tf.compat.v1.train.maybe_shuffle_batch

Session-like object that handles initialization, recovery and hooks.

tf.compat.v1.train.MonitoredSession(
    session_creator=None, hooks=None, stop_grace_period_secs=120
)

Example usage:

saver_hook = CheckpointSaverHook(...)
summary_hook = SummarySaverHook(...)
with MonitoredSession(session_creator=ChiefSessionCreator(...),
                      hooks=[saver_hook, summary_hook]) as sess:
  while not sess.should_stop():
    sess.run(train_op)

Initialization: At creation time the monitored session does following things in given order:

calls hook.begin() for each given hook
finalizes the graph via scaffold.finalize()
create session
initializes the model via initialization ops provided by Scaffold
restores variables if a checkpoint exists
launches queue runners
calls hook.after_create_session()

Run: When run() is called, the monitored session does following things:

calls hook.before_run()
calls TensorFlow session.run() with merged fetches and feed_dict
calls hook.after_run()
returns result of session.run() asked by user
if AbortedError or UnavailableError occurs, it recovers or reinitializes the session before executing the run() call again

Exit: At the close(), the monitored session does following things in order:

calls hook.end()
closes the queue runners and the session
suppresses OutOfRange error which indicates that all inputs have been processed if the monitored_session is used as a context

How to set tf.compat.v1.Session arguments:

In most cases you can set session arguments as follows:

MonitoredSession(
  session_creator=ChiefSessionCreator(master=..., config=...))

In distributed setting for a non-chief worker, you can use following:

MonitoredSession(
  session_creator=WorkerSessionCreator(master=..., config=...))

See MonitoredTrainingSession for an example usage based on chief or worker.

it cannot be set as default session.
it cannot be sent to saver.save.
it cannot be sent to tf.train.start_queue_runners.

Args
`session_creator`	A factory object to create session. Typically a `ChiefSessionCreator` which is the default one.
`hooks`	An iterable of `SessionRunHook' objects.

Returns
A MonitoredSession object.

Attributes
`graph`	The graph that was launched in this session.

Child Classes

class StepContext

Methods

`close`

View source

close()

`run`

View source

run(
    fetches, feed_dict=None, options=None, run_metadata=None
)

Run ops in the monitored session.

This method is completely compatible with the tf.Session.run() method.

Args
`fetches`	Same as `tf.Session.run()`.
`feed_dict`	Same as `tf.Session.run()`.
`options`	Same as `tf.Session.run()`.
`run_metadata`	Same as `tf.Session.run()`.

Returns
Same as `tf.Session.run()`.

`run_step_fn`

View source

run_step_fn(
    step_fn
)

Run ops using a step function.

Args

Args
`step_fn`	A function or a method with a single argument of type `StepContext`. The function may use methods of the argument to perform computations with access to a raw session. The returned value of the `step_fn` will be returned from `run_step_fn`, unless a stop is requested. In that case, the next `should_stop` call will return True. Example usage: `with tf.Graph().as_default(): c = tf.compat.v1.placeholder(dtypes.float32) v = tf.add(c, 4.0) w = tf.add(c, 0.5) def step_fn(step_context): a = step_context.session.run(fetches=v, feed_dict={c: 0.5}) if a <= 4.5: step_context.request_stop() return step_context.run_with_hooks(fetches=w, feed_dict={c: 0.1}) with tf.MonitoredSession() as session: while not session.should_stop(): a = session.run_step_fn(step_fn)` Hooks interact with the `run_with_hooks()` call inside the `step_fn` as they do with a `MonitoredSession.run` call.

step_fn

A function or a method with a single argument of type StepContext. The function may use methods of the argument to perform computations with access to a raw session. The returned value of the step_fn will be returned from run_step_fn, unless a stop is requested. In that case, the next should_stop call will return True. Example usage:

with tf.Graph().as_default():
c = tf.compat.v1.placeholder(dtypes.float32)
v = tf.add(c, 4.0)
w = tf.add(c, 0.5)
def step_fn(step_context):
a = step_context.session.run(fetches=v, feed_dict={c: 0.5})
if a <= 4.5:
step_context.request_stop()
return step_context.run_with_hooks(fetches=w,
feed_dict={c: 0.1})

with tf.MonitoredSession() as session:
while not session.should_stop():
a = session.run_step_fn(step_fn)

Hooks interact with the run_with_hooks() call inside the step_fn as they do with a MonitoredSession.run call.

Returns
Returns the returned value of `step_fn`.

Raises
`StopIteration`	if `step_fn` has called `request_stop()`. It may be caught by `with tf.MonitoredSession()` to close the session.
`ValueError`	if `step_fn` doesn't have a single argument called `step_context`. It may also optionally have `self` for cases when it belongs to an object.

`should_stop`

View source

should_stop()

`enter`

View source

__enter__()

`exit`

View source

__exit__(
    exception_type, exception_value, traceback
)

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.compat / v1 / v1.train.maybe_shuffle_batch_join

Example usage:

Args

Returns

Attributes

Child Classes

Methods

close

run

run_step_fn

should_stop

__enter__

__exit__

`close`

`run`

`run_step_fn`

`should_stop`

`enter`

`exit`