Executor

class texar.tf.run.Executor(model, data_hparams, config, model_hparams=None, train_hooks=None, eval_hooks=None, session_config=None)

Class that executes training, evaluation, prediction, export, and other actions of Estimator.

Parameters:

• model – An instance of a subclass of ModelBase.
• data_hparams – A dict or an instance of HParams containing the hyperparameters of data. It must contain train and/or eval fields for relevant processes. For example, for train_and_evaluate(), both fields are required.
• config – An instance of tf.estimator.RunConfig, used as the config argument of Estimator.
• model_hparams (optional) – A dict or an instance of HParams containing the hyperparameters of the model. If None, uses model.hparams. Used as the params argument of Estimator.
• train_hooks (optional) – Iterable of tf.train.SessionRunHook objects to run during training.
• eval_hooks (optional) – Iterable of tf.train.SessionRunHook objects to run during evaluation.
• session_config (optional) – An instance of tf.ConfigProto, used as the config argument of tf session.

Example

model = BasicSeq2seq(data_hparams, model_hparams)
exor = Executor(
    model=model,
    data_hparams=data_hparams,
    config=run_config)
exor.train_and_evaluate(
    max_train_steps=10000,
    eval_steps=100)

See bin/train.py for the usage in detail.

train(max_steps=None)

Trains the model. See tf.estimator.Estimator.train for more details.

Parameters:max_steps (int, optional) – Total number of steps for which to train model. If None, train forever or until the train data generates the OutOfRange exception. If OutOfRange occurs in the middle, training stops before max_steps steps.

evaluate(steps=None, checkpoint_path=None)

Evaluates the model. See tf.estimator.Estimator.evaluate for more details.

Parameters:

• steps (int, optional) – Number of steps for which to evaluate model. If None, evaluates until the eval data raises an OutOfRange exception.
• checkpoint_path (str, optional) – Path of a specific checkpoint to evaluate. If None, the the latest checkpoint in config.model_dir is used. If there are no checkpoints in model_dir, evaluation is run with newly initialized variables instead of restored from checkpoint.

train_and_evaluate(max_train_steps=None, eval_steps=None)

Trains and evaluates the model. See tf.estimator.train_and_evaluate for more details.

Parameters:

• max_train_steps (int, optional) – Total number of steps for which to train model. If None, train forever or until the train data generates the OutOfRange exception. If OutOfRange occurs in the middle, training stops before max_steps steps.
• eval_steps (int, optional) – Number of steps for which to evaluate model. If None, evaluates until the eval data raises an OutOfRange exception.