Estimator class is the basic TensorFlow model trainer/evaluator.
- - -

#### `tf.contrib.learn.Estimator.__init__(model_fn=None, model_dir=None, config=None, params=None)` {#Estimator.__init__}

Constructs an Estimator instance.

##### Args:


*  <b>`model_fn`</b>: 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

      * `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
             models). If mode is `ModeKeys.INFER`, `targets=None` will be
             passed. If the `model_fn`'s signature does not accept
             `mode`, the `model_fn` must still be able to handle
             `targets=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
    continue training a previously saved model.
*  <b>`config`</b>: Configuration object.
*  <b>`params`</b>: `dict` of hyper parameters that will be passed into `model_fn`.
          Keys are names of parameters, values are basic python types.

##### Raises:


*  <b>`ValueError`</b>: parameters of `model_fn` don't match `params`.


- - -

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

See `Evaluable`.

##### 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.Estimator.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None)` {#Estimator.fit}

See `Trainable`.

##### Raises:


*  <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`.


- - -

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

##### Returns:

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


- - -

#### `tf.contrib.learn.Estimator.get_variable_names()` {#Estimator.get_variable_names}

Returns list of all variable names in this model.

##### Returns:

  List of names.


- - -

#### `tf.contrib.learn.Estimator.get_variable_value(name)` {#Estimator.get_variable_value}

Returns value of the variable given by name.

##### Args:


*  <b>`name`</b>: string, name of the tensor.

##### Returns:

  Numpy array - value of the tensor.


- - -

#### `tf.contrib.learn.Estimator.model_dir` {#Estimator.model_dir}




- - -

#### `tf.contrib.learn.Estimator.partial_fit(x=None, y=None, input_fn=None, steps=1, batch_size=None, monitors=None)` {#Estimator.partial_fit}

Incremental fit on a batch of samples.

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 targets. The training target 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.Estimator.predict(x=None, input_fn=None, batch_size=None, outputs=None, as_iterable=False)` {#Estimator.predict}

Returns predictions for given features.

##### 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>`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).

##### Returns:

  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.

##### Raises:


*  <b>`ValueError`</b>: If x and input_fn are both provided or both `None`.


- - -

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

##### Returns:

  self

##### Raises:


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


