The standard library uses various well-known names to collect and retrieve values associated with a graph.
For example, the
tf$Optimizer subclasses default to optimizing the
variables collected under
specified, but it is also possible to pass an explicit list of variables.
The following standard keys are defined:
GLOBAL_VARIABLES: the default collection of
Variable objects, shared
across distributed environment (model variables are subset of these). See
tf$global_variables for more details. Commonly, all
variables will be in
MODEL_VARIABLES, and all
will be in
LOCAL_VARIABLES: the subset of
Variable objects that are local to each
machine. Usually used for temporarily variables, like counters. Note: use
tf$contrib$framework$local_variable to add to this collection.
MODEL_VARIABLES: the subset of
Variable objects that are used in the
model for inference (feed forward). Note: use
tf$contrib$framework$model_variable to add to this collection.
TRAINABLE_VARIABLES: the subset of
Variable objects that will be
trained by an optimizer. See
tf$trainable_variables for more details.
SUMMARIES: the summary
Tensor objects that have been created in the
tf$summary$merge_all for more details.
QueueRunner objects that are used to produce input
for a computation. See
tf$train$start_queue_runners for more details.
MOVING_AVERAGE_VARIABLES: the subset of
Variable objects that will also
keep moving averages. See
tf$moving_average_variables for more details.
REGULARIZATION_LOSSES: regularization losses collected during graph
construction. The following standard keys are defined, but their
collections are not automatically populated as many of the others are:
Other utility functions:
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