diff options
| author |  A. Unique TensorFlower <gardener@tensorflow.org> | 2016-07-19 18:06:18 -0800 |
|---|---|---|
| committer | TensorFlower Gardener <gardener@tensorflow.org> | 2016-07-19 19:18:34 -0700 |
| commit | b10e652bbaff0a1d60256a43bdf7db7a341071ab (patch) | |
| tree | a9ad92c8a4c11684f5eeb1755840145389b3d1a9 | |
| parent | 8c413daa09318c6ad021eb830650e3c66ee90891 (diff) | |
Update generated Python Op docs.
Change: 127901773
18 files changed, 462 insertions, 454 deletions
diff --git a/tensorflow/g3doc/api_docs/python/contrib.distributions.md b/tensorflow/g3doc/api_docs/python/contrib.distributions.md index ec6d739a7b..4df0e40068 100644 --- a/tensorflow/g3doc/api_docs/python/contrib.distributions.md +++ b/tensorflow/g3doc/api_docs/python/contrib.distributions.md @@ -102,11 +102,11 @@ a = tf.exp(tf.matmul(logits, weights_a)) b = tf.exp(tf.matmul(logits, weights_b)) # Will raise exception if ANY batch member has a < 1 or b < 1. -dist = distributions.beta(a, b, strict_statistics=True) # default is True +dist = distributions.beta(a, b, allow_nan_stats=False) # default is False mode = dist.mode().eval() # Will return NaN for batch members with either a < 1 or b < 1. -dist = distributions.beta(a, b, strict_statistics=False) +dist = distributions.beta(a, b, allow_nan_stats=True) mode = dist.mode().eval() ``` @@ -115,11 +115,18 @@ In all cases, an exception is raised if *invalid* parameters are passed, e.g. ```python # Will raise an exception if any Op is run. negative_a = -1.0 * a # beta distribution by definition has a > 0. -dist = distributions.beta(negative_a, b, strict_statistics=False) +dist = distributions.beta(negative_a, b, allow_nan_stats=True) dist.mean().eval() ``` - - - +#### `tf.contrib.distributions.Distribution.allow_nan_stats` {#Distribution.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.Distribution.batch_shape(name='batch_shape')` {#Distribution.batch_shape} Batch dimensions of this instance as a 1-D int32 `Tensor`. @@ -329,20 +336,13 @@ Standard deviation of the distribution. - - - -#### `tf.contrib.distributions.Distribution.strict` {#Distribution.strict} +#### `tf.contrib.distributions.Distribution.validate_args` {#Distribution.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.Distribution.strict_statistics` {#Distribution.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.Distribution.variance(name='variance')` {#Distribution.variance} Variance of the distribution. @@ -366,7 +366,7 @@ Note, the following methods of the base class aren't implemented: * log_cdf - - - -#### `tf.contrib.distributions.Bernoulli.__init__(logits=None, p=None, dtype=tf.int32, strict=True, strict_statistics=True, name='Bernoulli')` {#Bernoulli.__init__} +#### `tf.contrib.distributions.Bernoulli.__init__(logits=None, p=None, dtype=tf.int32, validate_args=True, allow_nan_stats=False, name='Bernoulli')` {#Bernoulli.__init__} Construct Bernoulli distributions. @@ -381,11 +381,11 @@ Construct Bernoulli distributions. event. Each entry in the `Tensor` parameterizes an independent Bernoulli distribution. * <b>`dtype`</b>: dtype for samples. -* <b>`strict`</b>: Whether to assert that `0 <= p <= 1`. If not strict, `log_pmf` may - return nans. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if +* <b>`validate_args`</b>: Whether to assert that `0 <= p <= 1`. If not validate_args, + `log_pmf` may return nans. +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: A name for this distribution. @@ -397,6 +397,13 @@ Construct Bernoulli distributions. - - - +#### `tf.contrib.distributions.Bernoulli.allow_nan_stats` {#Bernoulli.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.Bernoulli.batch_shape(name='batch_shape')` {#Bernoulli.batch_shape} @@ -664,20 +671,13 @@ Standard deviation of the distribution. - - - -#### `tf.contrib.distributions.Bernoulli.strict` {#Bernoulli.strict} +#### `tf.contrib.distributions.Bernoulli.validate_args` {#Bernoulli.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.Bernoulli.strict_statistics` {#Bernoulli.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.Bernoulli.variance(name='variance')` {#Bernoulli.variance} Variance of the distribution. @@ -760,7 +760,7 @@ dist.pdf(x) # Shape [2] ``` - - - -#### `tf.contrib.distributions.Beta.__init__(a, b, strict=True, strict_statistics=True, name='Beta')` {#Beta.__init__} +#### `tf.contrib.distributions.Beta.__init__(a, b, validate_args=True, allow_nan_stats=False, name='Beta')` {#Beta.__init__} Initialize a batch of Beta distributions. @@ -774,12 +774,12 @@ Initialize a batch of Beta distributions. * <b>`b`</b>: Positive `float` or `double` tensor with shape broadcastable to `[N1,..., Nm]` `m >= 0`. Defines this as a batch of `N1 x ... x Nm` different Beta distributions. -* <b>`strict`</b>: Whether to assert valid values for parameters `a` and `b`, and - `x` in `prob` and `log_prob`. If False, correct behavior is not +* <b>`validate_args`</b>: Whether to assert valid values for parameters `a` and `b`, + and `x` in `prob` and `log_prob`. If False, correct behavior is not guaranteed. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: The name to prefix Ops created by this distribution class. @@ -804,6 +804,13 @@ Shape parameter. - - - +#### `tf.contrib.distributions.Beta.allow_nan_stats` {#Beta.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.Beta.b` {#Beta.b} Shape parameter. @@ -962,7 +969,7 @@ Mode of the distribution. Note that the mode for the Beta distribution is only defined when `a > 1`, `b > 1`. This returns the mode when `a > 1` and `b > 1`, -and NaN otherwise. If `self.strict_statistics` is `True`, an exception +and NaN otherwise. If `self.allow_nan_stats` is `False`, an exception will be raised rather than returning `NaN`. ##### Args: @@ -1070,20 +1077,13 @@ Standard deviation of the distribution. - - - -#### `tf.contrib.distributions.Beta.strict` {#Beta.strict} +#### `tf.contrib.distributions.Beta.validate_args` {#Beta.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.Beta.strict_statistics` {#Beta.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.Beta.variance(name='variance')` {#Beta.variance} Variance of the distribution. @@ -1105,7 +1105,7 @@ Note, the following methods of the base class aren't implemented: * log_cdf - - - -#### `tf.contrib.distributions.Categorical.__init__(logits, dtype=tf.int32, strict=True, strict_statistics=True, name='Categorical')` {#Categorical.__init__} +#### `tf.contrib.distributions.Categorical.__init__(logits, dtype=tf.int32, validate_args=True, allow_nan_stats=False, name='Categorical')` {#Categorical.__init__} Initialize Categorical distributions using class log-probabilities. @@ -1117,16 +1117,23 @@ Initialize Categorical distributions using class log-probabilities. index into a batch of independent distributions and the last dimension indexes into the classes. * <b>`dtype`</b>: The type of the event samples (default: int32). -* <b>`strict`</b>: Unused in this distribution. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if +* <b>`validate_args`</b>: Unused in this distribution. +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: A name for this distribution (optional). - - - +#### `tf.contrib.distributions.Categorical.allow_nan_stats` {#Categorical.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.Categorical.batch_shape(name='batch_shape')` {#Categorical.batch_shape} @@ -1344,20 +1351,13 @@ Standard deviation of the distribution. - - - -#### `tf.contrib.distributions.Categorical.strict` {#Categorical.strict} +#### `tf.contrib.distributions.Categorical.validate_args` {#Categorical.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.Categorical.strict_statistics` {#Categorical.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.Categorical.variance(name='variance')` {#Categorical.variance} Variance of the distribution. @@ -1378,7 +1378,7 @@ Note that the Chi2 distribution is a special case of the Gamma distribution, with Chi2(df) = Gamma(df/2, 1/2). - - - -#### `tf.contrib.distributions.Chi2.__init__(df, strict=True, strict_statistics=True, name='Chi2')` {#Chi2.__init__} +#### `tf.contrib.distributions.Chi2.__init__(df, validate_args=True, allow_nan_stats=False, name='Chi2')` {#Chi2.__init__} Construct Chi2 distributions with parameter `df`. @@ -1387,18 +1387,25 @@ Construct Chi2 distributions with parameter `df`. * <b>`df`</b>: `float` or `double` tensor, the degrees of freedom of the distribution(s). `df` must contain only positive values. -* <b>`strict`</b>: Whether to assert that `df > 0`, and that `x > 0` in the - methods `prob(x)` and `log_prob(x)`. If `strict` is False +* <b>`validate_args`</b>: Whether to assert that `df > 0`, and that `x > 0` in the + methods `prob(x)` and `log_prob(x)`. If `validate_args` is False and the inputs are invalid, correct behavior is not guaranteed. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: The name to prepend to all ops created by this distribution. - - - +#### `tf.contrib.distributions.Chi2.allow_nan_stats` {#Chi2.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.Chi2.alpha` {#Chi2.alpha} Shape parameter. @@ -1613,7 +1620,7 @@ Mean of each batch member. Mode of each batch member. The mode of a gamma distribution is `(alpha - 1) / beta` when `alpha > 1`, -and `NaN` otherwise. If `self.strict_statistics` is `True`, an exception +and `NaN` otherwise. If `self.allow_nan_stats` is `False`, an exception will be raised rather than returning `NaN`. ##### Args: @@ -1726,20 +1733,13 @@ Standard deviation of this distribution. - - - -#### `tf.contrib.distributions.Chi2.strict` {#Chi2.strict} +#### `tf.contrib.distributions.Chi2.validate_args` {#Chi2.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.Chi2.strict_statistics` {#Chi2.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.Chi2.variance(name='variance')` {#Chi2.variance} Variance of each batch member. @@ -1760,7 +1760,7 @@ Note that the Exponential distribution is a special case of the Gamma distribution, with Exponential(lam) = Gamma(1, lam). - - - -#### `tf.contrib.distributions.Exponential.__init__(lam, strict=True, strict_statistics=True, name='Exponential')` {#Exponential.__init__} +#### `tf.contrib.distributions.Exponential.__init__(lam, validate_args=True, allow_nan_stats=False, name='Exponential')` {#Exponential.__init__} Construct Exponential distribution with parameter `lam`. @@ -1769,18 +1769,25 @@ Construct Exponential distribution with parameter `lam`. * <b>`lam`</b>: `float` or `double` tensor, the rate of the distribution(s). `lam` must contain only positive values. -* <b>`strict`</b>: Whether to assert that `lam > 0`, and that `x > 0` in the - methods `prob(x)` and `log_prob(x)`. If `strict` is False +* <b>`validate_args`</b>: Whether to assert that `lam > 0`, and that `x > 0` in the + methods `prob(x)` and `log_prob(x)`. If `validate_args` is False and the inputs are invalid, correct behavior is not guaranteed. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: The name to prepend to all ops created by this distribution. - - - +#### `tf.contrib.distributions.Exponential.allow_nan_stats` {#Exponential.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.Exponential.alpha` {#Exponential.alpha} Shape parameter. @@ -1995,7 +2002,7 @@ Mean of each batch member. Mode of each batch member. The mode of a gamma distribution is `(alpha - 1) / beta` when `alpha > 1`, -and `NaN` otherwise. If `self.strict_statistics` is `True`, an exception +and `NaN` otherwise. If `self.allow_nan_stats` is `False`, an exception will be raised rather than returning `NaN`. ##### Args: @@ -2105,20 +2112,13 @@ Standard deviation of this distribution. - - - -#### `tf.contrib.distributions.Exponential.strict` {#Exponential.strict} +#### `tf.contrib.distributions.Exponential.validate_args` {#Exponential.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.Exponential.strict_statistics` {#Exponential.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.Exponential.variance(name='variance')` {#Exponential.variance} Variance of each batch member. @@ -2151,7 +2151,7 @@ dist2 = Gamma(alpha=[3.0, 4.0], beta=[2.0, 3.0]) ``` - - - -#### `tf.contrib.distributions.Gamma.__init__(alpha, beta, strict=True, strict_statistics=True, name='Gamma')` {#Gamma.__init__} +#### `tf.contrib.distributions.Gamma.__init__(alpha, beta, validate_args=True, allow_nan_stats=False, name='Gamma')` {#Gamma.__init__} Construct Gamma distributions with parameters `alpha` and `beta`. @@ -2167,12 +2167,12 @@ broadcasting (e.g. `alpha + beta` is a valid operation). * <b>`beta`</b>: `float` or `double` tensor, the inverse scale params of the distribution(s). beta must contain only positive values. -* <b>`strict`</b>: Whether to assert that `a > 0, b > 0`, and that `x > 0` in the - methods `prob(x)` and `log_prob(x)`. If `strict` is False +* <b>`validate_args`</b>: Whether to assert that `a > 0, b > 0`, and that `x > 0` in + the methods `prob(x)` and `log_prob(x)`. If `validate_args` is False and the inputs are invalid, correct behavior is not guaranteed. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: The name to prepend to all ops created by this distribution. @@ -2184,6 +2184,13 @@ broadcasting (e.g. `alpha + beta` is a valid operation). - - - +#### `tf.contrib.distributions.Gamma.allow_nan_stats` {#Gamma.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.Gamma.alpha` {#Gamma.alpha} Shape parameter. @@ -2391,7 +2398,7 @@ Mean of each batch member. Mode of each batch member. The mode of a gamma distribution is `(alpha - 1) / beta` when `alpha > 1`, -and `NaN` otherwise. If `self.strict_statistics` is `True`, an exception +and `NaN` otherwise. If `self.allow_nan_stats` is `False`, an exception will be raised rather than returning `NaN`. ##### Args: @@ -2504,20 +2511,13 @@ Standard deviation of this distribution. - - - -#### `tf.contrib.distributions.Gamma.strict` {#Gamma.strict} +#### `tf.contrib.distributions.Gamma.validate_args` {#Gamma.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.Gamma.strict_statistics` {#Gamma.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.Gamma.variance(name='variance')` {#Gamma.variance} Variance of each batch member. @@ -2550,7 +2550,7 @@ dist2 = InverseGamma(alpha=[3.0, 4.0], beta=[2.0, 3.0]) ``` - - - -#### `tf.contrib.distributions.InverseGamma.__init__(alpha, beta, strict=True, strict_statistics=True, name='InverseGamma')` {#InverseGamma.__init__} +#### `tf.contrib.distributions.InverseGamma.__init__(alpha, beta, validate_args=True, allow_nan_stats=False, name='InverseGamma')` {#InverseGamma.__init__} Construct InverseGamma distributions with parameters `alpha` and `beta`. @@ -2565,12 +2565,12 @@ broadcasting (e.g. `alpha + beta` is a valid operation). alpha must contain only positive values. * <b>`beta`</b>: `float` or `double` tensor, the scale params of the distribution(s). beta must contain only positive values. -* <b>`strict`</b>: Whether to assert that `a > 0, b > 0`, and that `x > 0` in the - methods `prob(x)` and `log_prob(x)`. If `strict` is False +* <b>`validate_args`</b>: Whether to assert that `a > 0, b > 0`, and that `x > 0` in + the methods `prob(x)` and `log_prob(x)`. If `validate_args` is False and the inputs are invalid, correct behavior is not guaranteed. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: The name to prepend to all ops created by this distribution. @@ -2582,6 +2582,13 @@ broadcasting (e.g. `alpha + beta` is a valid operation). - - - +#### `tf.contrib.distributions.InverseGamma.allow_nan_stats` {#InverseGamma.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.InverseGamma.alpha` {#InverseGamma.alpha} Shape parameter. @@ -2782,8 +2789,8 @@ Log prob of observations in `x` under these InverseGamma distribution(s). Mean of each batch member. The mean of an inverse gamma distribution is `beta / (alpha - 1)`, -when `alpha > 1`, and `NaN` otherwise. If `self.strict_statistics` is -`True`, an exception will be raised rather than returning `NaN` +when `alpha > 1`, and `NaN` otherwise. If `self.allow_nan_stats` is +`False`, an exception will be raised rather than returning `NaN` ##### Args: @@ -2913,26 +2920,19 @@ Standard deviation of the distribution. - - - -#### `tf.contrib.distributions.InverseGamma.strict` {#InverseGamma.strict} +#### `tf.contrib.distributions.InverseGamma.validate_args` {#InverseGamma.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.InverseGamma.strict_statistics` {#InverseGamma.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.InverseGamma.variance(name='variance')` {#InverseGamma.variance} Variance of each batch member. Variance for inverse gamma is defined only for `alpha > 2`. If -`self.strict_statistics` is `True`, an exception will be raised rather +`self.allow_nan_stats` is `False`, an exception will be raised rather than returning `NaN`. ##### Args: @@ -2962,7 +2962,7 @@ Note that the Laplace distribution can be thought of two exponential distributions spliced together "back-to-back." - - - -#### `tf.contrib.distributions.Laplace.__init__(loc, scale, strict=True, strict_statistics=True, name='Laplace')` {#Laplace.__init__} +#### `tf.contrib.distributions.Laplace.__init__(loc, scale, validate_args=True, allow_nan_stats=False, name='Laplace')` {#Laplace.__init__} Construct Laplace distribution with parameters `loc` and `scale`. @@ -2976,11 +2976,12 @@ broadcasting (e.g., `loc / scale` is a valid operation). of the distribution. * <b>`scale`</b>: `float` or `double`, positive-valued tensor which characterzes the spread of the distribution. -* <b>`strict`</b>: Whether to validate input with asserts. If `strict` is `False`, - and the inputs are invalid, correct behavior is not guaranteed. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if +* <b>`validate_args`</b>: Whether to validate input with asserts. If `validate_args` + is `False`, and the inputs are invalid, correct behavior is not + guaranteed. +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: The name to give Ops created by the initializer. @@ -2992,6 +2993,13 @@ broadcasting (e.g., `loc / scale` is a valid operation). - - - +#### `tf.contrib.distributions.Laplace.allow_nan_stats` {#Laplace.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.Laplace.batch_shape(name='batch_shape')` {#Laplace.batch_shape} Batch dimensions of this instance as a 1-D int32 `Tensor`. @@ -3284,20 +3292,13 @@ Standard deviation of this distribution. - - - -#### `tf.contrib.distributions.Laplace.strict` {#Laplace.strict} +#### `tf.contrib.distributions.Laplace.validate_args` {#Laplace.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.Laplace.strict_statistics` {#Laplace.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.Laplace.variance(name='variance')` {#Laplace.variance} Variance of this distribution. @@ -3352,7 +3353,7 @@ dist.pdf(3.0) ``` - - - -#### `tf.contrib.distributions.Normal.__init__(mu, sigma, strict=True, strict_statistics=True, name='Normal')` {#Normal.__init__} +#### `tf.contrib.distributions.Normal.__init__(mu, sigma, validate_args=True, allow_nan_stats=False, name='Normal')` {#Normal.__init__} Construct Normal distributions with mean and stddev `mu` and `sigma`. @@ -3365,11 +3366,11 @@ broadcasting (e.g. `mu + sigma` is a valid operation). * <b>`mu`</b>: `float` or `double` tensor, the means of the distribution(s). * <b>`sigma`</b>: `float` or `double` tensor, the stddevs of the distribution(s). sigma must contain only positive values. -* <b>`strict`</b>: Whether to assert that `sigma > 0`. If `strict` is False, - correct output is not guaranteed when input is invalid. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if +* <b>`validate_args`</b>: Whether to assert that `sigma > 0`. If `validate_args` is + False, correct output is not guaranteed when input is invalid. +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: The name to give Ops created by the initializer. @@ -3381,6 +3382,13 @@ broadcasting (e.g. `mu + sigma` is a valid operation). - - - +#### `tf.contrib.distributions.Normal.allow_nan_stats` {#Normal.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.Normal.batch_shape(name='batch_shape')` {#Normal.batch_shape} Batch dimensions of this instance as a 1-D int32 `Tensor`. @@ -3666,20 +3674,13 @@ Standard deviation of this distribution. - - - -#### `tf.contrib.distributions.Normal.strict` {#Normal.strict} +#### `tf.contrib.distributions.Normal.validate_args` {#Normal.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.Normal.strict_statistics` {#Normal.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.Normal.variance(name='variance')` {#Normal.variance} Variance of this distribution. @@ -3737,7 +3738,7 @@ dist.pdf(3.0) ``` - - - -#### `tf.contrib.distributions.StudentT.__init__(df, mu, sigma, strict=True, strict_statistics=True, name='StudentT')` {#StudentT.__init__} +#### `tf.contrib.distributions.StudentT.__init__(df, mu, sigma, validate_args=True, allow_nan_stats=False, name='StudentT')` {#StudentT.__init__} Construct Student's t distributions. @@ -3755,11 +3756,12 @@ broadcasting (e.g. `df + mu + sigma` is a valid operation). * <b>`sigma`</b>: `float` or `double` tensor, the scaling factor for the distribution(s). `sigma` must contain only positive values. Note that `sigma` is not the standard deviation of this distribution. -* <b>`strict`</b>: Whether to assert that `df > 0, sigma > 0`. If `strict` is False - and inputs are invalid, correct behavior is not guaranteed. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if +* <b>`validate_args`</b>: Whether to assert that `df > 0, sigma > 0`. If + `validate_args` is False and inputs are invalid, correct behavior is not + guaranteed. +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: The name to give Ops created by the initializer. @@ -3771,6 +3773,13 @@ broadcasting (e.g. `df + mu + sigma` is a valid operation). - - - +#### `tf.contrib.distributions.StudentT.allow_nan_stats` {#StudentT.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.StudentT.batch_shape(name='batch_shape')` {#StudentT.batch_shape} @@ -3895,7 +3904,7 @@ Log prob of observations in `x` under these Student's t-distribution(s). Mean of the distribution. The mean of Student's T equals `mu` if `df > 1`, otherwise it is `NaN`. If -`self.strict_statistics=True`, then an exception will be raised rather than +`self.allow_nan_stats=False`, then an exception will be raised rather than returning `NaN`. ##### Args: @@ -4022,20 +4031,13 @@ Scaling factors of these Student's t distribution(s). - - - -#### `tf.contrib.distributions.StudentT.strict` {#StudentT.strict} +#### `tf.contrib.distributions.StudentT.validate_args` {#StudentT.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.StudentT.strict_statistics` {#StudentT.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.StudentT.variance(name='variance')` {#StudentT.variance} Variance of the distribution. @@ -4049,7 +4051,7 @@ NaN, when df <= 1 ``` The NaN state occurs because mean is undefined for `df <= 1`, and if -`self.strict_statistics` is `True`, an exception will be raised if any batch +`self.allow_nan_stats` is `False`, an exception will be raised if any batch members fall into this state. ##### Args: @@ -4072,7 +4074,7 @@ Uniform distribution with `a` and `b` parameters. The PDF of this distribution is constant between [`a`, `b`], and 0 elsewhere. - - - -#### `tf.contrib.distributions.Uniform.__init__(a=0.0, b=1.0, strict=True, strict_statistics=True, name='Uniform')` {#Uniform.__init__} +#### `tf.contrib.distributions.Uniform.__init__(a=0.0, b=1.0, validate_args=True, allow_nan_stats=False, name='Uniform')` {#Uniform.__init__} Construct Uniform distributions with `a` and `b`. @@ -4102,18 +4104,18 @@ u1 = Uniform(3.0, [5.0, 6.0, 7.0]) # 3 distributions * <b>`a`</b>: `float` or `double` tensor, the minimum endpoint. * <b>`b`</b>: `float` or `double` tensor, the maximum endpoint. Must be > `a`. -* <b>`strict`</b>: Whether to assert that `a > b`. If `strict` is False and inputs - are invalid, correct behavior is not guaranteed. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if +* <b>`validate_args`</b>: Whether to assert that `a > b`. If `validate_args` is False + and inputs are invalid, correct behavior is not guaranteed. +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: The name to prefix Ops created by this distribution class. ##### Raises: -* <b>`InvalidArgumentError`</b>: if `a >= b` and `strict=True`. +* <b>`InvalidArgumentError`</b>: if `a >= b` and `validate_args=True`. - - - @@ -4125,6 +4127,13 @@ u1 = Uniform(3.0, [5.0, 6.0, 7.0]) # 3 distributions - - - +#### `tf.contrib.distributions.Uniform.allow_nan_stats` {#Uniform.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.Uniform.b` {#Uniform.b} @@ -4357,20 +4366,13 @@ Sample `n` observations from the Uniform Distributions. - - - -#### `tf.contrib.distributions.Uniform.strict` {#Uniform.strict} +#### `tf.contrib.distributions.Uniform.validate_args` {#Uniform.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.Uniform.strict_statistics` {#Uniform.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.Uniform.variance(name='variance')` {#Uniform.variance} @@ -4429,7 +4431,7 @@ dist.pdf(x) ``` - - - -#### `tf.contrib.distributions.MultivariateNormalFull.__init__(mu, sigma, strict=True, strict_statistics=True, name='MultivariateNormalFull')` {#MultivariateNormalFull.__init__} +#### `tf.contrib.distributions.MultivariateNormalFull.__init__(mu, sigma, validate_args=True, allow_nan_stats=False, name='MultivariateNormalFull')` {#MultivariateNormalFull.__init__} Multivariate Normal distributions on `R^k`. @@ -4442,11 +4444,12 @@ User must provide means `mu` and `sigma`, the mean and covariance. `b >= 0`. * <b>`sigma`</b>: `(N+2)-D` `Tensor` with same `dtype` as `mu` and shape `[N1,...,Nb, k, k]`. -* <b>`strict`</b>: Whether to validate input with asserts. If `strict` is `False`, - and the inputs are invalid, correct behavior is not guaranteed. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if +* <b>`validate_args`</b>: Whether to validate input with asserts. If `validate_args` + is `False`, and the inputs are invalid, correct behavior is not + guaranteed. +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: The name to give Ops created by the initializer. @@ -4458,6 +4461,13 @@ User must provide means `mu` and `sigma`, the mean and covariance. - - - +#### `tf.contrib.distributions.MultivariateNormalFull.allow_nan_stats` {#MultivariateNormalFull.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.MultivariateNormalFull.batch_shape(name='batch_shape')` {#MultivariateNormalFull.batch_shape} Batch dimensions of this instance as a 1-D int32 `Tensor`. @@ -4720,20 +4730,13 @@ Standard deviation of the distribution. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.strict` {#MultivariateNormalFull.strict} +#### `tf.contrib.distributions.MultivariateNormalFull.validate_args` {#MultivariateNormalFull.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.strict_statistics` {#MultivariateNormalFull.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.MultivariateNormalFull.variance(name='variance')` {#MultivariateNormalFull.variance} Variance of each batch member. @@ -4791,7 +4794,7 @@ Trainable (batch) Choesky matrices can be created with `tf.contrib.distributions.batch_matrix_diag_transform()` - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.__init__(mu, chol, strict=True, strict_statistics=True, name='MultivariateNormalCholesky')` {#MultivariateNormalCholesky.__init__} +#### `tf.contrib.distributions.MultivariateNormalCholesky.__init__(mu, chol, validate_args=True, allow_nan_stats=False, name='MultivariateNormalCholesky')` {#MultivariateNormalCholesky.__init__} Multivariate Normal distributions on `R^k`. @@ -4805,11 +4808,12 @@ factors `S`, such that the covariance of each batch member is `S S^*`. `b >= 0`. * <b>`chol`</b>: `(N+2)-D` `Tensor` with same `dtype` as `mu` and shape `[N1,...,Nb, k, k]`. -* <b>`strict`</b>: Whether to validate input with asserts. If `strict` is `False`, +* <b>`validate_args`</b>: Whether to validate input with asserts. If `validate_args` + is `False`, and the inputs are invalid, correct behavior is not guaranteed. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: The name to give Ops created by the initializer. @@ -4821,6 +4825,13 @@ factors `S`, such that the covariance of each batch member is `S S^*`. - - - +#### `tf.contrib.distributions.MultivariateNormalCholesky.allow_nan_stats` {#MultivariateNormalCholesky.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.MultivariateNormalCholesky.batch_shape(name='batch_shape')` {#MultivariateNormalCholesky.batch_shape} Batch dimensions of this instance as a 1-D int32 `Tensor`. @@ -5083,20 +5094,13 @@ Standard deviation of the distribution. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.strict` {#MultivariateNormalCholesky.strict} +#### `tf.contrib.distributions.MultivariateNormalCholesky.validate_args` {#MultivariateNormalCholesky.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.strict_statistics` {#MultivariateNormalCholesky.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.MultivariateNormalCholesky.variance(name='variance')` {#MultivariateNormalCholesky.variance} Variance of each batch member. @@ -5229,7 +5233,7 @@ dist.prob(x) # Shape [2] ``` - - - -#### `tf.contrib.distributions.Dirichlet.__init__(alpha, strict=True, strict_statistics=True, name='Dirichlet')` {#Dirichlet.__init__} +#### `tf.contrib.distributions.Dirichlet.__init__(alpha, validate_args=True, allow_nan_stats=False, name='Dirichlet')` {#Dirichlet.__init__} Initialize a batch of Dirichlet distributions. @@ -5239,12 +5243,12 @@ Initialize a batch of Dirichlet distributions. * <b>`alpha`</b>: Positive `float` or `double` tensor with shape broadcastable to `[N1,..., Nm, k]` `m >= 0`. Defines this as a batch of `N1 x ... x Nm` different `k` class Dirichlet distributions. -* <b>`strict`</b>: Whether to assert valid values for parameters `alpha` and +* <b>`validate_args`</b>: Whether to assert valid values for parameters `alpha` and `x` in `prob` and `log_prob`. If False, correct behavior is not guaranteed. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: The name to prefix Ops created by this distribution class. @@ -5263,6 +5267,13 @@ dist = Dirichlet([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]) - - - +#### `tf.contrib.distributions.Dirichlet.allow_nan_stats` {#Dirichlet.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.Dirichlet.alpha` {#Dirichlet.alpha} Shape parameter. @@ -5420,7 +5431,7 @@ Mode of the distribution. Note that the mode for the Beta distribution is only defined when `alpha > 1`. This returns the mode when `alpha > 1`, -and NaN otherwise. If `self.strict_statistics` is `True`, an exception +and NaN otherwise. If `self.allow_nan_stats` is `False`, an exception will be raised rather than returning `NaN`. ##### Args: @@ -5527,20 +5538,13 @@ Standard deviation of the distribution. - - - -#### `tf.contrib.distributions.Dirichlet.strict` {#Dirichlet.strict} +#### `tf.contrib.distributions.Dirichlet.validate_args` {#Dirichlet.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.Dirichlet.strict_statistics` {#Dirichlet.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.Dirichlet.variance(name='variance')` {#Dirichlet.variance} Variance of the distribution. @@ -5620,7 +5624,7 @@ dist.pmf(counts) # Shape [2] ``` - - - -#### `tf.contrib.distributions.DirichletMultinomial.__init__(n, alpha, allow_arbitrary_counts=False, strict=True, strict_statistics=True, name='DirichletMultinomial')` {#DirichletMultinomial.__init__} +#### `tf.contrib.distributions.DirichletMultinomial.__init__(n, alpha, allow_arbitrary_counts=False, validate_args=True, allow_nan_stats=False, name='DirichletMultinomial')` {#DirichletMultinomial.__init__} Initialize a batch of DirichletMultinomial distributions. @@ -5637,14 +5641,14 @@ Initialize a batch of DirichletMultinomial distributions. * <b>`allow_arbitrary_counts`</b>: Boolean. This represents whether the pmf/cdf allows for the `counts` tensor to be non-integral values. The pmf/cdf are functions that can be evaluated at non-integral values, - but are only a distribution over non-negative integers. If `strict` is - `False`, this assertion is turned off. -* <b>`strict`</b>: Whether to assert valid values for parameters `alpha` and `n`, and - `x` in `prob` and `log_prob`. If False, correct behavior is not - guaranteed. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if + but are only a distribution over non-negative integers. If + `validate_args` is `False`, this assertion is turned off. +* <b>`validate_args`</b>: Whether to assert valid values for parameters `alpha` and + `n`, and `x` in `prob` and `log_prob`. If False, correct behavior is + not guaranteed. +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: The name to prefix Ops created by this distribution class. @@ -5663,6 +5667,13 @@ dist = DirichletMultinomial([3., 4], [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]) - - - +#### `tf.contrib.distributions.DirichletMultinomial.allow_nan_stats` {#DirichletMultinomial.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.DirichletMultinomial.alpha` {#DirichletMultinomial.alpha} Parameter defining this distribution. @@ -5936,20 +5947,13 @@ Standard deviation of the distribution. - - - -#### `tf.contrib.distributions.DirichletMultinomial.strict` {#DirichletMultinomial.strict} +#### `tf.contrib.distributions.DirichletMultinomial.validate_args` {#DirichletMultinomial.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.DirichletMultinomial.strict_statistics` {#DirichletMultinomial.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.DirichletMultinomial.variance(name='variance')` {#DirichletMultinomial.variance} Variance of the distribution. @@ -6027,6 +6031,13 @@ Construct a Transformed Distribution. - - - +#### `tf.contrib.distributions.TransformedDistribution.allow_nan_stats` {#TransformedDistribution.allow_nan_stats} + + + + +- - - + #### `tf.contrib.distributions.TransformedDistribution.base_distribution` {#TransformedDistribution.base_distribution} Base distribution, p(x). @@ -6298,24 +6309,17 @@ Standard deviation of the distribution. - - - -#### `tf.contrib.distributions.TransformedDistribution.strict` {#TransformedDistribution.strict} - +#### `tf.contrib.distributions.TransformedDistribution.transform` {#TransformedDistribution.transform} +Function transforming x => y. - - - -#### `tf.contrib.distributions.TransformedDistribution.strict_statistics` {#TransformedDistribution.strict_statistics} - +#### `tf.contrib.distributions.TransformedDistribution.validate_args` {#TransformedDistribution.validate_args} -- - - - -#### `tf.contrib.distributions.TransformedDistribution.transform` {#TransformedDistribution.transform} - -Function transforming x => y. - - - - 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 0162e518d3..1c16241d89 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 @@ -8,7 +8,7 @@ Note, the following methods of the base class aren't implemented: * log_cdf - - - -#### `tf.contrib.distributions.Bernoulli.__init__(logits=None, p=None, dtype=tf.int32, strict=True, strict_statistics=True, name='Bernoulli')` {#Bernoulli.__init__} +#### `tf.contrib.distributions.Bernoulli.__init__(logits=None, p=None, dtype=tf.int32, validate_args=True, allow_nan_stats=False, name='Bernoulli')` {#Bernoulli.__init__} Construct Bernoulli distributions. @@ -23,11 +23,11 @@ Construct Bernoulli distributions. event. Each entry in the `Tensor` parameterizes an independent Bernoulli distribution. * <b>`dtype`</b>: dtype for samples. -* <b>`strict`</b>: Whether to assert that `0 <= p <= 1`. If not strict, `log_pmf` may - return nans. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if +* <b>`validate_args`</b>: Whether to assert that `0 <= p <= 1`. If not validate_args, + `log_pmf` may return nans. +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: A name for this distribution. @@ -39,6 +39,13 @@ Construct Bernoulli distributions. - - - +#### `tf.contrib.distributions.Bernoulli.allow_nan_stats` {#Bernoulli.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.Bernoulli.batch_shape(name='batch_shape')` {#Bernoulli.batch_shape} @@ -306,20 +313,13 @@ Standard deviation of the distribution. - - - -#### `tf.contrib.distributions.Bernoulli.strict` {#Bernoulli.strict} +#### `tf.contrib.distributions.Bernoulli.validate_args` {#Bernoulli.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.Bernoulli.strict_statistics` {#Bernoulli.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.Bernoulli.variance(name='variance')` {#Bernoulli.variance} Variance of the distribution. 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 1fe3d6f56b..05da054e76 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 @@ -61,7 +61,7 @@ dist.prob(x) # Shape [2] ``` - - - -#### `tf.contrib.distributions.Dirichlet.__init__(alpha, strict=True, strict_statistics=True, name='Dirichlet')` {#Dirichlet.__init__} +#### `tf.contrib.distributions.Dirichlet.__init__(alpha, validate_args=True, allow_nan_stats=False, name='Dirichlet')` {#Dirichlet.__init__} Initialize a batch of Dirichlet distributions. @@ -71,12 +71,12 @@ Initialize a batch of Dirichlet distributions. * <b>`alpha`</b>: Positive `float` or `double` tensor with shape broadcastable to `[N1,..., Nm, k]` `m >= 0`. Defines this as a batch of `N1 x ... x Nm` different `k` class Dirichlet distributions. -* <b>`strict`</b>: Whether to assert valid values for parameters `alpha` and +* <b>`validate_args`</b>: Whether to assert valid values for parameters `alpha` and `x` in `prob` and `log_prob`. If False, correct behavior is not guaranteed. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: The name to prefix Ops created by this distribution class. @@ -95,6 +95,13 @@ dist = Dirichlet([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]) - - - +#### `tf.contrib.distributions.Dirichlet.allow_nan_stats` {#Dirichlet.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.Dirichlet.alpha` {#Dirichlet.alpha} Shape parameter. @@ -252,7 +259,7 @@ Mode of the distribution. Note that the mode for the Beta distribution is only defined when `alpha > 1`. This returns the mode when `alpha > 1`, -and NaN otherwise. If `self.strict_statistics` is `True`, an exception +and NaN otherwise. If `self.allow_nan_stats` is `False`, an exception will be raised rather than returning `NaN`. ##### Args: @@ -359,20 +366,13 @@ Standard deviation of the distribution. - - - -#### `tf.contrib.distributions.Dirichlet.strict` {#Dirichlet.strict} +#### `tf.contrib.distributions.Dirichlet.validate_args` {#Dirichlet.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.Dirichlet.strict_statistics` {#Dirichlet.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.Dirichlet.variance(name='variance')` {#Dirichlet.variance} Variance of the distribution. 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 27559a8e4d..16daf04b4d 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 @@ -84,11 +84,11 @@ a = tf.exp(tf.matmul(logits, weights_a)) b = tf.exp(tf.matmul(logits, weights_b)) # Will raise exception if ANY batch member has a < 1 or b < 1. -dist = distributions.beta(a, b, strict_statistics=True) # default is True +dist = distributions.beta(a, b, allow_nan_stats=False) # default is False mode = dist.mode().eval() # Will return NaN for batch members with either a < 1 or b < 1. -dist = distributions.beta(a, b, strict_statistics=False) +dist = distributions.beta(a, b, allow_nan_stats=True) mode = dist.mode().eval() ``` @@ -97,11 +97,18 @@ In all cases, an exception is raised if *invalid* parameters are passed, e.g. ```python # Will raise an exception if any Op is run. negative_a = -1.0 * a # beta distribution by definition has a > 0. -dist = distributions.beta(negative_a, b, strict_statistics=False) +dist = distributions.beta(negative_a, b, allow_nan_stats=True) dist.mean().eval() ``` - - - +#### `tf.contrib.distributions.Distribution.allow_nan_stats` {#Distribution.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.Distribution.batch_shape(name='batch_shape')` {#Distribution.batch_shape} Batch dimensions of this instance as a 1-D int32 `Tensor`. @@ -311,20 +318,13 @@ Standard deviation of the distribution. - - - -#### `tf.contrib.distributions.Distribution.strict` {#Distribution.strict} +#### `tf.contrib.distributions.Distribution.validate_args` {#Distribution.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.Distribution.strict_statistics` {#Distribution.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.Distribution.variance(name='variance')` {#Distribution.variance} Variance of the distribution. 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 f93ae572dd..8d177ca82d 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 @@ -45,7 +45,7 @@ Trainable (batch) Choesky matrices can be created with `tf.contrib.distributions.batch_matrix_diag_transform()` - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.__init__(mu, chol, strict=True, strict_statistics=True, name='MultivariateNormalCholesky')` {#MultivariateNormalCholesky.__init__} +#### `tf.contrib.distributions.MultivariateNormalCholesky.__init__(mu, chol, validate_args=True, allow_nan_stats=False, name='MultivariateNormalCholesky')` {#MultivariateNormalCholesky.__init__} Multivariate Normal distributions on `R^k`. @@ -59,11 +59,12 @@ factors `S`, such that the covariance of each batch member is `S S^*`. `b >= 0`. * <b>`chol`</b>: `(N+2)-D` `Tensor` with same `dtype` as `mu` and shape `[N1,...,Nb, k, k]`. -* <b>`strict`</b>: Whether to validate input with asserts. If `strict` is `False`, +* <b>`validate_args`</b>: Whether to validate input with asserts. If `validate_args` + is `False`, and the inputs are invalid, correct behavior is not guaranteed. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: The name to give Ops created by the initializer. @@ -75,6 +76,13 @@ factors `S`, such that the covariance of each batch member is `S S^*`. - - - +#### `tf.contrib.distributions.MultivariateNormalCholesky.allow_nan_stats` {#MultivariateNormalCholesky.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.MultivariateNormalCholesky.batch_shape(name='batch_shape')` {#MultivariateNormalCholesky.batch_shape} Batch dimensions of this instance as a 1-D int32 `Tensor`. @@ -337,20 +345,13 @@ Standard deviation of the distribution. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.strict` {#MultivariateNormalCholesky.strict} +#### `tf.contrib.distributions.MultivariateNormalCholesky.validate_args` {#MultivariateNormalCholesky.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.strict_statistics` {#MultivariateNormalCholesky.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.MultivariateNormalCholesky.variance(name='variance')` {#MultivariateNormalCholesky.variance} Variance of each batch member. 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 6120886ce4..0b00a17938 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 @@ -45,7 +45,7 @@ dist.pdf(3.0) ``` - - - -#### `tf.contrib.distributions.StudentT.__init__(df, mu, sigma, strict=True, strict_statistics=True, name='StudentT')` {#StudentT.__init__} +#### `tf.contrib.distributions.StudentT.__init__(df, mu, sigma, validate_args=True, allow_nan_stats=False, name='StudentT')` {#StudentT.__init__} Construct Student's t distributions. @@ -63,11 +63,12 @@ broadcasting (e.g. `df + mu + sigma` is a valid operation). * <b>`sigma`</b>: `float` or `double` tensor, the scaling factor for the distribution(s). `sigma` must contain only positive values. Note that `sigma` is not the standard deviation of this distribution. -* <b>`strict`</b>: Whether to assert that `df > 0, sigma > 0`. If `strict` is False - and inputs are invalid, correct behavior is not guaranteed. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if +* <b>`validate_args`</b>: Whether to assert that `df > 0, sigma > 0`. If + `validate_args` is False and inputs are invalid, correct behavior is not + guaranteed. +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: The name to give Ops created by the initializer. @@ -79,6 +80,13 @@ broadcasting (e.g. `df + mu + sigma` is a valid operation). - - - +#### `tf.contrib.distributions.StudentT.allow_nan_stats` {#StudentT.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.StudentT.batch_shape(name='batch_shape')` {#StudentT.batch_shape} @@ -203,7 +211,7 @@ Log prob of observations in `x` under these Student's t-distribution(s). Mean of the distribution. The mean of Student's T equals `mu` if `df > 1`, otherwise it is `NaN`. If -`self.strict_statistics=True`, then an exception will be raised rather than +`self.allow_nan_stats=False`, then an exception will be raised rather than returning `NaN`. ##### Args: @@ -330,20 +338,13 @@ Scaling factors of these Student's t distribution(s). - - - -#### `tf.contrib.distributions.StudentT.strict` {#StudentT.strict} +#### `tf.contrib.distributions.StudentT.validate_args` {#StudentT.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.StudentT.strict_statistics` {#StudentT.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.StudentT.variance(name='variance')` {#StudentT.variance} Variance of the distribution. @@ -357,7 +358,7 @@ NaN, when df <= 1 ``` The NaN state occurs because mean is undefined for `df <= 1`, and if -`self.strict_statistics` is `True`, an exception will be raised if any batch +`self.allow_nan_stats` is `False`, an exception will be raised if any batch members fall into this state. ##### Args: 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 5d11a754be..52cc8ec983 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 @@ -62,6 +62,13 @@ Construct a Transformed Distribution. - - - +#### `tf.contrib.distributions.TransformedDistribution.allow_nan_stats` {#TransformedDistribution.allow_nan_stats} + + + + +- - - + #### `tf.contrib.distributions.TransformedDistribution.base_distribution` {#TransformedDistribution.base_distribution} Base distribution, p(x). @@ -333,23 +340,16 @@ Standard deviation of the distribution. - - - -#### `tf.contrib.distributions.TransformedDistribution.strict` {#TransformedDistribution.strict} - +#### `tf.contrib.distributions.TransformedDistribution.transform` {#TransformedDistribution.transform} +Function transforming x => y. - - - -#### `tf.contrib.distributions.TransformedDistribution.strict_statistics` {#TransformedDistribution.strict_statistics} - - - +#### `tf.contrib.distributions.TransformedDistribution.validate_args` {#TransformedDistribution.validate_args} -- - - - -#### `tf.contrib.distributions.TransformedDistribution.transform` {#TransformedDistribution.transform} -Function transforming x => y. - - - 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 a7e027473c..052af1eb55 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 @@ -9,7 +9,7 @@ Note, the following methods of the base class aren't implemented: * log_cdf - - - -#### `tf.contrib.distributions.Categorical.__init__(logits, dtype=tf.int32, strict=True, strict_statistics=True, name='Categorical')` {#Categorical.__init__} +#### `tf.contrib.distributions.Categorical.__init__(logits, dtype=tf.int32, validate_args=True, allow_nan_stats=False, name='Categorical')` {#Categorical.__init__} Initialize Categorical distributions using class log-probabilities. @@ -21,16 +21,23 @@ Initialize Categorical distributions using class log-probabilities. index into a batch of independent distributions and the last dimension indexes into the classes. * <b>`dtype`</b>: The type of the event samples (default: int32). -* <b>`strict`</b>: Unused in this distribution. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if +* <b>`validate_args`</b>: Unused in this distribution. +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: A name for this distribution (optional). - - - +#### `tf.contrib.distributions.Categorical.allow_nan_stats` {#Categorical.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.Categorical.batch_shape(name='batch_shape')` {#Categorical.batch_shape} @@ -248,20 +255,13 @@ Standard deviation of the distribution. - - - -#### `tf.contrib.distributions.Categorical.strict` {#Categorical.strict} +#### `tf.contrib.distributions.Categorical.validate_args` {#Categorical.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.Categorical.strict_statistics` {#Categorical.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.Categorical.variance(name='variance')` {#Categorical.variance} Variance of the distribution. 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 b1c9f857e6..2f692a15f9 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 @@ -8,7 +8,7 @@ Note that the Chi2 distribution is a special case of the Gamma distribution, with Chi2(df) = Gamma(df/2, 1/2). - - - -#### `tf.contrib.distributions.Chi2.__init__(df, strict=True, strict_statistics=True, name='Chi2')` {#Chi2.__init__} +#### `tf.contrib.distributions.Chi2.__init__(df, validate_args=True, allow_nan_stats=False, name='Chi2')` {#Chi2.__init__} Construct Chi2 distributions with parameter `df`. @@ -17,18 +17,25 @@ Construct Chi2 distributions with parameter `df`. * <b>`df`</b>: `float` or `double` tensor, the degrees of freedom of the distribution(s). `df` must contain only positive values. -* <b>`strict`</b>: Whether to assert that `df > 0`, and that `x > 0` in the - methods `prob(x)` and `log_prob(x)`. If `strict` is False +* <b>`validate_args`</b>: Whether to assert that `df > 0`, and that `x > 0` in the + methods `prob(x)` and `log_prob(x)`. If `validate_args` is False and the inputs are invalid, correct behavior is not guaranteed. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: The name to prepend to all ops created by this distribution. - - - +#### `tf.contrib.distributions.Chi2.allow_nan_stats` {#Chi2.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.Chi2.alpha` {#Chi2.alpha} Shape parameter. @@ -243,7 +250,7 @@ Mean of each batch member. Mode of each batch member. The mode of a gamma distribution is `(alpha - 1) / beta` when `alpha > 1`, -and `NaN` otherwise. If `self.strict_statistics` is `True`, an exception +and `NaN` otherwise. If `self.allow_nan_stats` is `False`, an exception will be raised rather than returning `NaN`. ##### Args: @@ -356,20 +363,13 @@ Standard deviation of this distribution. - - - -#### `tf.contrib.distributions.Chi2.strict` {#Chi2.strict} +#### `tf.contrib.distributions.Chi2.validate_args` {#Chi2.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.Chi2.strict_statistics` {#Chi2.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.Chi2.variance(name='variance')` {#Chi2.variance} Variance of each batch member. 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 dc74a688ea..9862309eed 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 @@ -3,7 +3,7 @@ Uniform distribution with `a` and `b` parameters. The PDF of this distribution is constant between [`a`, `b`], and 0 elsewhere. - - - -#### `tf.contrib.distributions.Uniform.__init__(a=0.0, b=1.0, strict=True, strict_statistics=True, name='Uniform')` {#Uniform.__init__} +#### `tf.contrib.distributions.Uniform.__init__(a=0.0, b=1.0, validate_args=True, allow_nan_stats=False, name='Uniform')` {#Uniform.__init__} Construct Uniform distributions with `a` and `b`. @@ -33,18 +33,18 @@ u1 = Uniform(3.0, [5.0, 6.0, 7.0]) # 3 distributions * <b>`a`</b>: `float` or `double` tensor, the minimum endpoint. * <b>`b`</b>: `float` or `double` tensor, the maximum endpoint. Must be > `a`. -* <b>`strict`</b>: Whether to assert that `a > b`. If `strict` is False and inputs - are invalid, correct behavior is not guaranteed. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if +* <b>`validate_args`</b>: Whether to assert that `a > b`. If `validate_args` is False + and inputs are invalid, correct behavior is not guaranteed. +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: The name to prefix Ops created by this distribution class. ##### Raises: -* <b>`InvalidArgumentError`</b>: if `a >= b` and `strict=True`. +* <b>`InvalidArgumentError`</b>: if `a >= b` and `validate_args=True`. - - - @@ -56,6 +56,13 @@ u1 = Uniform(3.0, [5.0, 6.0, 7.0]) # 3 distributions - - - +#### `tf.contrib.distributions.Uniform.allow_nan_stats` {#Uniform.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.Uniform.b` {#Uniform.b} @@ -288,20 +295,13 @@ Sample `n` observations from the Uniform Distributions. - - - -#### `tf.contrib.distributions.Uniform.strict` {#Uniform.strict} +#### `tf.contrib.distributions.Uniform.validate_args` {#Uniform.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.Uniform.strict_statistics` {#Uniform.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.Uniform.variance(name='variance')` {#Uniform.variance} 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 56a82357bc..f63ce5e9e6 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 @@ -67,7 +67,7 @@ dist.pmf(counts) # Shape [2] ``` - - - -#### `tf.contrib.distributions.DirichletMultinomial.__init__(n, alpha, allow_arbitrary_counts=False, strict=True, strict_statistics=True, name='DirichletMultinomial')` {#DirichletMultinomial.__init__} +#### `tf.contrib.distributions.DirichletMultinomial.__init__(n, alpha, allow_arbitrary_counts=False, validate_args=True, allow_nan_stats=False, name='DirichletMultinomial')` {#DirichletMultinomial.__init__} Initialize a batch of DirichletMultinomial distributions. @@ -84,14 +84,14 @@ Initialize a batch of DirichletMultinomial distributions. * <b>`allow_arbitrary_counts`</b>: Boolean. This represents whether the pmf/cdf allows for the `counts` tensor to be non-integral values. The pmf/cdf are functions that can be evaluated at non-integral values, - but are only a distribution over non-negative integers. If `strict` is - `False`, this assertion is turned off. -* <b>`strict`</b>: Whether to assert valid values for parameters `alpha` and `n`, and - `x` in `prob` and `log_prob`. If False, correct behavior is not - guaranteed. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if + but are only a distribution over non-negative integers. If + `validate_args` is `False`, this assertion is turned off. +* <b>`validate_args`</b>: Whether to assert valid values for parameters `alpha` and + `n`, and `x` in `prob` and `log_prob`. If False, correct behavior is + not guaranteed. +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: The name to prefix Ops created by this distribution class. @@ -110,6 +110,13 @@ dist = DirichletMultinomial([3., 4], [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]) - - - +#### `tf.contrib.distributions.DirichletMultinomial.allow_nan_stats` {#DirichletMultinomial.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.DirichletMultinomial.alpha` {#DirichletMultinomial.alpha} Parameter defining this distribution. @@ -383,20 +390,13 @@ Standard deviation of the distribution. - - - -#### `tf.contrib.distributions.DirichletMultinomial.strict` {#DirichletMultinomial.strict} +#### `tf.contrib.distributions.DirichletMultinomial.validate_args` {#DirichletMultinomial.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.DirichletMultinomial.strict_statistics` {#DirichletMultinomial.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.DirichletMultinomial.variance(name='variance')` {#DirichletMultinomial.variance} Variance of the distribution. 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 565a007f2e..e785e49b2d 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 @@ -8,7 +8,7 @@ Note that the Exponential distribution is a special case of the Gamma distribution, with Exponential(lam) = Gamma(1, lam). - - - -#### `tf.contrib.distributions.Exponential.__init__(lam, strict=True, strict_statistics=True, name='Exponential')` {#Exponential.__init__} +#### `tf.contrib.distributions.Exponential.__init__(lam, validate_args=True, allow_nan_stats=False, name='Exponential')` {#Exponential.__init__} Construct Exponential distribution with parameter `lam`. @@ -17,18 +17,25 @@ Construct Exponential distribution with parameter `lam`. * <b>`lam`</b>: `float` or `double` tensor, the rate of the distribution(s). `lam` must contain only positive values. -* <b>`strict`</b>: Whether to assert that `lam > 0`, and that `x > 0` in the - methods `prob(x)` and `log_prob(x)`. If `strict` is False +* <b>`validate_args`</b>: Whether to assert that `lam > 0`, and that `x > 0` in the + methods `prob(x)` and `log_prob(x)`. If `validate_args` is False and the inputs are invalid, correct behavior is not guaranteed. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: The name to prepend to all ops created by this distribution. - - - +#### `tf.contrib.distributions.Exponential.allow_nan_stats` {#Exponential.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.Exponential.alpha` {#Exponential.alpha} Shape parameter. @@ -243,7 +250,7 @@ Mean of each batch member. Mode of each batch member. The mode of a gamma distribution is `(alpha - 1) / beta` when `alpha > 1`, -and `NaN` otherwise. If `self.strict_statistics` is `True`, an exception +and `NaN` otherwise. If `self.allow_nan_stats` is `False`, an exception will be raised rather than returning `NaN`. ##### Args: @@ -353,20 +360,13 @@ Standard deviation of this distribution. - - - -#### `tf.contrib.distributions.Exponential.strict` {#Exponential.strict} +#### `tf.contrib.distributions.Exponential.validate_args` {#Exponential.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.Exponential.strict_statistics` {#Exponential.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.Exponential.variance(name='variance')` {#Exponential.variance} Variance of each batch member. 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 8d03608c93..741d4d8c08 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 @@ -20,7 +20,7 @@ dist2 = Gamma(alpha=[3.0, 4.0], beta=[2.0, 3.0]) ``` - - - -#### `tf.contrib.distributions.Gamma.__init__(alpha, beta, strict=True, strict_statistics=True, name='Gamma')` {#Gamma.__init__} +#### `tf.contrib.distributions.Gamma.__init__(alpha, beta, validate_args=True, allow_nan_stats=False, name='Gamma')` {#Gamma.__init__} Construct Gamma distributions with parameters `alpha` and `beta`. @@ -36,12 +36,12 @@ broadcasting (e.g. `alpha + beta` is a valid operation). * <b>`beta`</b>: `float` or `double` tensor, the inverse scale params of the distribution(s). beta must contain only positive values. -* <b>`strict`</b>: Whether to assert that `a > 0, b > 0`, and that `x > 0` in the - methods `prob(x)` and `log_prob(x)`. If `strict` is False +* <b>`validate_args`</b>: Whether to assert that `a > 0, b > 0`, and that `x > 0` in + the methods `prob(x)` and `log_prob(x)`. If `validate_args` is False and the inputs are invalid, correct behavior is not guaranteed. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: The name to prepend to all ops created by this distribution. @@ -53,6 +53,13 @@ broadcasting (e.g. `alpha + beta` is a valid operation). - - - +#### `tf.contrib.distributions.Gamma.allow_nan_stats` {#Gamma.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.Gamma.alpha` {#Gamma.alpha} Shape parameter. @@ -260,7 +267,7 @@ Mean of each batch member. Mode of each batch member. The mode of a gamma distribution is `(alpha - 1) / beta` when `alpha > 1`, -and `NaN` otherwise. If `self.strict_statistics` is `True`, an exception +and `NaN` otherwise. If `self.allow_nan_stats` is `False`, an exception will be raised rather than returning `NaN`. ##### Args: @@ -373,20 +380,13 @@ Standard deviation of this distribution. - - - -#### `tf.contrib.distributions.Gamma.strict` {#Gamma.strict} +#### `tf.contrib.distributions.Gamma.validate_args` {#Gamma.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.Gamma.strict_statistics` {#Gamma.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.Gamma.variance(name='variance')` {#Gamma.variance} Variance of each batch member. 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 68e28c3253..11b7ce9156 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 @@ -20,7 +20,7 @@ dist2 = InverseGamma(alpha=[3.0, 4.0], beta=[2.0, 3.0]) ``` - - - -#### `tf.contrib.distributions.InverseGamma.__init__(alpha, beta, strict=True, strict_statistics=True, name='InverseGamma')` {#InverseGamma.__init__} +#### `tf.contrib.distributions.InverseGamma.__init__(alpha, beta, validate_args=True, allow_nan_stats=False, name='InverseGamma')` {#InverseGamma.__init__} Construct InverseGamma distributions with parameters `alpha` and `beta`. @@ -35,12 +35,12 @@ broadcasting (e.g. `alpha + beta` is a valid operation). alpha must contain only positive values. * <b>`beta`</b>: `float` or `double` tensor, the scale params of the distribution(s). beta must contain only positive values. -* <b>`strict`</b>: Whether to assert that `a > 0, b > 0`, and that `x > 0` in the - methods `prob(x)` and `log_prob(x)`. If `strict` is False +* <b>`validate_args`</b>: Whether to assert that `a > 0, b > 0`, and that `x > 0` in + the methods `prob(x)` and `log_prob(x)`. If `validate_args` is False and the inputs are invalid, correct behavior is not guaranteed. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: The name to prepend to all ops created by this distribution. @@ -52,6 +52,13 @@ broadcasting (e.g. `alpha + beta` is a valid operation). - - - +#### `tf.contrib.distributions.InverseGamma.allow_nan_stats` {#InverseGamma.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.InverseGamma.alpha` {#InverseGamma.alpha} Shape parameter. @@ -252,8 +259,8 @@ Log prob of observations in `x` under these InverseGamma distribution(s). Mean of each batch member. The mean of an inverse gamma distribution is `beta / (alpha - 1)`, -when `alpha > 1`, and `NaN` otherwise. If `self.strict_statistics` is -`True`, an exception will be raised rather than returning `NaN` +when `alpha > 1`, and `NaN` otherwise. If `self.allow_nan_stats` is +`False`, an exception will be raised rather than returning `NaN` ##### Args: @@ -383,26 +390,19 @@ Standard deviation of the distribution. - - - -#### `tf.contrib.distributions.InverseGamma.strict` {#InverseGamma.strict} +#### `tf.contrib.distributions.InverseGamma.validate_args` {#InverseGamma.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.InverseGamma.strict_statistics` {#InverseGamma.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.InverseGamma.variance(name='variance')` {#InverseGamma.variance} Variance of each batch member. Variance for inverse gamma is defined only for `alpha > 2`. If -`self.strict_statistics` is `True`, an exception will be raised rather +`self.allow_nan_stats` is `False`, an exception will be raised rather than returning `NaN`. ##### Args: 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 d866384032..aa40420ff8 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 @@ -60,7 +60,7 @@ dist.pdf(x) # Shape [2] ``` - - - -#### `tf.contrib.distributions.Beta.__init__(a, b, strict=True, strict_statistics=True, name='Beta')` {#Beta.__init__} +#### `tf.contrib.distributions.Beta.__init__(a, b, validate_args=True, allow_nan_stats=False, name='Beta')` {#Beta.__init__} Initialize a batch of Beta distributions. @@ -74,12 +74,12 @@ Initialize a batch of Beta distributions. * <b>`b`</b>: Positive `float` or `double` tensor with shape broadcastable to `[N1,..., Nm]` `m >= 0`. Defines this as a batch of `N1 x ... x Nm` different Beta distributions. -* <b>`strict`</b>: Whether to assert valid values for parameters `a` and `b`, and - `x` in `prob` and `log_prob`. If False, correct behavior is not +* <b>`validate_args`</b>: Whether to assert valid values for parameters `a` and `b`, + and `x` in `prob` and `log_prob`. If False, correct behavior is not guaranteed. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: The name to prefix Ops created by this distribution class. @@ -104,6 +104,13 @@ Shape parameter. - - - +#### `tf.contrib.distributions.Beta.allow_nan_stats` {#Beta.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.Beta.b` {#Beta.b} Shape parameter. @@ -262,7 +269,7 @@ Mode of the distribution. Note that the mode for the Beta distribution is only defined when `a > 1`, `b > 1`. This returns the mode when `a > 1` and `b > 1`, -and NaN otherwise. If `self.strict_statistics` is `True`, an exception +and NaN otherwise. If `self.allow_nan_stats` is `False`, an exception will be raised rather than returning `NaN`. ##### Args: @@ -370,20 +377,13 @@ Standard deviation of the distribution. - - - -#### `tf.contrib.distributions.Beta.strict` {#Beta.strict} +#### `tf.contrib.distributions.Beta.validate_args` {#Beta.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.Beta.strict_statistics` {#Beta.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.Beta.variance(name='variance')` {#Beta.variance} Variance of the distribution. 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 1e1953caa3..273e23714f 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 @@ -10,7 +10,7 @@ Note that the Laplace distribution can be thought of two exponential distributions spliced together "back-to-back." - - - -#### `tf.contrib.distributions.Laplace.__init__(loc, scale, strict=True, strict_statistics=True, name='Laplace')` {#Laplace.__init__} +#### `tf.contrib.distributions.Laplace.__init__(loc, scale, validate_args=True, allow_nan_stats=False, name='Laplace')` {#Laplace.__init__} Construct Laplace distribution with parameters `loc` and `scale`. @@ -24,11 +24,12 @@ broadcasting (e.g., `loc / scale` is a valid operation). of the distribution. * <b>`scale`</b>: `float` or `double`, positive-valued tensor which characterzes the spread of the distribution. -* <b>`strict`</b>: Whether to validate input with asserts. If `strict` is `False`, - and the inputs are invalid, correct behavior is not guaranteed. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if +* <b>`validate_args`</b>: Whether to validate input with asserts. If `validate_args` + is `False`, and the inputs are invalid, correct behavior is not + guaranteed. +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: The name to give Ops created by the initializer. @@ -40,6 +41,13 @@ broadcasting (e.g., `loc / scale` is a valid operation). - - - +#### `tf.contrib.distributions.Laplace.allow_nan_stats` {#Laplace.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.Laplace.batch_shape(name='batch_shape')` {#Laplace.batch_shape} Batch dimensions of this instance as a 1-D int32 `Tensor`. @@ -332,20 +340,13 @@ Standard deviation of this distribution. - - - -#### `tf.contrib.distributions.Laplace.strict` {#Laplace.strict} +#### `tf.contrib.distributions.Laplace.validate_args` {#Laplace.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.Laplace.strict_statistics` {#Laplace.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.Laplace.variance(name='variance')` {#Laplace.variance} Variance of this distribution. 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 706654c65d..e056b6a5ad 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 @@ -41,7 +41,7 @@ dist.pdf(x) ``` - - - -#### `tf.contrib.distributions.MultivariateNormalFull.__init__(mu, sigma, strict=True, strict_statistics=True, name='MultivariateNormalFull')` {#MultivariateNormalFull.__init__} +#### `tf.contrib.distributions.MultivariateNormalFull.__init__(mu, sigma, validate_args=True, allow_nan_stats=False, name='MultivariateNormalFull')` {#MultivariateNormalFull.__init__} Multivariate Normal distributions on `R^k`. @@ -54,11 +54,12 @@ User must provide means `mu` and `sigma`, the mean and covariance. `b >= 0`. * <b>`sigma`</b>: `(N+2)-D` `Tensor` with same `dtype` as `mu` and shape `[N1,...,Nb, k, k]`. -* <b>`strict`</b>: Whether to validate input with asserts. If `strict` is `False`, - and the inputs are invalid, correct behavior is not guaranteed. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if +* <b>`validate_args`</b>: Whether to validate input with asserts. If `validate_args` + is `False`, and the inputs are invalid, correct behavior is not + guaranteed. +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: The name to give Ops created by the initializer. @@ -70,6 +71,13 @@ User must provide means `mu` and `sigma`, the mean and covariance. - - - +#### `tf.contrib.distributions.MultivariateNormalFull.allow_nan_stats` {#MultivariateNormalFull.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.MultivariateNormalFull.batch_shape(name='batch_shape')` {#MultivariateNormalFull.batch_shape} Batch dimensions of this instance as a 1-D int32 `Tensor`. @@ -332,20 +340,13 @@ Standard deviation of the distribution. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.strict` {#MultivariateNormalFull.strict} +#### `tf.contrib.distributions.MultivariateNormalFull.validate_args` {#MultivariateNormalFull.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.strict_statistics` {#MultivariateNormalFull.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.MultivariateNormalFull.variance(name='variance')` {#MultivariateNormalFull.variance} Variance of each batch member. 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 7370b72a8c..3826c2812f 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 @@ -42,7 +42,7 @@ dist.pdf(3.0) ``` - - - -#### `tf.contrib.distributions.Normal.__init__(mu, sigma, strict=True, strict_statistics=True, name='Normal')` {#Normal.__init__} +#### `tf.contrib.distributions.Normal.__init__(mu, sigma, validate_args=True, allow_nan_stats=False, name='Normal')` {#Normal.__init__} Construct Normal distributions with mean and stddev `mu` and `sigma`. @@ -55,11 +55,11 @@ broadcasting (e.g. `mu + sigma` is a valid operation). * <b>`mu`</b>: `float` or `double` tensor, the means of the distribution(s). * <b>`sigma`</b>: `float` or `double` tensor, the stddevs of the distribution(s). sigma must contain only positive values. -* <b>`strict`</b>: Whether to assert that `sigma > 0`. If `strict` is False, - correct output is not guaranteed when input is invalid. -* <b>`strict_statistics`</b>: Boolean, default True. If True, raise an exception if +* <b>`validate_args`</b>: Whether to assert that `sigma > 0`. If `validate_args` is + False, correct output is not guaranteed when input is invalid. +* <b>`allow_nan_stats`</b>: Boolean, default False. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. - If False, batch members with valid parameters leading to undefined + If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. * <b>`name`</b>: The name to give Ops created by the initializer. @@ -71,6 +71,13 @@ broadcasting (e.g. `mu + sigma` is a valid operation). - - - +#### `tf.contrib.distributions.Normal.allow_nan_stats` {#Normal.allow_nan_stats} + +Boolean describing behavior when a stat is undefined for batch member. + + +- - - + #### `tf.contrib.distributions.Normal.batch_shape(name='batch_shape')` {#Normal.batch_shape} Batch dimensions of this instance as a 1-D int32 `Tensor`. @@ -356,20 +363,13 @@ Standard deviation of this distribution. - - - -#### `tf.contrib.distributions.Normal.strict` {#Normal.strict} +#### `tf.contrib.distributions.Normal.validate_args` {#Normal.validate_args} Boolean describing behavior on invalid input. - - - -#### `tf.contrib.distributions.Normal.strict_statistics` {#Normal.strict_statistics} - -Boolean describing behavior when a stat is undefined for batch member. - - -- - - - #### `tf.contrib.distributions.Normal.variance(name='variance')` {#Normal.variance} Variance of this distribution. |