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Update generated Python Op docs.
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tensorflower-gardener committed Dec 6, 2016
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240 changes: 27 additions & 213 deletions tensorflow/g3doc/api_docs/python/contrib.learn.md
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Expand Up @@ -1068,9 +1068,9 @@ altogether. The behavior of this flag is described below.

##### Returns:

Numpy array of predicted classes (or an iterable of predicted classes if
as_iterable is True). Each predicted class is represented by its class
index (i.e. integer from 0 to n_classes-1).
Numpy array of predicted classes with shape [batch_size] (or an iterable
of predicted classes if as_iterable is True). Each predicted class is
represented by its class index (i.e. integer from 0 to n_classes-1).


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Expand Down Expand Up @@ -1098,9 +1098,8 @@ altogether. The behavior of this flag is described below.

##### Returns:

Numpy array of predicted probabilities (or an iterable of predicted
probabilities if as_iterable is True). Each predicted class is represented
by its class index (i.e. integer from 0 to n_classes-1).
Numpy array of predicted probabilities with shape [batch_size, n_classes]
(or an iterable of predicted probabilities if as_iterable is True).


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Expand Down Expand Up @@ -1172,7 +1171,7 @@ Input of `fit` and `evaluate` should have following features,
whose `value` is a `Tensor`.
- - -

#### `tf.contrib.learn.DNNRegressor.__init__(hidden_units, feature_columns, model_dir=None, weight_column_name=None, optimizer=None, activation_fn=relu, dropout=None, gradient_clip_norm=None, enable_centered_bias=False, config=None, feature_engineering_fn=None, label_dimension=1)` {#DNNRegressor.__init__}
#### `tf.contrib.learn.DNNRegressor.__init__(hidden_units, feature_columns, model_dir=None, weight_column_name=None, optimizer=None, activation_fn=relu, dropout=None, gradient_clip_norm=None, enable_centered_bias=False, config=None, feature_engineering_fn=None, label_dimension=1, embedding_lr_multipliers=None)` {#DNNRegressor.__init__}

Initializes a `DNNRegressor` instance.

Expand Down Expand Up @@ -1209,30 +1208,15 @@ Initializes a `DNNRegressor` instance.
returns features and labels which will be fed
into the model.
* <b>`label_dimension`</b>: Dimension of the label for multilabels. Defaults to 1.
* <b>`embedding_lr_multipliers`</b>: Optional. A dictionary from `EbeddingColumn` to
a `float` multiplier. Multiplier will be used to multiply with
learning rate for the embedding variables.

##### Returns:

A `DNNRegressor` estimator.


- - -

#### `tf.contrib.learn.DNNRegressor.__repr__()` {#DNNRegressor.__repr__}




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#### `tf.contrib.learn.DNNRegressor.bias_` {#DNNRegressor.bias_}

DEPRECATED FUNCTION

THIS FUNCTION IS DEPRECATED. It will be removed after 2016-10-30.
Instructions for updating:
This method will be removed after the deprecation date. To inspect variables, use get_variable_names() and get_variable_value().


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#### `tf.contrib.learn.DNNRegressor.config` {#DNNRegressor.config}
Expand All @@ -1242,98 +1226,23 @@ This method will be removed after the deprecation date. To inspect variables, us

- - -

#### `tf.contrib.learn.DNNRegressor.dnn_bias_` {#DNNRegressor.dnn_bias_}

Returns bias of deep neural network part. (deprecated)

THIS FUNCTION IS DEPRECATED. It will be removed after 2016-10-30.
Instructions for updating:
This method will be removed after the deprecation date. To inspect variables, use get_variable_names() and get_variable_value().


- - -

#### `tf.contrib.learn.DNNRegressor.dnn_weights_` {#DNNRegressor.dnn_weights_}

Returns weights of deep neural network part. (deprecated)

THIS FUNCTION IS DEPRECATED. It will be removed after 2016-10-30.
Instructions for updating:
This method will be removed after the deprecation date. To inspect variables, use get_variable_names() and get_variable_value().


- - -

#### `tf.contrib.learn.DNNRegressor.evaluate(*args, **kwargs)` {#DNNRegressor.evaluate}

See `Evaluable`. (deprecated arguments)

SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.

##### Example conversion:

est = Estimator(...) -> est = SKCompat(Estimator(...))

##### Raises:


* <b>`ValueError`</b>: If at least one of `x` or `y` is provided, and at least one of
`input_fn` or `feed_fn` is provided.
Or if `metrics` is not `None` or `dict`.


- - -

#### `tf.contrib.learn.DNNRegressor.export(export_dir, input_fn=None, input_feature_key=None, use_deprecated_input_fn=True, signature_fn=None, default_batch_size=None, exports_to_keep=None)` {#DNNRegressor.export}

#### `tf.contrib.learn.DNNRegressor.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None, checkpoint_path=None)` {#DNNRegressor.evaluate}

See evaluable.Evaluable.


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#### `tf.contrib.learn.DNNRegressor.fit(*args, **kwargs)` {#DNNRegressor.fit}

See `Trainable`. (deprecated arguments)

SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.

##### Example conversion:

est = Estimator(...) -> est = SKCompat(Estimator(...))

##### Raises:

#### `tf.contrib.learn.DNNRegressor.export(export_dir, input_fn=None, input_feature_key=None, use_deprecated_input_fn=True, signature_fn=None, default_batch_size=1, exports_to_keep=None)` {#DNNRegressor.export}

* <b>`ValueError`</b>: If `x` or `y` are not `None` while `input_fn` is not `None`.
* <b>`ValueError`</b>: If both `steps` and `max_steps` are not `None`.
See BaseEstimator.export.


- - -

#### `tf.contrib.learn.DNNRegressor.get_params(deep=True)` {#DNNRegressor.get_params}

Get parameters for this estimator.

##### Args:


* <b>`deep`</b>: boolean, optional

If `True`, will return the parameters for this estimator and
contained subobjects that are estimators.
#### `tf.contrib.learn.DNNRegressor.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None)` {#DNNRegressor.fit}

##### Returns:

params : mapping of string to any
Parameter names mapped to their values.
See trainable.Trainable.


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Expand All @@ -1360,29 +1269,7 @@ Returns value of the variable given by name.

##### Returns:

Numpy array - value of the tensor.


- - -

#### `tf.contrib.learn.DNNRegressor.linear_bias_` {#DNNRegressor.linear_bias_}

Returns bias of the linear part. (deprecated)

THIS FUNCTION IS DEPRECATED. It will be removed after 2016-10-30.
Instructions for updating:
This method will be removed after the deprecation date. To inspect variables, use get_variable_names() and get_variable_value().


- - -

#### `tf.contrib.learn.DNNRegressor.linear_weights_` {#DNNRegressor.linear_weights_}

Returns weights per feature of the linear part. (deprecated)

THIS FUNCTION IS DEPRECATED. It will be removed after 2016-10-30.
Instructions for updating:
This method will be removed after the deprecation date. To inspect variables, use get_variable_names() and get_variable_value().
`Tensor` object.


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Expand All @@ -1392,107 +1279,34 @@ This method will be removed after the deprecation date. To inspect variables, us



- - -

#### `tf.contrib.learn.DNNRegressor.partial_fit(*args, **kwargs)` {#DNNRegressor.partial_fit}

Incremental fit on a batch of samples. (deprecated arguments)

SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.

##### Example conversion:

est = Estimator(...) -> est = SKCompat(Estimator(...))

This method is expected to be called several times consecutively
on different or the same chunks of the dataset. This either can
implement iterative training or out-of-core/online training.

This is especially useful when the whole dataset is too big to
fit in memory at the same time. Or when model is taking long time
to converge, and you want to split up training into subparts.

##### Args:


* <b>`x`</b>: Matrix of shape [n_samples, n_features...]. Can be iterator that
returns arrays of features. The training input samples for fitting the
model. If set, `input_fn` must be `None`.
* <b>`y`</b>: Vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
iterator that returns array of labels. The training label values
(class labels in classification, real numbers in regression). If set,
`input_fn` must be `None`.
* <b>`input_fn`</b>: Input function. If set, `x`, `y`, and `batch_size` must be
`None`.
* <b>`steps`</b>: Number of steps for which to train model. If `None`, train forever.
* <b>`batch_size`</b>: minibatch size to use on the input, defaults to first
dimension of `x`. Must be `None` if `input_fn` is provided.
* <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
inside the training loop.

##### Returns:

`self`, for chaining.

##### Raises:


* <b>`ValueError`</b>: If at least one of `x` and `y` is provided, and `input_fn` is
provided.


- - -

#### `tf.contrib.learn.DNNRegressor.predict(*args, **kwargs)` {#DNNRegressor.predict}

Runs inference to determine the predicted class. (deprecated arguments)
Returns predicted scores for given features. (deprecated arguments)

SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-09-15.
Instructions for updating:
The default behavior of predict() is changing. The default value for
as_iterable will change to True, and then the flag will be removed
altogether. The behavior of this flag is described below.


- - -

#### `tf.contrib.learn.DNNRegressor.set_params(**params)` {#DNNRegressor.set_params}

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects
(such as pipelines). The former have parameters of the form
``<component>__<parameter>`` so that it's possible to update each
component of a nested object.

##### Args:


* <b>`**params`</b>: Parameters.
* <b>`x`</b>: features.
* <b>`input_fn`</b>: Input function. If set, x must be None.
* <b>`batch_size`</b>: Override default batch size.
* <b>`as_iterable`</b>: If True, return an iterable which keeps yielding predictions
for each example until inputs are exhausted. Note: The inputs must
terminate if you want the iterable to terminate (e.g. be sure to pass
num_epochs=1 if you are using something like read_batch_features).

##### Returns:

self

##### Raises:


* <b>`ValueError`</b>: If params contain invalid names.


- - -

#### `tf.contrib.learn.DNNRegressor.weights_` {#DNNRegressor.weights_}

DEPRECATED FUNCTION

THIS FUNCTION IS DEPRECATED. It will be removed after 2016-10-30.
Instructions for updating:
This method will be removed after the deprecation date. To inspect variables, use get_variable_names() and get_variable_value().
Numpy array of predicted scores (or an iterable of predicted scores if
as_iterable is True). If `label_dimension == 1`, the shape of the output
is `[batch_size]`, otherwise the shape is `[batch_size, label_dimension]`.



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11 changes: 5 additions & 6 deletions .../api_docs/python/functions_and_classes/shard4/tf.contrib.learn.DNNClassifier.md
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Expand Up @@ -212,9 +212,9 @@ altogether. The behavior of this flag is described below.

##### Returns:

Numpy array of predicted classes (or an iterable of predicted classes if
as_iterable is True). Each predicted class is represented by its class
index (i.e. integer from 0 to n_classes-1).
Numpy array of predicted classes with shape [batch_size] (or an iterable
of predicted classes if as_iterable is True). Each predicted class is
represented by its class index (i.e. integer from 0 to n_classes-1).


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Expand Down Expand Up @@ -242,9 +242,8 @@ altogether. The behavior of this flag is described below.

##### Returns:

Numpy array of predicted probabilities (or an iterable of predicted
probabilities if as_iterable is True). Each predicted class is represented
by its class index (i.e. integer from 0 to n_classes-1).
Numpy array of predicted probabilities with shape [batch_size, n_classes]
(or an iterable of predicted probabilities if as_iterable is True).


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