View source: R/estimator_keys.R
| graph_keys | R Documentation |
The standard library uses various well-known names to collect and retrieve values associated with a graph.
graph_keys()
For example, the tf$Optimizer subclasses default to optimizing the
variables collected undergraph_keys()$TRAINABLE_VARIABLES if NULL is
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$global_variables for more details. Commonly, all TRAINABLE_VARIABLES
variables will be in MODEL_VARIABLES, and all MODEL_VARIABLES variables
will be in GLOBAL_VARIABLES.
LOCAL_VARIABLES: the subset of Variable objects that are local to each
machine. Usually used for temporarily variables, like counters. Note: use
tf$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
graph. See tf$summary$merge_all for more details.
QUEUE_RUNNERS: the 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$moving_average_variables for more details.
REGULARIZATION_LOSSES: regularization losses collected during graph
construction. The following standard keys are defined, but their
collections are not automatically populated as many of the others are:
WEIGHTS
BIASES
ACTIVATIONS
Other utility functions:
latest_checkpoint()
## Not run:
graph_keys()
graph_keys()$LOSSES
## End(Not run)
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