tf.estimator.Estimator

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Estimator class to train and evaluate TensorFlow models.

Inherits From: Estimator

tf.estimator.Estimator(
    model_fn, model_dir=None, config=None, params=None, warm_start_from=None
)

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 tf.estimator.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.

Args:

Attributes:

Raises:

Eager Compatibility

Calling methods of Estimator will work while eager execution is enabled. However, the model_fn and input_fn is not executed eagerly, Estimator will switch to graph mode before calling all user-provided functions (incl. hooks), so their code has to be compatible with graph mode execution. Note that input_fn code using tf.data generally works in both graph and eager modes.

Methods

eval_dir

View source

eval_dir(
    name=None
)

Shows the directory name where evaluation metrics are dumped.

Args:

Returns:

A string which is the path of directory contains evaluation metrics.

evaluate

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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 (tf.errors.OutOfRangeError or StopIteration).

Args:

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. For canned estimators, the dict contains the loss (mean loss per mini-batch) and the average_loss (mean loss per sample). Canned classifiers also return the accuracy. Canned regressors also return the label/mean and the prediction/mean.

Raises:

experimental_export_all_saved_models

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experimental_export_all_saved_models(
    export_dir_base, input_receiver_fn_map, assets_extra=None, as_text=False,
    checkpoint_path=None
)

Exports a SavedModel with tf.MetaGraphDefs for each requested mode.

For each mode passed in via the input_receiver_fn_map, this method builds a new graph by calling the input_receiver_fn to obtain feature and label Tensors. Next, this method calls the Estimator's model_fn in the passed mode to generate the model graph based on those features and labels, and restores the given checkpoint (or, lacking that, the most recent checkpoint) into the graph. Only one of the modes is used for saving variables to the SavedModel (order of preference: tf.estimator.ModeKeys.TRAIN, tf.estimator.ModeKeys.EVAL, then tf.estimator.ModeKeys.PREDICT), such that up to three tf.MetaGraphDefs are saved with a single set of variables in a single SavedModel directory.

For the variables and tf.MetaGraphDefs, a timestamped export directory below export_dir_base, and writes a SavedModel into it containing the tf.MetaGraphDef for the given mode and its associated signatures.

For prediction, 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 tf.saved_model.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 tf.estimator.export.ExportOutputs, and the inputs are always the input receivers provided by the serving_input_receiver_fn.

For training and evaluation, the train_op is stored in an extra collection, and loss, metrics, and predictions are included in a SignatureDef for the mode in question.

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:

Returns:

The string path to the exported directory.

Raises:

export_saved_model

View source

export_saved_model(
    export_dir_base, serving_input_receiver_fn, assets_extra=None, as_text=False,
    checkpoint_path=None, experimental_mode=ModeKeys.PREDICT
)

Exports inference graph as a SavedModel into the 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 tf.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 tf.saved_model.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 tf.estimator.export.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'}.

The experimental_mode parameter can be used to export a single train/eval/predict graph as a SavedModel. See experimental_export_all_saved_models for full docs.

Args:

Returns:

The string path to the exported directory.

Raises:

get_variable_names

View source

get_variable_names()

Returns list of all variable names in this model.

Returns:

List of names.

Raises:

get_variable_value

View source

get_variable_value(
    name
)

Returns value of the variable given by name.

Args:

Returns:

Numpy array - value of the tensor.

Raises:

latest_checkpoint

View source

latest_checkpoint()

Finds the filename of the latest saved checkpoint file in model_dir.

Returns:

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

predict

View source

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

Yields predictions for given features.

Please note that interleaving two predict outputs does not work. See: issue/20506

Args:

Yields:

Evaluated values of predictions tensors.

Raises:

train

View source

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

Trains a model given training data input_fn.

Args:

Returns:

self, for chaining.

Raises: