1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
|
Session-like object that handles initialization, restoring, and hooks.
Please note that this utility is not recommended for distributed settings.
For distributed settings, please use `tf.train.MonitoredSession`. The
differences between `MonitoredSession` and `SingularMonitoredSession` are:
* `MonitoredSession` handles `AbortedError` 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.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:
```python
saver_hook = CheckpointSaverHook(...)
summary_hook = SummaryHook(...)
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
* surpresses `OutOfRange` error which indicates that all inputs have been
processed if the `SingularMonitoredSession` is used as a context.
- - -
#### `tf.train.SingularMonitoredSession.__enter__()` {#SingularMonitoredSession.__enter__}
- - -
#### `tf.train.SingularMonitoredSession.__exit__(exception_type, exception_value, traceback)` {#SingularMonitoredSession.__exit__}
- - -
#### `tf.train.SingularMonitoredSession.__init__(hooks=None, scaffold=None, master='', config=None, checkpoint_dir=None)` {#SingularMonitoredSession.__init__}
Creates a SingularMonitoredSession.
##### Args:
* <b>`hooks`</b>: An iterable of `SessionRunHook' objects.
* <b>`scaffold`</b>: A `Scaffold` used for gathering or building supportive ops. If
not specified a default one is created. It's used to finalize the graph.
* <b>`master`</b>: `String` representation of the TensorFlow master to use.
* <b>`config`</b>: `ConfigProto` proto used to configure the session.
* <b>`checkpoint_dir`</b>: A string. Optional path to a directory where to restore
variables.
- - -
#### `tf.train.SingularMonitoredSession.close()` {#SingularMonitoredSession.close}
- - -
#### `tf.train.SingularMonitoredSession.graph` {#SingularMonitoredSession.graph}
The graph that was launched in this session.
- - -
#### `tf.train.SingularMonitoredSession.raw_session()` {#SingularMonitoredSession.raw_session}
Returns underlying `TensorFlow.Session` object.
- - -
#### `tf.train.SingularMonitoredSession.run(fetches, feed_dict=None, options=None, run_metadata=None)` {#SingularMonitoredSession.run}
Run ops in the monitored session.
This method is completely compatible with the `tf.Session.run()` method.
##### Args:
* <b>`fetches`</b>: Same as `tf.Session.run()`.
* <b>`feed_dict`</b>: Same as `tf.Session.run()`.
* <b>`options`</b>: Same as `tf.Session.run()`.
* <b>`run_metadata`</b>: Same as `tf.Session.run()`.
##### Returns:
Same as `tf.Session.run()`.
- - -
#### `tf.train.SingularMonitoredSession.should_stop()` {#SingularMonitoredSession.should_stop}
|
