tf.estimator.Estimator

Class Estimator

Defined in tensorflow/python/estimator/estimator.py.

See the guide: Regression Examples

Estimator class to train and evaluate TensorFlow models.

The Estimator object wraps a model which is specified by a model_fn, which, given inputs and a number of other parameters, returns the ops necessary to perform training, evaluation, or predictions.

All outputs (checkpoints, event files, etc.) are written to model_dir, or a subdirectory thereof. If model_dir is not set, a temporary directory is used.

The config argument can be passed RunConfig object containing information about the execution environment. It is passed on to the model_fn, if the model_fn has a parameter named "config" (and input functions in the same manner). If the config parameter is not passed, it is instantiated by the Estimator. Not passing config means that defaults useful for local execution are used. Estimator makes config available to the model (for instance, to allow specialization based on the number of workers available), and also uses some of its fields to control internals, especially regarding checkpointing.

The params argument contains hyperparameters. It is passed to the model_fn, if the model_fn has a parameter named "params", and to the input functions in the same manner. Estimator only passes params along, it does not inspect it. The structure of params is therefore entirely up to the developer.

None of Estimator's methods can be overridden in subclasses (its constructor enforces this). Subclasses should use model_fn to configure the base class, and may add methods implementing specialized functionality.

Eager Compatibility

Estimators are not compatible with eager execution.

Properties

config

model_dir

model_fn

Returns the model_fn which is bound to self.params.

Returns:

The model_fn with following signature: def model_fn(features, labels, mode, config)

params

Methods

__init__

__init__(
    model_fn,
    model_dir=None,
    config=None,
    params=None,
    warm_start_from=None
)

Constructs an Estimator instance.

See Estimators for more information. To warm-start an Estimator:

estimator = tf.estimator.DNNClassifier(
    feature_columns=[categorical_feature_a_emb, categorical_feature_b_emb],
    hidden_units=[1024, 512, 256],
    warm_start_from="/path/to/checkpoint/dir")

For more details on warm-start configuration, see WarmStartSettings.

Args:

  • model_fn: Model function. Follows the signature:

    • Args:

    • features: This is the first item returned from the input_fn passed to train, evaluate, and predict. This should be a single Tensor or dict of same.

    • labels: This is the second item returned from the input_fn passed to train, evaluate, and predict. This should be a single Tensor or dict of same (for multi-head models). If mode is ModeKeys.PREDICT, 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: Optional. Specifies if this training, evaluation or prediction. See ModeKeys.
    • params: Optional dict of hyperparameters. Will receive what is passed to Estimator in params parameter. This allows to configure Estimators from hyper parameter tuning.
    • config: Optional configuration object. Will receive what is passed to Estimator in config parameter, or the default config. Allows updating things in your model_fn based on configuration such as num_ps_replicas, or model_dir.

    • Returns: EstimatorSpec

  • model_dir: 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. If PathLike object, the path will be resolved. If None, the model_dir in config will be used if set. If both are set, they must be same. If both are None, a temporary directory will be used.

  • 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.
  • warm_start_from: Optional string filepath to a checkpoint to warm-start from, or a tf.estimator.WarmStartSettings object to fully configure warm-starting. If the string filepath is provided instead of a WarmStartSettings, then all variables are warm-started, and it is assumed that vocabularies and Tensor names are unchanged.

Raises:

  • RuntimeError: If eager execution is enabled.
  • ValueError: parameters of model_fn don't match params.
  • ValueError: if this is called via a subclass and if that class overrides a member of Estimator.

evaluate

evaluate(
    input_fn,
    steps=None,
    hooks=None,
    checkpoint_path=None,
    name=None
)

Evaluates the model given evaluation data input_fn.

For each step, calls input_fn, which returns one batch of data. Evaluates until: - steps batches are processed, or - input_fn raises an end-of-input exception (OutOfRangeError or StopIteration).

Args:

  • input_fn: A function that constructs the input data for evaluation. See Get Started with Estimators for more information. The function should construct and return one of the following:

    • A 'tf.data.Dataset' object: Outputs of Dataset object must be a tuple (features, labels) with same constraints as below.
    • A tuple (features, labels): Where features is a Tensor or a dictionary of string feature name to Tensor and labels is a Tensor or a dictionary of string label name to Tensor. Both features and labels are consumed by model_fn. They should satisfy the expectation of model_fn from inputs.
  • steps: Number of steps for which to evaluate model. If None, evaluates until input_fn raises an end-of-input exception.

  • hooks: List of SessionRunHook subclass instances. Used for callbacks inside the evaluation call.
  • checkpoint_path: Path of a specific checkpoint to evaluate. If None, the latest checkpoint in model_dir is used.
  • name: Name of the evaluation if user needs to run multiple evaluations on different data sets, such as on training data vs test data. Metrics for different evaluations are saved in separate folders, and appear separately in tensorboard.

Returns:

A dict containing the evaluation metrics specified in model_fn keyed by name, as well as an entry global_step which contains the value of the global step for which this evaluation was performed.

Raises:

  • ValueError: If steps <= 0.
  • ValueError: If no model has been trained, namely model_dir, or the given checkpoint_path is empty.

export_savedmodel

export_savedmodel(
    export_dir_base,
    serving_input_receiver_fn,
    assets_extra=None,
    as_text=False,
    checkpoint_path=None,
    strip_default_attrs=False
)

Exports inference graph as a SavedModel into given dir.

For a detailed guide, see Using SavedModel with Estimators.

This method builds a new graph by first calling the serving_input_receiver_fn to obtain feature Tensors, and then calling this Estimator's model_fn to generate the model graph based on those features. It restores the given checkpoint (or, lacking that, the most recent checkpoint) into this graph in a fresh session. Finally it creates a timestamped export directory below the given export_dir_base, and writes a SavedModel into it containing a single MetaGraphDef saved from this session.

The exported MetaGraphDef will provide one SignatureDef for each element of the export_outputs dict returned from the model_fn, named using the same keys. One of these keys is always signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY, indicating which signature will be served when a serving request does not specify one. For each signature, the outputs are provided by the corresponding ExportOutputs, and the inputs are always the input receivers provided by the serving_input_receiver_fn.

Extra assets may be written into the SavedModel via the assets_extra argument. This should be a dict, where each key gives a destination path (including the filename) relative to the assets.extra directory. The corresponding value gives the full path of the source file to be copied. For example, the simple case of copying a single file without renaming it is specified as {'my_asset_file.txt': '/path/to/my_asset_file.txt'}.

Args:

  • export_dir_base: A string containing a directory in which to create timestamped subdirectories containing exported SavedModels.
  • serving_input_receiver_fn: A function that takes no argument and returns a ServingInputReceiver or TensorServingInputReceiver.
  • assets_extra: A dict specifying how to populate the assets.extra directory within the exported SavedModel, or None if no extra assets are needed.
  • as_text: whether to write the SavedModel proto in text format.
  • checkpoint_path: The checkpoint path to export. If None (the default), the most recent checkpoint found within the model directory is chosen.
  • strip_default_attrs: Boolean. If True, default-valued attributes will be removed from the NodeDefs. For a detailed guide, see Stripping Default-Valued Attributes.

Returns:

The string path to the exported directory.

Raises:

  • ValueError: if no serving_input_receiver_fn is provided, no export_outputs are provided, or no checkpoint can be found.

get_variable_names

get_variable_names()

Returns list of all variable names in this model.

Returns:

List of names.

Raises:

  • ValueError: If the Estimator has not produced a checkpoint yet.

get_variable_value

get_variable_value(name)

Returns value of the variable given by name.

Args:

  • name: string or a list of string, name of the tensor.

Returns:

Numpy array - value of the tensor.

Raises:

  • ValueError: If the Estimator has not produced a checkpoint yet.

latest_checkpoint

latest_checkpoint()

Finds the filename of latest saved checkpoint file in model_dir.

Returns:

The full path to the latest checkpoint or None if no checkpoint was found.

predict

predict(
    input_fn,
    predict_keys=None,
    hooks=None,
    checkpoint_path=None,
    yield_single_examples=True
)

Yields predictions for given features.

Args:

  • input_fn: A function that constructs the features. Prediction continues until input_fn raises an end-of-input exception (OutOfRangeError or StopIteration). See Get Started with Estimators for more information. The function should construct and return one of the following:

    • A 'tf.data.Dataset' object: Outputs of Dataset object must have same constraints as below.
    • features: A Tensor or a dictionary of string feature name to Tensor. features are consumed by model_fn. They should satisfy the expectation of model_fn from inputs.
    • A tuple, in which case the first item is extracted as features.
  • predict_keys: list of str, name of the keys to predict. It is used if the EstimatorSpec.predictions is a dict. If predict_keys is used then rest of the predictions will be filtered from the dictionary. If None, returns all.

  • hooks: List of SessionRunHook subclass instances. Used for callbacks inside the prediction call.
  • checkpoint_path: Path of a specific checkpoint to predict. If None, the latest checkpoint in model_dir is used.
  • yield_single_examples: If False, yield the whole batch as returned by the model_fn instead of decomposing the batch into individual elements. This is useful if model_fn returns some tensors whose first dimension is not equal to the batch size.

Yields:

Evaluated values of predictions tensors.

Raises:

  • ValueError: Could not find a trained model in model_dir.
  • ValueError: If batch length of predictions is not the same and yield_single_examples is True.
  • ValueError: If there is a conflict between predict_keys and predictions. For example if predict_keys is not None but EstimatorSpec.predictions is not a dict.

train

train(
    input_fn,
    hooks=None,
    steps=None,
    max_steps=None,
    saving_listeners=None
)

Trains a model given training data input_fn.

Args:

  • input_fn: A function that provides input data for training as minibatches. See Get Started with Estimators for more information. The function should construct and return one of the following:

    • A 'tf.data.Dataset' object: Outputs of Dataset object must be a tuple (features, labels) with same constraints as below.
    • A tuple (features, labels): Where features is a Tensor or a dictionary of string feature name to Tensor and labels is a Tensor or a dictionary of string label name to Tensor. Both features and labels are consumed by model_fn. They should satisfy the expectation of model_fn from inputs.
  • hooks: List of SessionRunHook subclass instances. Used for callbacks inside the training loop.

  • steps: Number of steps for which to train model. If None, train forever or train until input_fn generates the OutOfRange error or StopIteration exception. 'steps' works incrementally. If you call two times train(steps=10) then training occurs in total 20 steps. If OutOfRange or StopIteration occurs in the middle, training stops before 20 steps. If you don't want to have incremental behavior please set max_steps instead. If set, max_steps must be None.
  • max_steps: Number of total steps for which to train model. If None, train forever or train until input_fn generates the OutOfRange error or StopIteration exception. If set, steps must be None. If OutOfRange or StopIteration occurs in the middle, training stops before max_steps steps. Two calls to train(steps=100) means 200 training iterations. On the other hand, two calls to train(max_steps=100) means that the second call will not do any iteration since first call did all 100 steps.
  • saving_listeners: list of CheckpointSaverListener objects. Used for callbacks that run immediately before or after checkpoint savings.

Returns:

self, for chaining.

Raises:

  • ValueError: If both steps and max_steps are not None.
  • ValueError: If either steps or max_steps is <= 0.