def test_metrics_consistent(self):
# Tests that the identity metrics used to report in-sample predictions match
# the behavior of standard metrics.
g = ops.Graph()
with g.as_default():
features = {
feature_keys.TrainEvalFeatures.TIMES:
array_ops.zeros((1, 1)),
feature_keys.TrainEvalFeatures.VALUES:
array_ops.zeros((1, 1, 1)),
"ticker":
array_ops.reshape(
math_ops.cast(variables.VariableV1(
name="ticker",
initial_value=0,
dtype=dtypes.int64,
collections=[ops.GraphKeys.LOCAL_VARIABLES
]).count_up_to(10),
dtype=dtypes.float32), (1, 1, 1))
}
model_fn = ts_head_lib.TimeSeriesRegressionHead(
model=_TickerModel(),
state_manager=state_management.PassthroughStateManager(),
optimizer=train.GradientDescentOptimizer(
0.001)).create_estimator_spec
outputs = model_fn(features=features,
labels=None,
mode=estimator_lib.ModeKeys.EVAL)
metric_update_ops = [
metric[1] for metric in outputs.eval_metric_ops.values()
]
loss_mean, loss_update = metrics.mean(outputs.loss)
metric_update_ops.append(loss_update)
with self.cached_session() as sess:
coordinator = coordinator_lib.Coordinator()
queue_runner_impl.start_queue_runners(sess, coord=coordinator)
variables.local_variables_initializer().run()
sess.run(metric_update_ops)
loss_evaled, metric_evaled, nested_metric_evaled = sess.run(
(loss_mean, outputs.eval_metric_ops["ticker"][0],
outputs.eval_metric_ops[
feature_keys.FilteringResults.STATE_TUPLE][0][0]))
# The custom model_utils metrics for in-sample predictions should be in
# sync with the Estimator's mean metric for model loss.
self.assertAllClose(0., loss_evaled)
self.assertAllClose((((0., ), ), ), metric_evaled)
self.assertAllClose((((0., ), ), ), nested_metric_evaled)
coordinator.request_stop()
coordinator.join()
def _VariableRankTest(self,
np_scatter,
tf_scatter,
vtype,
itype,
repeat_indices=False):
np.random.seed(8)
ref_shapes = [(3, 6), (3, 6), (3, 6, 9), (3, 6, 9), (3, 6, 9), (3, 6, 9)]
indices_shapes = [(2,), (2, 2), (2,), (2, 2), (2, 3), (2, 3, 3)]
with self.cached_session(use_gpu=True):
for ref_shape, indices_shape in zip(ref_shapes, indices_shapes):
num_updates = indices_shape[0]
ixdim = indices_shape[-1]
indexable_area_shape = ()
for i in range(ixdim):
indexable_area_shape += (ref_shape[i],)
all_indices = [
list(coord)
for coord, _ in np.ndenumerate(
np.empty(indexable_area_shape, vtype))
]
np.random.shuffle(all_indices)
indices = np.array(all_indices[:num_updates])
if num_updates > 1 and repeat_indices:
indices = indices[:num_updates // 2]
for _ in range(num_updates - num_updates // 2):
indices = np.append(
indices, [indices[np.random.randint(num_updates // 2)]], axis=0)
np.random.shuffle(indices)
indices = _AsType(indices[:num_updates], itype)
updates_shape = (num_updates,)
for i in range(ixdim, len(ref_shape)):
updates_shape += (ref_shape[i],)
updates = _AsType(np.random.randn(*(updates_shape)), vtype)
ref = _AsType(np.random.randn(*(ref_shape)), vtype)
# Scatter via numpy
new = ref.copy()
np_scatter(new, indices, updates)
# Scatter via tensorflow
ref_var = variables.VariableV1(ref)
ref_var.initializer.run()
tf_scatter(ref_var, indices, updates).eval()
# Compare
self.assertAllClose(new, self.evaluate(ref_var))
def testGrpcDebugWrapperSessionWithoutWatchFnWorks(self):
u = variables.VariableV1(2.1, name="u")
v = variables.VariableV1(20.0, name="v")
w = math_ops.multiply(u, v, name="w")
sess = session.Session(
config=session_debug_testlib.no_rewrite_session_config())
sess.run(u.initializer)
sess.run(v.initializer)
sess = grpc_wrapper.GrpcDebugWrapperSession(
sess, "localhost:%d" % self._server_port)
w_result = sess.run(w)
self.assertAllClose(42.0, w_result)
dump = debug_data.DebugDumpDir(self._dump_root)
self.assertLessEqual(5, dump.size)
self.assertAllClose([2.1], dump.get_tensors("u", 0, "DebugIdentity"))
self.assertAllClose([2.1],
dump.get_tensors("u/read", 0, "DebugIdentity"))
self.assertAllClose([20.0], dump.get_tensors("v", 0, "DebugIdentity"))
self.assertAllClose([20.0],
dump.get_tensors("v/read", 0, "DebugIdentity"))
self.assertAllClose([42.0], dump.get_tensors("w", 0, "DebugIdentity"))
def _create_slots(self):
"""Make unshrinked internal variables (slots)."""
# Unshrinked variables have the updates before applying L1 regularization.
# Each unshrinked slot variable is either a `Variable` or list of
# `Variable`, depending on the value of its corresponding primary variable.
# We avoid using `PartitionedVariable` for the unshrinked slots since we do
# not need any of the extra information.
self._slots = collections.defaultdict(list)
for name in ['sparse_features_weights', 'dense_features_weights']:
for var in self._variables[name]:
# Our primary variable may be either a PartitionedVariable, or a list
# of Variables (each representing a partition).
if (isinstance(var, var_ops.PartitionedVariable)
or isinstance(var, list)):
var_list = []
# pylint: disable=protected-access
for v in var:
with ops.colocate_with(v):
# TODO(andreasst): remove SDCAOptimizer suffix once bug 30843109
# is fixed.
slot_var = var_ops.VariableV1(
initial_value=array_ops.zeros_like(
v.initialized_value(), dtypes.float32),
name=v.op.name + '_unshrinked/SDCAOptimizer')
var_list.append(slot_var)
self._slots['unshrinked_' + name].append(var_list)
# pylint: enable=protected-access
else:
with ops.device(var.device):
# TODO(andreasst): remove SDCAOptimizer suffix once bug 30843109 is
# fixed.
self._slots['unshrinked_' + name].append(
var_ops.VariableV1(array_ops.zeros_like(
var.initialized_value(), dtypes.float32),
name=var.op.name +
'_unshrinked/SDCAOptimizer'))
def setUp(self):
"""Write test SavedModels to a temp directory."""
with session.Session(graph=ops.Graph()) as sess:
x = variables.VariableV1(5, name="x")
y = variables.VariableV1(11, name="y")
z = x + y
sess.run(variables.global_variables_initializer())
foo_sig_def = signature_def_utils.build_signature_def(
{"foo_input": utils.build_tensor_info(x)},
{"foo_output": utils.build_tensor_info(z)})
bar_sig_def = signature_def_utils.build_signature_def(
{
"bar_x": utils.build_tensor_info(x),
"bar_y": utils.build_tensor_info(y)
}, {"bar_z": utils.build_tensor_info(z)})
builder = saved_model_builder.SavedModelBuilder(
SIMPLE_ADD_SAVED_MODEL)
builder.add_meta_graph_and_variables(sess, ["foo_graph"], {
"foo": foo_sig_def,
"bar": bar_sig_def
})
builder.save()
# Write SavedModel with a main_op
assign_op = control_flow_ops.group(state_ops.assign(y, 7))
builder = saved_model_builder.SavedModelBuilder(
SAVED_MODEL_WITH_MAIN_OP)
builder.add_meta_graph_and_variables(sess, ["foo_graph"], {
"foo": foo_sig_def,
"bar": bar_sig_def
},
main_op=assign_op)
builder.save()
def testLocalInitOp(self):
logdir = self._test_dir("default_local_init_op")
with ops.Graph().as_default():
# A local variable.
v = variables.VariableV1(
[1.0, 2.0, 3.0],
trainable=False,
collections=[ops.GraphKeys.LOCAL_VARIABLES])
# An entity which is initialized through a TABLE_INITIALIZER.
w = variables.VariableV1([4, 5, 6], trainable=False, collections=[])
ops.add_to_collection(ops.GraphKeys.TABLE_INITIALIZERS, w.initializer)
# This shouldn't add a variable to the VARIABLES collection responsible
# for variables that are saved/restored from checkpoints.
self.assertEquals(len(variables.global_variables()), 0)
# Suppress normal variable inits to make sure the local one is
# initialized via local_init_op.
sv = supervisor.Supervisor(logdir=logdir, init_op=None)
sess = sv.prepare_or_wait_for_session("")
self.assertAllClose([1.0, 2.0, 3.0], sess.run(v))
self.assertAllClose([4, 5, 6], sess.run(w))
sv.stop()
def testExtendAfterQueueRunners(self):
server = self._cached_server
with session.Session(server.target) as sess:
input_queue = input_ops.input_producer(
constant_op.constant([0.], dtype=dtypes.float32))
self.assertIsNotNone(input_queue)
var = variables.VariableV1(1.,
dtype=dtypes.float32,
trainable=False,
name="var")
sess.run(variables.global_variables_initializer())
queue_runner_impl.start_queue_runners(sess)
sess.run(var.assign(3.0))
def testIgnoreMultiStarts(self):
with self.cached_session() as sess:
# CountUpTo will raise OUT_OF_RANGE when it reaches the count.
zero64 = constant_op.constant(0, dtype=dtypes.int64)
var = variables.VariableV1(zero64)
count_up_to = var.count_up_to(3)
queue = data_flow_ops.FIFOQueue(10, dtypes.float32)
variables.global_variables_initializer().run()
coord = coordinator.Coordinator()
qr = queue_runner_impl.QueueRunner(queue, [count_up_to])
threads = []
# NOTE that this test does not actually start the threads.
threads.extend(qr.create_threads(sess, coord=coord))
new_threads = qr.create_threads(sess, coord=coord)
self.assertEqual([], new_threads)
def testLogdirButExplicitlyNoSummaryWriter(self):
logdir = self._test_dir("explicit_no_summary_writer")
with ops.Graph().as_default():
variables.VariableV1([1.0], name="foo")
summary.scalar("c1", constant_op.constant(1))
summary.scalar("c2", constant_op.constant(2))
summary.scalar("c3", constant_op.constant(3))
summ = summary.merge_all()
sv = supervisor.Supervisor(logdir=logdir, summary_writer=None)
sess = sv.prepare_or_wait_for_session("")
# Check that a checkpoint is still be generated.
self._wait_for_glob(sv.save_path, 3.0)
# Check that we cannot write a summary
with self.assertRaisesRegexp(RuntimeError, "requires a summary writer"):
sv.summary_computed(sess, sess.run(summ))
def get_session(is_chief):
g = ops.Graph()
with g.as_default():
with ops.device("/job:local"):
v = variables.VariableV1(
1.0,
name="ready_for_local_init_op_restore_v_" + str(uid))
vadd = v.assign_add(1)
w = variables.VariableV1(
v,
trainable=False,
collections=[ops.GraphKeys.LOCAL_VARIABLES],
name="ready_for_local_init_op_restore_w_" + str(uid))
ready_for_local_init_op = variables.report_uninitialized_variables(
variables.global_variables())
sv = supervisor.Supervisor(
logdir=logdir,
is_chief=is_chief,
graph=g,
recovery_wait_secs=1,
ready_for_local_init_op=ready_for_local_init_op)
sess = sv.prepare_or_wait_for_session(server.target)
return sv, sess, v, vadd, w
def setUp(self):
"""Test setup.
Structure of the forward graph:
f
| |
----- -----
| |
d e
| | | |
--- --------- ---
| | |
a b c
Construct a backward graph using the GradientDescentOptimizer.
"""
self.a = variables.VariableV1(1.0, name="a")
self.b = variables.VariableV1(2.0, name="b")
self.c = variables.VariableV1(4.0, name="c")
self.d = math_ops.multiply(self.a, self.b, name="d")
self.e = math_ops.multiply(self.b, self.c, name="e")
self.f = math_ops.multiply(self.d, self.e, name="f")
# Gradient descent optimizer that minimizes g.
gradient_descent.GradientDescentOptimizer(0.01).minimize(
self.f, name="optim")
rewriter_config = rewriter_config_pb2.RewriterConfig(
disable_model_pruning=True,
arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF,
constant_folding=rewriter_config_pb2.RewriterConfig.OFF)
graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_config)
config = config_pb2.ConfigProto(graph_options=graph_options)
self.sess = session.Session(config=config)
self.sess.run(variables.global_variables_initializer())
def testColocationConstraints(self):
with ops.Graph().as_default() as g:
c = constant_op.constant([10])
v = variables.VariableV1([3], dtype=dtypes.int32)
i = gen_array_ops.ref_identity(v)
a = state_ops.assign(i, c)
train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
train_op.append(a)
mg = meta_graph.create_meta_graph_def(graph=g)
grappler_item = item.Item(mg)
groups = grappler_item.GetColocationGroups()
self.assertEqual(len(groups), 1)
self.assertItemsEqual(
groups[0],
['Assign', 'RefIdentity', 'Variable', 'Variable/Assign'])
def testTwoOps(self):
with self.cached_session() as sess:
# CountUpTo will raise OUT_OF_RANGE when it reaches the count.
zero64 = constant_op.constant(0, dtype=dtypes.int64)
var0 = variables.VariableV1(zero64)
count_up_to_3 = var0.count_up_to(3)
var1 = variables.VariableV1(zero64)
count_up_to_30 = var1.count_up_to(30)
queue = data_flow_ops.FIFOQueue(10, dtypes.float32)
qr = queue_runner_impl.QueueRunner(queue,
[count_up_to_3, count_up_to_30])
threads = qr.create_threads(sess)
self.assertEqual(sorted(t.name for t in threads), [
"QueueRunnerThread-fifo_queue-CountUpTo:0",
"QueueRunnerThread-fifo_queue-CountUpTo_1:0"
])
self.evaluate(variables.global_variables_initializer())
for t in threads:
t.start()
for t in threads:
t.join()
self.assertEqual(0, len(qr.exceptions_raised))
self.assertEqual(3, self.evaluate(var0))
self.assertEqual(30, self.evaluate(var1))
def testVariables(self, use_resource):
cluster = data_service_test_base.TestCluster(num_workers=1)
if not use_resource:
with variable_scope.variable_scope("foo", use_resource=False):
v = variables.VariableV1(10, dtype=dtypes.int64)
else:
v = variables.Variable(10, dtype=dtypes.int64)
ds = dataset_ops.Dataset.range(3)
ds = ds.map(lambda x: x + v)
ds = self.make_distributed_dataset(ds, cluster)
self.evaluate(v.initializer)
self.assertDatasetProduces(ds,
list(range(10, 13)),
requires_initialization=True)
def testToggleEnableTwoDebugWatchesNoCrosstalkBetweenServers(self):
with session.Session(config=session_debug_testlib.
no_rewrite_session_config()) as sess:
v = variables.VariableV1(50.0, name="v")
delta = constant_op.constant(5.0, name="delta")
inc_v = state_ops.assign_add(v, delta, name="inc_v")
sess.run(v.initializer)
run_metadata = config_pb2.RunMetadata()
run_options = config_pb2.RunOptions(output_partition_graphs=True)
debug_utils.watch_graph(
run_options,
sess.graph,
debug_ops=["DebugIdentity(gated_grpc=true)"],
debug_urls=[
self._debug_server_url_1, self._debug_server_url_2
])
for i in xrange(4):
self._server_1.clear_data()
self._server_2.clear_data()
if i % 2 == 0:
self._server_1.request_watch("delta", 0, "DebugIdentity")
self._server_2.request_watch("v", 0, "DebugIdentity")
else:
self._server_1.request_unwatch("delta", 0, "DebugIdentity")
self._server_2.request_unwatch("v", 0, "DebugIdentity")
sess.run(inc_v, options=run_options, run_metadata=run_metadata)
if i % 2 == 0:
self.assertEqual(1,
len(self._server_1.debug_tensor_values))
self.assertEqual(1,
len(self._server_2.debug_tensor_values))
self.assertAllClose(
[5.0], self._server_1.
debug_tensor_values["delta:0:DebugIdentity"])
self.assertAllClose([
50 + 5.0 * i
], self._server_2.debug_tensor_values["v:0:DebugIdentity"])
else:
self.assertEqual(0,
len(self._server_1.debug_tensor_values))
self.assertEqual(0,
len(self._server_2.debug_tensor_values))
def _testOneSimpleInference(self, rnn_mode, num_layers, num_units,
input_size, batch_size, seq_length, dir_count,
dropout, expected, tolerance):
random_seed.set_random_seed(5678)
model = _CreateModel(
rnn_mode,
num_layers,
num_units,
input_size,
input_mode="auto_select",
direction=(cudnn_rnn_ops.CUDNN_RNN_UNIDIRECTION if dir_count == 1
else cudnn_rnn_ops.CUDNN_RNN_BIDIRECTION),
dropout=dropout)
has_input_c = (rnn_mode == cudnn_rnn_ops.CUDNN_LSTM)
params_size_t = model.params_size()
input_data = array_ops.ones([seq_length, batch_size, input_size])
input_h = array_ops.ones(
[num_layers * dir_count, batch_size, num_units])
params = variables.VariableV1(array_ops.ones([params_size_t]),
validate_shape=False)
if has_input_c:
input_c = array_ops.ones(
[num_layers * dir_count, batch_size, num_units])
output, output_h, output_c = model(input_data=input_data,
input_h=input_h,
input_c=input_c,
params=params,
is_training=False)
else:
output, output_h = model(input_data=input_data,
input_h=input_h,
params=params,
is_training=False)
output_sum = math_ops.reduce_sum(output)
output_h_sum = math_ops.reduce_sum(output_h)
total_sum = output_sum + output_h_sum
if has_input_c:
output_c_sum = math_ops.reduce_sum(output_c)
total_sum += output_c_sum
with self.test_session(use_gpu=True,
graph=ops.get_default_graph()) as sess:
sess.run(variables.global_variables_initializer())
total_sum_v = sess.run([total_sum])
self.assertAllClose(total_sum_v[0],
expected,
atol=tolerance,
rtol=tolerance)
def test_optimizer_kwargs(self):
# Checks that the 'method' argument is stil present
# after running optimizer.minimize().
# Bug reference: b/64065260
vector_initial_value = [7., 7.]
vector = variables.VariableV1(vector_initial_value, 'vector')
loss = math_ops.reduce_sum(math_ops.square(vector))
optimizer = external_optimizer.ScipyOptimizerInterface(loss,
method='SLSQP')
with self.cached_session() as sess:
sess.run(variables.global_variables_initializer())
optimizer.minimize(sess)
method = optimizer.optimizer_kwargs.get('method')
self.assertEqual('SLSQP', method)
def test_vector_bounds(self):
vector_initial_value = [7., 7.]
vector = variables.VariableV1(vector_initial_value, 'vector')
# Make norm as small as possible.
loss = math_ops.reduce_sum(math_ops.square(vector))
var_to_bounds = {vector: ([None, 2.], None)}
optimizer = external_optimizer.ScipyOptimizerInterface(
loss, var_to_bounds=var_to_bounds)
with self.cached_session() as sess:
sess.run(variables.global_variables_initializer())
optimizer.minimize(sess)
self.assertAllClose([0., 2.], sess.run(vector))
def _model_fn(features, labels, mode, config):
del labels
del mode
del config
global_step = training_util.get_or_create_global_step()
update_global_step_op = global_step.assign_add(1)
latest_feature = variables.VariableV1(
0, name='latest_feature', dtype=dtypes.int64)
store_latest_feature_op = latest_feature.assign(features)
ops.add_to_collection('my_vars', global_step)
ops.add_to_collection('my_vars', latest_feature)
return model_fn.EstimatorSpec(
mode='train',
train_op=control_flow_ops.group(
[update_global_step_op, store_latest_feature_op]),
loss=constant_op.constant(2.0))
def _GetGraph(self):
"""Get the graph for testing."""
g = ops.Graph()
with g.as_default():
with g.device("/GPU:0"):
inp = array_ops.placeholder(dtype=dtypes.float32,
shape=[None, 1, 1],
name="input")
var = variables.VariableV1([[[1.0]]],
dtype=dtypes.float32,
name="v1")
add = inp + var.value()
mul = inp * add
add = mul + add
out = array_ops.identity(add, name="output")
return g, var, inp, out
def test_stop(self):
hook = wals_lib._StopAtSweepHook(last_sweep=10)
completed_sweeps = variables.VariableV1(
8, name=wals_lib.WALSMatrixFactorization.COMPLETED_SWEEPS)
train_op = state_ops.assign_add(completed_sweeps, 1)
hook.begin()
with self.cached_session() as sess:
sess.run([variables.global_variables_initializer()])
mon_sess = monitored_session._HookedSession(sess, [hook])
mon_sess.run(train_op)
# completed_sweeps is 9 after running train_op.
self.assertFalse(mon_sess.should_stop())
mon_sess.run(train_op)
# completed_sweeps is 10 after running train_op.
self.assertTrue(mon_sess.should_stop())
def setUp(self):
self.session_root = tempfile.mkdtemp()
self.v = variables.VariableV1(10.0, dtype=dtypes.float32, name="v")
self.delta = constant_op.constant(1.0,
dtype=dtypes.float32,
name="delta")
self.eta = constant_op.constant(-1.4, dtype=dtypes.float32, name="eta")
self.inc_v = state_ops.assign_add(self.v, self.delta, name="inc_v")
self.dec_v = state_ops.assign_add(self.v, self.eta, name="dec_v")
self.ph = array_ops.placeholder(dtypes.float32, shape=(), name="ph")
self.inc_w_ph = state_ops.assign_add(self.v, self.ph, name="inc_w_ph")
self.sess = session.Session()
self.sess.run(self.v.initializer)
def model_fn(features, labels, mode):
_, _ = features, labels
w = variables.VariableV1(
initial_value=[0.],
trainable=False,
collections=[ops.GraphKeys.SAVEABLE_OBJECTS])
init_op = control_flow_ops.group(
[w.initializer, training.get_global_step().initializer])
return estimator_lib.EstimatorSpec(
mode,
loss=constant_op.constant(3.),
scaffold=training.Scaffold(init_op=init_op),
train_op=constant_op.constant(5.),
eval_metric_ops={
'mean_of_features': metrics_lib.mean(constant_op.constant(2.))
})
def get_synthetic_inputs(self, input_name, nclass):
inputs = tf.random_uniform(self.get_input_shapes('train')[0],
dtype=self.get_input_data_types('train')[0])
inputs = variables.VariableV1(
inputs,
trainable=False,
collections=[tf.GraphKeys.LOCAL_VARIABLES],
name=input_name)
labels = tf.convert_to_tensor(
np.random.randint(28,
size=[self.batch_size, self.max_label_length]))
input_lengths = tf.convert_to_tensor([self.max_time_steps] *
self.batch_size)
label_lengths = tf.convert_to_tensor([self.max_label_length] *
self.batch_size)
return [inputs, labels, input_lengths, label_lengths]
def testPrepareSessionWithCyclicInitializer(self):
# Regression test. Previously Variable._build_initializer_expr would enter
# into an infinite recursion when the variable's initial_value involved
# cyclic dependencies.
with ops.Graph().as_default():
i = control_flow_ops.while_loop(lambda i: i < 1, lambda i: i + 1, [0])
v = variables.VariableV1(array_ops.identity(i), name="v")
with self.cached_session():
self.assertEqual(False, variables.is_variable_initialized(v).eval())
sm = session_manager.SessionManager(
ready_op=variables.report_uninitialized_variables())
sess = sm.prepare_session("", init_op=v.initializer)
self.assertEqual(1, sess.run(v))
self.assertEqual(
True,
variables.is_variable_initialized(
sess.graph.get_tensor_by_name("v:0")).eval(session=sess))
def tfadd_with_ckpt_saver(out_dir):
x = array_ops.placeholder(dtypes.int32, name='x_hold')
y = variables.VariableV1(constant_op.constant([0]), name='y_saved')
math_ops.add(x, y, name='x_y_sum')
init_op = variables.global_variables_initializer()
saver = saver_lib.Saver(name='abcprefix', write_version=saver_pb2.SaverDef.V1)
with session.Session() as sess:
sess.run(init_op)
sess.run(y.assign(y + 42))
# Without the checkpoint, the variable won't be set to 42.
ckpt_file = os.path.join(out_dir, 'test_graph_tfadd_with_ckpt_saver.ckpt')
saver.save(sess, ckpt_file)
# Without the SaverDef, the restore op won't be named correctly.
saver_file = os.path.join(out_dir, 'test_graph_tfadd_with_ckpt_saver.saver')
with open(saver_file, 'wb') as f:
f.write(six.ensure_binary(saver.as_saver_def().SerializeToString()))
def test_scalar_bounds(self):
vector_initial_value = [7., 7.]
vector = variables.VariableV1(vector_initial_value, 'vector')
# Make norm as small as possible.
loss = math_ops.reduce_sum(math_ops.square(vector))
# Make the minimum value of each component be 1.
var_to_bounds = {vector: (1., np.infty)}
optimizer = external_optimizer.ScipyOptimizerInterface(
loss, var_to_bounds=var_to_bounds)
with self.cached_session() as sess:
sess.run(variables.global_variables_initializer())
optimizer.minimize(sess)
self.assertAllClose(np.ones(2), sess.run(vector))
def testStartQueueRunners(self):
# CountUpTo will raise OUT_OF_RANGE when it reaches the count.
zero64 = constant_op.constant(0, dtype=dtypes.int64)
var = variables.VariableV1(zero64)
count_up_to = var.count_up_to(3)
queue = data_flow_ops.FIFOQueue(10, dtypes.float32)
init_op = variables.global_variables_initializer()
qr = queue_runner_impl.QueueRunner(queue, [count_up_to])
queue_runner_impl.add_queue_runner(qr)
with self.cached_session() as sess:
init_op.run()
threads = queue_runner_impl.start_queue_runners(sess)
for t in threads:
t.join()
self.assertEqual(0, len(qr.exceptions_raised))
# The variable should be 3.
self.assertEqual(3, var.eval())
def testVariables(self):
step = variables.VariableV1(1)
assign_1 = step.assign(1)
assign_2 = step.assign(2)
assign_100 = step.assign(100)
decayed_lr = learning_rate_decay.exponential_decay(
.1, step, 3, 0.96, staircase=True)
self.evaluate(variables.global_variables_initializer())
# No change to learning rate
self.evaluate(assign_1.op)
self.assertAllClose(self.evaluate(decayed_lr), .1, 1e-6)
self.evaluate(assign_2.op)
self.assertAllClose(self.evaluate(decayed_lr), .1, 1e-6)
# Decayed learning rate
self.evaluate(assign_100.op)
expected = .1 * 0.96**(100 // 3)
self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6)
def testPartitionedVariableMasking(self):
partitioner = partitioned_variables.variable_axis_size_partitioner(40)
with self.cached_session() as session:
with variable_scope.variable_scope("", partitioner=partitioner):
sparsity = variables.VariableV1(0.5, name="Sparsity")
weights = variable_scope.get_variable(
"weights", initializer=math_ops.linspace(1.0, 100.0, 100))
masked_weights = pruning.apply_mask(
weights, scope=variable_scope.get_variable_scope())
p = pruning.Pruning(sparsity=sparsity)
p._spec.threshold_decay = 0.0
mask_update_op = p.mask_update_op()
variables.global_variables_initializer().run()
masked_weights_val = masked_weights.eval()
session.run(mask_update_op)
masked_weights_val = masked_weights.eval()
self.assertAllEqual(np.count_nonzero(masked_weights_val), 50)
