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description: Session-like object that handles initialization, restoring, and hooks.
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View source on GitHub
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Session-like object that handles initialization, restoring, and hooks.
tf.compat.v1.train.SingularMonitoredSession(
hooks=None, scaffold=None, master='', config=None,
checkpoint_dir=None, stop_grace_period_secs=120,
checkpoint_filename_with_path=None
)
Please note that this utility is not recommended for distributed settings.
For distributed settings, please use tf.compat.v1.train.MonitoredSession.
The
differences between MonitoredSession and SingularMonitoredSession are:
MonitoredSession handles AbortedError and UnavailableError for
distributed settings, but SingularMonitoredSession does not.MonitoredSession can be created in chief or worker modes.
SingularMonitoredSession is always created as chief.tf.compat.v1.Session object used by
SingularMonitoredSession, whereas in MonitoredSession the raw session is
private. This can be used:
run without hooks.saver_hook = CheckpointSaverHook(...)
summary_hook = SummarySaverHook(...)
with SingularMonitoredSession(hooks=[saver_hook, summary_hook]) as sess:
while not sess.should_stop():
sess.run(train_op)
Initialization: At creation time the hooked session does following things in given order:
hook.begin() for each given hookscaffold.finalize()ScaffoldRun: When run() is called, the hooked session does following things:
hook.before_run()session.run() with merged fetches and feed_dicthook.after_run()session.run() asked by userExit: At the close(), the hooked session does following things in order:
hook.end()OutOfRange error which indicates that all inputs have been
processed if the SingularMonitoredSession is used as a context.Args | ||
|---|---|---|
hooks
|
An iterable of SessionRunHook' objects.
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scaffold
</td>
<td>
AScaffoldused for gathering or building supportive ops. If
not specified a default one is created. It's used to finalize the graph.
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<td>
master
</td>
<td>
Stringrepresentation of the TensorFlow master to use.
</td>
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<td>
config
</td>
<td>
ConfigProtoproto used to configure the session.
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<td>
checkpoint_dir
</td>
<td>
A string. Optional path to a directory where to restore
variables.
</td>
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stop_grace_period_secs
</td>
<td>
Number of seconds given to threads to stop after
close()has been called.
</td>
</tr><tr>
<td>
checkpoint_filename_with_path`
|
A string. Optional path to a checkpoint file from which to restore variables. |
Attributes | |
|---|---|
graph
|
The graph that was launched in this session. |
closeclose()
raw_sessionraw_session()
Returns underlying TensorFlow.Session object.
runrun(
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_fnrun_step_fn(
step_fn
)
Run ops using a step function.
| 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:
```python
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)
`` |
| 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_stopshould_stop()
__enter____enter__()
__exit____exit__(
exception_type, exception_value, traceback
)