def train(self,
input_fn,
steps=None,
hooks=None,
max_steps=None,
saving_listeners=None,
save_best_ckpt=False):
with context.graph_mode():
if (steps is not None) and (max_steps is not None):
raise ValueError('Can not provide both steps and max_steps.')
if steps is not None and steps <= 0:
raise ValueError(
'Must specify steps > 0, given: {}'.format(steps))
if max_steps is not None and max_steps <= 0:
raise ValueError(
'Must specify max_steps > 0, given: {}'.format(max_steps))
if max_steps is not None:
start_step = _load_global_step_from_checkpoint_dir(
self._model_dir)
if max_steps <= start_step:
logging.info(
'Skipping training since max_steps has already saved.')
return self
hooks = estimator_lib._check_hooks_type(hooks)
hooks.append(training.StopAtStepHook(steps, max_steps))
saving_listeners = estimator_lib._check_listeners_type(
saving_listeners)
loss = self._train_model(input_fn, hooks, saving_listeners,
save_best_ckpt)
logging.info('Loss for final step: %s.', loss)
return self
def fit(self, input_fn, hooks=None, steps=None, max_steps=None):
"""Trains a model given training data input_fn.
Args:
input_fn: Input function returning a tuple of:
features - `Tensor` or dictionary of string feature name to `Tensor`.
labels - `Tensor` or dictionary of `Tensor` with labels.
hooks: List of `SessionRunHook` subclass instances. Used for callbacks
inside the training loop.
steps: Number of steps for which to train model. If `None`, train forever
or train until input_fn generates the `OutOfRange` or `StopIteration`
error. 'steps' works incrementally. If you call two times fit(steps=10)
then training occurs in total 20 steps. If `OutOfRange` or
`StopIteration` error occurs in the middle, training stops before 20
steps. If you don't want to have incremental behaviour please set
`max_steps` instead. If set, `max_steps` must be `None`.
max_steps: Number of total steps for which to train model. If `None`,
train forever or train until input_fn generates the `OutOfRange` or
`StopIteration` error. If set, `steps` must be `None`. If `OutOfRange`
or `StopIteration` error occurs in the middle, training stops before
`max_steps` steps.
Two calls to `fit(steps=100)` means 200 training
iterations. On the other hand, two calls to `fit(max_steps=100)` means
that the second call will not do any iteration since first call did
all 100 steps.
Returns:
`self`, for chaining.
Raises:
ValueError: If both `steps` and `max_steps` are not `None`.
ValueError: If either `steps` or `max_steps` is <= 0.
"""
if (steps is not None) and (max_steps is not None):
raise ValueError('Can not provide both steps and max_steps.')
if steps is not None and steps <= 0:
raise ValueError(
'Must specify steps >= 0, given: {}'.format(steps))
if max_steps is not None and max_steps <= 0:
raise ValueError(
'Must specify max_steps >= 0, given: {}'.format(max_steps))
if max_steps is not None:
start_step = _load_global_step_from_checkpoint_dir(self._model_dir)
if max_steps <= start_step:
logging.info(
'Skipping training since max_steps has already saved.')
return self
hooks = hooks[:] if hooks else []
if steps is not None or max_steps is not None:
hooks.append(training.StopAtStepHook(steps, max_steps))
loss = self._train_model(input_fn=input_fn, hooks=hooks)
logging.info('Loss for final step: %s.', loss)
return self
def make_stop_at_checkpoint_step_hook(estimator,
last_step,
wait_after_file_check_secs=30):
"""Creates a proper StopAtCheckpointStepHook based on chief status."""
if estimator.config.is_chief:
return training.StopAtStepHook(last_step=last_step)
return _StopAtCheckpointStepHook(
model_dir=estimator.model_dir,
last_step=last_step,
wait_after_file_check_secs=wait_after_file_check_secs)
def _convert_train_steps_to_hooks(self, steps, max_steps):
if steps is not None or max_steps is not None:
return [training.StopAtStepHook(steps, max_steps)]
else:
return []
