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Structure to create or gather pieces commonly needed to train a model.
tf.compat.v1.train.Scaffold(
init_op=None, init_feed_dict=None, init_fn=None, ready_op=None,
ready_for_local_init_op=None, local_init_op=None, summary_op=None, saver=None,
copy_from_scaffold=None, local_init_feed_dict=None
)
When you build a model for training you usually need ops to initialize
variables, a Saver
to checkpoint them, an op to collect summaries for
the visualizer, and so on.
Various libraries built on top of the core TensorFlow library take care of
creating some or all of these pieces and storing them in well known
collections in the graph. The Scaffold
class helps pick these pieces from
the graph collections, creating and adding them to the collections if needed.
If you call the scaffold constructor without any arguments, it will pick
pieces from the collections, creating default ones if needed when
scaffold.finalize()
is called. You can pass arguments to the constructor to
provide your own pieces. Pieces that you pass to the constructor are not
added to the graph collections.
The following pieces are directly accessible as attributes of the Scaffold
object:
saver
: Atf.compat.v1.train.Saver
object taking care of saving the variables. Picked from and stored into theSAVERS
collection in the graph by default.init_op
: An op to run to initialize the variables. Picked from and stored into theINIT_OP
collection in the graph by default.ready_op
: An op to verify that the variables are initialized. Picked from and stored into theREADY_OP
collection in the graph by default.ready_for_local_init_op
: An op to verify that global state has been initialized and it is alright to runlocal_init_op
. Picked from and stored into theREADY_FOR_LOCAL_INIT_OP
collection in the graph by default. This is needed when the initialization of local variables depends on the values of global variables.local_init_op
: An op to initialize the local variables. Picked from and stored into theLOCAL_INIT_OP
collection in the graph by default.summary_op
: An op to run and merge the summaries in the graph. Picked from and stored into theSUMMARY_OP
collection in the graph by default.
You can also pass the following additional pieces to the constructor:
init_feed_dict
: A session feed dictionary that should be used when running the init op.init_fn
: A callable to run after the init op to perform additional initializations. The callable will be called asinit_fn(scaffold, session)
.
Args | |
---|---|
init_op
|
Optional op for initializing variables. |
init_feed_dict
|
Optional session feed dictionary to use when running the init_op. |
init_fn
|
Optional function to use to initialize the model after running
the init_op. Will be called as init_fn(scaffold, session) .
|
ready_op
|
Optional op to verify that the variables are initialized. Must return an empty 1D string tensor when the variables are initialized, or a non-empty 1D string tensor listing the names of the non-initialized variables. |
ready_for_local_init_op
|
Optional op to verify that the global variables
are initialized and local_init_op can be run. Must return an empty 1D
string tensor when the global variables are initialized, or a non-empty
1D string tensor listing the names of the non-initialized global
variables.
|
local_init_op
|
Optional op to initialize local variables. |
summary_op
|
Optional op to gather all summaries. Must return a scalar
string tensor containing a serialized Summary proto.
|
saver
|
Optional tf.compat.v1.train.Saver object to use to save and
restore variables. May also be a tf.train.Checkpoint object, in which
case object-based checkpoints are saved. This will also load some
object-based checkpoints saved from elsewhere, but that loading may be
fragile since it uses fixed keys rather than performing a full
graph-based match. For example if a variable has two paths from the
Checkpoint object because two Model objects share the Layer object
that owns it, removing one Model may change the keys and break
checkpoint loading through this API, whereas a graph-based match would
match the variable through the other Model .
|
copy_from_scaffold
|
Optional scaffold object to copy fields from. Its fields will be overwritten by the provided fields in this function. |
local_init_feed_dict
|
Optional session feed dictionary to use when running the local_init_op. |
Attributes | |
---|---|
init_feed_dict
|
|
init_fn
|
|
init_op
|
|
local_init_feed_dict
|
|
local_init_op
|
|
ready_for_local_init_op
|
|
ready_op
|
|
saver
|
|
summary_op
|
Methods
default_local_init_op
@staticmethod
default_local_init_op()
Returns an op that groups the default local init ops.
This op is used during session initialization when a Scaffold is
initialized without specifying the local_init_op arg. It includes
tf.compat.v1.local_variables_initializer
,
tf.compat.v1.tables_initializer
, and also
initializes local session resources.
Returns | |
---|---|
The default Scaffold local init op. |
finalize
finalize()
Creates operations if needed and finalizes the graph.
get_or_default
@staticmethod
get_or_default( arg_name, collection_key, default_constructor )
Get from cache or create a default operation.