org.platanios.tensorflow.api.learn.hooks
Evaluator
Related Docs:
object Evaluator
| package hooks
If true, the step rate is logged using the current logging configuration.
If provided, summaries for the step rate will be saved in this directory. This is useful for visualization using TensorBoard, for example.
Datasets over which to evaluate and which produce elements of the same type as the train dataset elements.
Evaluation metrics to use.
Hook trigger specifying when this hook is triggered (i.e., when it executes). If you only
want to trigger this hook at the end of a run and not during, then you should set trigger
to NoHookTrigger and triggerAtEnd to true.
If true, the hook will be triggered at the end of the run. Note that if this flag is set
to true, then the global step must be computable without using a feed map for the
Session.run() call (which should always be the case by default).
Number of decimal points to use when logging floating point values.
Random number generator seed to use.
Name to use for the evaluation hook when logging and saving metric values. This must follow the same formatting guidelines as the name scopes used when constructing graphs.
Called after a new session is created.
Called after a new session is created. This is called to signal the hooks that a new session has been created.
This callback has two essential differences with the situation in which begin() is called:
The session that has been created.
Called after each call to Session.run().
Called after each call to Session.run().
The runContext argument is the same one passed to beforeSessionRun(). runContext.requestStop() can be called
to stop the iteration.
The runResult argument contains fetched values for the tensors requested by beforeSessionRun().
If Session.run() throws any exception, then afterSessionRun() will not be called. Note the difference between
the end() and the afterSessionRun() behavior when Session.run() throws an OutOfRangeException. In
that case, end() is called but afterSessionRun() is not called.
Provides information about the run call (i.e., the originally requested ops/tensors, the
session, etc.). Same value as that passed to beforeSessionRun.
Result of the Session.run() call that includes the fetched values for the tensors requested
by beforeSessionRun().
Called before each call to Session.run().
Called before each call to Session.run(). You can return from this call a Hook.SessionRunArgs object
indicating ops or tensors to add to the upcoming run call. These ops/tensors will be run together with the
ops/tensors originally passed to the original run call. The run arguments you return can also contain feeds to be
added to the run call.
The runContext argument is a Hook.SessionRunContext that provides information about the upcoming run call
(i.e., the originally requested ops/tensors, the session, etc.).
At this point the graph is finalized and you should not add any new ops.
Provides information about the upcoming run call (i.e., the originally requested ops/tensors, the session, etc.).
Called once before creating the session.
Called once before creating the session. When called, the default graph is the one that will be launched in the
session. The hook can modify the graph by adding new operations to it. After the begin call the graph will be
finalized and the other callbacks will not be able to modify the graph anymore. A second begin call on the same
graph, should not change that graph.
Datasets over which to evaluate and which produce elements of the same type as the train dataset elements.
Called at the end of the session usage (i.e., Session.run() will not be invoked again after this call).
Called at the end of the session usage (i.e., Session.run() will not be invoked again after this call).
The session argument can be used in case the hook wants to execute any final ops, such as saving a last
checkpoint.
If Session.run() throws any exception other than OutOfRangeException then end() will not be called.
Note the difference between the end() and the afterSessionRun() behavior when Session.run() throws an
OutOfRangeException. In that case, end() is called but afterSessionRun() is not called.
Session that will not be used again after this call.
If true, the step rate is logged using the current logging configuration.
Evaluation metrics to use.
Name to use for the evaluation hook when logging and saving metric values.
Name to use for the evaluation hook when logging and saving metric values. This must follow the same formatting guidelines as the name scopes used when constructing graphs.
Number of decimal points to use when logging floating point values.
Random number generator seed to use.
If provided, summaries for the step rate will be saved in this directory.
If provided, summaries for the step rate will be saved in this directory. This is useful for visualization using TensorBoard, for example.
Hook trigger specifying when this hook is triggered (i.e., when it executes).
Hook trigger specifying when this hook is triggered (i.e., when it executes). If you only
want to trigger this hook at the end of a run and not during, then you should set trigger
to NoHookTrigger and triggerAtEnd to true.
If true, the hook will be triggered at the end of the run.
If true, the hook will be triggered at the end of the run. Note that if this flag is set
to true, then the global step must be computable without using a feed map for the
Session.run() call (which should always be the case by default).
Hooks that can be used to evaluate the performance of an estimator for a separate dataset, while training. This hook creates a new session whenever invoked that loads the latest saved checkpoint and evaluates performance using the provided set of evaluation metrics.