Configuration for the "train" part for the train_and_evaluate call.
tf.estimator.TrainSpec(
input_fn, max_steps=None, hooks=None
)
TrainSpec determines the input data for the training, as well as the
duration. Optional hooks run at various stages of training.
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.
|
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
|
|
TensorFlow 2 version
View source on GitHub