Update generated Python Op docs.

Change: 139097110

3 files changed, 54 insertions, 180 deletions

diff --git a/tensorflow/g3doc/api_docs/python/contrib.learn.md b/tensorflow/g3doc/api_docs/python/contrib.learn.md
index c52b3436d3..6c23a336c8 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.learn.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.learn.md
@@ -322,13 +322,30 @@ Estimator class is the basic TensorFlow model trainer/evaluator.
 
 #### `tf.contrib.learn.Estimator.__init__(model_fn=None, model_dir=None, config=None, params=None, feature_engineering_fn=None)` {#Estimator.__init__}
 
-Constructs an Estimator instance.
+Constructs an `Estimator` instance.
 
 ##### Args:
 
 
-*  <b>`model_fn`</b>: Model function, takes features and labels tensors or dicts of
-            tensors and returns tuple of:
+*  <b>`model_fn`</b>: Model function. Follows the signature:
+    * Args:
+      * `features` are single `Tensor` or `dict` of `Tensor`s
+             (depending on data passed to `fit`),
+      * `labels` are `Tensor` or `dict` of `Tensor`s (for multi-head
+             models). If mode is `ModeKeys.INFER`, `labels=None` will be
+             passed. If the `model_fn`'s signature does not accept
+             `mode`, the `model_fn` must still be able to handle
+             `labels=None`.
+      * `mode` specifies if this training, evaluation or
+             prediction. See `ModeKeys`.
+      * `params` is a `dict` of hyperparameters. Will receive what
+             is passed to Estimator in `params` parameter. This allows
+             to configure Estimators from hyper parameter tuning.
+
+    * Returns:
+      `ModelFnOps`
+
+    Also supports a legacy signature which returns tuple of:
 
       * predictions: `Tensor`, `SparseTensor` or dictionary of same.
           Can also be any type that is convertible to a `Tensor` or
@@ -336,27 +353,12 @@ Constructs an Estimator instance.
       * loss: Scalar loss `Tensor`.
       * train_op: Training update `Tensor` or `Operation`.
 
-     Supports next three signatures for the function:
+    Supports next three signatures for the function:
 
       * `(features, labels) -> (predictions, loss, train_op)`
       * `(features, labels, mode) -> (predictions, loss, train_op)`
       * `(features, labels, mode, params) -> (predictions, loss, train_op)`
 
-    Where
-
-      * `features` are single `Tensor` or `dict` of `Tensor`s
-             (depending on data passed to `fit`),
-      * `labels` are `Tensor` or `dict` of `Tensor`s (for multi-head
-             models). If mode is `ModeKeys.INFER`, `labels=None` will be
-             passed. If the `model_fn`'s signature does not accept
-             `mode`, the `model_fn` must still be able to handle
-             `labels=None`.
-      * `mode` represents if this training, evaluation or
-             prediction. See `ModeKeys`.
-      * `params` is a `dict` of hyperparameters. Will receive what
-             is passed to Estimator in `params` parameter. This allows
-             to configure Estimators from hyper parameter tunning.
-
 
 *  <b>`model_dir`</b>: Directory to save model parameters, graph and etc. This can
     also be used to load checkpoints from the directory into a estimator to
@@ -1083,44 +1085,9 @@ available in the SKCompat class, Estimator will only accept input_fn.
 
 - - -
 
-#### `tf.contrib.learn.DNNRegressor.export(*args, **kwargs)` {#DNNRegressor.export}
+#### `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}
 
-Exports inference graph into given dir. (deprecated arguments)
 
-SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-09-23.
-Instructions for updating:
-The signature of the input_fn accepted by export is changing to be consistent with what's used by tf.Learn Estimator's train/evaluate. input_fn (and in most cases, input_feature_key) will become required args, and use_deprecated_input_fn will default to False and be removed altogether.
-
-    Args:
-      export_dir: A string containing a directory to write the exported graph
-        and checkpoints.
-      input_fn: If `use_deprecated_input_fn` is true, then a function that given
-        `Tensor` of `Example` strings, parses it into features that are then
-        passed to the model. Otherwise, a function that takes no argument and
-        returns a tuple of (features, labels), where features is a dict of
-        string key to `Tensor` and labels is a `Tensor` that's currently not
-        used (and so can be `None`).
-      input_feature_key: Only used if `use_deprecated_input_fn` is false. String
-        key into the features dict returned by `input_fn` that corresponds to a
-        the raw `Example` strings `Tensor` that the exported model will take as
-        input. Can only be `None` if you're using a custom `signature_fn` that
-        does not use the first arg (examples).
-      use_deprecated_input_fn: Determines the signature format of `input_fn`.
-      signature_fn: Function that returns a default signature and a named
-        signature map, given `Tensor` of `Example` strings, `dict` of `Tensor`s
-        for features and `Tensor` or `dict` of `Tensor`s for predictions.
-      prediction_key: The key for a tensor in the `predictions` dict (output
-        from the `model_fn`) to use as the `predictions` input to the
-        `signature_fn`. Optional. If `None`, predictions will pass to
-        `signature_fn` without filtering.
-      default_batch_size: Default batch size of the `Example` placeholder.
-      exports_to_keep: Number of exports to keep.
-
-    Returns:
-      The string path to the exported directory. NB: this functionality was
-      added ca. 2016/09/25; clients that depend on the return value may need
-      to handle the case where this function returns None because subclasses
-      are not returning a value.
 
 
 - - -
@@ -1276,43 +1243,13 @@ available in the SKCompat class, Estimator will only accept input_fn.
 
 #### `tf.contrib.learn.DNNRegressor.predict(*args, **kwargs)` {#DNNRegressor.predict}
 
-Returns predictions for given features. (deprecated arguments)
+Runs inference to determine the predicted class. (deprecated arguments)
 
-SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-12-01.
+SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-09-15.
 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(...))
-
-
-*  <b>`Args`</b>: 
-*  <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>`input_fn`</b>: Input function. If set, `x` and 'batch_size' must be `None`.
-*  <b>`batch_size`</b>: Override default batch size. If set, 'input_fn' must be
-        'None'.
-*  <b>`outputs`</b>: list of `str`, name of the output to predict.
-        If `None`, returns all.
-*  <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).
-
-
-*  <b>`Returns`</b>: 
-      A numpy array of predicted classes or regression values if the
-      constructor's `model_fn` returns a `Tensor` for `predictions` or a `dict`
-      of numpy arrays if `model_fn` returns a `dict`. Returns an iterable of
-      predictions if as_iterable is True.
-
-
-*  <b>`Raises`</b>: 
-*  <b>`ValueError`</b>: If x and input_fn are both provided or both `None`.
+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.
 
 
 - - -
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.Estimator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.Estimator.md
index b3a87b64af..aa3c101dbf 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.Estimator.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.Estimator.md
@@ -3,13 +3,30 @@ Estimator class is the basic TensorFlow model trainer/evaluator.
 
 #### `tf.contrib.learn.Estimator.__init__(model_fn=None, model_dir=None, config=None, params=None, feature_engineering_fn=None)` {#Estimator.__init__}
 
-Constructs an Estimator instance.
+Constructs an `Estimator` instance.
 
 ##### Args:
 
 
-*  <b>`model_fn`</b>: Model function, takes features and labels tensors or dicts of
-            tensors and returns tuple of:
+*  <b>`model_fn`</b>: Model function. Follows the signature:
+    * Args:
+      * `features` are single `Tensor` or `dict` of `Tensor`s
+             (depending on data passed to `fit`),
+      * `labels` are `Tensor` or `dict` of `Tensor`s (for multi-head
+             models). If mode is `ModeKeys.INFER`, `labels=None` will be
+             passed. If the `model_fn`'s signature does not accept
+             `mode`, the `model_fn` must still be able to handle
+             `labels=None`.
+      * `mode` specifies if this training, evaluation or
+             prediction. See `ModeKeys`.
+      * `params` is a `dict` of hyperparameters. Will receive what
+             is passed to Estimator in `params` parameter. This allows
+             to configure Estimators from hyper parameter tuning.
+
+    * Returns:
+      `ModelFnOps`
+
+    Also supports a legacy signature which returns tuple of:
 
       * predictions: `Tensor`, `SparseTensor` or dictionary of same.
           Can also be any type that is convertible to a `Tensor` or
@@ -17,27 +34,12 @@ Constructs an Estimator instance.
       * loss: Scalar loss `Tensor`.
       * train_op: Training update `Tensor` or `Operation`.
 
-     Supports next three signatures for the function:
+    Supports next three signatures for the function:
 
       * `(features, labels) -> (predictions, loss, train_op)`
       * `(features, labels, mode) -> (predictions, loss, train_op)`
       * `(features, labels, mode, params) -> (predictions, loss, train_op)`
 
-    Where
-
-      * `features` are single `Tensor` or `dict` of `Tensor`s
-             (depending on data passed to `fit`),
-      * `labels` are `Tensor` or `dict` of `Tensor`s (for multi-head
-             models). If mode is `ModeKeys.INFER`, `labels=None` will be
-             passed. If the `model_fn`'s signature does not accept
-             `mode`, the `model_fn` must still be able to handle
-             `labels=None`.
-      * `mode` represents if this training, evaluation or
-             prediction. See `ModeKeys`.
-      * `params` is a `dict` of hyperparameters. Will receive what
-             is passed to Estimator in `params` parameter. This allows
-             to configure Estimators from hyper parameter tunning.
-
 
 *  <b>`model_dir`</b>: Directory to save model parameters, graph and etc. This can
     also be used to load checkpoints from the directory into a estimator to
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.DNNRegressor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.DNNRegressor.md
index 0c2ec5563e..b20f235de6 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.DNNRegressor.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.DNNRegressor.md
@@ -166,44 +166,9 @@ available in the SKCompat class, Estimator will only accept input_fn.
 
 - - -
 
-#### `tf.contrib.learn.DNNRegressor.export(*args, **kwargs)` {#DNNRegressor.export}
+#### `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}
 
-Exports inference graph into given dir. (deprecated arguments)
 
-SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-09-23.
-Instructions for updating:
-The signature of the input_fn accepted by export is changing to be consistent with what's used by tf.Learn Estimator's train/evaluate. input_fn (and in most cases, input_feature_key) will become required args, and use_deprecated_input_fn will default to False and be removed altogether.
-
-    Args:
-      export_dir: A string containing a directory to write the exported graph
-        and checkpoints.
-      input_fn: If `use_deprecated_input_fn` is true, then a function that given
-        `Tensor` of `Example` strings, parses it into features that are then
-        passed to the model. Otherwise, a function that takes no argument and
-        returns a tuple of (features, labels), where features is a dict of
-        string key to `Tensor` and labels is a `Tensor` that's currently not
-        used (and so can be `None`).
-      input_feature_key: Only used if `use_deprecated_input_fn` is false. String
-        key into the features dict returned by `input_fn` that corresponds to a
-        the raw `Example` strings `Tensor` that the exported model will take as
-        input. Can only be `None` if you're using a custom `signature_fn` that
-        does not use the first arg (examples).
-      use_deprecated_input_fn: Determines the signature format of `input_fn`.
-      signature_fn: Function that returns a default signature and a named
-        signature map, given `Tensor` of `Example` strings, `dict` of `Tensor`s
-        for features and `Tensor` or `dict` of `Tensor`s for predictions.
-      prediction_key: The key for a tensor in the `predictions` dict (output
-        from the `model_fn`) to use as the `predictions` input to the
-        `signature_fn`. Optional. If `None`, predictions will pass to
-        `signature_fn` without filtering.
-      default_batch_size: Default batch size of the `Example` placeholder.
-      exports_to_keep: Number of exports to keep.
-
-    Returns:
-      The string path to the exported directory. NB: this functionality was
-      added ca. 2016/09/25; clients that depend on the return value may need
-      to handle the case where this function returns None because subclasses
-      are not returning a value.
 
 
 - - -
@@ -359,43 +324,13 @@ available in the SKCompat class, Estimator will only accept input_fn.
 
 #### `tf.contrib.learn.DNNRegressor.predict(*args, **kwargs)` {#DNNRegressor.predict}
 
-Returns predictions for given features. (deprecated arguments)
+Runs inference to determine the predicted class. (deprecated arguments)
 
-SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-12-01.
+SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-09-15.
 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(...))
-
-
-*  <b>`Args`</b>: 
-*  <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>`input_fn`</b>: Input function. If set, `x` and 'batch_size' must be `None`.
-*  <b>`batch_size`</b>: Override default batch size. If set, 'input_fn' must be
-        'None'.
-*  <b>`outputs`</b>: list of `str`, name of the output to predict.
-        If `None`, returns all.
-*  <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).
-
-
-*  <b>`Returns`</b>: 
-      A numpy array of predicted classes or regression values if the
-      constructor's `model_fn` returns a `Tensor` for `predictions` or a `dict`
-      of numpy arrays if `model_fn` returns a `dict`. Returns an iterable of
-      predictions if as_iterable is True.
-
-
-*  <b>`Raises`</b>: 
-*  <b>`ValueError`</b>: If x and input_fn are both provided or both `None`.
+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.
 
 
 - - -