def testUnknown(self):
tf_val = gen_state_ops.variable(shape=[3, 4, 7],
dtype=dtypes.float32,
name="tf_val",
container="",
shared_name="")
self.assertIs(None, tensor_util.constant_value(tf_val))
def testUnknown(self):
tf_val = gen_state_ops.variable(
shape=[3, 4, 7],
dtype=dtypes.float32,
name="tf_val",
container="",
shared_name="")
self.assertIs(None, tensor_util.constant_value(tf_val))
def variable_op(shape, dtype, name="Variable", set_shape=True, container="",
shared_name=""):
"""Deprecated. Used variable_op_v2 instead."""
if not set_shape:
shape = tensor_shape.unknown_shape()
ret = gen_state_ops.variable(shape=shape, dtype=dtype, name=name,
container=container, shared_name=shared_name)
# TODO(mrry): Move this to where it is used, so we can get rid of this op
# wrapper?
if set_shape:
ret.set_shape(shape)
return ret
def variable_op(shape, dtype, name="Variable", set_shape=True, container="",
shared_name=""):
"""Deprecated. Used variable_op_v2 instead."""
if not set_shape:
shape = tensor_shape.unknown_shape()
ret = gen_state_ops.variable(shape=shape, dtype=dtype, name=name,
container=container, shared_name=shared_name)
# TODO(mrry): Move this to where it is used, so we can get rid of this op
# wrapper?
if set_shape:
ret.set_shape(shape)
return ret
def testTwoDeviceFunctions(self):
with ops.Graph().as_default() as g:
var_0 = gen_state_ops.variable(
shape=[1],
dtype=dtypes.float32,
name="var_0",
container="",
shared_name="")
with g.device(TestDeviceFuncPinVariableToCpu):
var_1 = gen_state_ops.variable(
shape=[1],
dtype=dtypes.float32,
name="var_1",
container="",
shared_name="")
var_2 = gen_state_ops.variable(
shape=[1],
dtype=dtypes.float32,
name="var_2",
container="",
shared_name="")
var_3 = gen_state_ops.variable(
shape=[1],
dtype=dtypes.float32,
name="var_3",
container="",
shared_name="")
with g.device(TestDeviceFuncPinVariableToCpu):
var_4 = gen_state_ops.variable(
shape=[1],
dtype=dtypes.float32,
name="var_4",
container="",
shared_name="")
with g.device("/device:GPU:0"):
var_5 = gen_state_ops.variable(
shape=[1],
dtype=dtypes.float32,
name="var_5",
container="",
shared_name="")
var_6 = gen_state_ops.variable(
shape=[1],
dtype=dtypes.float32,
name="var_6",
container="",
shared_name="")
self.assertDeviceEqual(var_0.device, None)
self.assertDeviceEqual(var_1.device, "/device:CPU:0")
self.assertDeviceEqual(var_2.device, None)
self.assertDeviceEqual(var_3.device, None)
self.assertDeviceEqual(var_4.device, "/device:CPU:0")
self.assertDeviceEqual(var_5.device, "/device:GPU:0")
self.assertDeviceEqual(var_6.device, "/device:CPU:0")
def testTwoDeviceFunctions(self):
with ops.Graph().as_default() as g:
var_0 = gen_state_ops.variable(
shape=[1],
dtype=dtypes.float32,
name="var_0",
container="",
shared_name="")
with g.device(test_device_func_pin_variable_to_cpu):
var_1 = gen_state_ops.variable(
shape=[1],
dtype=dtypes.float32,
name="var_1",
container="",
shared_name="")
var_2 = gen_state_ops.variable(
shape=[1],
dtype=dtypes.float32,
name="var_2",
container="",
shared_name="")
var_3 = gen_state_ops.variable(
shape=[1],
dtype=dtypes.float32,
name="var_3",
container="",
shared_name="")
with g.device(test_device_func_pin_variable_to_cpu):
var_4 = gen_state_ops.variable(
shape=[1],
dtype=dtypes.float32,
name="var_4",
container="",
shared_name="")
with g.device("/device:GPU:0"):
var_5 = gen_state_ops.variable(
shape=[1],
dtype=dtypes.float32,
name="var_5",
container="",
shared_name="")
var_6 = gen_state_ops.variable(
shape=[1],
dtype=dtypes.float32,
name="var_6",
container="",
shared_name="")
self.assertDeviceEqual(var_0.device, None)
self.assertDeviceEqual(var_1.device, "/device:CPU:0")
self.assertDeviceEqual(var_2.device, None)
self.assertDeviceEqual(var_3.device, None)
self.assertDeviceEqual(var_4.device, "/device:CPU:0")
self.assertDeviceEqual(var_5.device, "/device:GPU:0")
self.assertDeviceEqual(var_6.device, "/device:CPU:0")
def testDecay(self):
initial_lr = 0.1
k = 10
decay_rate = 0.96
step = gen_state_ops.variable(
shape=[], dtype=dtypes.int32, name="step", container="", shared_name="")
assign_step = state_ops.assign(step, 0)
increment_step = state_ops.assign_add(step, 1)
decayed_lr = learning_rate_decay.natural_exp_decay(initial_lr, step,
k, decay_rate)
with self.test_session():
assign_step.op.run()
for i in range(k+1):
expected = initial_lr * math.exp(-i / k * decay_rate)
self.assertAllClose(decayed_lr.eval(), expected, 1e-6)
increment_step.op.run()
def testStaircase(self):
with self.test_session():
step = gen_state_ops.variable(shape=[], dtype=dtypes.int32,
name="step", container="", shared_name="")
assign_100 = state_ops.assign(step, 100)
assign_1 = state_ops.assign(step, 1)
assign_2 = state_ops.assign(step, 2)
decayed_lr = learning_rate_decay.exponential_decay(.1, step, 3, 0.96,
staircase=True)
# No change to learning rate
assign_1.op.run()
self.assertAllClose(decayed_lr.eval(), .1, 1e-6)
assign_2.op.run()
self.assertAllClose(decayed_lr.eval(), .1, 1e-6)
# Decayed learning rate
assign_100.op.run()
expected = .1 * 0.96 ** (100 // 3)
self.assertAllClose(decayed_lr.eval(), expected, 1e-6)
def testAverageVariablesDeviceAssignment(self):
with ops.device("/job:dev_v0"):
v0 = variables.Variable(10.0, name="v0")
with ops.device("/job:dev_v1"):
v1 = gen_state_ops.variable(shape=[1],
dtype=dtypes.float32,
name="v1",
container="",
shared_name="")
v1.set_shape([1])
tensor2 = v0 + v1
ema = moving_averages.ExponentialMovingAverage(0.25, name="foo_avg")
with ops.device("/job:default"):
ema.apply([v0, v1, tensor2])
self.assertDeviceEqual("/job:dev_v0", ema.average(v0).device)
self.assertDeviceEqual("/job:dev_v1", ema.average(v1).device)
# However, the colocation property is maintained.
self.assertEqual([b"loc:@v1"], ema.average(v1).op.colocation_groups())
self.assertDeviceEqual("/job:default", ema.average(tensor2).device)
def testStaircase(self):
initial_lr = 0.1
k = 10
decay_rate = 0.96
step = gen_state_ops.variable(
shape=[], dtype=dtypes.int32, name="step", container="", shared_name="")
assign_step = state_ops.assign(step, 0)
increment_step = state_ops.assign_add(step, 1)
decayed_lr = learning_rate_decay.inverse_time_decay(initial_lr,
step,
k,
decay_rate,
staircase=True)
with self.test_session():
assign_step.op.run()
for i in range(k+1):
expected = initial_lr / (1 + decay_rate * (i // k))
self.assertAllClose(decayed_lr.eval(), expected, 1e-6)
increment_step.op.run()
def testAverageVariablesDeviceAssignment(self):
with ops.device("/job:dev_v0"):
v0 = variables.Variable(10.0, name="v0")
with ops.device("/job:dev_v1"):
v1 = gen_state_ops.variable(
shape=[1],
dtype=dtypes.float32,
name="v1",
container="",
shared_name="")
v1.set_shape([1])
tensor2 = v0 + v1
ema = moving_averages.ExponentialMovingAverage(0.25, name="foo_avg")
with ops.device("/job:default"):
ema.apply([v0, v1, tensor2])
self.assertDeviceEqual("/job:dev_v0", ema.average(v0).device)
self.assertDeviceEqual("/job:dev_v1", ema.average(v1).device)
# However, the colocation property is maintained.
self.assertEqual([b"loc:@v1"], ema.average(v1).op.colocation_groups())
self.assertDeviceEqual("/job:default", ema.average(tensor2).device)
def testContainer(self):
with ops.Graph().as_default():
v0 = variables.Variable([0])
with ops.container("l1"):
v1 = variables.Variable([1])
with ops.container("l2"):
v2 = variables.Variable([2])
special_v = gen_state_ops.variable(
shape=[1],
dtype=dtypes.float32,
name="VariableInL3",
container="l3",
shared_name="")
v3 = variables.Variable([3])
v4 = variables.Variable([4])
self.assertEqual(compat.as_bytes(""), v0.op.get_attr("container"))
self.assertEqual(compat.as_bytes("l1"), v1.op.get_attr("container"))
self.assertEqual(compat.as_bytes("l2"), v2.op.get_attr("container"))
self.assertEqual(compat.as_bytes("l3"), special_v.op.get_attr("container"))
self.assertEqual(compat.as_bytes("l1"), v3.op.get_attr("container"))
self.assertEqual(compat.as_bytes(""), v4.op.get_attr("container"))
def testContainer(self):
with ops.Graph().as_default():
v0 = variables.Variable([0])
with ops.container("l1"):
v1 = variables.Variable([1])
with ops.container("l2"):
v2 = variables.Variable([2])
special_v = gen_state_ops.variable(
shape=[1],
dtype=dtypes.float32,
name="VariableInL3",
container="l3",
shared_name="")
v3 = variables.Variable([3])
v4 = variables.Variable([4])
self.assertEqual(compat.as_bytes(""), v0.op.get_attr("container"))
self.assertEqual(compat.as_bytes("l1"), v1.op.get_attr("container"))
self.assertEqual(compat.as_bytes("l2"), v2.op.get_attr("container"))
self.assertEqual(compat.as_bytes("l3"), special_v.op.get_attr("container"))
self.assertEqual(compat.as_bytes("l1"), v3.op.get_attr("container"))
self.assertEqual(compat.as_bytes(""), v4.op.get_attr("container"))
def testRefDtype(self):
with context.graph_mode(), self.cached_session():
x = gen_state_ops.variable(shape=[1], dtype=dtypes.float32)
result = math_ops.cast(x, dtypes.float32)
self.assertEqual(x.dtype, dtypes.float32_ref)
self.assertEqual(result.dtype, dtypes.float32)
