def test_local_variable(self):
with self.test_session() as sess:
self.assertEquals([], variables_lib.local_variables())
value0 = 42
variables_lib2.local_variable(value0)
value1 = 43
variables_lib2.local_variable(value1)
variables = variables_lib.local_variables()
self.assertEquals(2, len(variables))
self.assertRaises(errors_impl.OpError, sess.run, variables)
variables_lib.initialize_variables(variables).run()
self.assertAllEqual(set([value0, value1]), set(sess.run(variables)))
def testVars(self):
classification.f1_score(
predictions=array_ops.ones((10, 1)),
labels=array_ops.ones((10, 1)),
num_thresholds=3)
expected = {'f1/true_positives:0', 'f1/false_positives:0',
'f1/false_negatives:0'}
self.assertEquals(
expected, set(v.name for v in variables.local_variables()))
self.assertEquals(
set(expected), set(v.name for v in variables.local_variables()))
self.assertEquals(
set(expected),
set(v.name for v in ops.get_collection(ops.GraphKeys.METRIC_VARIABLES)))
def testVariableDevicePlacement(self):
classes = np.random.randint(5, size=(20000,)) # Uniformly sampled
target_dist = [0.9, 0.05, 0.05, 0.0, 0.0]
with ops.device(
device_setter.replica_device_setter(ps_tasks=1, ps_device="/cpu:0")):
dataset = (dataset_ops.Dataset.from_tensor_slices(classes)
.shuffle(200, seed=21)
.map(lambda c: (c, string_ops.as_string(c))))
dataset = dataset_ops.rejection_resample(
dataset, target_dist=target_dist, initial_dist=None,
class_func=lambda c, _: c, seed=27)
self.assertEqual(1, len(variables.local_variables()))
self.assertEqual(b"",
compat.as_bytes(variables.local_variables()[0].device))
def testNotInLocalVariables(self):
with self.test_session():
with variable_scope.variable_scope('A'):
a = variables_lib2.model_variable('a', [5])
self.assertTrue(a in variables_lib.global_variables())
self.assertTrue(a in ops.get_collection(ops.GraphKeys.MODEL_VARIABLES))
self.assertFalse(a in variables_lib.local_variables())
def get_epoch_variable():
"""Returns the epoch variable, or [0] if not defined."""
# Grab epoch variable defined in
# //third_party/tensorflow/python/training/input.py::limit_epochs
for v in tf_variables.local_variables():
if 'limit_epochs/epoch' in v.op.name:
return array_ops.reshape(v, [1])
# TODO(thomaswc): Access epoch from the data feeder.
return [0]
def testCreateVariable(self):
with self.test_session():
with variable_scope.variable_scope('A'):
a = variables_lib2.variable('a', [5])
self.assertEquals(a.op.name, 'A/a')
self.assertListEqual(a.get_shape().as_list(), [5])
self.assertTrue(a in ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES))
self.assertFalse(a in ops.get_collection(ops.GraphKeys.MODEL_VARIABLES))
self.assertFalse(a in variables_lib.local_variables())
def _get_variable_for(v):
"""Returns the ResourceVariable responsible for v, or v if not necessary."""
if v.op.type == "ResourceGather":
for var in variables.global_variables() + variables.local_variables():
if (isinstance(var, resource_variable_ops.ResourceVariable)
and var.handle is v.op.inputs[0]):
return var
raise ValueError("Got embedding lookup %s but"
" could not locate source variable." % (str(v)))
return v
def _get_variable_for(v):
"""Returns the ResourceVariable responsible for v, or v if not necessary."""
if v.op.type == "ResourceGather":
for var in variables.global_variables() + variables.local_variables():
if (isinstance(var, resource_variable_ops.ResourceVariable)
and var.handle is v.op.inputs[0]):
return var
raise ValueError("Got embedding lookup %s but"
" could not locate source variable." % (str(v)))
return v
def run(self,
num_batches=None,
graph=None,
session=None,
start_queues=True,
initialize_variables=True,
**kwargs):
"""Builds and runs the columns of the `DataFrame` and yields batches.
This is a generator that yields a dictionary mapping column names to
evaluated columns.
Args:
num_batches: the maximum number of batches to produce. If none specified,
the returned value will iterate through infinite batches.
graph: the `Graph` in which the `DataFrame` should be built.
session: the `Session` in which to run the columns of the `DataFrame`.
start_queues: if true, queues will be started before running and halted
after producting `n` batches.
initialize_variables: if true, variables will be initialized.
**kwargs: Additional keyword arguments e.g. `num_epochs`.
Yields:
A dictionary, mapping column names to the values resulting from running
each column for a single batch.
"""
if graph is None:
graph = ops.get_default_graph()
with graph.as_default():
if session is None:
session = sess.Session()
self_built = self.build(**kwargs)
keys = list(self_built.keys())
cols = list(self_built.values())
if initialize_variables:
if variables.local_variables():
session.run(variables.initialize_local_variables())
if variables.all_variables():
session.run(variables.initialize_all_variables())
if start_queues:
coord = coordinator.Coordinator()
threads = qr.start_queue_runners(sess=session, coord=coord)
i = 0
while num_batches is None or i < num_batches:
i += 1
try:
values = session.run(cols)
yield collections.OrderedDict(zip(keys, values))
except errors.OutOfRangeError:
break
if start_queues:
coord.request_stop()
coord.join(threads)
def testLocalVariableNotInVariablesToRestore(self):
with self.test_session():
with variable_scope.variable_scope('A'):
a = variables_lib2.local_variable(0)
self.assertFalse(a in variables_lib2.get_variables_to_restore())
self.assertTrue(a in variables_lib.local_variables())
def testLocalVariableNotInAllVariables(self):
with self.test_session():
with variable_scope.variable_scope('A'):
a = variables_lib2.local_variable(0)
self.assertFalse(a in variables_lib.global_variables())
self.assertTrue(a in variables_lib.local_variables())
