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Update generated Python Op docs.

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A. Unique TensorFlower 2016-09-22 09:09:41 -08:00 committed by TensorFlower Gardener
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55
tensorflow/g3doc/api_docs/python/contrib.distributions.md
View File

@ -14285,45 +14285,17 @@ mixture probabilities) and a list of `Distribution` objects
all having matching dtype, batch shape, event shape, and continuity
properties (the components).
The user does not pass the list of distributions directly, but rather a
list of `(constructor, batch_tensor_params_dict)` pairs,
called `components`. The list of distributions is created via:
```python
distributions = [
c(**params_dict) for (c, params_dict) in zip(*components)
]
```
This form allows for certain types of batch-shape optimizations within
this class.
An example of `components`:
```python
components = [
(tf.contrib.distributions.Normal, {"mu": 3.0, "sigma": 1.0}),
(functools.partial(tf.contrib.distributions.Normal, validate_args=False),
{"mu": 3.0, "sigma": 2.0}),
(tf.contrib.distributions.Normal.from_params,
{"mu": 1.0, "sigma": -1.0})
]
```
The `num_classes` of `cat` must be possible to infer at graph construction
time and match `len(distributions)`.
time and match `len(components)`.
##### Args:
* <b>`cat`</b>: A `Categorical` distribution instance, representing the probabilities
of `distributions`.
* <b>`components`</b>: A list or tuple of `(constructor, batch_tensor_params)`
tuples. The `constructor` must be a callable, and `batch_tensor_params`
must be a dict mapping constructor kwargs to batchwise parameters.
Each `Distribution` instance created by calling
`constructor(**batch_tensor_params)` must have the same type, be defined
on the same domain, and have matching `event_shape` and `batch_shape`.
* <b>`components`</b>: A list or tuple of `Distribution` instances.
Each instance must have the same type, be defined on the same domain,
and have matching `event_shape` and `batch_shape`.
* <b>`validate_args`</b>: `Boolean`, default `False`. If `True`, raise a runtime
error if batch or event ranks are inconsistent between cat and any of
the distributions. This is only checked if the ranks cannot be
@ -14339,16 +14311,13 @@ time and match `len(distributions)`.
* <b>`TypeError`</b>: If cat is not a `Categorical`, or `components` is not
a list or tuple, or the elements of `components` are not
tuples of the form `(callable, dict)`, or the objects resulting
from calling `callable(**dict)` are not instances of `Distribution`, or
the resulting instances of `Distribution` do not have matching
continuity properties, or do not have matching `dtype`.
* <b>`ValueError`</b>: If `components` is an empty list or tuple, or the
distributions created from `components` do have a statically known event
rank. If `cat.num_classes` cannot be inferred at graph creation time,
instances of `Distribution`, or do not have matching `dtype`.
* <b>`ValueError`</b>: If `components` is an empty list or tuple, or its
elements do not have a statically known event rank.
If `cat.num_classes` cannot be inferred at graph creation time,
or the constant value of `cat.num_classes` is not equal to
`len(distributions)`, or all `distributions` and `cat` do not have
matching static batch shapes, or all components' distributions do not
`len(components)`, or all `components` and `cat` do not have
matching static batch shapes, or all components do not
have matching static event shapes.
@ -14427,7 +14396,7 @@ cdf(x) := P[X <= x]
- - -
#### `tf.contrib.distributions.Mixture.distributions` {#Mixture.distributions}
#### `tf.contrib.distributions.Mixture.components` {#Mixture.components}
@ -14453,7 +14422,7 @@ Shanon entropy in nats.
A lower bound on the entropy of this mixture model.
The bound below is not always very tight, and its usefulness depends
on the mixture probabilities and the distributions in use.
on the mixture probabilities and the components in use.
A lower bound is useful for ELBO when the `Mixture` is the variational
distribution:

55
tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.Mixture.md
View File

@ -17,45 +17,17 @@ mixture probabilities) and a list of `Distribution` objects
all having matching dtype, batch shape, event shape, and continuity
properties (the components).
The user does not pass the list of distributions directly, but rather a
list of `(constructor, batch_tensor_params_dict)` pairs,
called `components`. The list of distributions is created via:
```python
distributions = [
c(**params_dict) for (c, params_dict) in zip(*components)
]
```
This form allows for certain types of batch-shape optimizations within
this class.
An example of `components`:
```python
components = [
(tf.contrib.distributions.Normal, {"mu": 3.0, "sigma": 1.0}),
(functools.partial(tf.contrib.distributions.Normal, validate_args=False),
{"mu": 3.0, "sigma": 2.0}),
(tf.contrib.distributions.Normal.from_params,
{"mu": 1.0, "sigma": -1.0})
]
```
The `num_classes` of `cat` must be possible to infer at graph construction
time and match `len(distributions)`.
time and match `len(components)`.
##### Args:
* <b>`cat`</b>: A `Categorical` distribution instance, representing the probabilities
of `distributions`.
* <b>`components`</b>: A list or tuple of `(constructor, batch_tensor_params)`
tuples. The `constructor` must be a callable, and `batch_tensor_params`
must be a dict mapping constructor kwargs to batchwise parameters.
Each `Distribution` instance created by calling
`constructor(**batch_tensor_params)` must have the same type, be defined
on the same domain, and have matching `event_shape` and `batch_shape`.
* <b>`components`</b>: A list or tuple of `Distribution` instances.
Each instance must have the same type, be defined on the same domain,
and have matching `event_shape` and `batch_shape`.
* <b>`validate_args`</b>: `Boolean`, default `False`. If `True`, raise a runtime
error if batch or event ranks are inconsistent between cat and any of
the distributions. This is only checked if the ranks cannot be
@ -71,16 +43,13 @@ time and match `len(distributions)`.
* <b>`TypeError`</b>: If cat is not a `Categorical`, or `components` is not
a list or tuple, or the elements of `components` are not
tuples of the form `(callable, dict)`, or the objects resulting
from calling `callable(**dict)` are not instances of `Distribution`, or
the resulting instances of `Distribution` do not have matching
continuity properties, or do not have matching `dtype`.
* <b>`ValueError`</b>: If `components` is an empty list or tuple, or the
distributions created from `components` do have a statically known event
rank. If `cat.num_classes` cannot be inferred at graph creation time,
instances of `Distribution`, or do not have matching `dtype`.
* <b>`ValueError`</b>: If `components` is an empty list or tuple, or its
elements do not have a statically known event rank.
If `cat.num_classes` cannot be inferred at graph creation time,
or the constant value of `cat.num_classes` is not equal to
`len(distributions)`, or all `distributions` and `cat` do not have
matching static batch shapes, or all components' distributions do not
`len(components)`, or all `components` and `cat` do not have
matching static batch shapes, or all components do not
have matching static event shapes.
@ -159,7 +128,7 @@ cdf(x) := P[X <= x]
- - -
#### `tf.contrib.distributions.Mixture.distributions` {#Mixture.distributions}
#### `tf.contrib.distributions.Mixture.components` {#Mixture.components}
@ -185,7 +154,7 @@ Shanon entropy in nats.
A lower bound on the entropy of this mixture model.
The bound below is not always very tight, and its usefulness depends
on the mixture probabilities and the distributions in use.
on the mixture probabilities and the components in use.
A lower bound is useful for ELBO when the `Mixture` is the variational
distribution: