From ff3821ca25cdc0ee0667c6f7fea4e84d2a09c01c Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 14 Jun 2016 11:21:09 -0800 Subject: [PATCH] Update generated Python Op docs. Change: 124868009 --- .../g3doc/api_docs/python/contrib.learn.md | 18 ++++++---- .../shard2/tf.contrib.learn.BaseEstimator.md | 9 +++-- .../shard3/tf.contrib.learn.Estimator.md | 33 +++++++++++-------- 3 files changed, 38 insertions(+), 22 deletions(-) diff --git a/tensorflow/g3doc/api_docs/python/contrib.learn.md b/tensorflow/g3doc/api_docs/python/contrib.learn.md index f5fe4dfb7c8..76243f56fcc 100644 --- a/tensorflow/g3doc/api_docs/python/contrib.learn.md +++ b/tensorflow/g3doc/api_docs/python/contrib.learn.md @@ -27,10 +27,11 @@ Concrete implementation of this class should provide following functions: Initializes a BaseEstimator instance. -##### Parameters: +##### Args: * `model_dir`: Directory to save model parameters, graph and etc. +* `config`: A RunConfig instance. - - - @@ -293,16 +294,21 @@ Constructs an Estimator instance. (depending on data passed to `fit`), * `targets` are `Tensor` or `dict` of `Tensor`s (for multi-head model). - * `mode` represents if this training, evaluation or prediction. - See `ModeKeys` for example keys. - * `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. + * `mode` represents if this training, evaluation or + prediction. See `ModeKeys` for example keys. + * `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. * `model_dir`: Directory to save model parameters, graph and etc. * `config`: Configuration object. * `params`: `dict` of hyper parameters that will be passed into `model_fn`. Keys are names of parameters, values are basic python types. +##### Raises: + + +* `ValueError`: parameters of `model_fn` don't match `params`. + - - - diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.BaseEstimator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.BaseEstimator.md index b29f52ca915..2d416c79b67 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.BaseEstimator.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.BaseEstimator.md @@ -6,14 +6,17 @@ Concrete implementation of this class should provide following functions: * _get_predict_ops `Estimator` implemented below is a good example of how to use this class. - -Parameters: - model_dir: Directory to save model parameters, graph and etc. - - - #### `tf.contrib.learn.BaseEstimator.__init__(model_dir=None, config=None)` {#BaseEstimator.__init__} +Initializes a BaseEstimator instance. +##### Args: + + +* `model_dir`: Directory to save model parameters, graph and etc. +* `config`: A RunConfig instance. - - - 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 d5a45103cfc..9e576094da2 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 @@ -1,32 +1,39 @@ Estimator class is the basic TensorFlow model trainer/evaluator. +- - - -Parameters: - model_fn: Model function, takes features and targets tensors or dicts of +#### `tf.contrib.learn.Estimator.__init__(model_fn=None, model_dir=None, config=None, params=None)` {#Estimator.__init__} + +Constructs an Estimator instance. + +##### Args: + + +* `model_fn`: Model function, takes features and targets tensors or dicts of tensors and returns predictions and loss tensors. Supports next three signatures for the function: * `(features, targets) -> (predictions, loss, train_op)` * `(features, targets, mode) -> (predictions, loss, train_op)` * `(features, targets, mode, params) -> (predictions, loss, train_op)` - Where: +* `Where`: * `features` are single `Tensor` or `dict` of `Tensor`s (depending on data passed to `fit`), * `targets` are `Tensor` or `dict` of `Tensor`s (for multi-head model). - * `mode` represents if this training, evaluation or prediction. - See `ModeKeys` for example keys. - * `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. - model_dir: Directory to save model parameters, graph and etc. - config: Configuration object. - params: `dict` of hyper parameters that will be passed into `model_fn`. + * `mode` represents if this training, evaluation or + prediction. See `ModeKeys` for example keys. + * `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. +* `model_dir`: Directory to save model parameters, graph and etc. +* `config`: Configuration object. +* `params`: `dict` of hyper parameters that will be passed into `model_fn`. Keys are names of parameters, values are basic python types. -- - - -#### `tf.contrib.learn.Estimator.__init__(model_fn=None, model_dir=None, config=None, params=None)` {#Estimator.__init__} +##### Raises: +* `ValueError`: parameters of `model_fn` don't match `params`. - - -