tf.contrib.gan.estimator.GANEstimator

Class GANEstimator

Inherits From: Estimator

Aliases:

  • Class tf.contrib.gan.estimator.GANEstimator
  • Class tf.contrib.gan.estimator.gan_estimator.GANEstimator

Defined in tensorflow/contrib/gan/python/estimator/python/gan_estimator_impl.py.

An estimator for Generative Adversarial Networks (GANs).

This Estimator is backed by TFGAN. The network functions follow the TFGAN API except for one exception: if either generator_fn or discriminator_fn have an argument called mode, then the tf.Estimator mode is passed in for that argument. This helps with operations like batch normalization, which have different train and evaluation behavior.

Example:

    import tensorflow as tf
    tfgan = tf.contrib.gan

    # See TFGAN's `train.py` for a description of the generator and
    # discriminator API.
    def generator_fn(generator_inputs):
      ...
      return generated_data

    def discriminator_fn(data, conditioning):
      ...
      return logits

    # Create GAN estimator.
    gan_estimator = tfgan.estimator.GANEstimator(
        model_dir,
        generator_fn=generator_fn,
        discriminator_fn=discriminator_fn,
        generator_loss_fn=tfgan.losses.wasserstein_generator_loss,
        discriminator_loss_fn=tfgan.losses.wasserstein_discriminator_loss,
        generator_optimizer=tf.train.AdamOptimizer(0.1, 0.5),
        discriminator_optimizer=tf.train.AdamOptimizer(0.1, 0.5))

    # Train estimator.
    gan_estimator.train(train_input_fn, steps)

    # Evaluate resulting estimator.
    gan_estimator.evaluate(eval_input_fn)

    # Generate samples from generator.
    predictions = np.array([
        x for x in gan_estimator.predict(predict_input_fn)])

__init__

__init__(
    model_dir=None,
    generator_fn=None,
    discriminator_fn=None,
    generator_loss_fn=None,
    discriminator_loss_fn=None,
    generator_optimizer=None,
    discriminator_optimizer=None,
    get_hooks_fn=None,
    get_eval_metric_ops_fn=None,
    add_summaries=None,
    use_loss_summaries=True,
    config=None
)

Initializes a GANEstimator instance.

Args:

  • 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.
  • generator_fn: A python function that takes a Tensor, Tensor list, or Tensor dictionary as inputs and returns the outputs of the GAN generator. See TFGAN for more details and examples. Additionally, if it has an argument called mode, the Estimator's mode will be passed in (ex TRAIN, EVAL, PREDICT). This is useful for things like batch normalization.
  • discriminator_fn: A python function that takes the output of generator_fn or real data in the GAN setup, and generator_inputs. Outputs a Tensor in the range [-inf, inf]. See TFGAN for more details and examples.
  • generator_loss_fn: The loss function on the generator. Takes a GANModel tuple.
  • discriminator_loss_fn: The loss function on the discriminator. Takes a GANModel tuple.
  • generator_optimizer: The optimizer for generator updates, or a function that takes no arguments and returns an optimizer. This function will be called when the default graph is the GANEstimator's graph, so utilities like tf.contrib.framework.get_or_create_global_step will work.
  • discriminator_optimizer: Same as generator_optimizer, but for the discriminator updates.
  • get_hooks_fn: A function that takes a GANTrainOps tuple and returns a list of hooks. These hooks are run on the generator and discriminator train ops, and can be used to implement the GAN training scheme. Defaults to train.get_sequential_train_hooks().
  • get_eval_metric_ops_fn: A function that takes a GANModel, and returns a dict of metric results keyed by name. The output of this function is passed into tf.estimator.EstimatorSpec during evaluation.
  • add_summaries: None, a single SummaryType, or a list of SummaryType.
  • use_loss_summaries: If True, add loss summaries. If False, does not. If None, uses defaults.
  • config: RunConfig object to configure the runtime settings.

Raises:

  • ValueError: If loss functions aren't callable.
  • ValueError: If use_loss_summaries isn't boolean or None.
  • ValueError: If get_hooks_fn isn't callable or None.

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

eval_dir

eval_dir(name=None)

Shows the directory name where evaluation metrics are dumped.

Args:

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

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 (tf.errors.OutOfRangeError or StopIteration).

Args:

  • input_fn: A function that constructs the input data for evaluation. See Premade 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 tf.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 tf.train.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. If there are no checkpoints in model_dir, evaluation is run with newly initialized Variables instead of ones restored from checkpoint.
  • 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. 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:

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

export_saved_model

export_saved_model(
    export_dir_base,
    serving_input_receiver_fn,
    assets_extra=None,
    as_text=False,
    checkpoint_path=None
)

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'}.

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 tf.estimator.export.ServingInputReceiver or tf.estimator.export.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.

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.

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 the given dir.

Note that export_to_savedmodel will be renamed to export_to_saved_model in TensorFlow 2.0. At that time, export_to_savedmodel without the additional underscore will be available only through tf.compat.v1.

Please see tf.estimator.Estimator.export_saved_model for more information.

There is one additional arg versus the new method: strip_default_attrs: This parameter is going away in TF 2.0, and the new behavior will automatically strip all default attributes. Boolean. If True, default-valued attributes will be removed from the NodeDefs. For a detailed guide, see Stripping Default-Valued Attributes.

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 the 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 (tf.errors.OutOfRangeError or StopIteration). See Premade 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 tf.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 tf.estimator.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 tf.train.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. If there are no checkpoints in model_dir, prediction is run with newly initialized Variables instead of ones restored from checkpoint.

  • yield_single_examples: If False, yields 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 tf.estimator.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 Premade 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 tf.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 tf.train.SessionRunHook subclass instances. Used for callbacks inside the training loop.
  • steps: Number of steps for which to train the model. If None, train forever or train until input_fn generates the tf.errors.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 tf.errors.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 <= 0.