Update generated Python Op docs.
Change: 145835977
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A. Unique TensorFlower
TensorFlower Gardener
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@ -775,29 +775,41 @@ Variance.
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Binomial distribution.
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This distribution is parameterized by a vector `p` of probabilities and `n`,
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the total counts.
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This distribution is parameterized by `probs`, a (batch of) probabilities for
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drawing a `1` and `total_count`, the number of trials per draw from the
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Binomial.
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#### Mathematical details
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#### Mathematical Details
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The Binomial is a distribution over the number of successes in `n` independent
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trials, with each trial having the same probability of success `p`.
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The probability mass function (pmf):
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The Binomial is a distribution over the number of `1`'s in `total_count`
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independent trials, with each trial having the same probability of `1`, i.e.,
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`probs`.
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```pmf(k) = n! / (k! * (n - k)!) * (p)^k * (1 - p)^(n - k)```
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The probability mass function (pmf) is,
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```none
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pmf(k; n, p) = p**k (1 - p)**(n - k) / Z
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Z = k! (n - k)! / n!
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```
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where:
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* `total_count = n`,
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* `probs = p`,
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* `Z` is the normalizaing constant, and,
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* `n!` is the factorial of `n`.
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#### Examples
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Create a single distribution, corresponding to 5 coin flips.
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```python
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dist = Binomial(n=5., p=.5)
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dist = Binomial(total_count=5., probs=.5)
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```
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Create a single distribution (using logits), corresponding to 5 coin flips.
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```python
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dist = Binomial(n=5., logits=0.)
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dist = Binomial(total_count=5., logits=0.)
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```
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Creates 3 distributions with the third distribution most likely to have
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@ -806,7 +818,7 @@ successes.
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```python
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p = [.2, .3, .8]
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# n will be broadcast to [4., 4., 4.], to match p.
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dist = Binomial(n=4., p=p)
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dist = Binomial(total_count=4., probs=p)
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```
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The distribution functions can be evaluated on counts.
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@ -826,45 +838,35 @@ dist.prob(counts) # Shape [5, 7, 3]
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```
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- - -
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#### `tf.contrib.distributions.Binomial.__init__(n, logits=None, p=None, validate_args=False, allow_nan_stats=True, name='Binomial')` {#Binomial.__init__}
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#### `tf.contrib.distributions.Binomial.__init__(total_count, logits=None, probs=None, validate_args=False, allow_nan_stats=True, name='Binomial')` {#Binomial.__init__}
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Initialize a batch of Binomial distributions.
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##### Args:
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* <b>`n`</b>: Non-negative floating point tensor with shape broadcastable to
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`[N1,..., Nm]` with `m >= 0` and the same dtype as `p` or `logits`.
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Defines this as a batch of `N1 x ... x Nm` different Binomial
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* <b>`total_count`</b>: Non-negative floating point tensor with shape broadcastable
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to `[N1,..., Nm]` with `m >= 0` and the same dtype as `probs` or
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`logits`. Defines this as a batch of `N1 x ... x Nm` different Binomial
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distributions. Its components should be equal to integer values.
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* <b>`logits`</b>: Floating point tensor representing the log-odds of a
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positive event with shape broadcastable to `[N1,..., Nm]` `m >= 0`, and
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the same dtype as `n`. Each entry represents logits for the probability
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of success for independent Binomial distributions. Only one of
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`logits` or `p` should be passed in.
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* <b>`p`</b>: Positive floating point tensor with shape broadcastable to
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`[N1,..., Nm]` `m >= 0`, `p in [0, 1]`. Each entry represents the
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the same dtype as `total_count`. Each entry represents logits for the
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probability of success for independent Binomial distributions. Only one
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of `logits` or `p` should be passed in.
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* <b>`validate_args`</b>: `Boolean`, default `False`. Whether to assert valid values
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for parameters `n`, `p`, and `x` in `prob` and `log_prob`.
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If `False` and inputs are invalid, correct behavior is not guaranteed.
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* <b>`allow_nan_stats`</b>: `Boolean`, default `True`. If `False`, raise an
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exception if a statistic (e.g. mean/mode/etc...) is undefined for any
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batch member. If `True`, batch members with valid parameters leading to
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undefined statistics will return NaN for this statistic.
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* <b>`name`</b>: The name to prefix Ops created by this distribution class.
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* <b>`Examples`</b>:
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```python
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# Define 1-batch of a binomial distribution.
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dist = Binomial(n=2., p=.9)
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# Define a 2-batch.
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dist = Binomial(n=[4., 5], p=[.1, .3])
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```
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of `logits` or `probs` should be passed in.
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* <b>`probs`</b>: Positive floating point tensor with shape broadcastable to
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`[N1,..., Nm]` `m >= 0`, `probs in [0, 1]`. Each entry represents the
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probability of success for independent Binomial distributions. Only one
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of `logits` or `probs` should be passed in.
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* <b>`validate_args`</b>: Python `Boolean`, default `False`. When `True` distribution
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parameters are checked for validity despite possibly degrading runtime
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performance. When `False` invalid inputs may silently render incorrect
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outputs.
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* <b>`allow_nan_stats`</b>: Python `Boolean`, default `True`. When `True`, statistics
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(e.g., mean, mode, variance) use the value "`NaN`" to indicate the
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result is undefined. When `False`, an exception is raised if one or
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more of the statistic's batch members are undefined.
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* <b>`name`</b>: `String` name prefixed to Ops created by this class.
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- - -
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@ -1142,15 +1144,15 @@ Log probability density/mass function (depending on `is_continuous`).
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Additional documentation from `Binomial`:
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For each batch member of counts `value`, `P[counts]` is the probability that
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after sampling `n` draws from this Binomial distribution, the number of
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successes is `k`. Note that different sequences of draws can result in the
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same counts, thus the probability includes a combinatorial coefficient.
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For each batch member of counts `value`, `P[value]` is the probability that
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after sampling `self.total_count` draws from this Binomial distribution, the
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number of successes is `value`. Since different sequences of draws can result in
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the same counts, the probability includes a combinatorial coefficient.
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`value` must be a non-negative tensor with dtype `dtype` and whose shape
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can be broadcast with `self.p` and `self.n`. `counts` is only legal if it is
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less than or equal to `n` and its components are equal to integer
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values.
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Note: `value` must be a non-negative tensor with dtype `dtype` and whose shape
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can be broadcast with `self.probs` and `self.total_count`. `value` is only legal
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if it is less than or equal to `self.total_count` and its components are equal
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to integer values.
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##### Args:
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@ -1198,7 +1200,7 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`.
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#### `tf.contrib.distributions.Binomial.logits` {#Binomial.logits}
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Log-odds of success.
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Log-odds of drawing a `1`.
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- - -
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@ -1216,16 +1218,10 @@ Mode.
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Additional documentation from `Binomial`:
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Note that when `(n + 1) * p` is an integer, there are actually two
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modes. Namely, `(n + 1) * p` and `(n + 1) * p - 1` are both modes. Here
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we return only the larger of the two modes.
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- - -
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#### `tf.contrib.distributions.Binomial.n` {#Binomial.n}
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Number of trials.
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Note that when `(1 + total_count) * probs` is an integer, there are
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actually two modes. Namely, `(1 + total_count) * probs` and
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`(1 + total_count) * probs - 1` are both modes. Here we return only the
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larger of the two modes.
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- - -
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@ -1235,13 +1231,6 @@ Number of trials.
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Name prepended to all ops created by this `Distribution`.
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- - -
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#### `tf.contrib.distributions.Binomial.p` {#Binomial.p}
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Probability of success.
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- - -
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#### `tf.contrib.distributions.Binomial.param_shapes(cls, sample_shape, name='DistributionParamShapes')` {#Binomial.param_shapes}
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@ -1360,15 +1349,15 @@ Probability density/mass function (depending on `is_continuous`).
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Additional documentation from `Binomial`:
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For each batch member of counts `value`, `P[counts]` is the probability that
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after sampling `n` draws from this Binomial distribution, the number of
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successes is `k`. Note that different sequences of draws can result in the
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same counts, thus the probability includes a combinatorial coefficient.
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For each batch member of counts `value`, `P[value]` is the probability that
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after sampling `self.total_count` draws from this Binomial distribution, the
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number of successes is `value`. Since different sequences of draws can result in
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the same counts, the probability includes a combinatorial coefficient.
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`value` must be a non-negative tensor with dtype `dtype` and whose shape
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can be broadcast with `self.p` and `self.n`. `counts` is only legal if it is
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less than or equal to `n` and its components are equal to integer
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values.
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Note: `value` must be a non-negative tensor with dtype `dtype` and whose shape
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can be broadcast with `self.probs` and `self.total_count`. `value` is only legal
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if it is less than or equal to `self.total_count` and its components are equal
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to integer values.
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##### Args:
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@ -1383,6 +1372,13 @@ values.
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values of type `self.dtype`.
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- - -
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#### `tf.contrib.distributions.Binomial.probs` {#Binomial.probs}
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Probability of of drawing a `1`.
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- - -
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#### `tf.contrib.distributions.Binomial.reparameterization_type` {#Binomial.reparameterization_type}
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@ -1453,6 +1449,13 @@ survival_function(x) = P[X > x]
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`self.dtype`.
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- - -
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#### `tf.contrib.distributions.Binomial.total_count` {#Binomial.total_count}
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Number of trials.
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- - -
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#### `tf.contrib.distributions.Binomial.validate_args` {#Binomial.validate_args}
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@ -1474,34 +1477,35 @@ Variance.
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Bernoulli distribution.
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The Bernoulli distribution is parameterized by p, the probability of a
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positive event.
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The Bernoulli distribution with `probs` parameter, i.e., the probability of a
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`1` outcome (vs a `0` outcome).
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- - -
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#### `tf.contrib.distributions.Bernoulli.__init__(logits=None, p=None, dtype=tf.int32, validate_args=False, allow_nan_stats=True, name='Bernoulli')` {#Bernoulli.__init__}
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#### `tf.contrib.distributions.Bernoulli.__init__(logits=None, probs=None, dtype=tf.int32, validate_args=False, allow_nan_stats=True, name='Bernoulli')` {#Bernoulli.__init__}
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Construct Bernoulli distributions.
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##### Args:
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* <b>`logits`</b>: An N-D `Tensor` representing the log-odds
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of a positive event. Each entry in the `Tensor` parametrizes
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an independent Bernoulli distribution where the probability of an event
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is sigmoid(logits). Only one of `logits` or `p` should be passed in.
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* <b>`p`</b>: An N-D `Tensor` representing the probability of a positive
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event. Each entry in the `Tensor` parameterizes an independent
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Bernoulli distribution. Only one of `logits` or `p` should be passed
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in.
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* <b>`dtype`</b>: dtype for samples.
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* <b>`validate_args`</b>: `Boolean`, default `False`. Whether to validate that
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`0 <= p <= 1`. If `validate_args` is `False`, and the inputs are
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invalid, methods like `log_pmf` may return `NaN` values.
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* <b>`allow_nan_stats`</b>: `Boolean`, default `True`. If `False`, raise an
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exception if a statistic (e.g. mean/mode/etc...) is undefined for any
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batch member. If `True`, batch members with valid parameters leading to
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undefined statistics will return NaN for this statistic.
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* <b>`name`</b>: A name for this distribution.
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* <b>`logits`</b>: An N-D `Tensor` representing the log-odds of a `1` event. Each
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entry in the `Tensor` parametrizes an independent Bernoulli distribution
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where the probability of an event is sigmoid(logits). Only one of
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`logits` or `probs` should be passed in.
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* <b>`probs`</b>: An N-D `Tensor` representing the probability of a `1`
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event. Each entry in the `Tensor` parameterizes an independent
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Bernoulli distribution. Only one of `logits` or `probs` should be passed
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in.
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* <b>`dtype`</b>: The type of the event samples. Default: `int32`.
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* <b>`validate_args`</b>: Python `Boolean`, default `False`. When `True` distribution
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parameters are checked for validity despite possibly degrading runtime
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performance. When `False` invalid inputs may silently render incorrect
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outputs.
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* <b>`allow_nan_stats`</b>: Python `Boolean`, default `True`. When `True`,
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statistics (e.g., mean, mode, variance) use the value "`NaN`" to
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indicate the result is undefined. When `False`, an exception is raised
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if one or more of the statistic's batch members are undefined.
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* <b>`name`</b>: `String` name prefixed to Ops created by this class.
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##### Raises:
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@ -1827,7 +1831,7 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`.
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#### `tf.contrib.distributions.Bernoulli.logits` {#Bernoulli.logits}
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Log-odds of success.
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Log-odds of a `1` outcome (vs `0`).
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- - -
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@ -1845,7 +1849,7 @@ Mode.
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Additional documentation from `Bernoulli`:
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Returns `1` if `p > 1-p` and `0` otherwise.
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Returns `1` if `prob > 0.5` and `0` otherwise.
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- - -
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@ -1855,13 +1859,6 @@ Returns `1` if `p > 1-p` and `0` otherwise.
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Name prepended to all ops created by this `Distribution`.
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- - -
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#### `tf.contrib.distributions.Bernoulli.p` {#Bernoulli.p}
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Probability of success.
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- - -
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#### `tf.contrib.distributions.Bernoulli.param_shapes(cls, sample_shape, name='DistributionParamShapes')` {#Bernoulli.param_shapes}
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@ -1992,9 +1989,9 @@ Probability density/mass function (depending on `is_continuous`).
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- - -
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#### `tf.contrib.distributions.Bernoulli.q` {#Bernoulli.q}
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#### `tf.contrib.distributions.Bernoulli.probs` {#Bernoulli.probs}
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1-p.
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Probability of a `1` outcome (vs `0`).
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- - -
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@ -2084,19 +2081,19 @@ Variance.
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- - -
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### `class tf.contrib.distributions.BernoulliWithSigmoidP` {#BernoulliWithSigmoidP}
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### `class tf.contrib.distributions.BernoulliWithSigmoidProbs` {#BernoulliWithSigmoidProbs}
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Bernoulli with `p = sigmoid(p)`.
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Bernoulli with `probs = nn.sigmoid(logits)`.
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- - -
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#### `tf.contrib.distributions.BernoulliWithSigmoidP.__init__(p=None, dtype=tf.int32, validate_args=False, allow_nan_stats=True, name='BernoulliWithSigmoidP')` {#BernoulliWithSigmoidP.__init__}
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#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.__init__(logits=None, dtype=tf.int32, validate_args=False, allow_nan_stats=True, name='BernoulliWithSigmoidProbs')` {#BernoulliWithSigmoidProbs.__init__}
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- - -
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#### `tf.contrib.distributions.BernoulliWithSigmoidP.allow_nan_stats` {#BernoulliWithSigmoidP.allow_nan_stats}
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#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.allow_nan_stats` {#BernoulliWithSigmoidProbs.allow_nan_stats}
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Python boolean describing behavior when a stat is undefined.
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@ -2117,7 +2114,7 @@ undefined.
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- - -
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#### `tf.contrib.distributions.BernoulliWithSigmoidP.batch_shape(name='batch_shape')` {#BernoulliWithSigmoidP.batch_shape}
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#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.batch_shape(name='batch_shape')` {#BernoulliWithSigmoidProbs.batch_shape}
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Shape of a single sample from a single event index as a 1-D `Tensor`.
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@ -2137,7 +2134,7 @@ independent distributions of this kind the instance represents.
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- - -
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#### `tf.contrib.distributions.BernoulliWithSigmoidP.cdf(value, name='cdf')` {#BernoulliWithSigmoidP.cdf}
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#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.cdf(value, name='cdf')` {#BernoulliWithSigmoidProbs.cdf}
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Cumulative distribution function.
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@ -2162,7 +2159,7 @@ cdf(x) := P[X <= x]
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- - -
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#### `tf.contrib.distributions.BernoulliWithSigmoidP.copy(**override_parameters_kwargs)` {#BernoulliWithSigmoidP.copy}
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#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.copy(**override_parameters_kwargs)` {#BernoulliWithSigmoidProbs.copy}
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Creates a deep copy of the distribution.
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@ -2185,21 +2182,21 @@ intialization arguments.
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- - -
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#### `tf.contrib.distributions.BernoulliWithSigmoidP.dtype` {#BernoulliWithSigmoidP.dtype}
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#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.dtype` {#BernoulliWithSigmoidProbs.dtype}
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The `DType` of `Tensor`s handled by this `Distribution`.
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- - -
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#### `tf.contrib.distributions.BernoulliWithSigmoidP.entropy(name='entropy')` {#BernoulliWithSigmoidP.entropy}
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#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.entropy(name='entropy')` {#BernoulliWithSigmoidProbs.entropy}
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Shannon entropy in nats.
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- - -
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#### `tf.contrib.distributions.BernoulliWithSigmoidP.event_shape(name='event_shape')` {#BernoulliWithSigmoidP.event_shape}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.event_shape(name='event_shape')` {#BernoulliWithSigmoidProbs.event_shape}
|
||||
|
||||
Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
|
||||
|
||||
@ -2216,7 +2213,7 @@ Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.get_batch_shape()` {#BernoulliWithSigmoidP.get_batch_shape}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.get_batch_shape()` {#BernoulliWithSigmoidProbs.get_batch_shape}
|
||||
|
||||
Shape of a single sample from a single event index as a `TensorShape`.
|
||||
|
||||
@ -2230,7 +2227,7 @@ Same meaning as `batch_shape`. May be only partially defined.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.get_event_shape()` {#BernoulliWithSigmoidP.get_event_shape}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.get_event_shape()` {#BernoulliWithSigmoidProbs.get_event_shape}
|
||||
|
||||
Shape of a single sample from a single batch as a `TensorShape`.
|
||||
|
||||
@ -2244,14 +2241,14 @@ Same meaning as `event_shape`. May be only partially defined.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.is_continuous` {#BernoulliWithSigmoidP.is_continuous}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.is_continuous` {#BernoulliWithSigmoidProbs.is_continuous}
|
||||
|
||||
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.is_scalar_batch(name='is_scalar_batch')` {#BernoulliWithSigmoidP.is_scalar_batch}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.is_scalar_batch(name='is_scalar_batch')` {#BernoulliWithSigmoidProbs.is_scalar_batch}
|
||||
|
||||
Indicates that `batch_shape == []`.
|
||||
|
||||
@ -2268,7 +2265,7 @@ Indicates that `batch_shape == []`.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.is_scalar_event(name='is_scalar_event')` {#BernoulliWithSigmoidP.is_scalar_event}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.is_scalar_event(name='is_scalar_event')` {#BernoulliWithSigmoidProbs.is_scalar_event}
|
||||
|
||||
Indicates that `event_shape == []`.
|
||||
|
||||
@ -2285,7 +2282,7 @@ Indicates that `event_shape == []`.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_cdf(value, name='log_cdf')` {#BernoulliWithSigmoidP.log_cdf}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.log_cdf(value, name='log_cdf')` {#BernoulliWithSigmoidProbs.log_cdf}
|
||||
|
||||
Log cumulative distribution function.
|
||||
|
||||
@ -2314,7 +2311,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_pdf(value, name='log_pdf')` {#BernoulliWithSigmoidP.log_pdf}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.log_pdf(value, name='log_pdf')` {#BernoulliWithSigmoidProbs.log_pdf}
|
||||
|
||||
Log probability density function.
|
||||
|
||||
@ -2338,7 +2335,7 @@ Log probability density function.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_pmf(value, name='log_pmf')` {#BernoulliWithSigmoidP.log_pmf}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.log_pmf(value, name='log_pmf')` {#BernoulliWithSigmoidProbs.log_pmf}
|
||||
|
||||
Log probability mass function.
|
||||
|
||||
@ -2362,7 +2359,7 @@ Log probability mass function.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_prob(value, name='log_prob')` {#BernoulliWithSigmoidP.log_prob}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.log_prob(value, name='log_prob')` {#BernoulliWithSigmoidProbs.log_prob}
|
||||
|
||||
Log probability density/mass function (depending on `is_continuous`).
|
||||
|
||||
@ -2381,7 +2378,7 @@ Log probability density/mass function (depending on `is_continuous`).
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_survival_function(value, name='log_survival_function')` {#BernoulliWithSigmoidP.log_survival_function}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.log_survival_function(value, name='log_survival_function')` {#BernoulliWithSigmoidProbs.log_survival_function}
|
||||
|
||||
Log survival function.
|
||||
|
||||
@ -2410,46 +2407,39 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.logits` {#BernoulliWithSigmoidP.logits}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.logits` {#BernoulliWithSigmoidProbs.logits}
|
||||
|
||||
Log-odds of success.
|
||||
Log-odds of a `1` outcome (vs `0`).
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.mean(name='mean')` {#BernoulliWithSigmoidP.mean}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.mean(name='mean')` {#BernoulliWithSigmoidProbs.mean}
|
||||
|
||||
Mean.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.mode(name='mode')` {#BernoulliWithSigmoidP.mode}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.mode(name='mode')` {#BernoulliWithSigmoidProbs.mode}
|
||||
|
||||
Mode.
|
||||
|
||||
Additional documentation from `Bernoulli`:
|
||||
|
||||
Returns `1` if `p > 1-p` and `0` otherwise.
|
||||
Returns `1` if `prob > 0.5` and `0` otherwise.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.name` {#BernoulliWithSigmoidP.name}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.name` {#BernoulliWithSigmoidProbs.name}
|
||||
|
||||
Name prepended to all ops created by this `Distribution`.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.p` {#BernoulliWithSigmoidP.p}
|
||||
|
||||
Probability of success.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.param_shapes(cls, sample_shape, name='DistributionParamShapes')` {#BernoulliWithSigmoidP.param_shapes}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.param_shapes(cls, sample_shape, name='DistributionParamShapes')` {#BernoulliWithSigmoidProbs.param_shapes}
|
||||
|
||||
Shapes of parameters given the desired shape of a call to `sample()`.
|
||||
|
||||
@ -2473,7 +2463,7 @@ Subclasses should override class method `_param_shapes`.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.param_static_shapes(cls, sample_shape)` {#BernoulliWithSigmoidP.param_static_shapes}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.param_static_shapes(cls, sample_shape)` {#BernoulliWithSigmoidProbs.param_static_shapes}
|
||||
|
||||
param_shapes with static (i.e. `TensorShape`) shapes.
|
||||
|
||||
@ -2503,14 +2493,14 @@ constant-valued tensors when constant values are fed.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.parameters` {#BernoulliWithSigmoidP.parameters}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.parameters` {#BernoulliWithSigmoidProbs.parameters}
|
||||
|
||||
Dictionary of parameters used to instantiate this `Distribution`.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.pdf(value, name='pdf')` {#BernoulliWithSigmoidP.pdf}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.pdf(value, name='pdf')` {#BernoulliWithSigmoidProbs.pdf}
|
||||
|
||||
Probability density function.
|
||||
|
||||
@ -2534,7 +2524,7 @@ Probability density function.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.pmf(value, name='pmf')` {#BernoulliWithSigmoidP.pmf}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.pmf(value, name='pmf')` {#BernoulliWithSigmoidProbs.pmf}
|
||||
|
||||
Probability mass function.
|
||||
|
||||
@ -2558,7 +2548,7 @@ Probability mass function.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.prob(value, name='prob')` {#BernoulliWithSigmoidP.prob}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.prob(value, name='prob')` {#BernoulliWithSigmoidProbs.prob}
|
||||
|
||||
Probability density/mass function (depending on `is_continuous`).
|
||||
|
||||
@ -2577,14 +2567,14 @@ Probability density/mass function (depending on `is_continuous`).
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.q` {#BernoulliWithSigmoidP.q}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.probs` {#BernoulliWithSigmoidProbs.probs}
|
||||
|
||||
1-p.
|
||||
Probability of a `1` outcome (vs `0`).
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.reparameterization_type` {#BernoulliWithSigmoidP.reparameterization_type}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.reparameterization_type` {#BernoulliWithSigmoidProbs.reparameterization_type}
|
||||
|
||||
Describes how samples from the distribution are reparameterized.
|
||||
|
||||
@ -2599,7 +2589,7 @@ or `distributions.NOT_REPARAMETERIZED`.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.sample(sample_shape=(), seed=None, name='sample')` {#BernoulliWithSigmoidP.sample}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.sample(sample_shape=(), seed=None, name='sample')` {#BernoulliWithSigmoidProbs.sample}
|
||||
|
||||
Generate samples of the specified shape.
|
||||
|
||||
@ -2621,14 +2611,14 @@ sample.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.stddev(name='stddev')` {#BernoulliWithSigmoidP.stddev}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.stddev(name='stddev')` {#BernoulliWithSigmoidProbs.stddev}
|
||||
|
||||
Standard deviation.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.survival_function(value, name='survival_function')` {#BernoulliWithSigmoidP.survival_function}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.survival_function(value, name='survival_function')` {#BernoulliWithSigmoidProbs.survival_function}
|
||||
|
||||
Survival function.
|
||||
|
||||
@ -2654,14 +2644,14 @@ survival_function(x) = P[X > x]
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.validate_args` {#BernoulliWithSigmoidP.validate_args}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.validate_args` {#BernoulliWithSigmoidProbs.validate_args}
|
||||
|
||||
Python boolean indicated possibly expensive checks are enabled.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.variance(name='variance')` {#BernoulliWithSigmoidP.variance}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.variance(name='variance')` {#BernoulliWithSigmoidProbs.variance}
|
||||
|
||||
Variance.
|
||||
|
||||
@ -3980,7 +3970,7 @@ drawn from.
|
||||
|
||||
```python
|
||||
p = [0.1, 0.5, 0.4]
|
||||
dist = Categorical(p=p)
|
||||
dist = Categorical(probs=p)
|
||||
```
|
||||
|
||||
Creates a 3-class distiribution, with the 2nd class the most likely to be
|
||||
@ -3997,7 +3987,7 @@ The distribution functions can be evaluated on counts.
|
||||
```python
|
||||
# counts is a scalar.
|
||||
p = [0.1, 0.4, 0.5]
|
||||
dist = Categorical(p=p)
|
||||
dist = Categorical(probs=p)
|
||||
dist.pmf(0) # Shape []
|
||||
|
||||
# p will be broadcast to [[0.1, 0.4, 0.5], [0.1, 0.4, 0.5]] to match counts.
|
||||
@ -4010,7 +4000,7 @@ dist.pmf(counts) # Shape [5, 7, 3]
|
||||
```
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Categorical.__init__(logits=None, p=None, dtype=tf.int32, validate_args=False, allow_nan_stats=True, name='Categorical')` {#Categorical.__init__}
|
||||
#### `tf.contrib.distributions.Categorical.__init__(logits=None, probs=None, dtype=tf.int32, validate_args=False, allow_nan_stats=True, name='Categorical')` {#Categorical.__init__}
|
||||
|
||||
Initialize Categorical distributions using class log-probabilities.
|
||||
|
||||
@ -4018,22 +4008,25 @@ Initialize Categorical distributions using class log-probabilities.
|
||||
|
||||
|
||||
* <b>`logits`</b>: An N-D `Tensor`, `N >= 1`, representing the log probabilities
|
||||
of a set of Categorical distributions. The first `N - 1` dimensions
|
||||
index into a batch of independent distributions and the last dimension
|
||||
represents a vector of logits for each class. Only one of `logits` or
|
||||
`p` should be passed in.
|
||||
* <b>`p`</b>: An N-D `Tensor`, `N >= 1`, representing the probabilities
|
||||
of a set of Categorical distributions. The first `N - 1` dimensions
|
||||
index into a batch of independent distributions and the last dimension
|
||||
represents a vector of probabilities for each class. Only one of
|
||||
`logits` or `p` should be passed in.
|
||||
of a set of Categorical distributions. The first `N - 1` dimensions
|
||||
index into a batch of independent distributions and the last dimension
|
||||
represents a vector of logits for each class. Only one of `logits` or
|
||||
`probs` should be passed in.
|
||||
* <b>`probs`</b>: An N-D `Tensor`, `N >= 1`, representing the probabilities
|
||||
of a set of Categorical distributions. The first `N - 1` dimensions
|
||||
index into a batch of independent distributions and the last dimension
|
||||
represents a vector of probabilities for each class. Only one of
|
||||
`logits` or `probs` should be passed in.
|
||||
* <b>`dtype`</b>: The type of the event samples (default: int32).
|
||||
* <b>`validate_args`</b>: Unused in this distribution.
|
||||
* <b>`allow_nan_stats`</b>: `Boolean`, default `True`. If `False`, raise an
|
||||
exception if a statistic (e.g. mean/mode/etc...) is undefined for any
|
||||
batch member. 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).
|
||||
* <b>`validate_args`</b>: Python `Boolean`, default `False`. When `True` distribution
|
||||
parameters are checked for validity despite possibly degrading runtime
|
||||
performance. When `False` invalid inputs may silently render incorrect
|
||||
outputs.
|
||||
* <b>`allow_nan_stats`</b>: Python `Boolean`, default `True`. When `True`, statistics
|
||||
(e.g., mean, mode, variance) use the value "`NaN`" to indicate the
|
||||
result is undefined. When `False`, an exception is raised if one or
|
||||
more of the statistic's batch members are undefined.
|
||||
* <b>`name`</b>: `String` name prefixed to Ops created by this class.
|
||||
|
||||
|
||||
- - -
|
||||
@ -4385,15 +4378,6 @@ Name prepended to all ops created by this `Distribution`.
|
||||
Scalar `int32` tensor: the number of classes.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Categorical.p` {#Categorical.p}
|
||||
|
||||
Vector of probabilities summing to one.
|
||||
|
||||
Each element is the probability of drawing that coordinate.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Categorical.param_shapes(cls, sample_shape, name='DistributionParamShapes')` {#Categorical.param_shapes}
|
||||
@ -4522,6 +4506,15 @@ Probability density/mass function (depending on `is_continuous`).
|
||||
values of type `self.dtype`.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Categorical.probs` {#Categorical.probs}
|
||||
|
||||
Vector of probabilities summing to one.
|
||||
|
||||
Each element is the probability of drawing that coordinate.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Categorical.reparameterization_type` {#Categorical.reparameterization_type}
|
||||
@ -19248,36 +19241,50 @@ Cov(X_i, X_j) = -n * alpha_i * alpha_j / alpha_0 ** 2 *
|
||||
|
||||
Multinomial distribution.
|
||||
|
||||
This distribution is parameterized by a vector `p` of probability
|
||||
parameters for `k` classes and `n`, the counts per each class..
|
||||
This Multinomial distribution is parameterized by `probs`, a (batch of)
|
||||
length-`k` `prob` (probability) vectors (`k > 1`) such that
|
||||
`tf.reduce_sum(probs, -1) = 1`, and a `total_count` number of trials, i.e.,
|
||||
the number of trials per draw from the Multinomial. It is defined over a
|
||||
(batch of) length-`k` vector `counts` such that
|
||||
`tf.reduce_sum(counts, -1) = total_count`. The Multinomial is identically the
|
||||
Binomial distribution when `k = 2`.
|
||||
|
||||
#### Mathematical details
|
||||
#### Mathematical Details
|
||||
|
||||
The Multinomial is a distribution over k-class count data, meaning
|
||||
for each k-tuple of non-negative integer `counts = [n_1,...,n_k]`, we have a
|
||||
probability of these draws being made from the distribution. The distribution
|
||||
has hyperparameters `p = (p_1,...,p_k)`, and probability mass
|
||||
function (pmf):
|
||||
The Multinomial is a distribution over `k`-class counts, i.e., a length-`k`
|
||||
vector of non-negative integer `counts = n = [n_0, ..., n_{k-1}]`.
|
||||
|
||||
```pmf(counts) = n! / (n_1!...n_k!) * (p_1)^n_1*(p_2)^n_2*...(p_k)^n_k```
|
||||
The probability mass function (pmf) is,
|
||||
|
||||
where above `n = sum_j n_j`, `n!` is `n` factorial.
|
||||
```none
|
||||
pmf(n; pi, N) = prod_j (pi_j)**n_j / Z
|
||||
Z = (prod_j n_j!) / N!
|
||||
```
|
||||
|
||||
where:
|
||||
* `probs = pi = [pi_0, ..., pi_{k-1}]`, `pi_j > 0`, `sum_j pi_j = 1`,
|
||||
* `total_count = N`, `N` a positive integer,
|
||||
* `Z` is the normalization constant, and,
|
||||
* `N!` denotes `N` factorial.
|
||||
|
||||
Distribution parameters are automatically broadcast in all functions; see
|
||||
examples for details.
|
||||
|
||||
#### Examples
|
||||
|
||||
Create a 3-class distribution, with the 3rd class is most likely to be drawn,
|
||||
using logits..
|
||||
using logits.
|
||||
|
||||
```python
|
||||
logits = [-50., -43, 0]
|
||||
dist = Multinomial(n=4., logits=logits)
|
||||
dist = Multinomial(total_count=4., logits=logits)
|
||||
```
|
||||
|
||||
Create a 3-class distribution, with the 3rd class is most likely to be drawn.
|
||||
|
||||
```python
|
||||
p = [.2, .3, .5]
|
||||
dist = Multinomial(n=4., p=p)
|
||||
dist = Multinomial(total_count=4., probs=p)
|
||||
```
|
||||
|
||||
The distribution functions can be evaluated on counts.
|
||||
@ -19300,54 +19307,43 @@ Create a 2-batch of 3-class distributions.
|
||||
|
||||
```python
|
||||
p = [[.1, .2, .7], [.3, .3, .4]] # Shape [2, 3]
|
||||
dist = Multinomial(n=[4., 5], p=p)
|
||||
dist = Multinomial(total_count=[4., 5], probs=p)
|
||||
|
||||
counts = [[2., 1, 1], [3, 1, 1]]
|
||||
dist.prob(counts) # Shape [2]
|
||||
```
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Multinomial.__init__(n, logits=None, p=None, validate_args=False, allow_nan_stats=True, name='Multinomial')` {#Multinomial.__init__}
|
||||
#### `tf.contrib.distributions.Multinomial.__init__(total_count, logits=None, probs=None, validate_args=False, allow_nan_stats=True, name='Multinomial')` {#Multinomial.__init__}
|
||||
|
||||
Initialize a batch of Multinomial distributions.
|
||||
|
||||
##### Args:
|
||||
|
||||
|
||||
* <b>`n`</b>: Non-negative floating point tensor with shape broadcastable to
|
||||
`[N1,..., Nm]` with `m >= 0`. Defines this as a batch of
|
||||
* <b>`total_count`</b>: Non-negative floating point tensor with shape broadcastable
|
||||
to `[N1,..., Nm]` with `m >= 0`. Defines this as a batch of
|
||||
`N1 x ... x Nm` different Multinomial distributions. Its components
|
||||
should be equal to integer values.
|
||||
* <b>`logits`</b>: Floating point tensor representing the log-odds of a
|
||||
positive event with shape broadcastable to `[N1,..., Nm, k], m >= 0`,
|
||||
and the same dtype as `n`. Defines this as a batch of `N1 x ... x Nm`
|
||||
different `k` class Multinomial distributions. Only one of `logits` or
|
||||
`p` should be passed in.
|
||||
* <b>`p`</b>: Positive floating point tensor with shape broadcastable to
|
||||
`[N1,..., Nm, k]` `m >= 0` and same dtype as `n`. Defines this as
|
||||
a batch of `N1 x ... x Nm` different `k` class Multinomial
|
||||
distributions. `p`'s components in the last portion of its shape should
|
||||
sum up to 1. Only one of `logits` or `p` should be passed in.
|
||||
* <b>`validate_args`</b>: `Boolean`, default `False`. Whether to assert valid
|
||||
values for parameters `n` and `p`, and `x` in `prob` and `log_prob`.
|
||||
If `False`, correct behavior is not guaranteed.
|
||||
* <b>`allow_nan_stats`</b>: `Boolean`, default `True`. If `False`, raise an
|
||||
exception if a statistic (e.g. mean/mode/etc...) is undefined for any
|
||||
batch member. 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.
|
||||
|
||||
|
||||
* <b>`Examples`</b>:
|
||||
|
||||
```python
|
||||
# Define 1-batch of 2-class multinomial distribution,
|
||||
# also known as a Binomial distribution.
|
||||
dist = Multinomial(n=2., p=[.1, .9])
|
||||
|
||||
# Define a 2-batch of 3-class distributions.
|
||||
dist = Multinomial(n=[4., 5], p=[[.1, .3, .6], [.4, .05, .55]])
|
||||
```
|
||||
and the same dtype as `total_count`. Defines this as a batch of
|
||||
`N1 x ... x Nm` different `k` class Multinomial distributions. Only one
|
||||
of `logits` or `probs` should be passed in.
|
||||
* <b>`probs`</b>: Positive floating point tensor with shape broadcastable to
|
||||
`[N1,..., Nm, k]` `m >= 0` and same dtype as `total_count`. Defines
|
||||
this as a batch of `N1 x ... x Nm` different `k` class Multinomial
|
||||
distributions. `probs`'s components in the last portion of its shape
|
||||
should sum to `1`. Only one of `logits` or `probs` should be passed in.
|
||||
* <b>`validate_args`</b>: Python `Boolean`, default `False`. When `True` distribution
|
||||
parameters are checked for validity despite possibly degrading runtime
|
||||
performance. When `False` invalid inputs may silently render incorrect
|
||||
outputs.
|
||||
* <b>`allow_nan_stats`</b>: Python `Boolean`, default `True`. When `True`, statistics
|
||||
(e.g., mean, mode, variance) use the value "`NaN`" to indicate the
|
||||
result is undefined. When `False`, an exception is raised if one or
|
||||
more of the statistic's batch members are undefined.
|
||||
* <b>`name`</b>: `String` name prefixed to Ops created by this class.
|
||||
|
||||
|
||||
- - -
|
||||
@ -19625,17 +19621,18 @@ Log probability density/mass function (depending on `is_continuous`).
|
||||
|
||||
Additional documentation from `Multinomial`:
|
||||
|
||||
For each batch of counts `[n_1,...,n_k]`, `P[counts]` is the probability
|
||||
that after sampling `n` draws from this Multinomial distribution, the
|
||||
number of draws falling in class `j` is `n_j`. Note that different
|
||||
sequences of draws can result in the same counts, thus the probability
|
||||
includes a combinatorial coefficient.
|
||||
For each batch of counts, `value = [n_0, ...
|
||||
,n_{k-1}]`, `P[value]` is the probability that after sampling `self.total_count`
|
||||
draws from this Multinomial distribution, the number of draws falling in class
|
||||
`j` is `n_j`. Since this definition is [exchangeable](
|
||||
https://en.wikipedia.org/wiki/Exchangeable_random_variables); different
|
||||
sequences have the same counts so the probability includes a combinatorial
|
||||
coefficient.
|
||||
|
||||
Note that input "counts" must be a non-negative tensor with dtype `dtype`
|
||||
and whose shape can be broadcast with `self.p` and `self.n`. For fixed
|
||||
leading dimensions, the last dimension represents counts for the
|
||||
corresponding Multinomial distribution in `self.p`. `counts` is only legal
|
||||
if it sums up to `n` and its components are equal to integer values.
|
||||
Note: `value` must be a non-negative tensor with dtype `self.dtype`, have no
|
||||
fractional components, and such that
|
||||
`tf.reduce_sum(value, -1) = self.total_count`. Its shape must be broadcastable
|
||||
with `self.probs` and `self.total_count`.
|
||||
|
||||
##### Args:
|
||||
|
||||
@ -19700,13 +19697,6 @@ Mean.
|
||||
Mode.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Multinomial.n` {#Multinomial.n}
|
||||
|
||||
Number of trials.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Multinomial.name` {#Multinomial.name}
|
||||
@ -19714,15 +19704,6 @@ Number of trials.
|
||||
Name prepended to all ops created by this `Distribution`.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Multinomial.p` {#Multinomial.p}
|
||||
|
||||
Vector of probabilities summing to one.
|
||||
|
||||
Each element is the probability of drawing that coordinate.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Multinomial.param_shapes(cls, sample_shape, name='DistributionParamShapes')` {#Multinomial.param_shapes}
|
||||
@ -19841,17 +19822,18 @@ Probability density/mass function (depending on `is_continuous`).
|
||||
|
||||
Additional documentation from `Multinomial`:
|
||||
|
||||
For each batch of counts `[n_1,...,n_k]`, `P[counts]` is the probability
|
||||
that after sampling `n` draws from this Multinomial distribution, the
|
||||
number of draws falling in class `j` is `n_j`. Note that different
|
||||
sequences of draws can result in the same counts, thus the probability
|
||||
includes a combinatorial coefficient.
|
||||
For each batch of counts, `value = [n_0, ...
|
||||
,n_{k-1}]`, `P[value]` is the probability that after sampling `self.total_count`
|
||||
draws from this Multinomial distribution, the number of draws falling in class
|
||||
`j` is `n_j`. Since this definition is [exchangeable](
|
||||
https://en.wikipedia.org/wiki/Exchangeable_random_variables); different
|
||||
sequences have the same counts so the probability includes a combinatorial
|
||||
coefficient.
|
||||
|
||||
Note that input "counts" must be a non-negative tensor with dtype `dtype`
|
||||
and whose shape can be broadcast with `self.p` and `self.n`. For fixed
|
||||
leading dimensions, the last dimension represents counts for the
|
||||
corresponding Multinomial distribution in `self.p`. `counts` is only legal
|
||||
if it sums up to `n` and its components are equal to integer values.
|
||||
Note: `value` must be a non-negative tensor with dtype `self.dtype`, have no
|
||||
fractional components, and such that
|
||||
`tf.reduce_sum(value, -1) = self.total_count`. Its shape must be broadcastable
|
||||
with `self.probs` and `self.total_count`.
|
||||
|
||||
##### Args:
|
||||
|
||||
@ -19866,6 +19848,15 @@ if it sums up to `n` and its components are equal to integer values.
|
||||
values of type `self.dtype`.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Multinomial.probs` {#Multinomial.probs}
|
||||
|
||||
Vector of probabilities summing to one.
|
||||
|
||||
Each element is the probability of drawing that coordinate.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Multinomial.reparameterization_type` {#Multinomial.reparameterization_type}
|
||||
@ -19936,6 +19927,13 @@ survival_function(x) = P[X > x]
|
||||
`self.dtype`.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Multinomial.total_count` {#Multinomial.total_count}
|
||||
|
||||
Number of trials used to construct a sample.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Multinomial.validate_args` {#Multinomial.validate_args}
|
||||
|
||||
@ -1,33 +1,34 @@
|
||||
Bernoulli distribution.
|
||||
|
||||
The Bernoulli distribution is parameterized by p, the probability of a
|
||||
positive event.
|
||||
The Bernoulli distribution with `probs` parameter, i.e., the probability of a
|
||||
`1` outcome (vs a `0` outcome).
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Bernoulli.__init__(logits=None, p=None, dtype=tf.int32, validate_args=False, allow_nan_stats=True, name='Bernoulli')` {#Bernoulli.__init__}
|
||||
#### `tf.contrib.distributions.Bernoulli.__init__(logits=None, probs=None, dtype=tf.int32, validate_args=False, allow_nan_stats=True, name='Bernoulli')` {#Bernoulli.__init__}
|
||||
|
||||
Construct Bernoulli distributions.
|
||||
|
||||
##### Args:
|
||||
|
||||
|
||||
* <b>`logits`</b>: An N-D `Tensor` representing the log-odds
|
||||
of a positive event. Each entry in the `Tensor` parametrizes
|
||||
an independent Bernoulli distribution where the probability of an event
|
||||
is sigmoid(logits). Only one of `logits` or `p` should be passed in.
|
||||
* <b>`p`</b>: An N-D `Tensor` representing the probability of a positive
|
||||
event. Each entry in the `Tensor` parameterizes an independent
|
||||
Bernoulli distribution. Only one of `logits` or `p` should be passed
|
||||
in.
|
||||
* <b>`dtype`</b>: dtype for samples.
|
||||
* <b>`validate_args`</b>: `Boolean`, default `False`. Whether to validate that
|
||||
`0 <= p <= 1`. If `validate_args` is `False`, and the inputs are
|
||||
invalid, methods like `log_pmf` may return `NaN` values.
|
||||
* <b>`allow_nan_stats`</b>: `Boolean`, default `True`. If `False`, raise an
|
||||
exception if a statistic (e.g. mean/mode/etc...) is undefined for any
|
||||
batch member. 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.
|
||||
* <b>`logits`</b>: An N-D `Tensor` representing the log-odds of a `1` event. Each
|
||||
entry in the `Tensor` parametrizes an independent Bernoulli distribution
|
||||
where the probability of an event is sigmoid(logits). Only one of
|
||||
`logits` or `probs` should be passed in.
|
||||
* <b>`probs`</b>: An N-D `Tensor` representing the probability of a `1`
|
||||
event. Each entry in the `Tensor` parameterizes an independent
|
||||
Bernoulli distribution. Only one of `logits` or `probs` should be passed
|
||||
in.
|
||||
* <b>`dtype`</b>: The type of the event samples. Default: `int32`.
|
||||
* <b>`validate_args`</b>: Python `Boolean`, default `False`. When `True` distribution
|
||||
parameters are checked for validity despite possibly degrading runtime
|
||||
performance. When `False` invalid inputs may silently render incorrect
|
||||
outputs.
|
||||
* <b>`allow_nan_stats`</b>: Python `Boolean`, default `True`. When `True`,
|
||||
statistics (e.g., mean, mode, variance) use the value "`NaN`" to
|
||||
indicate the result is undefined. When `False`, an exception is raised
|
||||
if one or more of the statistic's batch members are undefined.
|
||||
* <b>`name`</b>: `String` name prefixed to Ops created by this class.
|
||||
|
||||
##### Raises:
|
||||
|
||||
@ -353,7 +354,7 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`.
|
||||
|
||||
#### `tf.contrib.distributions.Bernoulli.logits` {#Bernoulli.logits}
|
||||
|
||||
Log-odds of success.
|
||||
Log-odds of a `1` outcome (vs `0`).
|
||||
|
||||
|
||||
- - -
|
||||
@ -371,7 +372,7 @@ Mode.
|
||||
|
||||
Additional documentation from `Bernoulli`:
|
||||
|
||||
Returns `1` if `p > 1-p` and `0` otherwise.
|
||||
Returns `1` if `prob > 0.5` and `0` otherwise.
|
||||
|
||||
|
||||
- - -
|
||||
@ -381,13 +382,6 @@ Returns `1` if `p > 1-p` and `0` otherwise.
|
||||
Name prepended to all ops created by this `Distribution`.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Bernoulli.p` {#Bernoulli.p}
|
||||
|
||||
Probability of success.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Bernoulli.param_shapes(cls, sample_shape, name='DistributionParamShapes')` {#Bernoulli.param_shapes}
|
||||
@ -518,9 +512,9 @@ Probability density/mass function (depending on `is_continuous`).
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Bernoulli.q` {#Bernoulli.q}
|
||||
#### `tf.contrib.distributions.Bernoulli.probs` {#Bernoulli.probs}
|
||||
|
||||
1-p.
|
||||
Probability of a `1` outcome (vs `0`).
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
@ -10,7 +10,7 @@ drawn from.
|
||||
|
||||
```python
|
||||
p = [0.1, 0.5, 0.4]
|
||||
dist = Categorical(p=p)
|
||||
dist = Categorical(probs=p)
|
||||
```
|
||||
|
||||
Creates a 3-class distiribution, with the 2nd class the most likely to be
|
||||
@ -27,7 +27,7 @@ The distribution functions can be evaluated on counts.
|
||||
```python
|
||||
# counts is a scalar.
|
||||
p = [0.1, 0.4, 0.5]
|
||||
dist = Categorical(p=p)
|
||||
dist = Categorical(probs=p)
|
||||
dist.pmf(0) # Shape []
|
||||
|
||||
# p will be broadcast to [[0.1, 0.4, 0.5], [0.1, 0.4, 0.5]] to match counts.
|
||||
@ -40,7 +40,7 @@ dist.pmf(counts) # Shape [5, 7, 3]
|
||||
```
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Categorical.__init__(logits=None, p=None, dtype=tf.int32, validate_args=False, allow_nan_stats=True, name='Categorical')` {#Categorical.__init__}
|
||||
#### `tf.contrib.distributions.Categorical.__init__(logits=None, probs=None, dtype=tf.int32, validate_args=False, allow_nan_stats=True, name='Categorical')` {#Categorical.__init__}
|
||||
|
||||
Initialize Categorical distributions using class log-probabilities.
|
||||
|
||||
@ -48,22 +48,25 @@ Initialize Categorical distributions using class log-probabilities.
|
||||
|
||||
|
||||
* <b>`logits`</b>: An N-D `Tensor`, `N >= 1`, representing the log probabilities
|
||||
of a set of Categorical distributions. The first `N - 1` dimensions
|
||||
index into a batch of independent distributions and the last dimension
|
||||
represents a vector of logits for each class. Only one of `logits` or
|
||||
`p` should be passed in.
|
||||
* <b>`p`</b>: An N-D `Tensor`, `N >= 1`, representing the probabilities
|
||||
of a set of Categorical distributions. The first `N - 1` dimensions
|
||||
index into a batch of independent distributions and the last dimension
|
||||
represents a vector of probabilities for each class. Only one of
|
||||
`logits` or `p` should be passed in.
|
||||
of a set of Categorical distributions. The first `N - 1` dimensions
|
||||
index into a batch of independent distributions and the last dimension
|
||||
represents a vector of logits for each class. Only one of `logits` or
|
||||
`probs` should be passed in.
|
||||
* <b>`probs`</b>: An N-D `Tensor`, `N >= 1`, representing the probabilities
|
||||
of a set of Categorical distributions. The first `N - 1` dimensions
|
||||
index into a batch of independent distributions and the last dimension
|
||||
represents a vector of probabilities for each class. Only one of
|
||||
`logits` or `probs` should be passed in.
|
||||
* <b>`dtype`</b>: The type of the event samples (default: int32).
|
||||
* <b>`validate_args`</b>: Unused in this distribution.
|
||||
* <b>`allow_nan_stats`</b>: `Boolean`, default `True`. If `False`, raise an
|
||||
exception if a statistic (e.g. mean/mode/etc...) is undefined for any
|
||||
batch member. 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).
|
||||
* <b>`validate_args`</b>: Python `Boolean`, default `False`. When `True` distribution
|
||||
parameters are checked for validity despite possibly degrading runtime
|
||||
performance. When `False` invalid inputs may silently render incorrect
|
||||
outputs.
|
||||
* <b>`allow_nan_stats`</b>: Python `Boolean`, default `True`. When `True`, statistics
|
||||
(e.g., mean, mode, variance) use the value "`NaN`" to indicate the
|
||||
result is undefined. When `False`, an exception is raised if one or
|
||||
more of the statistic's batch members are undefined.
|
||||
* <b>`name`</b>: `String` name prefixed to Ops created by this class.
|
||||
|
||||
|
||||
- - -
|
||||
@ -415,15 +418,6 @@ Name prepended to all ops created by this `Distribution`.
|
||||
Scalar `int32` tensor: the number of classes.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Categorical.p` {#Categorical.p}
|
||||
|
||||
Vector of probabilities summing to one.
|
||||
|
||||
Each element is the probability of drawing that coordinate.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Categorical.param_shapes(cls, sample_shape, name='DistributionParamShapes')` {#Categorical.param_shapes}
|
||||
@ -552,6 +546,15 @@ Probability density/mass function (depending on `is_continuous`).
|
||||
values of type `self.dtype`.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Categorical.probs` {#Categorical.probs}
|
||||
|
||||
Vector of probabilities summing to one.
|
||||
|
||||
Each element is the probability of drawing that coordinate.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Categorical.reparameterization_type` {#Categorical.reparameterization_type}
|
||||
|
||||
@ -1,28 +1,40 @@
|
||||
Binomial distribution.
|
||||
|
||||
This distribution is parameterized by a vector `p` of probabilities and `n`,
|
||||
the total counts.
|
||||
This distribution is parameterized by `probs`, a (batch of) probabilities for
|
||||
drawing a `1` and `total_count`, the number of trials per draw from the
|
||||
Binomial.
|
||||
|
||||
#### Mathematical details
|
||||
#### Mathematical Details
|
||||
|
||||
The Binomial is a distribution over the number of successes in `n` independent
|
||||
trials, with each trial having the same probability of success `p`.
|
||||
The probability mass function (pmf):
|
||||
The Binomial is a distribution over the number of `1`'s in `total_count`
|
||||
independent trials, with each trial having the same probability of `1`, i.e.,
|
||||
`probs`.
|
||||
|
||||
```pmf(k) = n! / (k! * (n - k)!) * (p)^k * (1 - p)^(n - k)```
|
||||
The probability mass function (pmf) is,
|
||||
|
||||
```none
|
||||
pmf(k; n, p) = p**k (1 - p)**(n - k) / Z
|
||||
Z = k! (n - k)! / n!
|
||||
```
|
||||
|
||||
where:
|
||||
* `total_count = n`,
|
||||
* `probs = p`,
|
||||
* `Z` is the normalizaing constant, and,
|
||||
* `n!` is the factorial of `n`.
|
||||
|
||||
#### Examples
|
||||
|
||||
Create a single distribution, corresponding to 5 coin flips.
|
||||
|
||||
```python
|
||||
dist = Binomial(n=5., p=.5)
|
||||
dist = Binomial(total_count=5., probs=.5)
|
||||
```
|
||||
|
||||
Create a single distribution (using logits), corresponding to 5 coin flips.
|
||||
|
||||
```python
|
||||
dist = Binomial(n=5., logits=0.)
|
||||
dist = Binomial(total_count=5., logits=0.)
|
||||
```
|
||||
|
||||
Creates 3 distributions with the third distribution most likely to have
|
||||
@ -31,7 +43,7 @@ successes.
|
||||
```python
|
||||
p = [.2, .3, .8]
|
||||
# n will be broadcast to [4., 4., 4.], to match p.
|
||||
dist = Binomial(n=4., p=p)
|
||||
dist = Binomial(total_count=4., probs=p)
|
||||
```
|
||||
|
||||
The distribution functions can be evaluated on counts.
|
||||
@ -51,45 +63,35 @@ dist.prob(counts) # Shape [5, 7, 3]
|
||||
```
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Binomial.__init__(n, logits=None, p=None, validate_args=False, allow_nan_stats=True, name='Binomial')` {#Binomial.__init__}
|
||||
#### `tf.contrib.distributions.Binomial.__init__(total_count, logits=None, probs=None, validate_args=False, allow_nan_stats=True, name='Binomial')` {#Binomial.__init__}
|
||||
|
||||
Initialize a batch of Binomial distributions.
|
||||
|
||||
##### Args:
|
||||
|
||||
|
||||
* <b>`n`</b>: Non-negative floating point tensor with shape broadcastable to
|
||||
`[N1,..., Nm]` with `m >= 0` and the same dtype as `p` or `logits`.
|
||||
Defines this as a batch of `N1 x ... x Nm` different Binomial
|
||||
* <b>`total_count`</b>: Non-negative floating point tensor with shape broadcastable
|
||||
to `[N1,..., Nm]` with `m >= 0` and the same dtype as `probs` or
|
||||
`logits`. Defines this as a batch of `N1 x ... x Nm` different Binomial
|
||||
distributions. Its components should be equal to integer values.
|
||||
* <b>`logits`</b>: Floating point tensor representing the log-odds of a
|
||||
positive event with shape broadcastable to `[N1,..., Nm]` `m >= 0`, and
|
||||
the same dtype as `n`. Each entry represents logits for the probability
|
||||
of success for independent Binomial distributions. Only one of
|
||||
`logits` or `p` should be passed in.
|
||||
* <b>`p`</b>: Positive floating point tensor with shape broadcastable to
|
||||
`[N1,..., Nm]` `m >= 0`, `p in [0, 1]`. Each entry represents the
|
||||
the same dtype as `total_count`. Each entry represents logits for the
|
||||
probability of success for independent Binomial distributions. Only one
|
||||
of `logits` or `p` should be passed in.
|
||||
* <b>`validate_args`</b>: `Boolean`, default `False`. Whether to assert valid values
|
||||
for parameters `n`, `p`, and `x` in `prob` and `log_prob`.
|
||||
If `False` and inputs are invalid, correct behavior is not guaranteed.
|
||||
* <b>`allow_nan_stats`</b>: `Boolean`, default `True`. If `False`, raise an
|
||||
exception if a statistic (e.g. mean/mode/etc...) is undefined for any
|
||||
batch member. 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.
|
||||
|
||||
|
||||
* <b>`Examples`</b>:
|
||||
|
||||
```python
|
||||
# Define 1-batch of a binomial distribution.
|
||||
dist = Binomial(n=2., p=.9)
|
||||
|
||||
# Define a 2-batch.
|
||||
dist = Binomial(n=[4., 5], p=[.1, .3])
|
||||
```
|
||||
of `logits` or `probs` should be passed in.
|
||||
* <b>`probs`</b>: Positive floating point tensor with shape broadcastable to
|
||||
`[N1,..., Nm]` `m >= 0`, `probs in [0, 1]`. Each entry represents the
|
||||
probability of success for independent Binomial distributions. Only one
|
||||
of `logits` or `probs` should be passed in.
|
||||
* <b>`validate_args`</b>: Python `Boolean`, default `False`. When `True` distribution
|
||||
parameters are checked for validity despite possibly degrading runtime
|
||||
performance. When `False` invalid inputs may silently render incorrect
|
||||
outputs.
|
||||
* <b>`allow_nan_stats`</b>: Python `Boolean`, default `True`. When `True`, statistics
|
||||
(e.g., mean, mode, variance) use the value "`NaN`" to indicate the
|
||||
result is undefined. When `False`, an exception is raised if one or
|
||||
more of the statistic's batch members are undefined.
|
||||
* <b>`name`</b>: `String` name prefixed to Ops created by this class.
|
||||
|
||||
|
||||
- - -
|
||||
@ -367,15 +369,15 @@ Log probability density/mass function (depending on `is_continuous`).
|
||||
|
||||
Additional documentation from `Binomial`:
|
||||
|
||||
For each batch member of counts `value`, `P[counts]` is the probability that
|
||||
after sampling `n` draws from this Binomial distribution, the number of
|
||||
successes is `k`. Note that different sequences of draws can result in the
|
||||
same counts, thus the probability includes a combinatorial coefficient.
|
||||
For each batch member of counts `value`, `P[value]` is the probability that
|
||||
after sampling `self.total_count` draws from this Binomial distribution, the
|
||||
number of successes is `value`. Since different sequences of draws can result in
|
||||
the same counts, the probability includes a combinatorial coefficient.
|
||||
|
||||
`value` must be a non-negative tensor with dtype `dtype` and whose shape
|
||||
can be broadcast with `self.p` and `self.n`. `counts` is only legal if it is
|
||||
less than or equal to `n` and its components are equal to integer
|
||||
values.
|
||||
Note: `value` must be a non-negative tensor with dtype `dtype` and whose shape
|
||||
can be broadcast with `self.probs` and `self.total_count`. `value` is only legal
|
||||
if it is less than or equal to `self.total_count` and its components are equal
|
||||
to integer values.
|
||||
|
||||
##### Args:
|
||||
|
||||
@ -423,7 +425,7 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`.
|
||||
|
||||
#### `tf.contrib.distributions.Binomial.logits` {#Binomial.logits}
|
||||
|
||||
Log-odds of success.
|
||||
Log-odds of drawing a `1`.
|
||||
|
||||
|
||||
- - -
|
||||
@ -441,16 +443,10 @@ Mode.
|
||||
|
||||
Additional documentation from `Binomial`:
|
||||
|
||||
Note that when `(n + 1) * p` is an integer, there are actually two
|
||||
modes. Namely, `(n + 1) * p` and `(n + 1) * p - 1` are both modes. Here
|
||||
we return only the larger of the two modes.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Binomial.n` {#Binomial.n}
|
||||
|
||||
Number of trials.
|
||||
Note that when `(1 + total_count) * probs` is an integer, there are
|
||||
actually two modes. Namely, `(1 + total_count) * probs` and
|
||||
`(1 + total_count) * probs - 1` are both modes. Here we return only the
|
||||
larger of the two modes.
|
||||
|
||||
|
||||
- - -
|
||||
@ -460,13 +456,6 @@ Number of trials.
|
||||
Name prepended to all ops created by this `Distribution`.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Binomial.p` {#Binomial.p}
|
||||
|
||||
Probability of success.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Binomial.param_shapes(cls, sample_shape, name='DistributionParamShapes')` {#Binomial.param_shapes}
|
||||
@ -585,15 +574,15 @@ Probability density/mass function (depending on `is_continuous`).
|
||||
|
||||
Additional documentation from `Binomial`:
|
||||
|
||||
For each batch member of counts `value`, `P[counts]` is the probability that
|
||||
after sampling `n` draws from this Binomial distribution, the number of
|
||||
successes is `k`. Note that different sequences of draws can result in the
|
||||
same counts, thus the probability includes a combinatorial coefficient.
|
||||
For each batch member of counts `value`, `P[value]` is the probability that
|
||||
after sampling `self.total_count` draws from this Binomial distribution, the
|
||||
number of successes is `value`. Since different sequences of draws can result in
|
||||
the same counts, the probability includes a combinatorial coefficient.
|
||||
|
||||
`value` must be a non-negative tensor with dtype `dtype` and whose shape
|
||||
can be broadcast with `self.p` and `self.n`. `counts` is only legal if it is
|
||||
less than or equal to `n` and its components are equal to integer
|
||||
values.
|
||||
Note: `value` must be a non-negative tensor with dtype `dtype` and whose shape
|
||||
can be broadcast with `self.probs` and `self.total_count`. `value` is only legal
|
||||
if it is less than or equal to `self.total_count` and its components are equal
|
||||
to integer values.
|
||||
|
||||
##### Args:
|
||||
|
||||
@ -608,6 +597,13 @@ values.
|
||||
values of type `self.dtype`.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Binomial.probs` {#Binomial.probs}
|
||||
|
||||
Probability of of drawing a `1`.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Binomial.reparameterization_type` {#Binomial.reparameterization_type}
|
||||
@ -678,6 +674,13 @@ survival_function(x) = P[X > x]
|
||||
`self.dtype`.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Binomial.total_count` {#Binomial.total_count}
|
||||
|
||||
Number of trials.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Binomial.validate_args` {#Binomial.validate_args}
|
||||
|
||||
@ -1,35 +1,49 @@
|
||||
Multinomial distribution.
|
||||
|
||||
This distribution is parameterized by a vector `p` of probability
|
||||
parameters for `k` classes and `n`, the counts per each class..
|
||||
This Multinomial distribution is parameterized by `probs`, a (batch of)
|
||||
length-`k` `prob` (probability) vectors (`k > 1`) such that
|
||||
`tf.reduce_sum(probs, -1) = 1`, and a `total_count` number of trials, i.e.,
|
||||
the number of trials per draw from the Multinomial. It is defined over a
|
||||
(batch of) length-`k` vector `counts` such that
|
||||
`tf.reduce_sum(counts, -1) = total_count`. The Multinomial is identically the
|
||||
Binomial distribution when `k = 2`.
|
||||
|
||||
#### Mathematical details
|
||||
#### Mathematical Details
|
||||
|
||||
The Multinomial is a distribution over k-class count data, meaning
|
||||
for each k-tuple of non-negative integer `counts = [n_1,...,n_k]`, we have a
|
||||
probability of these draws being made from the distribution. The distribution
|
||||
has hyperparameters `p = (p_1,...,p_k)`, and probability mass
|
||||
function (pmf):
|
||||
The Multinomial is a distribution over `k`-class counts, i.e., a length-`k`
|
||||
vector of non-negative integer `counts = n = [n_0, ..., n_{k-1}]`.
|
||||
|
||||
```pmf(counts) = n! / (n_1!...n_k!) * (p_1)^n_1*(p_2)^n_2*...(p_k)^n_k```
|
||||
The probability mass function (pmf) is,
|
||||
|
||||
where above `n = sum_j n_j`, `n!` is `n` factorial.
|
||||
```none
|
||||
pmf(n; pi, N) = prod_j (pi_j)**n_j / Z
|
||||
Z = (prod_j n_j!) / N!
|
||||
```
|
||||
|
||||
where:
|
||||
* `probs = pi = [pi_0, ..., pi_{k-1}]`, `pi_j > 0`, `sum_j pi_j = 1`,
|
||||
* `total_count = N`, `N` a positive integer,
|
||||
* `Z` is the normalization constant, and,
|
||||
* `N!` denotes `N` factorial.
|
||||
|
||||
Distribution parameters are automatically broadcast in all functions; see
|
||||
examples for details.
|
||||
|
||||
#### Examples
|
||||
|
||||
Create a 3-class distribution, with the 3rd class is most likely to be drawn,
|
||||
using logits..
|
||||
using logits.
|
||||
|
||||
```python
|
||||
logits = [-50., -43, 0]
|
||||
dist = Multinomial(n=4., logits=logits)
|
||||
dist = Multinomial(total_count=4., logits=logits)
|
||||
```
|
||||
|
||||
Create a 3-class distribution, with the 3rd class is most likely to be drawn.
|
||||
|
||||
```python
|
||||
p = [.2, .3, .5]
|
||||
dist = Multinomial(n=4., p=p)
|
||||
dist = Multinomial(total_count=4., probs=p)
|
||||
```
|
||||
|
||||
The distribution functions can be evaluated on counts.
|
||||
@ -52,54 +66,43 @@ Create a 2-batch of 3-class distributions.
|
||||
|
||||
```python
|
||||
p = [[.1, .2, .7], [.3, .3, .4]] # Shape [2, 3]
|
||||
dist = Multinomial(n=[4., 5], p=p)
|
||||
dist = Multinomial(total_count=[4., 5], probs=p)
|
||||
|
||||
counts = [[2., 1, 1], [3, 1, 1]]
|
||||
dist.prob(counts) # Shape [2]
|
||||
```
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Multinomial.__init__(n, logits=None, p=None, validate_args=False, allow_nan_stats=True, name='Multinomial')` {#Multinomial.__init__}
|
||||
#### `tf.contrib.distributions.Multinomial.__init__(total_count, logits=None, probs=None, validate_args=False, allow_nan_stats=True, name='Multinomial')` {#Multinomial.__init__}
|
||||
|
||||
Initialize a batch of Multinomial distributions.
|
||||
|
||||
##### Args:
|
||||
|
||||
|
||||
* <b>`n`</b>: Non-negative floating point tensor with shape broadcastable to
|
||||
`[N1,..., Nm]` with `m >= 0`. Defines this as a batch of
|
||||
* <b>`total_count`</b>: Non-negative floating point tensor with shape broadcastable
|
||||
to `[N1,..., Nm]` with `m >= 0`. Defines this as a batch of
|
||||
`N1 x ... x Nm` different Multinomial distributions. Its components
|
||||
should be equal to integer values.
|
||||
* <b>`logits`</b>: Floating point tensor representing the log-odds of a
|
||||
positive event with shape broadcastable to `[N1,..., Nm, k], m >= 0`,
|
||||
and the same dtype as `n`. Defines this as a batch of `N1 x ... x Nm`
|
||||
different `k` class Multinomial distributions. Only one of `logits` or
|
||||
`p` should be passed in.
|
||||
* <b>`p`</b>: Positive floating point tensor with shape broadcastable to
|
||||
`[N1,..., Nm, k]` `m >= 0` and same dtype as `n`. Defines this as
|
||||
a batch of `N1 x ... x Nm` different `k` class Multinomial
|
||||
distributions. `p`'s components in the last portion of its shape should
|
||||
sum up to 1. Only one of `logits` or `p` should be passed in.
|
||||
* <b>`validate_args`</b>: `Boolean`, default `False`. Whether to assert valid
|
||||
values for parameters `n` and `p`, and `x` in `prob` and `log_prob`.
|
||||
If `False`, correct behavior is not guaranteed.
|
||||
* <b>`allow_nan_stats`</b>: `Boolean`, default `True`. If `False`, raise an
|
||||
exception if a statistic (e.g. mean/mode/etc...) is undefined for any
|
||||
batch member. 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.
|
||||
|
||||
|
||||
* <b>`Examples`</b>:
|
||||
|
||||
```python
|
||||
# Define 1-batch of 2-class multinomial distribution,
|
||||
# also known as a Binomial distribution.
|
||||
dist = Multinomial(n=2., p=[.1, .9])
|
||||
|
||||
# Define a 2-batch of 3-class distributions.
|
||||
dist = Multinomial(n=[4., 5], p=[[.1, .3, .6], [.4, .05, .55]])
|
||||
```
|
||||
and the same dtype as `total_count`. Defines this as a batch of
|
||||
`N1 x ... x Nm` different `k` class Multinomial distributions. Only one
|
||||
of `logits` or `probs` should be passed in.
|
||||
* <b>`probs`</b>: Positive floating point tensor with shape broadcastable to
|
||||
`[N1,..., Nm, k]` `m >= 0` and same dtype as `total_count`. Defines
|
||||
this as a batch of `N1 x ... x Nm` different `k` class Multinomial
|
||||
distributions. `probs`'s components in the last portion of its shape
|
||||
should sum to `1`. Only one of `logits` or `probs` should be passed in.
|
||||
* <b>`validate_args`</b>: Python `Boolean`, default `False`. When `True` distribution
|
||||
parameters are checked for validity despite possibly degrading runtime
|
||||
performance. When `False` invalid inputs may silently render incorrect
|
||||
outputs.
|
||||
* <b>`allow_nan_stats`</b>: Python `Boolean`, default `True`. When `True`, statistics
|
||||
(e.g., mean, mode, variance) use the value "`NaN`" to indicate the
|
||||
result is undefined. When `False`, an exception is raised if one or
|
||||
more of the statistic's batch members are undefined.
|
||||
* <b>`name`</b>: `String` name prefixed to Ops created by this class.
|
||||
|
||||
|
||||
- - -
|
||||
@ -377,17 +380,18 @@ Log probability density/mass function (depending on `is_continuous`).
|
||||
|
||||
Additional documentation from `Multinomial`:
|
||||
|
||||
For each batch of counts `[n_1,...,n_k]`, `P[counts]` is the probability
|
||||
that after sampling `n` draws from this Multinomial distribution, the
|
||||
number of draws falling in class `j` is `n_j`. Note that different
|
||||
sequences of draws can result in the same counts, thus the probability
|
||||
includes a combinatorial coefficient.
|
||||
For each batch of counts, `value = [n_0, ...
|
||||
,n_{k-1}]`, `P[value]` is the probability that after sampling `self.total_count`
|
||||
draws from this Multinomial distribution, the number of draws falling in class
|
||||
`j` is `n_j`. Since this definition is [exchangeable](
|
||||
https://en.wikipedia.org/wiki/Exchangeable_random_variables); different
|
||||
sequences have the same counts so the probability includes a combinatorial
|
||||
coefficient.
|
||||
|
||||
Note that input "counts" must be a non-negative tensor with dtype `dtype`
|
||||
and whose shape can be broadcast with `self.p` and `self.n`. For fixed
|
||||
leading dimensions, the last dimension represents counts for the
|
||||
corresponding Multinomial distribution in `self.p`. `counts` is only legal
|
||||
if it sums up to `n` and its components are equal to integer values.
|
||||
Note: `value` must be a non-negative tensor with dtype `self.dtype`, have no
|
||||
fractional components, and such that
|
||||
`tf.reduce_sum(value, -1) = self.total_count`. Its shape must be broadcastable
|
||||
with `self.probs` and `self.total_count`.
|
||||
|
||||
##### Args:
|
||||
|
||||
@ -452,13 +456,6 @@ Mean.
|
||||
Mode.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Multinomial.n` {#Multinomial.n}
|
||||
|
||||
Number of trials.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Multinomial.name` {#Multinomial.name}
|
||||
@ -466,15 +463,6 @@ Number of trials.
|
||||
Name prepended to all ops created by this `Distribution`.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Multinomial.p` {#Multinomial.p}
|
||||
|
||||
Vector of probabilities summing to one.
|
||||
|
||||
Each element is the probability of drawing that coordinate.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Multinomial.param_shapes(cls, sample_shape, name='DistributionParamShapes')` {#Multinomial.param_shapes}
|
||||
@ -593,17 +581,18 @@ Probability density/mass function (depending on `is_continuous`).
|
||||
|
||||
Additional documentation from `Multinomial`:
|
||||
|
||||
For each batch of counts `[n_1,...,n_k]`, `P[counts]` is the probability
|
||||
that after sampling `n` draws from this Multinomial distribution, the
|
||||
number of draws falling in class `j` is `n_j`. Note that different
|
||||
sequences of draws can result in the same counts, thus the probability
|
||||
includes a combinatorial coefficient.
|
||||
For each batch of counts, `value = [n_0, ...
|
||||
,n_{k-1}]`, `P[value]` is the probability that after sampling `self.total_count`
|
||||
draws from this Multinomial distribution, the number of draws falling in class
|
||||
`j` is `n_j`. Since this definition is [exchangeable](
|
||||
https://en.wikipedia.org/wiki/Exchangeable_random_variables); different
|
||||
sequences have the same counts so the probability includes a combinatorial
|
||||
coefficient.
|
||||
|
||||
Note that input "counts" must be a non-negative tensor with dtype `dtype`
|
||||
and whose shape can be broadcast with `self.p` and `self.n`. For fixed
|
||||
leading dimensions, the last dimension represents counts for the
|
||||
corresponding Multinomial distribution in `self.p`. `counts` is only legal
|
||||
if it sums up to `n` and its components are equal to integer values.
|
||||
Note: `value` must be a non-negative tensor with dtype `self.dtype`, have no
|
||||
fractional components, and such that
|
||||
`tf.reduce_sum(value, -1) = self.total_count`. Its shape must be broadcastable
|
||||
with `self.probs` and `self.total_count`.
|
||||
|
||||
##### Args:
|
||||
|
||||
@ -618,6 +607,15 @@ if it sums up to `n` and its components are equal to integer values.
|
||||
values of type `self.dtype`.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Multinomial.probs` {#Multinomial.probs}
|
||||
|
||||
Vector of probabilities summing to one.
|
||||
|
||||
Each element is the probability of drawing that coordinate.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Multinomial.reparameterization_type` {#Multinomial.reparameterization_type}
|
||||
@ -688,6 +686,13 @@ survival_function(x) = P[X > x]
|
||||
`self.dtype`.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Multinomial.total_count` {#Multinomial.total_count}
|
||||
|
||||
Number of trials used to construct a sample.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.Multinomial.validate_args` {#Multinomial.validate_args}
|
||||
|
||||
@ -1,14 +1,14 @@
|
||||
Bernoulli with `p = sigmoid(p)`.
|
||||
Bernoulli with `probs = nn.sigmoid(logits)`.
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.__init__(p=None, dtype=tf.int32, validate_args=False, allow_nan_stats=True, name='BernoulliWithSigmoidP')` {#BernoulliWithSigmoidP.__init__}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.__init__(logits=None, dtype=tf.int32, validate_args=False, allow_nan_stats=True, name='BernoulliWithSigmoidProbs')` {#BernoulliWithSigmoidProbs.__init__}
|
||||
|
||||
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.allow_nan_stats` {#BernoulliWithSigmoidP.allow_nan_stats}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.allow_nan_stats` {#BernoulliWithSigmoidProbs.allow_nan_stats}
|
||||
|
||||
Python boolean describing behavior when a stat is undefined.
|
||||
|
||||
@ -29,7 +29,7 @@ undefined.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.batch_shape(name='batch_shape')` {#BernoulliWithSigmoidP.batch_shape}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.batch_shape(name='batch_shape')` {#BernoulliWithSigmoidProbs.batch_shape}
|
||||
|
||||
Shape of a single sample from a single event index as a 1-D `Tensor`.
|
||||
|
||||
@ -49,7 +49,7 @@ independent distributions of this kind the instance represents.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.cdf(value, name='cdf')` {#BernoulliWithSigmoidP.cdf}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.cdf(value, name='cdf')` {#BernoulliWithSigmoidProbs.cdf}
|
||||
|
||||
Cumulative distribution function.
|
||||
|
||||
@ -74,7 +74,7 @@ cdf(x) := P[X <= x]
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.copy(**override_parameters_kwargs)` {#BernoulliWithSigmoidP.copy}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.copy(**override_parameters_kwargs)` {#BernoulliWithSigmoidProbs.copy}
|
||||
|
||||
Creates a deep copy of the distribution.
|
||||
|
||||
@ -97,21 +97,21 @@ intialization arguments.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.dtype` {#BernoulliWithSigmoidP.dtype}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.dtype` {#BernoulliWithSigmoidProbs.dtype}
|
||||
|
||||
The `DType` of `Tensor`s handled by this `Distribution`.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.entropy(name='entropy')` {#BernoulliWithSigmoidP.entropy}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.entropy(name='entropy')` {#BernoulliWithSigmoidProbs.entropy}
|
||||
|
||||
Shannon entropy in nats.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.event_shape(name='event_shape')` {#BernoulliWithSigmoidP.event_shape}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.event_shape(name='event_shape')` {#BernoulliWithSigmoidProbs.event_shape}
|
||||
|
||||
Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
|
||||
|
||||
@ -128,7 +128,7 @@ Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.get_batch_shape()` {#BernoulliWithSigmoidP.get_batch_shape}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.get_batch_shape()` {#BernoulliWithSigmoidProbs.get_batch_shape}
|
||||
|
||||
Shape of a single sample from a single event index as a `TensorShape`.
|
||||
|
||||
@ -142,7 +142,7 @@ Same meaning as `batch_shape`. May be only partially defined.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.get_event_shape()` {#BernoulliWithSigmoidP.get_event_shape}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.get_event_shape()` {#BernoulliWithSigmoidProbs.get_event_shape}
|
||||
|
||||
Shape of a single sample from a single batch as a `TensorShape`.
|
||||
|
||||
@ -156,14 +156,14 @@ Same meaning as `event_shape`. May be only partially defined.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.is_continuous` {#BernoulliWithSigmoidP.is_continuous}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.is_continuous` {#BernoulliWithSigmoidProbs.is_continuous}
|
||||
|
||||
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.is_scalar_batch(name='is_scalar_batch')` {#BernoulliWithSigmoidP.is_scalar_batch}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.is_scalar_batch(name='is_scalar_batch')` {#BernoulliWithSigmoidProbs.is_scalar_batch}
|
||||
|
||||
Indicates that `batch_shape == []`.
|
||||
|
||||
@ -180,7 +180,7 @@ Indicates that `batch_shape == []`.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.is_scalar_event(name='is_scalar_event')` {#BernoulliWithSigmoidP.is_scalar_event}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.is_scalar_event(name='is_scalar_event')` {#BernoulliWithSigmoidProbs.is_scalar_event}
|
||||
|
||||
Indicates that `event_shape == []`.
|
||||
|
||||
@ -197,7 +197,7 @@ Indicates that `event_shape == []`.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_cdf(value, name='log_cdf')` {#BernoulliWithSigmoidP.log_cdf}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.log_cdf(value, name='log_cdf')` {#BernoulliWithSigmoidProbs.log_cdf}
|
||||
|
||||
Log cumulative distribution function.
|
||||
|
||||
@ -226,7 +226,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_pdf(value, name='log_pdf')` {#BernoulliWithSigmoidP.log_pdf}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.log_pdf(value, name='log_pdf')` {#BernoulliWithSigmoidProbs.log_pdf}
|
||||
|
||||
Log probability density function.
|
||||
|
||||
@ -250,7 +250,7 @@ Log probability density function.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_pmf(value, name='log_pmf')` {#BernoulliWithSigmoidP.log_pmf}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.log_pmf(value, name='log_pmf')` {#BernoulliWithSigmoidProbs.log_pmf}
|
||||
|
||||
Log probability mass function.
|
||||
|
||||
@ -274,7 +274,7 @@ Log probability mass function.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_prob(value, name='log_prob')` {#BernoulliWithSigmoidP.log_prob}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.log_prob(value, name='log_prob')` {#BernoulliWithSigmoidProbs.log_prob}
|
||||
|
||||
Log probability density/mass function (depending on `is_continuous`).
|
||||
|
||||
@ -293,7 +293,7 @@ Log probability density/mass function (depending on `is_continuous`).
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_survival_function(value, name='log_survival_function')` {#BernoulliWithSigmoidP.log_survival_function}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.log_survival_function(value, name='log_survival_function')` {#BernoulliWithSigmoidProbs.log_survival_function}
|
||||
|
||||
Log survival function.
|
||||
|
||||
@ -322,46 +322,39 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.logits` {#BernoulliWithSigmoidP.logits}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.logits` {#BernoulliWithSigmoidProbs.logits}
|
||||
|
||||
Log-odds of success.
|
||||
Log-odds of a `1` outcome (vs `0`).
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.mean(name='mean')` {#BernoulliWithSigmoidP.mean}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.mean(name='mean')` {#BernoulliWithSigmoidProbs.mean}
|
||||
|
||||
Mean.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.mode(name='mode')` {#BernoulliWithSigmoidP.mode}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.mode(name='mode')` {#BernoulliWithSigmoidProbs.mode}
|
||||
|
||||
Mode.
|
||||
|
||||
Additional documentation from `Bernoulli`:
|
||||
|
||||
Returns `1` if `p > 1-p` and `0` otherwise.
|
||||
Returns `1` if `prob > 0.5` and `0` otherwise.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.name` {#BernoulliWithSigmoidP.name}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.name` {#BernoulliWithSigmoidProbs.name}
|
||||
|
||||
Name prepended to all ops created by this `Distribution`.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.p` {#BernoulliWithSigmoidP.p}
|
||||
|
||||
Probability of success.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.param_shapes(cls, sample_shape, name='DistributionParamShapes')` {#BernoulliWithSigmoidP.param_shapes}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.param_shapes(cls, sample_shape, name='DistributionParamShapes')` {#BernoulliWithSigmoidProbs.param_shapes}
|
||||
|
||||
Shapes of parameters given the desired shape of a call to `sample()`.
|
||||
|
||||
@ -385,7 +378,7 @@ Subclasses should override class method `_param_shapes`.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.param_static_shapes(cls, sample_shape)` {#BernoulliWithSigmoidP.param_static_shapes}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.param_static_shapes(cls, sample_shape)` {#BernoulliWithSigmoidProbs.param_static_shapes}
|
||||
|
||||
param_shapes with static (i.e. `TensorShape`) shapes.
|
||||
|
||||
@ -415,14 +408,14 @@ constant-valued tensors when constant values are fed.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.parameters` {#BernoulliWithSigmoidP.parameters}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.parameters` {#BernoulliWithSigmoidProbs.parameters}
|
||||
|
||||
Dictionary of parameters used to instantiate this `Distribution`.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.pdf(value, name='pdf')` {#BernoulliWithSigmoidP.pdf}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.pdf(value, name='pdf')` {#BernoulliWithSigmoidProbs.pdf}
|
||||
|
||||
Probability density function.
|
||||
|
||||
@ -446,7 +439,7 @@ Probability density function.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.pmf(value, name='pmf')` {#BernoulliWithSigmoidP.pmf}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.pmf(value, name='pmf')` {#BernoulliWithSigmoidProbs.pmf}
|
||||
|
||||
Probability mass function.
|
||||
|
||||
@ -470,7 +463,7 @@ Probability mass function.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.prob(value, name='prob')` {#BernoulliWithSigmoidP.prob}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.prob(value, name='prob')` {#BernoulliWithSigmoidProbs.prob}
|
||||
|
||||
Probability density/mass function (depending on `is_continuous`).
|
||||
|
||||
@ -489,14 +482,14 @@ Probability density/mass function (depending on `is_continuous`).
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.q` {#BernoulliWithSigmoidP.q}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.probs` {#BernoulliWithSigmoidProbs.probs}
|
||||
|
||||
1-p.
|
||||
Probability of a `1` outcome (vs `0`).
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.reparameterization_type` {#BernoulliWithSigmoidP.reparameterization_type}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.reparameterization_type` {#BernoulliWithSigmoidProbs.reparameterization_type}
|
||||
|
||||
Describes how samples from the distribution are reparameterized.
|
||||
|
||||
@ -511,7 +504,7 @@ or `distributions.NOT_REPARAMETERIZED`.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.sample(sample_shape=(), seed=None, name='sample')` {#BernoulliWithSigmoidP.sample}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.sample(sample_shape=(), seed=None, name='sample')` {#BernoulliWithSigmoidProbs.sample}
|
||||
|
||||
Generate samples of the specified shape.
|
||||
|
||||
@ -533,14 +526,14 @@ sample.
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.stddev(name='stddev')` {#BernoulliWithSigmoidP.stddev}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.stddev(name='stddev')` {#BernoulliWithSigmoidProbs.stddev}
|
||||
|
||||
Standard deviation.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.survival_function(value, name='survival_function')` {#BernoulliWithSigmoidP.survival_function}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.survival_function(value, name='survival_function')` {#BernoulliWithSigmoidProbs.survival_function}
|
||||
|
||||
Survival function.
|
||||
|
||||
@ -566,14 +559,14 @@ survival_function(x) = P[X > x]
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.validate_args` {#BernoulliWithSigmoidP.validate_args}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.validate_args` {#BernoulliWithSigmoidProbs.validate_args}
|
||||
|
||||
Python boolean indicated possibly expensive checks are enabled.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidP.variance(name='variance')` {#BernoulliWithSigmoidP.variance}
|
||||
#### `tf.contrib.distributions.BernoulliWithSigmoidProbs.variance(name='variance')` {#BernoulliWithSigmoidProbs.variance}
|
||||
|
||||
Variance.
|
||||
|
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@ -738,7 +738,7 @@
|
||||
|
||||
* **[Statistical Distributions (contrib)](../../api_docs/python/contrib.distributions.md)**:
|
||||
* [`Bernoulli`](../../api_docs/python/contrib.distributions.md#Bernoulli)
|
||||
* [`BernoulliWithSigmoidP`](../../api_docs/python/contrib.distributions.md#BernoulliWithSigmoidP)
|
||||
* [`BernoulliWithSigmoidProbs`](../../api_docs/python/contrib.distributions.md#BernoulliWithSigmoidProbs)
|
||||
* [`Beta`](../../api_docs/python/contrib.distributions.md#Beta)
|
||||
* [`BetaWithSoftplusAB`](../../api_docs/python/contrib.distributions.md#BetaWithSoftplusAB)
|
||||
* [`Binomial`](../../api_docs/python/contrib.distributions.md#Binomial)
|
||||
|
||||
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Block a user