tf.compat.v1.train.SingularMonitoredSession 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. You can access the raw tf.compat.v1.Session object used by SingularMonitoredSession, whereas in MonitoredSession the raw session is private. This can be used:  To run without hooks. To save and restore. 
 All other functionality is identical.  Example usage: 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:  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  Run: When run() is called, the hooked 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  Exit: At the close(), the hooked 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 SingularMonitoredSession is used as a context. 
 


 Args
  hooks   An iterable of SessionRunHook' objects. </td> </tr><tr> <td>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. </td> </tr><tr> <td>master</td> <td>Stringrepresentation of the TensorFlow master to use. </td> </tr><tr> <td>config</td> <td>ConfigProtoproto used to configure the session. </td> </tr><tr> <td>checkpoint_dir</td> <td> A string. Optional path to a directory where to restore variables. </td> </tr><tr> <td>stop_grace_period_secs</td> <td> Number of seconds given to threads to stop afterclose()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.    Child Classes class StepContext Methods close View source 
close()
 raw_session View source 
raw_session()
 Returns underlying TensorFlow.Session object. 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
  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
)