Given graph, a directory to write outputs to (output_dir), and some ops,
run a training loop. The given train_op performs one step of training on the
model. The loss_op represents the objective function of the training. It is
expected to increment the global_step_tensor, a scalar integer tensor
counting training steps. This function uses Supervisor to initialize the
graph (from a checkpoint if one is available in output_dir), write summaries
defined in the graph, and write regular checkpoints as defined by
supervisor_save_model_secs.
Training continues until global_step_tensor evaluates to max_steps, or, if
fail_on_nan_loss, until loss_op evaluates to NaN. In that case the
program is terminated with exit code 1.
Args
graph
A graph to train. It is expected that this graph is not in use
elsewhere.
output_dir
A directory to write outputs to.
train_op
An op that performs one training step when run.
loss_op
A scalar loss tensor.
global_step_tensor
A tensor representing the global step. If none is given,
one is extracted from the graph using the same logic as in Supervisor.
init_op
An op that initializes the graph. If None, use Supervisor's
default.
init_feed_dict
A dictionary that maps Tensor objects to feed values.
This feed dictionary will be used when init_op is evaluated.
init_fn
Optional callable passed to Supervisor to initialize the model.
log_every_steps
Output logs regularly. The logs contain timing data and the
current loss.
supervisor_is_chief
Whether the current process is the chief supervisor in
charge of restoring the model and running standard services.
supervisor_master
The master string to use when preparing the session.
supervisor_save_model_secs
Save a checkpoint every
supervisor_save_model_secs seconds when training.
keep_checkpoint_max
The maximum number of recent checkpoint files to
keep. As new files are created, older files are deleted. If None or 0,
all checkpoint files are kept. This is simply passed as the max_to_keep
arg to tf.compat.v1.train.Saver constructor.
supervisor_save_summaries_steps
Save summaries every
supervisor_save_summaries_steps seconds when training.
feed_fn
A function that is called every iteration to produce a feed_dict
passed to session.run calls. Optional.
steps
Trains for this many steps (e.g. current global step + steps).
fail_on_nan_loss
If true, raise NanLossDuringTrainingError if loss_op
evaluates to NaN. If false, continue training as if nothing happened.
monitors
List of BaseMonitor subclass instances. Used for callbacks
inside the training loop.
max_steps
Number of total steps for which to train model. If None,
train forever. Two calls fit(steps=100) means 200 training iterations.
On the other hand two calls of fit(max_steps=100) means, second call
will not do any iteration since first call did all 100 steps.
Returns
The final loss value.
Raises
ValueError
If output_dir, train_op, loss_op, or global_step_tensor
is not provided. See tf.contrib.framework.get_global_step for how we
look up the latter if not provided explicitly.
NanLossDuringTrainingError
If fail_on_nan_loss is True, and loss ever
evaluates to NaN.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2020-10-01 UTC."],[],[]]