tf.estimator.TrainSpec
Configuration for the "train" part for the train_and_evaluate call.
tf.estimator.TrainSpec(
input_fn, max_steps=None, hooks=None, saving_listeners=None
)
TrainSpec determines the input data for the training, as well as the duration. Optional hooks run at various stages of training.
Usage:
train_spec = tf.estimator.TrainSpec(
input_fn=lambda: 1,
max_steps=100,
hooks=[_StopAtSecsHook(stop_after_secs=10)],
saving_listeners=[_NewCheckpointListenerForEvaluate(None, 20, None)])
train_spec.saving_listeners[0]._eval_throttle_secs
20
train_spec.hooks[0]._stop_after_secs
10
train_spec.max_steps
100
| 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
Tensor or a dictionary of string feature name to Tensor and labels is a Tensor or a dictionary of string label name to Tensor.
|
max_steps | Int. Positive number of total steps for which to train model. If None, train forever. The training input_fn is not expected to generate OutOfRangeError or StopIteration exceptions. See the train_and_evaluate stop condition section for details. |
hooks | Iterable of tf.train.SessionRunHook objects to run on all workers (including chief) during training. |
saving_listeners | Iterable of tf.estimator.CheckpointSaverListener objects to run on chief during training. |
| Raises |
|---|
ValueError | If any of the input arguments is invalid. |
TypeError | If any of the arguments is not of the expected type. |
| Attributes |
|---|
input_fn |
|
max_steps |
|
hooks |
|
saving_listeners |
|