在 TensorFlow 中使用实验的优势
Advantage of using experiments in TensorFlow
许多 TensorFlow 示例应用程序通过调用 tf.contrib.data.learn_runner.run 创建 Experiment 和 运行 Experiment 的方法之一。看起来 Experiment 本质上是 Estimator.
的包装器
创建和 运行 Experiment 所需的代码看起来比创建、训练和评估 Estimator 所需的代码更复杂。我确信使用 Experiments 有好处，但我不知道它是什么。有人可以帮我填吗？
tf.contrib.learn.Experiment是分布式训练的高级API。这是它的文档：
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.
就像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，即tf.train.ClusterSpec、tf.train.Server、工作、任务等
您可以在没有 Experiment 的单台机器上训练和评估 tf.estimator.Estimator。请参阅 this tutorial.
中的示例
许多 TensorFlow 示例应用程序通过调用 tf.contrib.data.learn_runner.run 创建 Experiment 和 运行 Experiment 的方法之一。看起来 Experiment 本质上是 Estimator.
创建和 运行 Experiment 所需的代码看起来比创建、训练和评估 Estimator 所需的代码更复杂。我确信使用 Experiments 有好处，但我不知道它是什么。有人可以帮我填吗？
tf.contrib.learn.Experiment是分布式训练的高级API。这是它的文档：
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.
就像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，即tf.train.ClusterSpec、tf.train.Server、工作、任务等
您可以在没有 Experiment 的单台机器上训练和评估 tf.estimator.Estimator。请参阅 this tutorial.