in tensorflow_recommenders_addons/embedding_variable/python/ops/embedding_variable.py [0:0]
def get_variable(
self,
name,
shape=None, # embedding_dim
dtype=dtypes.float32,
ktype=dtypes.int64,
initializer=None,
regularizer=None,
reuse=None,
trainable=None,
collections=None,
caching_device=None,
partitioner=None,
validate_shape=True,
constraint=None,
synchronization=VariableSynchronization.AUTO,
aggregation=VariableAggregation.NONE):
"""Gets an existing variable with these parameters or create a new one.
If a variable with the given name is already stored, we return the stored
variable. Otherwise, we create a new one.
Set `reuse` to `True` when you only want to reuse existing Variables.
Set `reuse` to `False` when you only want to create new Variables.
Set `reuse` to None (the default) or tf.compat.v1.AUTO_REUSE when you want
variables to be created if they don't exist or returned if they do.
If initializer is `None` (the default), the default initializer passed in
the constructor is used. If that one is `None` too, we use a new
`glorot_uniform_initializer`. If initializer is a Tensor, we use
it as a value and derive the shape from the initializer.
If a partitioner is provided, a `PartitionedVariable` is returned.
Accessing this object as a `Tensor` returns the shards concatenated along
the partition axis.
Some useful partitioners are available. See, e.g.,
`variable_axis_size_partitioner` and `min_max_variable_partitioner`.
Args:
name: The name of the new or existing variable.
shape: Shape of the new or existing variable.
dtype: Type of the new or existing variable (defaults to `DT_FLOAT`).
initializer: Initializer for the variable.
regularizer: A (Tensor -> Tensor or None) function; the result of applying
it on a newly created variable will be added to the collection
GraphKeys.REGULARIZATION_LOSSES and can be used for regularization.
reuse: a Boolean, None, or tf.AUTO_REUSE. Controls reuse or creation of
variables. When eager execution is enabled this argument is always
forced to be False.
trainable: If `True` also add the variable to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). `trainable`
defaults to `True`, unless `synchronization` is set to `ON_READ`, in
which case it defaults to `False`.
collections: List of graph collections keys to add the `Variable` to.
Defaults to `[GraphKeys.GLOBAL_VARIABLES]` (see `tf.Variable`).
caching_device: Optional device string or function describing where the
Variable should be cached for reading. Defaults to the Variable's
device. If not `None`, caches on another device. Typical use is to
cache on the device where the Ops using the `Variable` reside, to
deduplicate copying through `Switch` and other conditional statements.
partitioner: Optional callable that accepts a fully defined `TensorShape`
and dtype of the `Variable` to be created, and returns a list of
partitions for each axis (currently only one axis can be partitioned).
validate_shape: If False, allows the variable to be initialized with a
value of unknown shape. If True, the default, the shape of initial_value
must be known.
use_resource: If False, creates a regular Variable. If True, creates
instead an experimental ResourceVariable which has well-defined
semantics. Defaults to False (will later change to True). When eager
execution is enabled this argument is always forced to be true.
custom_getter: Callable that takes as a first argument the true getter,
and allows overwriting the internal get_variable method. The signature
of `custom_getter` should match that of this method,
but the most future-proof version will allow for changes: `def
custom_getter(getter, *args, **kwargs)`. Direct access to
all `get_variable` parameters is also allowed: `def
custom_getter(getter, name, *args, **kwargs)`. A simple identity
custom getter that simply creates variables with modified names is:
```python
def custom_getter(getter, name, *args, **kwargs): return getter(name +
'_suffix', *args, **kwargs) ```
constraint: An optional projection function to be applied to the variable
after being updated by an `Optimizer` (e.g. used to implement norm
constraints or value constraints for layer weights). The function must
take as input the unprojected Tensor representing the value of the
variable and return the Tensor for the projected value (which must have
the same shape). Constraints are not safe to use when doing asynchronous
distributed training.
synchronization: Indicates when a distributed a variable will be
aggregated. Accepted values are constants defined in the class
`tf.VariableSynchronization`. By default the synchronization is set to
`AUTO` and the current `DistributionStrategy` chooses when to
synchronize.
aggregation: Indicates how a distributed variable will be aggregated.
Accepted values are constants defined in the class
`tf.VariableAggregation`.
Returns:
The created or existing `Variable` (or `PartitionedVariable`, if a
partitioner was used).
Raises:
ValueError: when creating a new variable and shape is not declared,
when reusing a variable and specifying a conflicting shape,
or when violating reuse during variable creation.
RuntimeError: when eager execution is enabled and not called from an
EagerVariableStore.
"""
# Note that it's fine to reuse eager variables whose initialization was
# lifted from a function-building graph into the eager context (that's why
# the following clause is not wrapped in an `init_scope`); lifted variables
# are tracked by the graph's `VariableStore`.
if context.executing_eagerly():
if not self._var_store._store_eager_variables and reuse:
raise RuntimeError(
"When eager execution is enabled variable reuse is only supported"
" when an EagerVariableStore is active. See the documentation on"
" EagerVariableStore for example usage.")
if self._var_store._store_eager_variables:
reuse = AUTO_REUSE
# If a *_ref type is passed in an error would be triggered further down the
# stack. We prevent this using base_dtype to get a non-ref version of the
# type, before doing anything else. When _ref types are removed in favor of
# resources, this line can be removed.
try:
dtype = dtype.base_dtype
ktype = ktype.base_dtype
except AttributeError:
# .base_dtype not existing means that we will try and use the raw dtype
# which was passed in - this might be a NumPy type which is valid.
pass
# This is the main logic of get_variable. However, custom_getter
# may override this logic. So we save it as a callable and pass
# it to custom_getter.
# Note: the parameters of _true_getter, and their documentation, match
# *exactly* item-for-item with the docstring of this method.
def _true_getter( # pylint: disable=missing-docstring
var_store,
name,
shape=None,
dtype=dtypes.float32,
ktype=dtypes.int64,
initializer=None,
regularizer=None,
reuse=None,
trainable=None,
collections=None,
caching_device=None,
partitioner=None,
validate_shape=True,
constraint=None,
synchronization=VariableSynchronization.AUTO,
aggregation=VariableAggregation.NONE):
is_scalar = (shape is not None
and isinstance(shape, collections_abc.Sequence)
and not shape)
# Partitioned variable case
if partitioner is not None and not is_scalar:
if not callable(partitioner):
raise ValueError("Partitioner must be callable, but received: %s" %
partitioner)
with ops.name_scope(None):
return self._get_partitioned_variable(var_store=var_store,
name=name,
shape=shape,
dtype=dtype,
ktype=ktype,
initializer=initializer,
regularizer=regularizer,
reuse=reuse,
trainable=trainable,
collections=collections,
caching_device=caching_device,
partitioner=partitioner,
validate_shape=validate_shape,
constraint=constraint,
synchronization=synchronization,
aggregation=aggregation)
# Special case for partitioned variable to allow reuse without having to
# specify partitioner.
if (reuse is True and partitioner is None
and name in self._var_store._partitioned_vars):
return self._get_partitioned_variable(var_store=var_store,
name=name,
shape=shape,
dtype=dtype,
ktype=dtypes.int64,
initializer=initializer,
regularizer=regularizer,
reuse=reuse,
trainable=trainable,
collections=collections,
caching_device=caching_device,
partitioner=None,
validate_shape=validate_shape,
constraint=constraint,
synchronization=synchronization,
aggregation=aggregation)
# Single variable case
if "%s/part_0" % name in var_store._vars:
raise ValueError(
"No partitioner was provided, but a partitioned version of the "
"variable was found: %s/part_0. Perhaps a variable of the same "
"name was already created with partitioning?" % name)
return self._get_single_variable(var_store=var_store,
name=name,
shape=shape,
dtype=dtype,
ktype=ktype,
initializer=initializer,
regularizer=regularizer,
reuse=reuse,
trainable=trainable,
collections=collections,
caching_device=caching_device,
validate_shape=validate_shape,
constraint=constraint,
synchronization=synchronization,
aggregation=aggregation)
synchronization, aggregation, trainable = (
variables.validate_synchronization_aggregation_trainable(
synchronization, aggregation, trainable, name))
return _true_getter(self._var_store,
name,
shape=shape,
dtype=dtype,
ktype=ktype,
initializer=initializer,
regularizer=regularizer,
reuse=reuse,
trainable=trainable,
collections=collections,
caching_device=caching_device,
partitioner=partitioner,
validate_shape=validate_shape,
constraint=constraint,
synchronization=synchronization,
aggregation=aggregation)