在TensorFlow中使用实验的优势

Question

Many of TensorFlow's example applications create Experiment s and run one of the Experiment 's methods by calling tf.contrib.data.learn_runner.run . It looks like an Experiment is essentially a wrapper for an Estimator .

The code needed to create and run an Experiment looks more complex than the code needed to create, train, and evaluate an Estimator . I'm sure there's an advantage to using Experiment s, but I can't figure out what it is. Could someone fill me in?

Answer 1

tf.contrib.learn.Experiment is a high-level API for distributed training. Here's from its doc:

Experiment is a class containing all information needed to train a model.

After an experiment is created (by passing an Estimator and inputs for training and evaluation), an Experiment instance knows how to invoke training and eval loops in a sensible fashion for distributed training .

Just like tf.estimator.Estimator (and the derived classes) is a high-level API that hides matrix multiplications, saving checkpoints and so on, tf.contrib.learn.Experiment tries to hide the boilerplate you'd need to do for distributed computation , namely tf.train.ClusterSpec , tf.train.Server , jobs, tasks, etc.

You can train and evaluate the tf.estimator.Estimator on a single machine without an Experiment . See the examples in this tutorial .