name='log_prob')` {#BetaWithSoftplusAB.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -4260,54 +3732,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.pdf(value, name='pdf')` {#BetaWithSoftplusAB.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.pmf(value, name='pmf')` {#BetaWithSoftplusAB.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.BetaWithSoftplusAB.prob(value, name='prob')` {#BetaWithSoftplusAB.prob} Probability density/mass function (depending on `is_continuous`). @@ -4492,15 +3916,15 @@ The distribution functions can be evaluated on counts. # counts is a scalar. p = [0.1, 0.4, 0.5] dist = Categorical(probs=p) -dist.pmf(0) # Shape [] +dist.prob(0) # Shape [] # p will be broadcast to [[0.1, 0.4, 0.5], [0.1, 0.4, 0.5]] to match counts. counts = [1, 0] -dist.pmf(counts) # Shape [2] +dist.prob(counts) # Shape [2] # p will be broadcast to shape [3, 5, 7, 3] to match counts. counts = [[...]] # Shape [5, 7, 3] -dist.pmf(counts) # Shape [5, 7, 3] +dist.prob(counts) # Shape [5, 7, 3] ``` - - - @@ -4797,54 +4221,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Categorical.log_pdf(value, name='log_pdf')` {#Categorical.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Categorical.log_pmf(value, name='log_pmf')` {#Categorical.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Categorical.log_prob(value, name='log_prob')` {#Categorical.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -4989,54 +4365,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Categorical.pdf(value, name='pdf')` {#Categorical.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Categorical.pmf(value, name='pmf')` {#Categorical.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Categorical.prob(value, name='prob')` {#Categorical.prob} Probability density/mass function (depending on `is_continuous`). @@ -5058,9 +4386,7 @@ Probability density/mass function (depending on `is_continuous`). #### `tf.contrib.distributions.Categorical.probs` {#Categorical.probs} -Vector of probabilities summing to one. - -Each element is the probability of drawing that coordinate. +Vector of coordinatewise probabilities. - - - @@ -5518,54 +4844,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Chi2.log_pdf(value, name='log_pdf')` {#Chi2.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Chi2.log_pmf(value, name='log_pmf')` {#Chi2.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Chi2.log_prob(value, name='log_prob')` {#Chi2.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -5702,54 +4980,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Chi2.pdf(value, name='pdf')` {#Chi2.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Chi2.pmf(value, name='pmf')` {#Chi2.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Chi2.prob(value, name='prob')` {#Chi2.prob} Probability density/mass function (depending on `is_continuous`). @@ -6200,54 +5430,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.log_pdf(value, name='log_pdf')` {#Chi2WithAbsDf.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.log_pmf(value, name='log_pmf')` {#Chi2WithAbsDf.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Chi2WithAbsDf.log_prob(value, name='log_prob')` {#Chi2WithAbsDf.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -6384,54 +5566,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.pdf(value, name='pdf')` {#Chi2WithAbsDf.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.pmf(value, name='pmf')` {#Chi2WithAbsDf.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Chi2WithAbsDf.prob(value, name='prob')` {#Chi2WithAbsDf.prob} Probability density/mass function (depending on `is_continuous`). @@ -6904,54 +6038,6 @@ 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} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Exponential.log_pmf(value, name='log_pmf')` {#Exponential.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Exponential.log_prob(value, name='log_prob')` {#Exponential.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -7088,54 +6174,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Exponential.pdf(value, name='pdf')` {#Exponential.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Exponential.pmf(value, name='pmf')` {#Exponential.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Exponential.prob(value, name='prob')` {#Exponential.prob} Probability density/mass function (depending on `is_continuous`). @@ -7586,54 +6624,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_pdf(value, name='log_pdf')` {#ExponentialWithSoftplusLam.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_pmf(value, name='log_pmf')` {#ExponentialWithSoftplusLam.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_prob(value, name='log_prob')` {#ExponentialWithSoftplusLam.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -7770,54 +6760,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.pdf(value, name='pdf')` {#ExponentialWithSoftplusLam.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.pmf(value, name='pmf')` {#ExponentialWithSoftplusLam.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.ExponentialWithSoftplusLam.prob(value, name='prob')` {#ExponentialWithSoftplusLam.prob} Probability density/mass function (depending on `is_continuous`). @@ -8310,54 +7252,6 @@ 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} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Gamma.log_pmf(value, name='log_pmf')` {#Gamma.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Gamma.log_prob(value, name='log_prob')` {#Gamma.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -8494,54 +7388,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Gamma.pdf(value, name='pdf')` {#Gamma.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Gamma.pmf(value, name='pmf')` {#Gamma.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Gamma.prob(value, name='prob')` {#Gamma.prob} Probability density/mass function (depending on `is_continuous`). @@ -8985,54 +7831,6 @@ 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} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf')` {#GammaWithSoftplusAlphaBeta.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob')` {#GammaWithSoftplusAlphaBeta.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -9169,54 +7967,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.pdf(value, name='pdf')` {#GammaWithSoftplusAlphaBeta.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.pmf(value, name='pmf')` {#GammaWithSoftplusAlphaBeta.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.prob(value, name='prob')` {#GammaWithSoftplusAlphaBeta.prob} Probability density/mass function (depending on `is_continuous`). @@ -9705,54 +8455,6 @@ 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} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.InverseGamma.log_pmf(value, name='log_pmf')` {#InverseGamma.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.InverseGamma.log_prob(value, name='log_prob')` {#InverseGamma.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -9893,54 +8595,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.InverseGamma.pdf(value, name='pdf')` {#InverseGamma.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.InverseGamma.pmf(value, name='pmf')` {#InverseGamma.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.InverseGamma.prob(value, name='prob')` {#InverseGamma.prob} Probability density/mass function (depending on `is_continuous`). @@ -10391,54 +9045,6 @@ 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} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf')` {#InverseGammaWithSoftplusAlphaBeta.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob')` {#InverseGammaWithSoftplusAlphaBeta.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -10579,54 +9185,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pdf(value, name='pdf')` {#InverseGammaWithSoftplusAlphaBeta.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pmf(value, name='pmf')` {#InverseGammaWithSoftplusAlphaBeta.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.prob(value, name='prob')` {#InverseGammaWithSoftplusAlphaBeta.prob} Probability density/mass function (depending on `is_continuous`). @@ -11107,54 +9665,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Laplace.log_pdf(value, name='log_pdf')` {#Laplace.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Laplace.log_pmf(value, name='log_pmf')` {#Laplace.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Laplace.log_prob(value, name='log_prob')` {#Laplace.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -11285,54 +9795,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Laplace.pdf(value, name='pdf')` {#Laplace.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Laplace.pmf(value, name='pmf')` {#Laplace.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Laplace.prob(value, name='prob')` {#Laplace.prob} Probability density/mass function (depending on `is_continuous`). @@ -11765,54 +10227,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_pdf(value, name='log_pdf')` {#LaplaceWithSoftplusScale.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_pmf(value, name='log_pmf')` {#LaplaceWithSoftplusScale.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_prob(value, name='log_prob')` {#LaplaceWithSoftplusScale.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -11943,54 +10357,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.pdf(value, name='pdf')` {#LaplaceWithSoftplusScale.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.pmf(value, name='pmf')` {#LaplaceWithSoftplusScale.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.LaplaceWithSoftplusScale.prob(value, name='prob')` {#LaplaceWithSoftplusScale.prob} Probability density/mass function (depending on `is_continuous`). @@ -12184,7 +10550,7 @@ dist = tf.contrib.distributions.Normal(loc=[1, 2.], scale=[11, 22.]) # Evaluate the pdf of the first distribution on 0, and the second on 1.5, # returning a length two tensor. -dist.pdf([0, 1.5]) +dist.prob([0, 1.5]) # Get 3 samples, returning a 3 x 2 tensor. dist.sample([3]) @@ -12199,7 +10565,7 @@ dist = tf.contrib.distributions.Normal(loc=1., scale=[11, 22.]) # Evaluate the pdf of both distributions on the same point, 3.0, # returning a length 2 tensor. -dist.pdf(3.0) +dist.prob(3.0) ``` - - - @@ -12503,54 +10869,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Normal.log_pdf(value, name='log_pdf')` {#Normal.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Normal.log_pmf(value, name='log_pmf')` {#Normal.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Normal.log_prob(value, name='log_prob')` {#Normal.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -12681,54 +10999,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Normal.pdf(value, name='pdf')` {#Normal.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Normal.pmf(value, name='pmf')` {#Normal.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Normal.prob(value, name='prob')` {#Normal.prob} Probability density/mass function (depending on `is_continuous`). @@ -13161,54 +11431,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.NormalWithSoftplusScale.log_pdf(value, name='log_pdf')` {#NormalWithSoftplusScale.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.NormalWithSoftplusScale.log_pmf(value, name='log_pmf')` {#NormalWithSoftplusScale.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.NormalWithSoftplusScale.log_prob(value, name='log_prob')` {#NormalWithSoftplusScale.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -13339,54 +11561,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.NormalWithSoftplusScale.pdf(value, name='pdf')` {#NormalWithSoftplusScale.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.NormalWithSoftplusScale.pmf(value, name='pmf')` {#NormalWithSoftplusScale.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.NormalWithSoftplusScale.prob(value, name='prob')` {#NormalWithSoftplusScale.prob} Probability density/mass function (depending on `is_continuous`). @@ -13562,8 +11736,8 @@ Construct Poisson distributions. * <b>`lam`</b>: Floating point tensor, the rate parameter of the distribution(s). `lam` must be positive. * <b>`validate_args`</b>: `Boolean`, default `False`. Whether to assert that - `lam > 0` as well as inputs to pmf computations are non-negative - integers. If validate_args is `False`, then `pmf` computations might + `lam > 0` as well as inputs to `prob` computations are non-negative + integers. If validate_args is `False`, then `prob` computations might return `NaN`, but can be evaluated at any real value. * <b>`allow_nan_stats`</b>: `Boolean`, default `True`. If `False`, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any @@ -13843,54 +12017,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Poisson.log_pdf(value, name='log_pdf')` {#Poisson.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Poisson.log_pmf(value, name='log_pmf')` {#Poisson.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Poisson.log_prob(value, name='log_prob')` {#Poisson.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -14034,54 +12160,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Poisson.pdf(value, name='pdf')` {#Poisson.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Poisson.pmf(value, name='pmf')` {#Poisson.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Poisson.prob(value, name='prob')` {#Poisson.prob} Probability density/mass function (depending on `is_continuous`). @@ -14277,7 +12355,7 @@ Examples of initialization of one or a batch of distributions. single_dist = tf.contrib.distributions.StudentT(df=3) # Evaluate the pdf at 1, returning a scalar Tensor. -single_dist.pdf(1.) +single_dist.prob(1.) # Define a batch of two scalar valued Student t's. # The first has degrees of freedom 2, mean 1, and scale 11. @@ -14288,7 +12366,7 @@ multi_dist = tf.contrib.distributions.StudentT(df=[2, 3], # Evaluate the pdf of the first distribution on 0, and the second on 1.5, # returning a length two tensor. -multi_dist.pdf([0, 1.5]) +multi_dist.prob([0, 1.5]) # Get 3 samples, returning a 3 x 2 tensor. multi_dist.sample(3) @@ -14303,7 +12381,7 @@ dist = tf.contrib.distributions.StudentT(df=2, loc=1, scale=[11, 22.]) # Evaluate the pdf of both distributions on the same point, 3.0, # returning a length 2 tensor. -dist.pdf(3.0) +dist.prob(3.0) ``` - - - @@ -14621,54 +12699,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.StudentT.log_pdf(value, name='log_pdf')` {#StudentT.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.StudentT.log_pmf(value, name='log_pmf')` {#StudentT.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.StudentT.log_prob(value, name='log_prob')` {#StudentT.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -14805,54 +12835,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.StudentT.pdf(value, name='pdf')` {#StudentT.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.StudentT.pmf(value, name='pmf')` {#StudentT.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.StudentT.prob(value, name='prob')` {#StudentT.prob} Probability density/mass function (depending on `is_continuous`). @@ -15303,54 +13285,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusScale.log_pdf(value, name='log_pdf')` {#StudentTWithAbsDfSoftplusScale.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusScale.log_pmf(value, name='log_pmf')` {#StudentTWithAbsDfSoftplusScale.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.StudentTWithAbsDfSoftplusScale.log_prob(value, name='log_prob')` {#StudentTWithAbsDfSoftplusScale.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -15487,54 +13421,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusScale.pdf(value, name='pdf')` {#StudentTWithAbsDfSoftplusScale.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusScale.pmf(value, name='pmf')` {#StudentTWithAbsDfSoftplusScale.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.StudentTWithAbsDfSoftplusScale.prob(value, name='prob')` {#StudentTWithAbsDfSoftplusScale.prob} Probability density/mass function (depending on `is_continuous`). @@ -16027,54 +13913,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Uniform.log_pdf(value, name='log_pdf')` {#Uniform.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Uniform.log_pmf(value, name='log_pmf')` {#Uniform.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Uniform.log_prob(value, name='log_prob')` {#Uniform.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -16205,54 +14043,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Uniform.pdf(value, name='pdf')` {#Uniform.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Uniform.pmf(value, name='pmf')` {#Uniform.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Uniform.prob(value, name='prob')` {#Uniform.prob} Probability density/mass function (depending on `is_continuous`). @@ -16753,54 +14543,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.log_pdf(value, name='log_pdf')` {#MultivariateNormalDiag.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.log_pmf(value, name='log_pmf')` {#MultivariateNormalDiag.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.MultivariateNormalDiag.log_prob(value, name='log_prob')` {#MultivariateNormalDiag.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -16961,54 +14703,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.pdf(value, name='pdf')` {#MultivariateNormalDiag.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.pmf(value, name='pmf')` {#MultivariateNormalDiag.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.MultivariateNormalDiag.prob(value, name='prob')` {#MultivariateNormalDiag.prob} Probability density/mass function (depending on `is_continuous`). @@ -17518,54 +15212,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalFull.log_pdf(value, name='log_pdf')` {#MultivariateNormalFull.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.MultivariateNormalFull.log_pmf(value, name='log_pmf')` {#MultivariateNormalFull.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.MultivariateNormalFull.log_prob(value, name='log_prob')` {#MultivariateNormalFull.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -17726,54 +15372,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.pdf(value, name='pdf')` {#MultivariateNormalFull.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.MultivariateNormalFull.pmf(value, name='pmf')` {#MultivariateNormalFull.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.MultivariateNormalFull.prob(value, name='prob')` {#MultivariateNormalFull.prob} Probability density/mass function (depending on `is_continuous`). @@ -18292,54 +15890,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.log_pdf(value, name='log_pdf')` {#MultivariateNormalCholesky.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.log_pmf(value, name='log_pmf')` {#MultivariateNormalCholesky.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.MultivariateNormalCholesky.log_prob(value, name='log_prob')` {#MultivariateNormalCholesky.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -18500,54 +16050,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.pdf(value, name='pdf')` {#MultivariateNormalCholesky.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.pmf(value, name='pmf')` {#MultivariateNormalCholesky.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.MultivariateNormalCholesky.prob(value, name='prob')` {#MultivariateNormalCholesky.prob} Probability density/mass function (depending on `is_continuous`). @@ -19092,54 +16594,6 @@ 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} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pmf(value, name='log_pmf')` {#MultivariateNormalDiagPlusVDVT.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_prob(value, name='log_prob')` {#MultivariateNormalDiagPlusVDVT.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -19300,54 +16754,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pdf(value, name='pdf')` {#MultivariateNormalDiagPlusVDVT.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pmf(value, name='pmf')` {#MultivariateNormalDiagPlusVDVT.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.prob(value, name='prob')` {#MultivariateNormalDiagPlusVDVT.prob} Probability density/mass function (depending on `is_continuous`). @@ -19796,54 +17202,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_pdf(value, name='log_pdf')` {#MultivariateNormalDiagWithSoftplusStDev.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_pmf(value, name='log_pmf')` {#MultivariateNormalDiagWithSoftplusStDev.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_prob(value, name='log_prob')` {#MultivariateNormalDiagWithSoftplusStDev.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -20004,54 +17362,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.pdf(value, name='pdf')` {#MultivariateNormalDiagWithSoftplusStDev.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.pmf(value, name='pmf')` {#MultivariateNormalDiagWithSoftplusStDev.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.prob(value, name='prob')` {#MultivariateNormalDiagWithSoftplusStDev.prob} Probability density/mass function (depending on `is_continuous`). @@ -20604,54 +17914,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Dirichlet.log_pdf(value, name='log_pdf')` {#Dirichlet.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Dirichlet.log_pmf(value, name='log_pmf')` {#Dirichlet.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Dirichlet.log_prob(value, name='log_prob')` {#Dirichlet.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -20797,54 +18059,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Dirichlet.pdf(value, name='pdf')` {#Dirichlet.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Dirichlet.pmf(value, name='pmf')` {#Dirichlet.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Dirichlet.prob(value, name='prob')` {#Dirichlet.prob} Probability density/mass function (depending on `is_continuous`). @@ -21026,9 +18240,9 @@ This class provides methods to create indexed batches of Dirichlet Multinomial distributions. If the provided `alpha` is rank 2 or higher, for every fixed set of leading dimensions, the last dimension represents one single Dirichlet Multinomial distribution. When calling distribution -functions (e.g. `dist.pmf(counts)`), `alpha` and `counts` are broadcast to the -same shape (if possible). In all cases, the last dimension of alpha/counts -represents single Dirichlet Multinomial distributions. +functions (e.g. `dist.prob(counts)`), `alpha` and `counts` are broadcast to +the same shape (if possible). In all cases, the last dimension of +alpha/counts represents single Dirichlet Multinomial distributions. #### Examples @@ -21044,15 +18258,15 @@ The distribution functions can be evaluated on counts. ```python # counts same shape as alpha. counts = [0, 0, 2] -dist.pmf(counts) # Shape [] +dist.prob(counts) # Shape [] # alpha will be broadcast to [[1, 2, 3], [1, 2, 3]] to match counts. counts = [[1, 1, 0], [1, 0, 1]] -dist.pmf(counts) # Shape [2] +dist.prob(counts) # Shape [2] # alpha will be broadcast to shape [5, 7, 3] to match counts. counts = [[...]] # Shape [5, 7, 3] -dist.pmf(counts) # Shape [5, 7] +dist.prob(counts) # Shape [5, 7] ``` Creates a 2-batch of 3-class distributions. @@ -21064,7 +18278,7 @@ dist = DirichletMultinomial(n, alpha) # counts will be broadcast to [[2, 1, 0], [2, 1, 0]] to match alpha. counts = [2, 1, 0] -dist.pmf(counts) # Shape [2] +dist.prob(counts) # Shape [2] ``` - - - @@ -21403,54 +18617,6 @@ 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} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.DirichletMultinomial.log_pmf(value, name='log_pmf')` {#DirichletMultinomial.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.DirichletMultinomial.log_prob(value, name='log_prob')` {#DirichletMultinomial.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -21603,54 +18769,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.DirichletMultinomial.pdf(value, name='pdf')` {#DirichletMultinomial.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.DirichletMultinomial.pmf(value, name='pmf')` {#DirichletMultinomial.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.DirichletMultinomial.prob(value, name='prob')` {#DirichletMultinomial.prob} Probability density/mass function (depending on `is_continuous`). @@ -22183,54 +19301,6 @@ 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} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Multinomial.log_pmf(value, name='log_pmf')` {#Multinomial.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Multinomial.log_prob(value, name='log_prob')` {#Multinomial.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -22384,54 +19454,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Multinomial.pdf(value, name='pdf')` {#Multinomial.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Multinomial.pmf(value, name='pmf')` {#Multinomial.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Multinomial.prob(value, name='prob')` {#Multinomial.prob} Probability density/mass function (depending on `is_continuous`). @@ -22648,12 +19670,12 @@ dist = tf.contrib.distributions.WishartCholesky(df=df, scale=chol_scale) # Evaluate this on an observation in R^3, returning a scalar. x = ... # A 3x3 positive definite matrix. -dist.pdf(x) # Shape is [], a scalar. +dist.prob(x) # Shape is [], a scalar. # Evaluate this on a two observations, each in R^{3x3}, returning a length two # Tensor. x = [x0, x1] # Shape is [2, 3, 3]. -dist.pdf(x) # Shape is [2]. +dist.prob(x) # Shape is [2]. # Initialize two 3x3 Wisharts with Cholesky factored scale matrices. df = [5, 4] @@ -22662,7 +19684,7 @@ dist = tf.contrib.distributions.WishartCholesky(df=df, scale=chol_scale) # Evaluate this on four observations. x = [[x0, x1], [x2, x3]] # Shape is [2, 2, 3, 3]. -dist.pdf(x) # Shape is [2, 2]. +dist.prob(x) # Shape is [2, 2]. # (*) - To efficiently create a trainable covariance matrix, see the example # in tf.contrib.distributions.matrix_diag_transform. @@ -22682,7 +19704,7 @@ Construct Wishart distributions. the symmetric positive definite scale matrix of the distribution. * <b>`cholesky_input_output_matrices`</b>: `Boolean`. Any function which whose input or output is a matrix assumes the input is Cholesky and returns a - Cholesky factored matrix. Example`log_pdf` input takes a Cholesky and + Cholesky factored matrix. Example `log_prob` input takes a Cholesky and `sample_n` returns a Cholesky when `cholesky_input_output_matrices=True`. * <b>`validate_args`</b>: `Boolean`, default `False`. Whether to validate input @@ -22987,54 +20009,6 @@ Computes the log normalizing constant, log(Z). - - - -#### `tf.contrib.distributions.WishartCholesky.log_pdf(value, name='log_pdf')` {#WishartCholesky.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.WishartCholesky.log_pmf(value, name='log_pmf')` {#WishartCholesky.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.WishartCholesky.log_prob(value, name='log_prob')` {#WishartCholesky.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -23172,54 +20146,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.WishartCholesky.pdf(value, name='pdf')` {#WishartCholesky.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.WishartCholesky.pmf(value, name='pmf')` {#WishartCholesky.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.WishartCholesky.prob(value, name='prob')` {#WishartCholesky.prob} Probability density/mass function (depending on `is_continuous`). @@ -23416,12 +20342,12 @@ dist = tf.contrib.distributions.WishartFull(df=df, scale=scale) # Evaluate this on an observation in R^3, returning a scalar. x = ... # A 3x3 positive definite matrix. -dist.pdf(x) # Shape is [], a scalar. +dist.prob(x) # Shape is [], a scalar. # Evaluate this on a two observations, each in R^{3x3}, returning a length two # Tensor. x = [x0, x1] # Shape is [2, 3, 3]. -dist.pdf(x) # Shape is [2]. +dist.prob(x) # Shape is [2]. # Initialize two 3x3 Wisharts with Full factored scale matrices. df = [5, 4] @@ -23430,7 +20356,7 @@ dist = tf.contrib.distributions.WishartFull(df=df, scale=scale) # Evaluate this on four observations. x = [[x0, x1], [x2, x3]] # Shape is [2, 2, 3, 3]; xi is positive definite. -dist.pdf(x) # Shape is [2, 2]. +dist.prob(x) # Shape is [2, 2]. # (*) - To efficiently create a trainable covariance matrix, see the example # in tf.contrib.distributions.matrix_diag_transform. @@ -23450,7 +20376,7 @@ Construct Wishart distributions. scale matrix of the distribution. * <b>`cholesky_input_output_matrices`</b>: `Boolean`. Any function which whose input or output is a matrix assumes the input is Cholesky and returns a - Cholesky factored matrix. Example`log_pdf` input takes a Cholesky and + Cholesky factored matrix. Example `log_prob` input takes a Cholesky and `sample_n` returns a Cholesky when `cholesky_input_output_matrices=True`. * <b>`validate_args`</b>: `Boolean`, default `False`. Whether to validate input with @@ -23755,54 +20681,6 @@ Computes the log normalizing constant, log(Z). - - - -#### `tf.contrib.distributions.WishartFull.log_pdf(value, name='log_pdf')` {#WishartFull.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.WishartFull.log_pmf(value, name='log_pmf')` {#WishartFull.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.WishartFull.log_prob(value, name='log_prob')` {#WishartFull.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -23940,54 +20818,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.WishartFull.pdf(value, name='pdf')` {#WishartFull.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.WishartFull.pmf(value, name='pmf')` {#WishartFull.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.WishartFull.prob(value, name='prob')` {#WishartFull.prob} Probability density/mass function (depending on `is_continuous`). @@ -24184,7 +21014,7 @@ dist = tf.contrib.distributions.MVNCholesky(mu, chol) # Standard log loss. Minimizing this will "train" mu and chol, and then dist # will be a distribution predicting labels as multivariate Gaussians. -loss = -1 * tf.reduce_mean(dist.log_pdf(labels)) +loss = -1 * tf.reduce_mean(dist.log_prob(labels)) ``` ##### Args: @@ -24646,54 +21476,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.TransformedDistribution.log_pdf(value, name='log_pdf')` {#TransformedDistribution.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.TransformedDistribution.log_pmf(value, name='log_pmf')` {#TransformedDistribution.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.TransformedDistribution.log_prob(value, name='log_prob')` {#TransformedDistribution.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -24833,54 +21615,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.TransformedDistribution.pdf(value, name='pdf')` {#TransformedDistribution.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.TransformedDistribution.pmf(value, name='pmf')` {#TransformedDistribution.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.TransformedDistribution.prob(value, name='prob')` {#TransformedDistribution.prob} Probability density/mass function (depending on `is_continuous`). @@ -25103,11 +21837,11 @@ the `distribution`. instance of `Distribution`. * <b>`lower_cutoff`</b>: `Tensor` with same `dtype` as this distribution and shape able to be added to samples. Should be a whole number. Default `None`. - If provided, base distribution's pdf/pmf should be defined at + If provided, base distribution's `prob` should be defined at `lower_cutoff`. * <b>`upper_cutoff`</b>: `Tensor` with same `dtype` as this distribution and shape able to be added to samples. Should be a whole number. Default `None`. - If provided, base distribution's pdf/pmf should be defined at + If provided, base distribution's `prob` should be defined at `upper_cutoff - 1`. `upper_cutoff` must be strictly greater than `lower_cutoff`. * <b>`validate_args`</b>: Python boolean. Whether to validate input with asserts. @@ -25430,54 +22164,6 @@ The base distribution's `log_cdf` method must be defined on `y - 1`. - - - -#### `tf.contrib.distributions.QuantizedDistribution.log_pdf(value, name='log_pdf')` {#QuantizedDistribution.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.QuantizedDistribution.log_pmf(value, name='log_pmf')` {#QuantizedDistribution.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.QuantizedDistribution.log_prob(value, name='log_prob')` {#QuantizedDistribution.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -25644,54 +22330,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.QuantizedDistribution.pdf(value, name='pdf')` {#QuantizedDistribution.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.QuantizedDistribution.pmf(value, name='pmf')` {#QuantizedDistribution.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.QuantizedDistribution.prob(value, name='prob')` {#QuantizedDistribution.prob} Probability density/mass function (depending on `is_continuous`). @@ -26256,54 +22894,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Mixture.log_pdf(value, name='log_pdf')` {#Mixture.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Mixture.log_pmf(value, name='log_pmf')` {#Mixture.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Mixture.log_prob(value, name='log_prob')` {#Mixture.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -26441,54 +23031,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Mixture.pdf(value, name='pdf')` {#Mixture.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Mixture.pmf(value, name='pmf')` {#Mixture.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Mixture.prob(value, name='prob')` {#Mixture.prob} Probability density/mass function (depending on `is_continuous`). @@ -27141,54 +23683,6 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.ConditionalDistribution.log_pdf(value, name='log_pdf')` {#ConditionalDistribution.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.ConditionalDistribution.log_pmf(value, name='log_pmf')` {#ConditionalDistribution.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.ConditionalDistribution.log_prob(*args, **kwargs)` {#ConditionalDistribution.log_prob} ##### `kwargs`: @@ -27289,54 +23783,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.ConditionalDistribution.pdf(value, name='pdf')` {#ConditionalDistribution.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.ConditionalDistribution.pmf(value, name='pmf')` {#ConditionalDistribution.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.ConditionalDistribution.prob(*args, **kwargs)` {#ConditionalDistribution.prob} ##### `kwargs`: @@ -27716,54 +24162,6 @@ Additional documentation from `ConditionalTransformedDistribution`: - - - -#### `tf.contrib.distributions.ConditionalTransformedDistribution.log_pdf(value, name='log_pdf')` {#ConditionalTransformedDistribution.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.ConditionalTransformedDistribution.log_pmf(value, name='log_pmf')` {#ConditionalTransformedDistribution.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.ConditionalTransformedDistribution.log_prob(*args, **kwargs)` {#ConditionalTransformedDistribution.log_prob} Additional documentation from `ConditionalTransformedDistribution`: @@ -27870,54 +24268,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.ConditionalTransformedDistribution.pdf(value, name='pdf')` {#ConditionalTransformedDistribution.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.ConditionalTransformedDistribution.pmf(value, name='pmf')` {#ConditionalTransformedDistribution.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.ConditionalTransformedDistribution.prob(*args, **kwargs)` {#ConditionalTransformedDistribution.prob} Additional documentation from `ConditionalTransformedDistribution`: diff --git a/tensorflow/g3doc/api_docs/python/contrib.learn.md b/tensorflow/g3doc/api_docs/python/contrib.learn.md index 95e08ceb58..a7fe7f358e 100644 --- a/tensorflow/g3doc/api_docs/python/contrib.learn.md +++ b/tensorflow/g3doc/api_docs/python/contrib.learn.md @@ -15,13 +15,7 @@ Train and evaluate TensorFlow models. Abstract BaseEstimator class to train and evaluate TensorFlow models. -Concrete implementation of this class should provide the following functions: - - * _get_train_ops - * _get_eval_ops - * _get_predict_ops - -`Estimator` implemented below is a good example of how to use this class. +Users should not instantiate or subclass this class. Instead, use `Estimator`. - - - #### `tf.contrib.learn.BaseEstimator.__init__(model_dir=None, config=None)` {#BaseEstimator.__init__} diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Bernoulli.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Bernoulli.md index 79443ef290..4b07870c3f 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Bernoulli.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Bernoulli.md @@ -300,54 +300,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Bernoulli.log_pdf(value, name='log_pdf')` {#Bernoulli.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Bernoulli.log_pmf(value, name='log_pmf')` {#Bernoulli.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Bernoulli.log_prob(value, name='log_prob')` {#Bernoulli.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -489,54 +441,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Bernoulli.pdf(value, name='pdf')` {#Bernoulli.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Bernoulli.pmf(value, name='pmf')` {#Bernoulli.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Bernoulli.prob(value, name='prob')` {#Bernoulli.prob} Probability density/mass function (depending on `is_continuous`). diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Chi2WithAbsDf.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Chi2WithAbsDf.md index b4668bbf51..6e8b9f8fe5 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Chi2WithAbsDf.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Chi2WithAbsDf.md @@ -302,54 +302,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.log_pdf(value, name='log_pdf')` {#Chi2WithAbsDf.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.log_pmf(value, name='log_pmf')` {#Chi2WithAbsDf.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Chi2WithAbsDf.log_prob(value, name='log_prob')` {#Chi2WithAbsDf.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -486,54 +438,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.pdf(value, name='pdf')` {#Chi2WithAbsDf.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.pmf(value, name='pmf')` {#Chi2WithAbsDf.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Chi2WithAbsDf.prob(value, name='prob')` {#Chi2WithAbsDf.prob} Probability density/mass function (depending on `is_continuous`). diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Dirichlet.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Dirichlet.md index 85465a5852..c13d5bef6a 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Dirichlet.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Dirichlet.md @@ -371,54 +371,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Dirichlet.log_pdf(value, name='log_pdf')` {#Dirichlet.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Dirichlet.log_pmf(value, name='log_pmf')` {#Dirichlet.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Dirichlet.log_prob(value, name='log_prob')` {#Dirichlet.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -564,54 +516,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Dirichlet.pdf(value, name='pdf')` {#Dirichlet.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Dirichlet.pmf(value, name='pmf')` {#Dirichlet.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Dirichlet.prob(value, name='prob')` {#Dirichlet.prob} Probability density/mass function (depending on `is_continuous`). diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Distribution.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Distribution.md index 0df5f4b55c..67e620438c 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Distribution.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Distribution.md @@ -412,54 +412,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Distribution.log_pdf(value, name='log_pdf')` {#Distribution.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Distribution.log_pmf(value, name='log_pmf')` {#Distribution.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Distribution.log_prob(value, name='log_prob')` {#Distribution.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -590,54 +542,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Distribution.pdf(value, name='pdf')` {#Distribution.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Distribution.pmf(value, name='pmf')` {#Distribution.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Distribution.prob(value, name='prob')` {#Distribution.prob} Probability density/mass function (depending on `is_continuous`). diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.MultivariateNormalCholesky.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.MultivariateNormalCholesky.md index ac2d038214..a1cf4b9438 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.MultivariateNormalCholesky.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.MultivariateNormalCholesky.md @@ -340,54 +340,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.log_pdf(value, name='log_pdf')` {#MultivariateNormalCholesky.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.log_pmf(value, name='log_pmf')` {#MultivariateNormalCholesky.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.MultivariateNormalCholesky.log_prob(value, name='log_prob')` {#MultivariateNormalCholesky.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -548,54 +500,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.pdf(value, name='pdf')` {#MultivariateNormalCholesky.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.pmf(value, name='pmf')` {#MultivariateNormalCholesky.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.MultivariateNormalCholesky.prob(value, name='prob')` {#MultivariateNormalCholesky.prob} Probability density/mass function (depending on `is_continuous`). diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.MultivariateNormalDiag.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.MultivariateNormalDiag.md index fd1347a576..1883e777f0 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.MultivariateNormalDiag.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.MultivariateNormalDiag.md @@ -340,54 +340,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.log_pdf(value, name='log_pdf')` {#MultivariateNormalDiag.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.log_pmf(value, name='log_pmf')` {#MultivariateNormalDiag.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.MultivariateNormalDiag.log_prob(value, name='log_prob')` {#MultivariateNormalDiag.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -548,54 +500,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.pdf(value, name='pdf')` {#MultivariateNormalDiag.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.pmf(value, name='pmf')` {#MultivariateNormalDiag.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.MultivariateNormalDiag.prob(value, name='prob')` {#MultivariateNormalDiag.prob} Probability density/mass function (depending on `is_continuous`). diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.QuantizedDistribution.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.QuantizedDistribution.md index 5ed362fc26..879f47fcda 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.QuantizedDistribution.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.QuantizedDistribution.md @@ -65,11 +65,11 @@ the `distribution`. instance of `Distribution`. * <b>`lower_cutoff`</b>: `Tensor` with same `dtype` as this distribution and shape able to be added to samples. Should be a whole number. Default `None`. - If provided, base distribution's pdf/pmf should be defined at + If provided, base distribution's `prob` should be defined at `lower_cutoff`. * <b>`upper_cutoff`</b>: `Tensor` with same `dtype` as this distribution and shape able to be added to samples. Should be a whole number. Default `None`. - If provided, base distribution's pdf/pmf should be defined at + If provided, base distribution's `prob` should be defined at `upper_cutoff - 1`. `upper_cutoff` must be strictly greater than `lower_cutoff`. * <b>`validate_args`</b>: Python boolean. Whether to validate input with asserts. @@ -392,54 +392,6 @@ The base distribution's `log_cdf` method must be defined on `y - 1`. - - - -#### `tf.contrib.distributions.QuantizedDistribution.log_pdf(value, name='log_pdf')` {#QuantizedDistribution.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.QuantizedDistribution.log_pmf(value, name='log_pmf')` {#QuantizedDistribution.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.QuantizedDistribution.log_prob(value, name='log_prob')` {#QuantizedDistribution.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -606,54 +558,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.QuantizedDistribution.pdf(value, name='pdf')` {#QuantizedDistribution.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.QuantizedDistribution.pmf(value, name='pmf')` {#QuantizedDistribution.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.QuantizedDistribution.prob(value, name='prob')` {#QuantizedDistribution.prob} Probability density/mass function (depending on `is_continuous`). diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.StudentT.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.StudentT.md index d239930923..53f4d424c6 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.StudentT.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.StudentT.md @@ -40,7 +40,7 @@ Examples of initialization of one or a batch of distributions. single_dist = tf.contrib.distributions.StudentT(df=3) # Evaluate the pdf at 1, returning a scalar Tensor. -single_dist.pdf(1.) +single_dist.prob(1.) # Define a batch of two scalar valued Student t's. # The first has degrees of freedom 2, mean 1, and scale 11. @@ -51,7 +51,7 @@ multi_dist = tf.contrib.distributions.StudentT(df=[2, 3], # Evaluate the pdf of the first distribution on 0, and the second on 1.5, # returning a length two tensor. -multi_dist.pdf([0, 1.5]) +multi_dist.prob([0, 1.5]) # Get 3 samples, returning a 3 x 2 tensor. multi_dist.sample(3) @@ -66,7 +66,7 @@ dist = tf.contrib.distributions.StudentT(df=2, loc=1, scale=[11, 22.]) # Evaluate the pdf of both distributions on the same point, 3.0, # returning a length 2 tensor. -dist.pdf(3.0) +dist.prob(3.0) ``` - - - @@ -384,54 +384,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.StudentT.log_pdf(value, name='log_pdf')` {#StudentT.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.StudentT.log_pmf(value, name='log_pmf')` {#StudentT.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.StudentT.log_prob(value, name='log_prob')` {#StudentT.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -568,54 +520,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.StudentT.pdf(value, name='pdf')` {#StudentT.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.StudentT.pmf(value, name='pmf')` {#StudentT.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.StudentT.prob(value, name='prob')` {#StudentT.prob} Probability density/mass function (depending on `is_continuous`). diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.StudentTWithAbsDfSoftplusScale.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.StudentTWithAbsDfSoftplusScale.md index d54ee3678c..0d019eb3d0 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.StudentTWithAbsDfSoftplusScale.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.StudentTWithAbsDfSoftplusScale.md @@ -284,54 +284,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusScale.log_pdf(value, name='log_pdf')` {#StudentTWithAbsDfSoftplusScale.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusScale.log_pmf(value, name='log_pmf')` {#StudentTWithAbsDfSoftplusScale.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.StudentTWithAbsDfSoftplusScale.log_prob(value, name='log_prob')` {#StudentTWithAbsDfSoftplusScale.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -468,54 +420,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusScale.pdf(value, name='pdf')` {#StudentTWithAbsDfSoftplusScale.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusScale.pmf(value, name='pmf')` {#StudentTWithAbsDfSoftplusScale.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.StudentTWithAbsDfSoftplusScale.prob(value, name='prob')` {#StudentTWithAbsDfSoftplusScale.prob} Probability density/mass function (depending on `is_continuous`). diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.TransformedDistribution.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.TransformedDistribution.md index 3c284d5400..37e0913552 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.TransformedDistribution.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.TransformedDistribution.md @@ -434,54 +434,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.TransformedDistribution.log_pdf(value, name='log_pdf')` {#TransformedDistribution.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.TransformedDistribution.log_pmf(value, name='log_pmf')` {#TransformedDistribution.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.TransformedDistribution.log_prob(value, name='log_prob')` {#TransformedDistribution.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -621,54 +573,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.TransformedDistribution.pdf(value, name='pdf')` {#TransformedDistribution.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.TransformedDistribution.pmf(value, name='pmf')` {#TransformedDistribution.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.TransformedDistribution.prob(value, name='prob')` {#TransformedDistribution.prob} Probability density/mass function (depending on `is_continuous`). diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Categorical.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Categorical.md index 24ab08c6ae..c64729af5e 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Categorical.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Categorical.md @@ -28,15 +28,15 @@ The distribution functions can be evaluated on counts. # counts is a scalar. p = [0.1, 0.4, 0.5] dist = Categorical(probs=p) -dist.pmf(0) # Shape [] +dist.prob(0) # Shape [] # p will be broadcast to [[0.1, 0.4, 0.5], [0.1, 0.4, 0.5]] to match counts. counts = [1, 0] -dist.pmf(counts) # Shape [2] +dist.prob(counts) # Shape [2] # p will be broadcast to shape [3, 5, 7, 3] to match counts. counts = [[...]] # Shape [5, 7, 3] -dist.pmf(counts) # Shape [5, 7, 3] +dist.prob(counts) # Shape [5, 7, 3] ``` - - - @@ -333,54 +333,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Categorical.log_pdf(value, name='log_pdf')` {#Categorical.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Categorical.log_pmf(value, name='log_pmf')` {#Categorical.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Categorical.log_prob(value, name='log_prob')` {#Categorical.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -525,54 +477,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Categorical.pdf(value, name='pdf')` {#Categorical.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Categorical.pmf(value, name='pmf')` {#Categorical.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Categorical.prob(value, name='prob')` {#Categorical.prob} Probability density/mass function (depending on `is_continuous`). @@ -594,9 +498,7 @@ Probability density/mass function (depending on `is_continuous`). #### `tf.contrib.distributions.Categorical.probs` {#Categorical.probs} -Vector of probabilities summing to one. - -Each element is the probability of drawing that coordinate. +Vector of coordinatewise probabilities. - - - diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Chi2.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Chi2.md index b356966511..a1978e859f 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Chi2.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Chi2.md @@ -324,54 +324,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Chi2.log_pdf(value, name='log_pdf')` {#Chi2.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Chi2.log_pmf(value, name='log_pmf')` {#Chi2.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Chi2.log_prob(value, name='log_prob')` {#Chi2.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -508,54 +460,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Chi2.pdf(value, name='pdf')` {#Chi2.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Chi2.pmf(value, name='pmf')` {#Chi2.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Chi2.prob(value, name='prob')` {#Chi2.prob} Probability density/mass function (depending on `is_continuous`). diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.ConditionalDistribution.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.ConditionalDistribution.md index 2768acd296..00434684b3 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.ConditionalDistribution.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.ConditionalDistribution.md @@ -268,54 +268,6 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.ConditionalDistribution.log_pdf(value, name='log_pdf')` {#ConditionalDistribution.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.ConditionalDistribution.log_pmf(value, name='log_pmf')` {#ConditionalDistribution.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.ConditionalDistribution.log_prob(*args, **kwargs)` {#ConditionalDistribution.log_prob} ##### `kwargs`: @@ -416,54 +368,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.ConditionalDistribution.pdf(value, name='pdf')` {#ConditionalDistribution.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.ConditionalDistribution.pmf(value, name='pmf')` {#ConditionalDistribution.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.ConditionalDistribution.prob(*args, **kwargs)` {#ConditionalDistribution.prob} ##### `kwargs`: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Uniform.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Uniform.md index f312e876d7..8539167fe2 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Uniform.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Uniform.md @@ -326,54 +326,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Uniform.log_pdf(value, name='log_pdf')` {#Uniform.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Uniform.log_pmf(value, name='log_pmf')` {#Uniform.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Uniform.log_prob(value, name='log_prob')` {#Uniform.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -504,54 +456,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Uniform.pdf(value, name='pdf')` {#Uniform.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Uniform.pmf(value, name='pmf')` {#Uniform.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Uniform.prob(value, name='prob')` {#Uniform.prob} Probability density/mass function (depending on `is_continuous`). diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.WishartCholesky.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.WishartCholesky.md index 0af916f72e..76ed5c44c6 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.WishartCholesky.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.WishartCholesky.md @@ -38,12 +38,12 @@ dist = tf.contrib.distributions.WishartCholesky(df=df, scale=chol_scale) # Evaluate this on an observation in R^3, returning a scalar. x = ... # A 3x3 positive definite matrix. -dist.pdf(x) # Shape is [], a scalar. +dist.prob(x) # Shape is [], a scalar. # Evaluate this on a two observations, each in R^{3x3}, returning a length two # Tensor. x = [x0, x1] # Shape is [2, 3, 3]. -dist.pdf(x) # Shape is [2]. +dist.prob(x) # Shape is [2]. # Initialize two 3x3 Wisharts with Cholesky factored scale matrices. df = [5, 4] @@ -52,7 +52,7 @@ dist = tf.contrib.distributions.WishartCholesky(df=df, scale=chol_scale) # Evaluate this on four observations. x = [[x0, x1], [x2, x3]] # Shape is [2, 2, 3, 3]. -dist.pdf(x) # Shape is [2, 2]. +dist.prob(x) # Shape is [2, 2]. # (*) - To efficiently create a trainable covariance matrix, see the example # in tf.contrib.distributions.matrix_diag_transform. @@ -72,7 +72,7 @@ Construct Wishart distributions. the symmetric positive definite scale matrix of the distribution. * <b>`cholesky_input_output_matrices`</b>: `Boolean`. Any function which whose input or output is a matrix assumes the input is Cholesky and returns a - Cholesky factored matrix. Example`log_pdf` input takes a Cholesky and + Cholesky factored matrix. Example `log_prob` input takes a Cholesky and `sample_n` returns a Cholesky when `cholesky_input_output_matrices=True`. * <b>`validate_args`</b>: `Boolean`, default `False`. Whether to validate input @@ -377,54 +377,6 @@ Computes the log normalizing constant, log(Z). - - - -#### `tf.contrib.distributions.WishartCholesky.log_pdf(value, name='log_pdf')` {#WishartCholesky.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.WishartCholesky.log_pmf(value, name='log_pmf')` {#WishartCholesky.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.WishartCholesky.log_prob(value, name='log_prob')` {#WishartCholesky.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -562,54 +514,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.WishartCholesky.pdf(value, name='pdf')` {#WishartCholesky.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.WishartCholesky.pmf(value, name='pmf')` {#WishartCholesky.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.WishartCholesky.prob(value, name='prob')` {#WishartCholesky.prob} Probability density/mass function (depending on `is_continuous`). diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.bijector.Bijector.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.bijector.Bijector.md index b52554f862..9bd9c7b635 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.bijector.Bijector.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.bijector.Bijector.md @@ -44,9 +44,9 @@ Example Use: - Computing a log-likelihood: ```python - def transformed_log_pdf(bijector, log_pdf, x): + def transformed_log_prob(bijector, log_prob, x): return (bijector.inverse_log_det_jacobian(x) + - log_pdf(bijector.inverse(x))) + log_prob(bijector.inverse(x))) ``` - Transforming a random outcome: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.BaseEstimator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.BaseEstimator.md index 0c24058d72..740be32d9b 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.BaseEstimator.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.BaseEstimator.md @@ -1,12 +1,6 @@ Abstract BaseEstimator class to train and evaluate TensorFlow models. -Concrete implementation of this class should provide the following functions: - - * _get_train_ops - * _get_eval_ops - * _get_predict_ops - -`Estimator` implemented below is a good example of how to use this class. +Users should not instantiate or subclass this class. Instead, use `Estimator`. - - - #### `tf.contrib.learn.BaseEstimator.__init__(model_dir=None, config=None)` {#BaseEstimator.__init__} 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 5c3f7090c3..2f0b0b33a4 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 @@ -299,54 +299,6 @@ 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} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.log_pmf(value, name='log_pmf')` {#BetaWithSoftplusAB.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.BetaWithSoftplusAB.log_prob(value, name='log_prob')` {#BetaWithSoftplusAB.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -484,54 +436,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.pdf(value, name='pdf')` {#BetaWithSoftplusAB.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.pmf(value, name='pmf')` {#BetaWithSoftplusAB.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.BetaWithSoftplusAB.prob(value, name='prob')` {#BetaWithSoftplusAB.prob} Probability density/mass function (depending on `is_continuous`). 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 f59f85f88b..542f7cecdd 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 @@ -358,54 +358,6 @@ 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} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Binomial.log_pmf(value, name='log_pmf')` {#Binomial.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Binomial.log_prob(value, name='log_prob')` {#Binomial.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -563,54 +515,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Binomial.pdf(value, name='pdf')` {#Binomial.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Binomial.pmf(value, name='pmf')` {#Binomial.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Binomial.prob(value, name='prob')` {#Binomial.prob} Probability density/mass function (depending on `is_continuous`). 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 d5699ef3d4..7496add194 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 @@ -25,9 +25,9 @@ This class provides methods to create indexed batches of Dirichlet Multinomial distributions. If the provided `alpha` is rank 2 or higher, for every fixed set of leading dimensions, the last dimension represents one single Dirichlet Multinomial distribution. When calling distribution -functions (e.g. `dist.pmf(counts)`), `alpha` and `counts` are broadcast to the -same shape (if possible). In all cases, the last dimension of alpha/counts -represents single Dirichlet Multinomial distributions. +functions (e.g. `dist.prob(counts)`), `alpha` and `counts` are broadcast to +the same shape (if possible). In all cases, the last dimension of +alpha/counts represents single Dirichlet Multinomial distributions. #### Examples @@ -43,15 +43,15 @@ The distribution functions can be evaluated on counts. ```python # counts same shape as alpha. counts = [0, 0, 2] -dist.pmf(counts) # Shape [] +dist.prob(counts) # Shape [] # alpha will be broadcast to [[1, 2, 3], [1, 2, 3]] to match counts. counts = [[1, 1, 0], [1, 0, 1]] -dist.pmf(counts) # Shape [2] +dist.prob(counts) # Shape [2] # alpha will be broadcast to shape [5, 7, 3] to match counts. counts = [[...]] # Shape [5, 7, 3] -dist.pmf(counts) # Shape [5, 7] +dist.prob(counts) # Shape [5, 7] ``` Creates a 2-batch of 3-class distributions. @@ -63,7 +63,7 @@ dist = DirichletMultinomial(n, alpha) # counts will be broadcast to [[2, 1, 0], [2, 1, 0]] to match alpha. counts = [2, 1, 0] -dist.pmf(counts) # Shape [2] +dist.prob(counts) # Shape [2] ``` - - - @@ -402,54 +402,6 @@ 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} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.DirichletMultinomial.log_pmf(value, name='log_pmf')` {#DirichletMultinomial.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.DirichletMultinomial.log_prob(value, name='log_prob')` {#DirichletMultinomial.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -602,54 +554,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.DirichletMultinomial.pdf(value, name='pdf')` {#DirichletMultinomial.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.DirichletMultinomial.pmf(value, name='pmf')` {#DirichletMultinomial.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.DirichletMultinomial.prob(value, name='prob')` {#DirichletMultinomial.prob} Probability density/mass function (depending on `is_continuous`). 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 44d2ff1499..920976843e 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 @@ -324,54 +324,6 @@ 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} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Exponential.log_pmf(value, name='log_pmf')` {#Exponential.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Exponential.log_prob(value, name='log_prob')` {#Exponential.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -508,54 +460,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Exponential.pdf(value, name='pdf')` {#Exponential.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Exponential.pmf(value, name='pmf')` {#Exponential.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Exponential.prob(value, name='prob')` {#Exponential.prob} Probability density/mass function (depending on `is_continuous`). 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 adde19f553..6c14ffbaaf 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 @@ -344,54 +344,6 @@ 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} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Gamma.log_pmf(value, name='log_pmf')` {#Gamma.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Gamma.log_prob(value, name='log_prob')` {#Gamma.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -528,54 +480,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Gamma.pdf(value, name='pdf')` {#Gamma.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Gamma.pmf(value, name='pmf')` {#Gamma.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Gamma.prob(value, name='prob')` {#Gamma.prob} Probability density/mass function (depending on `is_continuous`). 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 0c48531370..48867ffb37 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 @@ -295,54 +295,6 @@ 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} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf')` {#GammaWithSoftplusAlphaBeta.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob')` {#GammaWithSoftplusAlphaBeta.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -479,54 +431,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.pdf(value, name='pdf')` {#GammaWithSoftplusAlphaBeta.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.pmf(value, name='pmf')` {#GammaWithSoftplusAlphaBeta.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.prob(value, name='prob')` {#GammaWithSoftplusAlphaBeta.prob} Probability density/mass function (depending on `is_continuous`). 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 2b13df03d0..d5f5217988 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 @@ -340,54 +340,6 @@ 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} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.InverseGamma.log_pmf(value, name='log_pmf')` {#InverseGamma.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.InverseGamma.log_prob(value, name='log_prob')` {#InverseGamma.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -528,54 +480,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.InverseGamma.pdf(value, name='pdf')` {#InverseGamma.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.InverseGamma.pmf(value, name='pmf')` {#InverseGamma.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.InverseGamma.prob(value, name='prob')` {#InverseGamma.prob} Probability density/mass function (depending on `is_continuous`). 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 67e8d29a01..a04224b768 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 @@ -295,54 +295,6 @@ 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} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf')` {#InverseGammaWithSoftplusAlphaBeta.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob')` {#InverseGammaWithSoftplusAlphaBeta.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -483,54 +435,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pdf(value, name='pdf')` {#InverseGammaWithSoftplusAlphaBeta.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pmf(value, name='pmf')` {#InverseGammaWithSoftplusAlphaBeta.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.prob(value, name='prob')` {#InverseGammaWithSoftplusAlphaBeta.prob} Probability density/mass function (depending on `is_continuous`). 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 858d7c3ba4..eeef2d89ff 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 @@ -369,54 +369,6 @@ 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} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Multinomial.log_pmf(value, name='log_pmf')` {#Multinomial.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Multinomial.log_prob(value, name='log_prob')` {#Multinomial.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -570,54 +522,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Multinomial.pdf(value, name='pdf')` {#Multinomial.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Multinomial.pmf(value, name='pmf')` {#Multinomial.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Multinomial.prob(value, name='prob')` {#Multinomial.prob} Probability density/mass function (depending on `is_continuous`). 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 e8a3d076f9..a71f0b27b9 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 @@ -366,54 +366,6 @@ 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} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pmf(value, name='log_pmf')` {#MultivariateNormalDiagPlusVDVT.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_prob(value, name='log_prob')` {#MultivariateNormalDiagPlusVDVT.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -574,54 +526,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pdf(value, name='pdf')` {#MultivariateNormalDiagPlusVDVT.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pmf(value, name='pmf')` {#MultivariateNormalDiagPlusVDVT.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.prob(value, name='prob')` {#MultivariateNormalDiagPlusVDVT.prob} Probability density/mass function (depending on `is_continuous`). diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.NormalWithSoftplusScale.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.NormalWithSoftplusScale.md index 675a961a82..4b6d13c5c3 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.NormalWithSoftplusScale.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.NormalWithSoftplusScale.md @@ -277,54 +277,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.NormalWithSoftplusScale.log_pdf(value, name='log_pdf')` {#NormalWithSoftplusScale.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.NormalWithSoftplusScale.log_pmf(value, name='log_pmf')` {#NormalWithSoftplusScale.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.NormalWithSoftplusScale.log_prob(value, name='log_prob')` {#NormalWithSoftplusScale.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -455,54 +407,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.NormalWithSoftplusScale.pdf(value, name='pdf')` {#NormalWithSoftplusScale.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.NormalWithSoftplusScale.pmf(value, name='pmf')` {#NormalWithSoftplusScale.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.NormalWithSoftplusScale.prob(value, name='prob')` {#NormalWithSoftplusScale.prob} Probability density/mass function (depending on `is_continuous`). diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.BernoulliWithSigmoidProbs.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.BernoulliWithSigmoidProbs.md index 4eaceaa9e7..545a24197d 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.BernoulliWithSigmoidProbs.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.BernoulliWithSigmoidProbs.md @@ -270,54 +270,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.log_pdf(value, name='log_pdf')` {#BernoulliWithSigmoidProbs.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.log_pmf(value, name='log_pmf')` {#BernoulliWithSigmoidProbs.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.BernoulliWithSigmoidProbs.log_prob(value, name='log_prob')` {#BernoulliWithSigmoidProbs.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -459,54 +411,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.pdf(value, name='pdf')` {#BernoulliWithSigmoidProbs.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.pmf(value, name='pmf')` {#BernoulliWithSigmoidProbs.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.BernoulliWithSigmoidProbs.prob(value, name='prob')` {#BernoulliWithSigmoidProbs.prob} Probability density/mass function (depending on `is_continuous`). diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Beta.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Beta.md index 87a1d87aca..8bc40a15f1 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Beta.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Beta.md @@ -18,7 +18,7 @@ is the beta function. This class provides methods to create indexed batches of Beta distributions. One entry of the broadcasted shape represents of `a` and `b` represents one single Beta distribution. -When calling distribution functions (e.g. `dist.pdf(x)`), `a`, `b` +When calling distribution functions (e.g. `dist.prob(x)`), `a`, `b` and `x` are broadcast to the same shape (if possible). Every entry in a/b/x corresponds to a single Beta distribution. @@ -36,15 +36,15 @@ dist = Beta(a, b) ```python # x same shape as a. x = [.2, .3, .7] -dist.pdf(x) # Shape [3] +dist.prob(x) # Shape [3] # a/b will be broadcast to [[1, 2, 3], [1, 2, 3]] to match x. x = [[.1, .4, .5], [.2, .3, .5]] -dist.pdf(x) # Shape [2, 3] +dist.prob(x) # Shape [2, 3] # a/b will be broadcast to shape [5, 7, 3] to match x. x = [[...]] # Shape [5, 7, 3] -dist.pdf(x) # Shape [5, 7, 3] +dist.prob(x) # Shape [5, 7, 3] ``` Creates a 2-batch of 3-class distributions. @@ -56,7 +56,7 @@ dist = Beta(a, b) # x will be broadcast to [[.2, .3, .9], [.2, .3, .9]] to match a/b. x = [.2, .3, .9] -dist.pdf(x) # Shape [2] +dist.prob(x) # Shape [2] ``` - - - @@ -388,54 +388,6 @@ distribution in `self.a` and `self.b`. `x` is only legal if `0 < x < 1`. - - - -#### `tf.contrib.distributions.Beta.log_pdf(value, name='log_pdf')` {#Beta.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Beta.log_pmf(value, name='log_pmf')` {#Beta.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Beta.log_prob(value, name='log_prob')` {#Beta.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -573,54 +525,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Beta.pdf(value, name='pdf')` {#Beta.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Beta.pmf(value, name='pmf')` {#Beta.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Beta.prob(value, name='prob')` {#Beta.prob} Probability density/mass function (depending on `is_continuous`). diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Laplace.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Laplace.md index 23f403b471..8605038c7b 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Laplace.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Laplace.md @@ -325,54 +325,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Laplace.log_pdf(value, name='log_pdf')` {#Laplace.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Laplace.log_pmf(value, name='log_pmf')` {#Laplace.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Laplace.log_prob(value, name='log_prob')` {#Laplace.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -503,54 +455,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Laplace.pdf(value, name='pdf')` {#Laplace.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Laplace.pmf(value, name='pmf')` {#Laplace.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Laplace.prob(value, name='prob')` {#Laplace.prob} Probability density/mass function (depending on `is_continuous`). diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.LaplaceWithSoftplusScale.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.LaplaceWithSoftplusScale.md index cebf30a19a..49fe6c601f 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.LaplaceWithSoftplusScale.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.LaplaceWithSoftplusScale.md @@ -277,54 +277,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_pdf(value, name='log_pdf')` {#LaplaceWithSoftplusScale.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_pmf(value, name='log_pmf')` {#LaplaceWithSoftplusScale.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_prob(value, name='log_prob')` {#LaplaceWithSoftplusScale.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -455,54 +407,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.pdf(value, name='pdf')` {#LaplaceWithSoftplusScale.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.pmf(value, name='pmf')` {#LaplaceWithSoftplusScale.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.LaplaceWithSoftplusScale.prob(value, name='prob')` {#LaplaceWithSoftplusScale.prob} Probability density/mass function (depending on `is_continuous`). diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.ConditionalTransformedDistribution.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.ConditionalTransformedDistribution.md index 52cf3fed7a..69750baf03 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.ConditionalTransformedDistribution.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.ConditionalTransformedDistribution.md @@ -271,54 +271,6 @@ Additional documentation from `ConditionalTransformedDistribution`: - - - -#### `tf.contrib.distributions.ConditionalTransformedDistribution.log_pdf(value, name='log_pdf')` {#ConditionalTransformedDistribution.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.ConditionalTransformedDistribution.log_pmf(value, name='log_pmf')` {#ConditionalTransformedDistribution.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.ConditionalTransformedDistribution.log_prob(*args, **kwargs)` {#ConditionalTransformedDistribution.log_prob} Additional documentation from `ConditionalTransformedDistribution`: @@ -425,54 +377,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.ConditionalTransformedDistribution.pdf(value, name='pdf')` {#ConditionalTransformedDistribution.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.ConditionalTransformedDistribution.pmf(value, name='pmf')` {#ConditionalTransformedDistribution.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.ConditionalTransformedDistribution.prob(*args, **kwargs)` {#ConditionalTransformedDistribution.prob} Additional documentation from `ConditionalTransformedDistribution`: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.ExponentialWithSoftplusLam.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.ExponentialWithSoftplusLam.md index 042d31cc51..55478867c1 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.ExponentialWithSoftplusLam.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.ExponentialWithSoftplusLam.md @@ -302,54 +302,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_pdf(value, name='log_pdf')` {#ExponentialWithSoftplusLam.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_pmf(value, name='log_pmf')` {#ExponentialWithSoftplusLam.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_prob(value, name='log_prob')` {#ExponentialWithSoftplusLam.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -486,54 +438,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.pdf(value, name='pdf')` {#ExponentialWithSoftplusLam.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.pmf(value, name='pmf')` {#ExponentialWithSoftplusLam.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.ExponentialWithSoftplusLam.prob(value, name='prob')` {#ExponentialWithSoftplusLam.prob} Probability density/mass function (depending on `is_continuous`). diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.MultivariateNormalFull.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.MultivariateNormalFull.md index 0c1c09d567..4e8e20400f 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.MultivariateNormalFull.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.MultivariateNormalFull.md @@ -331,54 +331,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalFull.log_pdf(value, name='log_pdf')` {#MultivariateNormalFull.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.MultivariateNormalFull.log_pmf(value, name='log_pmf')` {#MultivariateNormalFull.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.MultivariateNormalFull.log_prob(value, name='log_prob')` {#MultivariateNormalFull.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -539,54 +491,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.pdf(value, name='pdf')` {#MultivariateNormalFull.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.MultivariateNormalFull.pmf(value, name='pmf')` {#MultivariateNormalFull.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.MultivariateNormalFull.prob(value, name='prob')` {#MultivariateNormalFull.prob} Probability density/mass function (depending on `is_continuous`). diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.Normal.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.Normal.md index 0dab0bfa73..325b86737f 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.Normal.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.Normal.md @@ -38,7 +38,7 @@ dist = tf.contrib.distributions.Normal(loc=[1, 2.], scale=[11, 22.]) # Evaluate the pdf of the first distribution on 0, and the second on 1.5, # returning a length two tensor. -dist.pdf([0, 1.5]) +dist.prob([0, 1.5]) # Get 3 samples, returning a 3 x 2 tensor. dist.sample([3]) @@ -53,7 +53,7 @@ dist = tf.contrib.distributions.Normal(loc=1., scale=[11, 22.]) # Evaluate the pdf of both distributions on the same point, 3.0, # returning a length 2 tensor. -dist.pdf(3.0) +dist.prob(3.0) ``` - - - @@ -357,54 +357,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Normal.log_pdf(value, name='log_pdf')` {#Normal.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Normal.log_pmf(value, name='log_pmf')` {#Normal.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Normal.log_prob(value, name='log_prob')` {#Normal.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -535,54 +487,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Normal.pdf(value, name='pdf')` {#Normal.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Normal.pmf(value, name='pmf')` {#Normal.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Normal.prob(value, name='prob')` {#Normal.prob} Probability density/mass function (depending on `is_continuous`). diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.Mixture.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.Mixture.md index d74abaeafa..d686caebda 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.Mixture.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.Mixture.md @@ -377,54 +377,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Mixture.log_pdf(value, name='log_pdf')` {#Mixture.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Mixture.log_pmf(value, name='log_pmf')` {#Mixture.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Mixture.log_prob(value, name='log_prob')` {#Mixture.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -562,54 +514,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Mixture.pdf(value, name='pdf')` {#Mixture.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Mixture.pmf(value, name='pmf')` {#Mixture.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Mixture.prob(value, name='prob')` {#Mixture.prob} Probability density/mass function (depending on `is_continuous`). diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.matrix_diag_transform.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.matrix_diag_transform.md index 1c155eebcb..6f0edd8304 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.matrix_diag_transform.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.matrix_diag_transform.md @@ -33,7 +33,7 @@ dist = tf.contrib.distributions.MVNCholesky(mu, chol) # Standard log loss. Minimizing this will "train" mu and chol, and then dist # will be a distribution predicting labels as multivariate Gaussians. -loss = -1 * tf.reduce_mean(dist.log_pdf(labels)) +loss = -1 * tf.reduce_mean(dist.log_prob(labels)) ``` ##### Args: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.md index 3ec9536979..2fc179945b 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.md @@ -270,54 +270,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_pdf(value, name='log_pdf')` {#MultivariateNormalDiagWithSoftplusStDev.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_pmf(value, name='log_pmf')` {#MultivariateNormalDiagWithSoftplusStDev.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_prob(value, name='log_prob')` {#MultivariateNormalDiagWithSoftplusStDev.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -478,54 +430,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.pdf(value, name='pdf')` {#MultivariateNormalDiagWithSoftplusStDev.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.pmf(value, name='pmf')` {#MultivariateNormalDiagWithSoftplusStDev.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.prob(value, name='prob')` {#MultivariateNormalDiagWithSoftplusStDev.prob} Probability density/mass function (depending on `is_continuous`). diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.Poisson.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.Poisson.md index 52ec73fbf0..e64febf42d 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.Poisson.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.Poisson.md @@ -20,8 +20,8 @@ Construct Poisson distributions. * <b>`lam`</b>: Floating point tensor, the rate parameter of the distribution(s). `lam` must be positive. * <b>`validate_args`</b>: `Boolean`, default `False`. Whether to assert that - `lam > 0` as well as inputs to pmf computations are non-negative - integers. If validate_args is `False`, then `pmf` computations might + `lam > 0` as well as inputs to `prob` computations are non-negative + integers. If validate_args is `False`, then `prob` computations might return `NaN`, but can be evaluated at any real value. * <b>`allow_nan_stats`</b>: `Boolean`, default `True`. If `False`, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any @@ -301,54 +301,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Poisson.log_pdf(value, name='log_pdf')` {#Poisson.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Poisson.log_pmf(value, name='log_pmf')` {#Poisson.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Poisson.log_prob(value, name='log_prob')` {#Poisson.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -492,54 +444,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Poisson.pdf(value, name='pdf')` {#Poisson.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.Poisson.pmf(value, name='pmf')` {#Poisson.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.Poisson.prob(value, name='prob')` {#Poisson.prob} Probability density/mass function (depending on `is_continuous`). diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.WishartFull.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.WishartFull.md index f1dd7066f7..633e5a7070 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.WishartFull.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.WishartFull.md @@ -34,12 +34,12 @@ dist = tf.contrib.distributions.WishartFull(df=df, scale=scale) # Evaluate this on an observation in R^3, returning a scalar. x = ... # A 3x3 positive definite matrix. -dist.pdf(x) # Shape is [], a scalar. +dist.prob(x) # Shape is [], a scalar. # Evaluate this on a two observations, each in R^{3x3}, returning a length two # Tensor. x = [x0, x1] # Shape is [2, 3, 3]. -dist.pdf(x) # Shape is [2]. +dist.prob(x) # Shape is [2]. # Initialize two 3x3 Wisharts with Full factored scale matrices. df = [5, 4] @@ -48,7 +48,7 @@ dist = tf.contrib.distributions.WishartFull(df=df, scale=scale) # Evaluate this on four observations. x = [[x0, x1], [x2, x3]] # Shape is [2, 2, 3, 3]; xi is positive definite. -dist.pdf(x) # Shape is [2, 2]. +dist.prob(x) # Shape is [2, 2]. # (*) - To efficiently create a trainable covariance matrix, see the example # in tf.contrib.distributions.matrix_diag_transform. @@ -68,7 +68,7 @@ Construct Wishart distributions. scale matrix of the distribution. * <b>`cholesky_input_output_matrices`</b>: `Boolean`. Any function which whose input or output is a matrix assumes the input is Cholesky and returns a - Cholesky factored matrix. Example`log_pdf` input takes a Cholesky and + Cholesky factored matrix. Example `log_prob` input takes a Cholesky and `sample_n` returns a Cholesky when `cholesky_input_output_matrices=True`. * <b>`validate_args`</b>: `Boolean`, default `False`. Whether to validate input with @@ -373,54 +373,6 @@ Computes the log normalizing constant, log(Z). - - - -#### `tf.contrib.distributions.WishartFull.log_pdf(value, name='log_pdf')` {#WishartFull.log_pdf} - -Log probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.WishartFull.log_pmf(value, name='log_pmf')` {#WishartFull.log_pmf} - -Log probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.WishartFull.log_prob(value, name='log_prob')` {#WishartFull.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -558,54 +510,6 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.WishartFull.pdf(value, name='pdf')` {#WishartFull.pdf} - -Probability density function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if not `is_continuous`. - - -- - - - -#### `tf.contrib.distributions.WishartFull.pmf(value, name='pmf')` {#WishartFull.pmf} - -Probability mass function. - -##### Args: - - -* <b>`value`</b>: `float` or `double` `Tensor`. -* <b>`name`</b>: The name to give this op. - -##### Returns: - - -* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with - values of type `self.dtype`. - -##### Raises: - - -* <b>`TypeError`</b>: if `is_continuous`. - - -- - - - #### `tf.contrib.distributions.WishartFull.prob(value, name='prob')` {#WishartFull.prob} Probability density/mass function (depending on `is_continuous`). |