def testIdentityOnVariable(self):
with self.test_scope():
v = resource_variable_ops.ResourceVariable(True)
i = array_ops.identity(v)
self.assertAllEqual(True, i.numpy())
def model():
return resource_variable_ops.ResourceVariable(name="same_name",
initial_value=1) + 1
def benchmark_tf_transpose_variable_2_by_2_GPU(self):
with context.device(GPU):
m = resource_variable_ops.ResourceVariable(self._m_2_by_2)
self._benchmark_transpose(m, num_iters=self._num_iters_2_by_2)
def testScatterSubStateOps(self):
with context.eager_mode():
v = resource_variable_ops.ResourceVariable([1.0, 2.0], name="sub")
state_ops.scatter_sub(v, [1], [3])
self.assertAllEqual([1.0, -1.0], v.numpy())
def testAssignIncompatibleShape(self):
v = resource_variable_ops.ResourceVariable([0, 1, 2, 3])
self.evaluate(v.initializer)
with self.assertRaisesRegexp(Exception, r"hapes must be equal"):
self.assertAllEqual(self.evaluate(v.assign_add(1)), [1, 2, 3, 4])
def testDtypeAfterFromProto(self):
v = resource_variable_ops.ResourceVariable(2.0)
w = resource_variable_ops.ResourceVariable.from_proto(v.to_proto())
self.assertIsInstance(w.dtype, dtypes.DType)
self.assertEqual(v.dtype, w.dtype)
def testEagerNameNotNeeded(self):
with context.eager_mode():
v0 = resource_variable_ops.ResourceVariable(1.0)
self.assertAllEqual(v0.numpy(), 1.0)
def testDifferentAssignGraph(self):
with ops.Graph().as_default():
v = resource_variable_ops.ResourceVariable(1.0)
ops.reset_default_graph()
v.assign(
2.0) # Note: this fails if we run convert_to_tensor on not the
def testUnreadOpName(self):
v = resource_variable_ops.ResourceVariable(1.0)
self.assertNotEqual(v.name, v.assign_add(1.0).name)
def testEagerBool(self):
with context.eager_mode():
v = resource_variable_ops.ResourceVariable(False, name="bool_test")
self.assertAllEqual(bool(v), False)
def testStridedSliceAssign(self):
v = resource_variable_ops.ResourceVariable([1.0, 2.0])
self.evaluate(variables.global_variables_initializer())
self.evaluate(v[0].assign(2.0))
self.assertAllEqual(self.evaluate(v), [2.0, 2.0])
def testEagerInitializedValue(self):
with context.eager_mode():
variable = resource_variable_ops.ResourceVariable(
1.0, name="eager-init")
self.assertAllEqual(variable.numpy(), 1.0)
self.assertAllEqual(variable.initialized_value().numpy(), 1.0)
def testAddVariable(self):
obj = NonLayerTrackable()
with self.assertRaisesRegex(ValueError, "do not specify shape"):
trackable_utils.add_variable(obj,
name="shape_specified_twice",
shape=[],
initializer=1)
constant_initializer = trackable_utils.add_variable(
obj, name="constant_initializer", initializer=1)
with variable_scope.variable_scope("some_variable_scope"):
ones_initializer = trackable_utils.add_variable(
obj,
name="ones_initializer",
shape=[2],
initializer=init_ops.ones_initializer(dtype=dtypes.float32))
bare_initializer = trackable_utils.add_variable(
obj,
name="bare_initializer",
shape=[2, 2],
dtype=dtypes.float64,
initializer=init_ops.zeros_initializer)
# Even in graph mode, there are no naming conflicts between objects, only
# naming conflicts within an object.
other_duplicate = resource_variable_ops.ResourceVariable(
name="duplicate", initial_value=1.)
duplicate = trackable_utils.add_variable(obj,
name="duplicate",
shape=[])
with self.assertRaisesRegex(ValueError,
"'duplicate'.*already declared"):
trackable_utils.add_variable(obj, name="duplicate", shape=[])
self.evaluate(trackable_utils.gather_initializers(obj))
self.assertEqual("constant_initializer:0", constant_initializer.name)
self.assertEqual(1, self.evaluate(constant_initializer))
self.assertEqual("some_variable_scope/ones_initializer:0",
ones_initializer.name)
self.assertAllEqual([1, 1], self.evaluate(ones_initializer))
self.assertAllEqual([[0., 0.], [0., 0.]],
self.evaluate(bare_initializer))
self.assertEqual("a_variable:0", obj.a_variable.name)
self.assertEqual("duplicate:0", other_duplicate.name)
if context.executing_eagerly():
# When executing eagerly, there's no uniquification of variable names. The
# checkpoint name will be the same.
self.assertEqual("duplicate:0", duplicate.name)
else:
# The .name attribute may be globally influenced, but the checkpoint name
# won't be (tested below).
self.assertEqual("duplicate_1:0", duplicate.name)
named_variables, _, _ = (
graph_view.ObjectGraphView(obj).serialize_object_graph())
expected_checkpoint_names = (
"a_variable/.ATTRIBUTES/VARIABLE_VALUE",
"bare_initializer/.ATTRIBUTES/VARIABLE_VALUE",
"constant_initializer/.ATTRIBUTES/VARIABLE_VALUE",
"duplicate/.ATTRIBUTES/VARIABLE_VALUE",
"ones_initializer/.ATTRIBUTES/VARIABLE_VALUE",
)
six.assertCountEqual(self, expected_checkpoint_names,
[v.name for v in named_variables])
def testAssignAddVariable(self):
with self.test_scope():
v = resource_variable_ops.ResourceVariable(1.0)
v.assign_add(2.0)
self.assertEqual(3.0, v.numpy())
def testLoad(self):
v = resource_variable_ops.ResourceVariable(1.0, name="var0")
self.evaluate(variables.global_variables_initializer())
v.load(2.0)
self.assertEqual(2.0, self.evaluate(v.value()))
def testInitFn(self):
with self.cached_session():
v = resource_variable_ops.ResourceVariable(initial_value=lambda: 1,
dtype=dtypes.float32)
self.assertEqual(v.handle.op.colocation_groups(),
v.initializer.inputs[1].op.colocation_groups())
def testGPUInt64(self):
if not context.context().num_gpus():
return
with context.eager_mode(), context.device("gpu:0"):
v = resource_variable_ops.ResourceVariable(1, dtype=dtypes.int64)
self.assertAllEqual(1, v.numpy())
def testHandleNumpy(self):
with context.eager_mode():
with self.assertRaises(ValueError):
resource_variable_ops.ResourceVariable(
1.0, name="handle-numpy").handle.numpy()
def testEagerNameNotIdentity(self):
with context.eager_mode():
v0 = resource_variable_ops.ResourceVariable(1.0, name="a")
v1 = resource_variable_ops.ResourceVariable(2.0, name="a")
self.assertAllEqual(v0.numpy(), 1.0)
self.assertAllEqual(v1.numpy(), 2.0)
def testCountUpToFunction(self):
with context.eager_mode():
v = resource_variable_ops.ResourceVariable(0, name="upto")
self.assertAllEqual(state_ops.count_up_to(v, 1), 0)
with self.assertRaises(errors.OutOfRangeError):
state_ops.count_up_to(v, 1)
def body(i, _):
zero = array_ops.zeros([], dtype=dtypes.int32)
v = resource_variable_ops.ResourceVariable(initial_value=zero)
return (i + 1, v.read_value())
def testInitFnDtype(self):
v = resource_variable_ops.ResourceVariable(initial_value=lambda: 1,
dtype=dtypes.float32,
name="var0")
self.assertEqual(dtypes.float32, v.value().dtype)
def testScatterUpdateCast(self):
with context.eager_mode():
v = resource_variable_ops.ResourceVariable([1.0, 2.0],
name="update")
state_ops.scatter_update(v, [1], [3])
self.assertAllEqual([1.0, 3.0], v.numpy())
def testInitFnNoDtype(self):
v = resource_variable_ops.ResourceVariable(initial_value=lambda: 1,
name="var2")
self.assertEqual(dtypes.int32, v.value().dtype)
def __init__(self):
self.v = resource_variable_ops.ResourceVariable(1.0)
def testInitializeAllVariables(self):
v = resource_variable_ops.ResourceVariable(1,
dtype=dtypes.float32,
name="var0")
self.evaluate(variables.global_variables_initializer())
self.assertEqual(1.0, self.evaluate(v.value()))
def benchmark_tf_zeros_like_variable_GPU(self): m = resource_variable_ops.ResourceVariable(self._m_2_by_2) self._benchmark_tf_zeros_like(m, device=GPU)
def testOperatorOverload(self):
v = resource_variable_ops.ResourceVariable(1.0, name="var0")
self.evaluate(variables.global_variables_initializer())
self.assertEqual(2.0, self.evaluate(v + v))
def benchmark_read_variable_op_2_by_2_CPU(self):
with context.device(CPU):
m = resource_variable_ops.ResourceVariable(self._m_2_by_2)
self._benchmark_read_variable(m, num_iters=self._num_iters_2_by_2)
def testGradientTapeVariable(self):
v = resource_variable_ops.ResourceVariable(1.0, name='v')
with backprop.GradientTape() as g:
y = v * v
grad = g.gradient(y, [v])[0]
self.assertAllEqual(grad, 2.0)
