def accumulate_privacy_spending(self, eps_delta, unused_sigma,
num_examples):
"""Accumulate the privacy spending.
Currently only support approximate privacy. Here we assume we use Gaussian
noise on randomly sampled batch so we get better composition: 1. the per
batch privacy is computed using privacy amplication via sampling bound;
2. the composition is done using the composition with Gaussian noise.
TODO(liqzhang) Add a link to a document that describes the bounds used.
Args:
eps_delta: EpsDelta pair which can be tensors.
unused_sigma: the noise sigma. Unused for this accountant.
num_examples: the number of examples involved.
Returns:
a TensorFlow operation for updating the privacy spending.
"""
eps, delta = eps_delta
with tf.control_dependencies(
[tf.Assert(tf.greater(delta, 0),
["delta needs to be greater than 0"])]):
amortize_ratio = (tf.cast(num_examples, tf.float32) * 1.0 /
self._total_examples)
# Use privacy amplification via sampling bound.
# See Lemma 2.2 in http://arxiv.org/pdf/1405.7085v2.pdf
# TODO(liqzhang) Add a link to a document with formal statement
# and proof.
amortize_eps = tf.reshape(tf.log(1.0 + amortize_ratio * (
tf.exp(eps) - 1.0)), [1])
amortize_delta = tf.reshape(amortize_ratio * delta, [1])
return tf.group(*[tf.assign_add(self._eps_squared_sum,
tf.square(amortize_eps)),
tf.assign_add(self._delta_sum, amortize_delta)])
def _apply_stats(self, statsUpdates, accumulate=False, accumulateCoeff=0.):
updateOps = []
# obtain the stats var list
for stats_var in statsUpdates:
stats_new = statsUpdates[stats_var]
if accumulate:
# simple superbatch averaging
update_op = tf.assign_add(
stats_var, accumulateCoeff * stats_new, use_locking=True)
else:
# exponential running averaging
update_op = tf.assign(
stats_var, stats_var * self._stats_decay, use_locking=True)
update_op = tf.assign_add(
update_op, (1. - self._stats_decay) * stats_new, use_locking=True)
updateOps.append(update_op)
with tf.control_dependencies(updateOps):
stats_step_op = tf.assign_add(self.stats_step, 1)
if KFAC_DEBUG:
stats_step_op = (tf.Print(stats_step_op,
[tf.convert_to_tensor('step:'),
self.global_step,
tf.convert_to_tensor('fac step:'),
self.factor_step,
tf.convert_to_tensor('sgd step:'),
self.sgd_step,
tf.convert_to_tensor('Accum:'),
tf.convert_to_tensor(accumulate),
tf.convert_to_tensor('Accum coeff:'),
tf.convert_to_tensor(accumulateCoeff),
tf.convert_to_tensor('stat step:'),
self.stats_step, updateOps[0], updateOps[1]]))
return [stats_step_op, ]
def evaluate_precision_recall(
input_layer, labels, threshold=0.5, per_example_weights=None, name=PROVIDED, phase=Phase.train
):
"""Computes the precision and recall of the prediction vs the labels.
Args:
input_layer: A Pretty Tensor object.
labels: The target labels to learn as a float tensor.
threshold: The threshold to use to decide if the prediction is true.
per_example_weights: A Tensor with a weight per example.
name: An optional name.
phase: The phase of this model; non training phases compute a total across
all examples.
Returns:
Precision and Recall.
"""
_ = name # Eliminate warning, name used for namescoping by PT.
selected, sum_retrieved, sum_relevant = _compute_precision_recall(
input_layer, labels, threshold, per_example_weights
)
if phase != Phase.train:
dtype = tf.float32
# Create the variables in all cases so that the load logic is easier.
relevant_count = tf.get_variable(
"relevant_count",
[],
dtype,
tf.zeros_initializer,
collections=[bookkeeper.GraphKeys.TEST_VARIABLES],
trainable=False,
)
retrieved_count = tf.get_variable(
"retrieved_count",
[],
dtype,
tf.zeros_initializer,
collections=[bookkeeper.GraphKeys.TEST_VARIABLES],
trainable=False,
)
selected_count = tf.get_variable(
"selected_count",
[],
dtype,
tf.zeros_initializer,
collections=[bookkeeper.GraphKeys.TEST_VARIABLES],
trainable=False,
)
with input_layer.g.device(selected_count.device):
selected = tf.assign_add(selected_count, selected)
with input_layer.g.device(retrieved_count.device):
sum_retrieved = tf.assign_add(retrieved_count, sum_retrieved)
with input_layer.g.device(relevant_count.device):
sum_relevant = tf.assign_add(relevant_count, sum_relevant)
return (
tf.select(tf.equal(sum_retrieved, 0), tf.zeros_like(selected), selected / sum_retrieved),
tf.select(tf.equal(sum_relevant, 0), tf.zeros_like(selected), selected / sum_relevant),
)
def test_summary_saver(self):
with tf.Graph().as_default() as g, tf.Session() as sess:
log_dir = 'log/dir'
summary_writer = testing.FakeSummaryWriter(log_dir, g)
var = tf.Variable(0.0)
tensor = tf.assign_add(var, 1.0)
summary_op = tf.scalar_summary('my_summary', tensor)
global_step = tf.contrib.framework.get_or_create_global_step()
train_op = tf.assign_add(global_step, 1)
hook = tf.train.SummarySaverHook(
summary_op=summary_op, save_steps=8, summary_writer=summary_writer)
hook.begin()
sess.run(tf.initialize_all_variables())
mon_sess = monitored_session._HookedSession(sess, [hook])
for i in range(30):
_ = i
mon_sess.run(train_op)
hook.end(sess)
summary_writer.assert_summaries(
test_case=self,
expected_logdir=log_dir,
expected_graph=g,
expected_summaries={
1: {'my_summary': 1.0},
9: {'my_summary': 2.0},
17: {'my_summary': 3.0},
25: {'my_summary': 4.0},
})
def apply(self, var_list):
"""Applies the running average to a list of variables
Creates shadow variables and update op. Returns a grouped update op for
all the averages in the list."""
update_ops = []
with tf.variable_scope('running_average'):
for var in var_list:
# add a shadow var that gets initialized to the same value
# and a count to keep track of how many times it's been updated
name = var.op.name
count = tf.get_variable(
name+'_count', dtype=tf.float32,
initializer=tf.constant_initializer(0.0),
shape=[], trainable=False)
shadow = tf.get_variable(
name+'_shadow', dtype=var.dtype,
initializer=var.initialized_value(),
collections=[tf.GraphKeys.MOVING_AVERAGE_VARIABLES,
tf.GraphKeys.VARIABLES],
trainable=False)
# now make the update ops
# increase the count
count_update = tf.assign_add(count, 1.0)
with tf.control_dependencies([count_update]):
difference = (var - shadow)/count
update = tf.assign_add(shadow, difference)
update_ops.append(update)
self.shadow_vars[var] = (shadow, count)
return update_ops
def test_train_skip_train_if_max_step_already_saved(self):
with tf.Graph().as_default() as g, self.test_session(g):
with tf.control_dependencies(self._build_inference_graph()):
train_op = tf.assign_add(tf.contrib.framework.get_global_step(), 1)
learn.graph_actions._monitored_train( # pylint: disable=protected-access
g,
output_dir=self._output_dir,
train_op=train_op,
loss_op=tf.constant(2.0),
max_steps=10)
step = checkpoints.load_variable(
self._output_dir, tf.contrib.framework.get_global_step().name)
self.assertEqual(10, step)
with tf.Graph().as_default() as g, self.test_session(g):
with tf.control_dependencies(self._build_inference_graph()):
train_op = tf.assign_add(tf.contrib.framework.get_global_step(), 1)
learn.graph_actions._monitored_train( # pylint: disable=protected-access
g,
output_dir=self._output_dir,
train_op=train_op,
loss_op=tf.constant(2.0),
max_steps=10)
step = checkpoints.load_variable(
self._output_dir, tf.contrib.framework.get_global_step().name)
self.assertEqual(10, step)
def _eval_metric(input_, topk, correct_predictions, examples, phase):
"""Creates the standard tracking varibles if in test and returns accuracy."""
my_parameters = {}
if phase in (Phase.test, Phase.infer):
dtype = tf.float32
# Create the variables using tf.Variable because we don't want to share.
count = tf.Variable(
tf.constant(0, dtype=dtype),
name="count_%d" % topk,
collections=[bookkeeper.GraphKeys.TEST_VARIABLES],
trainable=False,
)
correct = tf.Variable(
tf.constant(0, dtype=dtype),
name="correct_%d" % topk,
collections=[bookkeeper.GraphKeys.TEST_VARIABLES],
trainable=False,
)
my_parameters["count"] = count
my_parameters["correct"] = correct
with input_.g.device(count.device):
examples = tf.assign_add(count, examples)
with input_.g.device(correct.device):
correct_predictions = tf.assign_add(correct, correct_predictions)
return correct_predictions, examples, my_parameters
def running_mean(cost, tag_name, batch_size=1):
with tf.name_scope("running_mean_" + tag_name):
with tf.variable_scope(tag_name):
cost_sum = tf.get_variable(
"cost_sum",
initializer=tf.zeros_initializer,
dtype=tf.float64,
shape=(),
collections=[tf.GraphKeys.LOCAL_VARIABLES],
trainable=False)
batches = tf.get_variable(
"cost_num_batches",
initializer=tf.zeros_initializer,
dtype=tf.int32,
shape=(),
collections=[tf.GraphKeys.LOCAL_VARIABLES],
trainable=False)
cost_add = tf.assign_add(cost_sum, tf.cast(cost, dtype=tf.float64))
batches_add = tf.assign_add(batches, batch_size)
update_cost_mean = tf.group(cost_add, batches_add)
reset_batches = tf.assign(batches, 0)
reset_cost_sum = tf.assign(cost_sum, 0.0)
reset_cost_mean = tf.group(reset_batches, reset_cost_sum)
mean_cost = tf.divide(
cost_sum,
tf.cast(batches, dtype=tf.float64))
train_loss_summary = tf.summary.scalar(tag_name, mean_cost)
return reset_cost_mean, update_cost_mean, train_loss_summary
def loop_body(i):
asn1 = tf.assign_add(var_a, 1, name="a_add")
with tf.control_dependencies([asn1]):
asn2 = tf.assign_add(var_b, var_a, name="b_add")
with tf.control_dependencies([asn2]):
ni = tf.add(i, 1, name="i_add")
return ni
def __init__(self, epsilon=1e-2, shape=()):
self._sum = tf.get_variable(
dtype=tf.float64,
shape=shape,
initializer=tf.constant_initializer(0.0),
name="runningsum", trainable=False)
self._sumsq = tf.get_variable(
dtype=tf.float64,
shape=shape,
initializer=tf.constant_initializer(epsilon),
name="runningsumsq", trainable=False)
self._count = tf.get_variable(
dtype=tf.float64,
shape=(),
initializer=tf.constant_initializer(epsilon),
name="count", trainable=False)
self.shape = shape
self.mean = tf.to_float(self._sum / self._count)
self.std = tf.sqrt( tf.maximum( tf.to_float(self._sumsq / self._count) - tf.square(self.mean) , 1e-2 ))
newsum = tf.placeholder(shape=self.shape, dtype=tf.float64, name='sum')
newsumsq = tf.placeholder(shape=self.shape, dtype=tf.float64, name='var')
newcount = tf.placeholder(shape=[], dtype=tf.float64, name='count')
self.incfiltparams = U.function([newsum, newsumsq, newcount], [],
updates=[tf.assign_add(self._sum, newsum),
tf.assign_add(self._sumsq, newsumsq),
tf.assign_add(self._count, newcount)])
def test_capture_variable(self):
monitor = learn.monitors.CaptureVariable(var_name="my_assign_add:0", every_n=8, first_n=2)
with tf.Graph().as_default() as g, self.test_session(g):
var = tf.Variable(0.0, name="my_var")
var.initializer.run()
tf.assign_add(var, 1.0, name="my_assign_add")
self._run_monitor(monitor, num_epochs=3, num_steps_per_epoch=10)
self.assertEqual({0: 1.0, 1: 2.0, 2: 3.0, 10: 4.0, 18: 5.0, 26: 6.0, 29: 7.0}, monitor.values)
def loss(loss_value): """Calculates aggregated mean loss.""" total_loss = tf.Variable(0.0, False) loss_count = tf.Variable(0, False) total_loss_update = tf.assign_add(total_loss, loss_value) loss_count_update = tf.assign_add(loss_count, 1) loss_op = total_loss / tf.cast(loss_count, tf.float32) return [total_loss_update, loss_count_update], loss_op
def train_one_epoch(generator, discriminator,
generator_optimizer, discriminator_optimizer,
dataset, log_interval, noise_dim):
"""Trains `generator` and `discriminator` models on `dataset`.
Args:
generator: Generator model.
discriminator: Discriminator model.
generator_optimizer: Optimizer to use for generator.
discriminator_optimizer: Optimizer to use for discriminator.
dataset: Dataset of images to train on.
log_interval: How many global steps to wait between logging and collecting
summaries.
noise_dim: Dimension of noise vector to use.
"""
total_generator_loss = 0.0
total_discriminator_loss = 0.0
for (batch_index, images) in enumerate(tfe.Iterator(dataset)):
with tf.device('/cpu:0'):
tf.assign_add(tf.train.get_global_step(), 1)
with tf.contrib.summary.record_summaries_every_n_global_steps(log_interval):
current_batch_size = images.shape[0]
noise = tf.random_uniform(shape=[current_batch_size, noise_dim],
minval=-1., maxval=1., seed=batch_index)
with tfe.GradientTape(persistent=True) as g:
generated_images = generator(noise)
tf.contrib.summary.image('generated_images',
tf.reshape(generated_images, [-1, 28, 28, 1]),
max_images=10)
discriminator_gen_outputs = discriminator(generated_images)
discriminator_real_outputs = discriminator(images)
discriminator_loss_val = discriminator_loss(discriminator_real_outputs,
discriminator_gen_outputs)
total_discriminator_loss += discriminator_loss_val
generator_loss_val = generator_loss(discriminator_gen_outputs)
total_generator_loss += generator_loss_val
generator_grad = g.gradient(generator_loss_val, generator.variables)
discriminator_grad = g.gradient(discriminator_loss_val,
discriminator.variables)
with tf.variable_scope('generator'):
generator_optimizer.apply_gradients(zip(generator_grad,
generator.variables))
with tf.variable_scope('discriminator'):
discriminator_optimizer.apply_gradients(zip(discriminator_grad,
discriminator.variables))
if log_interval and batch_index > 0 and batch_index % log_interval == 0:
print('Batch #%d\tAverage Generator Loss: %.6f\t'
'Average Discriminator Loss: %.6f' % (
batch_index, total_generator_loss/batch_index,
total_discriminator_loss/batch_index))
def setUp(self):
tf.test.TestCase.setUp(self)
self.log_dir = 'log/dir'
self.summary_writer = testing.FakeSummaryWriter(self.log_dir)
var = tf.Variable(0.0)
tensor = tf.assign_add(var, 1.0)
self.summary_op = tf.summary.scalar('my_summary', tensor)
global_step = tf.contrib.framework.get_or_create_global_step()
self.train_op = tf.assign_add(global_step, 1)
def accuracy(logits, labels):
"""Calculates aggregated accuracy."""
is_correct = tf.nn.in_top_k(logits, labels, 1)
correct = tf.reduce_sum(tf.cast(is_correct, tf.int32))
incorrect = tf.reduce_sum(tf.cast(tf.logical_not(is_correct), tf.int32))
correct_count = tf.Variable(0, False)
incorrect_count = tf.Variable(0, False)
correct_count_update = tf.assign_add(correct_count, correct)
incorrect_count_update = tf.assign_add(incorrect_count, incorrect)
accuracy_op = tf.cast(correct_count, tf.float32) / tf.cast(
correct_count + incorrect_count, tf.float32)
return [correct_count_update, incorrect_count_update], accuracy_op
def advance_counters(self, total):
"""Returns ops to advance the per-component step and total counters.
Args:
total: Total number of actions to increment counters by.
Returns:
tf.Group op incrementing 'step' by 1 and 'total' by total.
"""
update_total = tf.assign_add(self._total, total, use_locking=True)
update_step = tf.assign_add(self._step, 1, use_locking=True)
return tf.group(update_total, update_step)
def test_train_loss(self):
with tf.Graph().as_default() as g, self.test_session(g):
tf.contrib.framework.create_global_step()
loss_var = tf.contrib.framework.local_variable(10.0)
train_op = tf.group(
tf.assign_add(tf.contrib.framework.get_global_step(), 1),
tf.assign_add(loss_var, -1.0))
self._assert_summaries(self._output_dir)
loss = learn.graph_actions.train(
g, output_dir=self._output_dir, train_op=train_op,
loss_op=loss_var.value(), steps=6)
self.assertEqual(4.0, loss)
self._assert_summaries(self._output_dir, expected_graphs=[g])
def session_run_job():
with tf.Session() as sess:
a = tf.Variable(10, dtype=tf.int32, name='a')
b = tf.Variable(20, dtype=tf.int32, name='b')
d = tf.constant(1, dtype=tf.int32, name='d')
inc_a = tf.assign_add(a, d, name='inc_a')
inc_b = tf.assign_add(b, d, name='inc_b')
inc_ab = tf.group([inc_a, inc_b], name="inc_ab")
sess.run(tf.global_variables_initializer())
sess = tf_debug.TensorBoardDebugWrapperSession(sess, self._debugger_url)
session_run_results.append(sess.run(inc_ab))
def testPlateauOpHook(self):
global_step = tf.train.create_global_step()
counter = tf.get_variable("count", initializer=0, dtype=tf.int32)
indicator = tf.get_variable("indicator", initializer=0, dtype=tf.int32)
tf.summary.scalar("count", counter)
incr_global_step = tf.assign_add(global_step, 1)
incr_counter = tf.assign_add(counter, 1)
incr_indicator = tf.assign_add(indicator, 1)
# Stop if the global step has not gone up by more than 1 in 20 steps.
ckpt_dir = self.ckpt_dir("plateauop")
stop_hook = metrics_hook.PlateauOpHook(
ckpt_dir,
"count_1",
incr_indicator,
num_plateau_steps=20,
plateau_delta=1.,
plateau_decrease=False,
every_n_steps=10)
with self.sess(stop_hook, ckpt_dir) as sess:
for _ in range(20):
sess.run((incr_global_step, incr_counter))
# Summary files should now have 2 values in them
self.flush()
# Run for more steps so that the hook gets triggered and we verify that we
# don't stop.
for _ in range(30):
sess.run((incr_global_step, incr_counter))
self.flush()
# Run without incrementing the counter
for _ in range(30):
sess.run(incr_global_step)
self.flush()
self.assertTrue(sess.run(indicator) < 1)
# Metrics should be written such that now the counter has gone >20 steps
# without being incremented.
# Check that we run the incr_indicator op several times
for _ in range(3):
for _ in range(10):
sess.run(incr_global_step)
self.flush()
self.assertTrue(sess.run(indicator) > 1)
def setUp(self):
self.model_dir = tempfile.mkdtemp()
self.graph = tf.Graph()
with self.graph.as_default():
self.scaffold = monitored_session.Scaffold()
self.global_step = tf.contrib.framework.get_or_create_global_step()
self.train_op = tf.assign_add(self.global_step, 1)
def test_recover_and_retry_on_aborted_error(self):
# Tests that we silently retry and recover on abort. This test uses
# a CheckpointSaver to have something to recover from.
logdir = self._test_dir('test_recover_and_retry_on_aborted_error')
with tf.Graph().as_default():
gstep = tf.contrib.framework.get_or_create_global_step()
do_step = tf.assign_add(gstep, 1)
scaffold = supervised_session.Scaffold()
abort_monitor = RaiseOnceAtStepN(
3, tf.errors.AbortedError(None, None, 'Abort'))
# Save after each step.
ckpt_monitor = tf.contrib.learn.monitors.CheckpointSaver(
1, scaffold.saver, logdir)
monitors = [abort_monitor, ckpt_monitor]
with supervised_session.SupervisedSession('', scaffold=scaffold,
checkpoint_dir=logdir,
monitors=monitors) as session:
self.assertEqual(0, session.run(gstep))
self.assertEqual(1, session.run(do_step))
self.assertEqual(2, session.run(do_step))
self.assertFalse(session.should_stop())
# Here at step 3, the monitor triggers and raises AbortedError. The
# SupervisedSession automatically restores and retries.
self.assertEqual(3, session.run(do_step))
self.assertTrue(abort_monitor.raised)
self.assertFalse(session.should_stop())
self.assertEqual(4, session.run(do_step))
self.assertFalse(session.should_stop())
def accumulate_privacy_spending(self, unused_eps_delta,
sigma, num_examples):
"""Accumulate privacy spending.
In particular, accounts for privacy spending when we assume there
are num_examples, and we are releasing the vector
(sum_{i=1}^{num_examples} x_i) + Normal(0, stddev=l2norm_bound*sigma)
where l2norm_bound is the maximum l2_norm of each example x_i, and
the num_examples have been randomly selected out of a pool of
self.total_examples.
Args:
unused_eps_delta: EpsDelta pair which can be tensors. Unused
in this accountant.
sigma: the noise sigma, in the multiples of the sensitivity (that is,
if the l2norm sensitivity is k, then the caller must have added
Gaussian noise with stddev=k*sigma to the result of the query).
num_examples: the number of examples involved.
Returns:
a TensorFlow operation for updating the privacy spending.
"""
q = tf.cast(num_examples, tf.float64) * 1.0 / self._total_examples
moments_accum_ops = []
for i in range(len(self._log_moments)):
moment = self._compute_log_moment(sigma, q, self._moment_orders[i])
moments_accum_ops.append(tf.assign_add(self._log_moments[i], moment))
return tf.group(*moments_accum_ops)
def testPeriodicTargetUpdate(self, use_locking, update_period):
"""Tests that the simple success case works as expected.
This is an integration test. The periodically and update parts are
unit-tested in the preceding.
Args:
use_locking: value for `periodic_target_update`'s `use_locking` argument.
update_period: how often an update should happen.
"""
target_variables = [tf.Variable(tf.zeros([1, 2]))]
source_variables = [tf.Variable(tf.random_normal([1, 2]))]
increment = tf.ones([1, 2])
update_source_op = tf.assign_add(source_variables[0], increment)
updated = target_update_ops.periodic_target_update(
target_variables,
source_variables,
update_period=update_period,
use_locking=use_locking)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
for step in range(3 * update_period):
sess.run(update_source_op)
sess.run(updated)
targets, sources = sess.run([target_variables, source_variables])
if step % update_period == 0:
self.assertAllClose(targets, sources)
else:
self.assertNotAllClose(targets, sources)
def __init__(self, train_time, time_limit=None): super(TrainTimeHook, self).__init__() self._train_time = train_time self._time_limit = time_limit self._increment_amount = tf.placeholder(tf.float32, None) self._increment_op = tf.assign_add(train_time, self._increment_amount) self._last_run_duration = None
def test_num_steps(self):
logdir = self._test_dir('test_num_steps')
with tf.Graph().as_default():
gstep = tf.contrib.framework.get_or_create_global_step()
do_step = tf.assign_add(gstep, 1)
scaffold = supervised_session.Scaffold()
# Do 3 steps and save.
monitors = [tf.contrib.learn.monitors.StopAtStep(num_steps=3)]
with supervised_session.SupervisedSession('', scaffold=scaffold,
monitors=monitors) as session:
session.run(do_step)
self.assertFalse(session.should_stop())
session.run(do_step)
self.assertFalse(session.should_stop())
session.run(do_step)
self.assertTrue(session.should_stop())
save_path = scaffold.saver.save(session.session,
os.path.join(logdir, 'step-3'))
# Restore and do 4 steps.
def load_ckpt(scaffold, sess):
scaffold.saver.restore(sess, save_path)
scaffold = supervised_session.Scaffold(init_fn=load_ckpt)
monitors = [tf.contrib.learn.monitors.StopAtStep(num_steps=4)]
with supervised_session.SupervisedSession('', scaffold=scaffold,
monitors=monitors) as session:
self.assertEqual(3, session.run(gstep))
session.run(do_step)
self.assertFalse(session.should_stop())
session.run(do_step)
self.assertFalse(session.should_stop())
session.run(do_step)
self.assertFalse(session.should_stop())
session.run(do_step)
self.assertTrue(session.should_stop())
def testEvaluateWithEvalFeedDict(self):
# Create a checkpoint.
checkpoint_dir = os.path.join(self.get_temp_dir(),
'evaluate_with_eval_feed_dict')
self._train_model(checkpoint_dir, num_steps=1)
# We need a variable that that the saver will try to restore.
tf.contrib.framework.get_or_create_global_step()
# Create a variable and an eval op that increments it with a placeholder.
my_var = tf.contrib.framework.local_variable(0.0, name='my_var')
increment = tf.placeholder(dtype=tf.float32)
eval_ops = tf.assign_add(my_var, increment)
increment_value = 3
num_evals = 5
expected_value = increment_value * num_evals
final_values = tf.contrib.training.evaluate_repeatedly(
checkpoint_dir=checkpoint_dir,
eval_ops=eval_ops,
feed_dict={increment: 3},
final_ops={'my_var': tf.identity(my_var)},
hooks=[
tf.contrib.training.StopAfterNEvalsHook(num_evals),
],
max_number_of_evaluations=1)
self.assertEqual(final_values['my_var'], expected_value)
def testCallsMonitorsWithLastStep(self):
with tf.Graph().as_default(), tf.Session() as sess:
global_step_tensor = tf.contrib.framework.create_global_step()
mock_mon = FakeMonitor()
mock_mon2 = FakeMonitor()
mon_sess = monitored_session.MonitoredSession(
sess=sess, monitors=[mock_mon, mock_mon2], global_step_tensor=global_step_tensor
)
inc_5 = tf.assign_add(global_step_tensor, 5)
# Initialize global_step_tensor to '0':
sess.run(tf.initialize_all_variables())
mon_sess.run(fetches=[inc_5])
for mon in [mock_mon, mock_mon2]:
self.assertEqual(mon.last_begin_step, 1)
self.assertEqual(mon.last_end_step, 1)
self.assertEqual(mon.last_post_step, 1)
mon_sess.run(fetches=[inc_5])
for mon in [mock_mon, mock_mon2]:
self.assertEqual(mon.last_begin_step, 6)
self.assertEqual(mon.last_end_step, 6)
self.assertEqual(mon.last_post_step, 6)
mon_sess.run(fetches=[inc_5])
for mon in [mock_mon, mock_mon2]:
self.assertEqual(mon.last_begin_step, 11)
self.assertEqual(mon.last_end_step, 11)
self.assertEqual(mon.last_post_step, 11)
def testStop(self):
global_step = tf.train.create_global_step()
tf.summary.scalar("global_step", global_step)
incr_global_step = tf.assign_add(global_step, 1)
ckpt_dir = self.ckpt_dir("stop")
dummy = DummyHook(ckpt_dir, every_n_steps=10)
with self.sess(dummy, ckpt_dir) as sess:
for _ in range(20):
sess.run(incr_global_step)
# Summary files should now have 2 global step values in them
self.flush()
# Run for 10 more so that the hook gets triggered again
for _ in range(10):
sess.run(incr_global_step)
# Check that the metrics have actually been collected.
self.assertTrue("" in dummy.test_metrics)
metrics = dummy.test_metrics[""]
self.assertTrue("global_step_1" in metrics)
steps, vals = metrics["global_step_1"]
self.assertTrue(len(steps) == len(vals))
self.assertTrue(len(steps) >= 2)
# Run for 10 more so that the hook triggers stoppage
for _ in range(10):
sess.run(incr_global_step)
with self.assertRaisesRegexp(RuntimeError, "after should_stop requested"):
sess.run(incr_global_step)
def testEvalOpAndFinalOp(self):
checkpoint_dir = os.path.join(self.get_temp_dir(), 'eval_ops_and_final_ops')
# Train a model for a single step to get a checkpoint.
self._train_model(checkpoint_dir, num_steps=1)
checkpoint_path = tf.contrib.training.wait_for_new_checkpoint(
checkpoint_dir)
# Create the model so we have something to restore.
inputs = tf.constant(self._inputs, dtype=tf.float32)
logistic_classifier(inputs)
num_evals = 5
final_increment = 9.0
my_var = tf.contrib.framework.local_variable(0.0, name='MyVar')
eval_ops = tf.assign_add(my_var, 1.0)
final_ops = tf.identity(my_var) + final_increment
final_ops_values = tf.contrib.training.evaluate_once(
checkpoint_path=checkpoint_path,
eval_ops=eval_ops,
final_ops={'value': final_ops},
hooks=[
tf.contrib.training.StopAfterNEvalsHook(num_evals),
])
self.assertEqual(final_ops_values['value'], num_evals + final_increment)
def _model_fn(features, labels, mode):
print("\t_model_fn:features=", features)
print("\t_model_fn:labels=", labels)
print("\t_model_fn:mode=", mode)
# Build a linear model and predict values
W = tf.get_variable("W", [1], dtype=tf.float64)
b = tf.get_variable("b", [1], dtype=tf.float64)
y = W*features['x'] + b
# Loss sub-graph
"""Clouds: what is "labels"? "labels" is the standard answer? where "y" is the predict answer?"""
loss = tf.reduce_sum(tf.square(y - labels))
# Training sub-graph
global_step = tf.train.get_global_step()
optimizer = tf.train.GradientDescentOptimizer(0.01)
"""Clouds: what is tf.group???"""
train = tf.group(optimizer.minimize(loss),
tf.assign_add(global_step, 1))
print("--------\n\t_model_fn:train group=", train )
print("--------\n\n")
# EstimatorSpec connects subgraphs we built to the
# appropriate functionality.
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=y,
loss=loss,
train_op=train)
def train_adv(model=None):
assert FLAGS.train_dir, 'train_dir must be given'
print('train dir is %s' % FLAGS.train_dir)
# global_step = tf.train.get_or_create_global_step()
global_step = tf.Variable(0, trainable=False)
add_global = tf.assign_add(global_step, 1)
action_prob_op, gene_loss_op, train_gene_op = model.train_generator(
global_step)
dis_loss_op, train_dis_op, reward_op = model.train_discriminator()
train_sentence_op, train_sentence_len_op, train_label_op = model.get_generator_data(
)
original_prob_op = model.get_original_prob()
dev_acc_op, dev_num_op, dev_init_op = model.build_dev_graph()
test_acc_op, test_num_op, test_init_op = model.build_test_graph()
train_ckpt_dir = FLAGS.train_dir + '/train_ckpt'
os.makedirs(train_ckpt_dir, exist_ok=True)
sum_writer = tf.summary.FileWriter(str(train_ckpt_dir),
graph=tf.get_default_graph())
best_dev_acc = 0.0
final_acc = 0.0
average_reward = 0
all_reward = 0
all_sent_num = 0
saver = tf.train.Saver(max_to_keep=1)
init = tf.global_variables_initializer()
with tf.Session(config=utils.get_config()) as sess:
tf.set_random_seed(FLAGS.random_seed)
np.random.seed(FLAGS.random_seed)
sess.run(init)
for _ in itertools.count(1):
this_global_step = sess.run(add_global)
if this_global_step >= FLAGS.max_steps + 1:
break
sentence, sentence_len, label = sess.run(
[train_sentence_op, train_sentence_len_op, train_label_op])
raw_sentence = sentence.copy()
if this_global_step < FLAGS.dis_warm_up_step: # discriminator warm up
dis_loss, _, = sess.run(
[dis_loss_op, train_dis_op],
feed_dict={
'discriminator/sentence:0': sentence,
'discriminator/sentence_len:0': sentence_len,
'discriminator/train_label:0': label,
})
gene_loss = 0.0
elif this_global_step < FLAGS.gene_warm_up_step + FLAGS.dis_warm_up_step: # generator warm up
original_prob = sess.run(original_prob_op,
feed_dict={
'sentence_original:0': sentence,
'sentence_len_original:0':
sentence_len,
'sentence_label_original:0': label
})
action = sess.run(action_prob_op,
feed_dict={
'generator/train_sentence:0':
sentence,
'generator/train_sentence_len:0':
sentence_len
})
sentence_new, action_idx = generate_new_sentence_with_action(
model.vocab, action, sentence, sentence_len)
reward = sess.run(reward_op,
feed_dict={
'discriminator/sentence:0':
sentence_new,
'discriminator/sentence_len:0':
sentence_len,
'discriminator/train_label:0':
label,
'discriminator/original_prob:0':
original_prob
})
all_sent_num += len(reward)
all_reward += np.sum(reward)
average_reward = all_reward / all_sent_num
reward -= average_reward
gene_loss, _ = sess.run(
[gene_loss_op, train_gene_op],
feed_dict={
'generator/train_sentence:0': raw_sentence,
'generator/train_sentence_len:0': sentence_len,
'generator/reward_score:0': reward,
'generator/action_idx:0': action_idx
})
dis_loss = 0
else: # adversarial train
rand_num = random.choice([1] * FLAGS.every + [0])
if rand_num != 0: # train with generated sentences
original_prob = sess.run(original_prob_op,
feed_dict={
'sentence_original:0':
sentence,
'sentence_len_original:0':
sentence_len,
'sentence_label_original:0':
label
})
action = sess.run(action_prob_op,
feed_dict={
'generator/train_sentence:0':
sentence,
'generator/train_sentence_len:0':
sentence_len
})
sentence_new, action_idx = generate_new_sentence_with_action(
model.vocab, action, sentence, sentence_len)
dis_loss, _, reward = sess.run(
[dis_loss_op, train_dis_op, reward_op],
feed_dict={
'discriminator/sentence:0': sentence_new,
'discriminator/sentence_len:0': sentence_len,
'discriminator/train_label:0': label,
'discriminator/original_prob:0': original_prob
})
all_sent_num += len(reward)
all_reward += np.sum(reward)
average_reward = all_reward / all_sent_num
reward -= average_reward
gene_loss, _ = sess.run(
[gene_loss_op, train_gene_op],
feed_dict={
'generator/train_sentence:0': raw_sentence,
'generator/train_sentence_len:0': sentence_len,
'generator/reward_score:0': reward,
'generator/action_idx:0': action_idx
})
else: # train with original sentence
dis_loss, _, = sess.run(
[dis_loss_op, train_dis_op],
feed_dict={
'discriminator/sentence:0': sentence,
'discriminator/sentence_len:0': sentence_len,
'discriminator/train_label:0': label,
})
gene_loss = 0.0
if this_global_step != 0 and this_global_step % FLAGS.test_steps == 0 and this_global_step > FLAGS.dis_warm_up_step + FLAGS.gene_warm_up_step:
number = 0
accuracy = 0.0
while True:
try:
acc, num = sess.run([dev_acc_op, dev_num_op])
number += num
accuracy += acc * num
except tf.errors.OutOfRangeError:
break
accuracy /= number
print('At step %d. dev num=%d acc=%f.' %
(this_global_step, number, accuracy))
if accuracy > best_dev_acc:
best_dev_acc = accuracy
print("best acc=%f At step %d." %
(best_dev_acc, this_global_step))
test_accuracy = 0.
test_number = 0
while True:
try:
test_acc, test_num = sess.run(
[test_acc_op, test_num_op])
test_number += test_num
test_accuracy += test_acc * test_num
except tf.errors.OutOfRangeError:
break
test_accuracy /= test_number
print('test num=%d acc=%f.' % (test_number, test_accuracy))
final_acc = test_accuracy
sess.run(test_init_op)
save_checkpoint(saver, sess, FLAGS.train_dir,
this_global_step)
summary = tf.Summary()
summary.value.add(tag='test_acc', simple_value=accuracy)
summary.value.add(tag='best_dev_acc',
simple_value=best_dev_acc)
sum_writer.add_summary(summary, this_global_step)
sess.run(dev_init_op)
sum_writer.close()
print('Accuracy of test set is %f .' % final_acc)
def model_fn(features, labels, mode, params):
"""The model_fn argument for creating an Estimator."""
tf.logging.info("features = %s labels = %s mode = %s params=%s" %
(features, labels, mode, params))
global_step = tf.train.get_global_step()
graph = mtf.Graph()
mesh = mtf.Mesh(graph, "my_mesh")
logits, loss = mnist_model(features, labels, mesh)
mesh_shape = mtf.convert_to_shape(FLAGS.mesh_shape)
layout_rules = mtf.convert_to_layout_rules(FLAGS.layout)
mesh_size = mesh_shape.size
mesh_devices = [""] * mesh_size
mesh_impl = placement_mesh_impl.PlacementMeshImpl(mesh_shape, layout_rules,
mesh_devices)
if mode == tf.estimator.ModeKeys.TRAIN:
var_grads = mtf.gradients(
[loss], [v.outputs[0] for v in graph.trainable_variables])
optimizer = mtf_optimize.AdafactorOptimizer()
update_ops = []
for grad, var in zip(var_grads, graph.trainable_variables):
update_ops.extend(optimizer.apply_grad(grad, var))
lowering = mtf.Lowering(graph, {mesh: mesh_impl})
restore_hook = mtf.MtfRestoreHook(lowering)
tf_logits = lowering.export_to_tf_tensor(logits)
if mode != tf.estimator.ModeKeys.PREDICT:
tf_loss = lowering.export_to_tf_tensor(loss)
tf.summary.scalar("loss", tf_loss)
if mode == tf.estimator.ModeKeys.TRAIN:
tf_update_ops = [lowering.lowered_operation(op) for op in update_ops]
tf_update_ops.append(tf.assign_add(global_step, 1))
train_op = tf.group(tf_update_ops)
saver = tf.train.Saver(tf.global_variables(),
sharded=True,
max_to_keep=10,
keep_checkpoint_every_n_hours=2,
defer_build=False,
save_relative_paths=True)
tf.add_to_collection(tf.GraphKeys.SAVERS, saver)
saver_listener = mtf.MtfCheckpointSaverListener(lowering)
saver_hook = tf.train.CheckpointSaverHook(FLAGS.model_dir,
save_steps=1000,
saver=saver,
listeners=[saver_listener])
accuracy = tf.metrics.accuracy(labels=labels,
predictions=tf.argmax(tf_logits,
axis=1))
# Name tensors to be logged with LoggingTensorHook.
tf.identity(tf_loss, "cross_entropy")
tf.identity(accuracy[1], name="train_accuracy")
# Save accuracy scalar to Tensorboard output.
tf.summary.scalar("train_accuracy", accuracy[1])
# restore_hook must come before saver_hook
return tf.estimator.EstimatorSpec(
tf.estimator.ModeKeys.TRAIN,
loss=tf_loss,
train_op=train_op,
training_chief_hooks=[restore_hook, saver_hook])
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {
"classes": tf.argmax(tf_logits, axis=1),
"probabilities": tf.nn.softmax(tf_logits),
}
return tf.estimator.EstimatorSpec(
mode=tf.estimator.ModeKeys.PREDICT,
predictions=predictions,
prediction_hooks=[restore_hook],
export_outputs={
"classify": tf.estimator.export.PredictOutput(predictions)
})
if mode == tf.estimator.ModeKeys.EVAL:
return tf.estimator.EstimatorSpec(
mode=tf.estimator.ModeKeys.EVAL,
loss=tf_loss,
evaluation_hooks=[restore_hook],
eval_metric_ops={
"accuracy":
tf.metrics.accuracy(labels=labels,
predictions=tf.argmax(tf_logits, axis=1)),
})
def __init__(self, sess, input_norm, config):
super(AE_Expert_Network, self).__init__(sess, config, config.expert_lr)
self.rng = np.random.RandomState(config.random_seed)
self.expert_layer1_dim = config.l1_dim
self.expert_layer2_dim = config.l2_dim
self.input_norm = input_norm
self.use_better_q_gd = False
if config.use_better_q_gd == "True":
self.use_better_q_gd = True
self.better_q_gd_alpha = 1e-2 # config.better_q_gd_alpha
self.better_q_gd_max_steps = 10 # config.better_q_gd_max_steps
self.better_q_gd_stop = 1e-3 # config.better_q_gd_stop
# original network
self.inputs, self.phase, self.action, self.q_prediction = self.build_network(
scope_name='ae_expert')
self.net_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='ae_expert')
# Target network
self.target_inputs, self.target_phase, self.target_action, self.target_q_prediction = self.build_network(
scope_name='target_ae_expert')
self.target_net_params = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, scope='target_ae_expert')
# Op for periodically updating target network with online network weights
self.update_target_net_params = [
tf.assign_add(
self.target_net_params[idx],
self.tau *
(self.net_params[idx] - self.target_net_params[idx]))
for idx in range(len(self.target_net_params))
]
# Op for init. target network with identical parameter as the original network
self.init_target_net_params = [
tf.assign(self.target_net_params[idx], self.net_params[idx])
for idx in range(len(self.target_net_params))
]
# TODO: Currently doesn't support batchnorm
if self.norm_type == 'batch':
raise NotImplementedError
else:
assert (self.norm_type == 'none' or self.norm_type == 'layer'
or self.norm_type == 'input_norm')
self.batchnorm_ops = [tf.no_op()]
self.update_target_batchnorm_params = tf.no_op()
self.predicted_q_value = tf.placeholder(tf.float32, [None, 1])
# Optimization Op
with tf.control_dependencies(self.batchnorm_ops):
# Expert Update
self.expert_loss = tf.reduce_mean(
tf.squared_difference(self.predicted_q_value,
self.q_prediction))
self.expert_optimize = tf.train.AdamOptimizer(
self.learning_rate).minimize(self.expert_loss)
# Get the gradient of the expert w.r.t. the action
self.action_grads = tf.gradients(self.q_prediction, self.action)
def main(args=None):
print(args)
tf.reset_default_graph()
"""
Read dataset parser
"""
flags.network_name = args[0].split('/')[-1].split('.')[0].split(
'main_')[-1]
flags.logs_dir = './logs_' + flags.network_name
dataset_parser = GANParser(flags=flags)
"""
Transform data to TFRecord format (Only do once.)
"""
if False:
dataset_parser.load_paths(is_jpg=True, load_val=True)
dataset_parser.data2record(name='{}_train.tfrecords'.format(
dataset_parser.dataset_name),
set_type='train',
test_num=None)
dataset_parser.data2record(name='{}_val.tfrecords'.format(
dataset_parser.dataset_name),
set_type='val',
test_num=None)
# coco_parser.data2record_test(name='coco_stuff2017_test-dev_all_label.tfrecords', is_dev=True, test_num=None)
# coco_parser.data2record_test(name='coco_stuff2017_test_all_label.tfrecords', is_dev=False, test_num=None)
return
"""
Build Graph
"""
with tf.Graph().as_default():
"""
Input (TFRecord)
"""
with tf.name_scope('TFRecord'):
# DatasetA
training_a_dataset = dataset_parser.tfrecord_get_dataset(
name='{}_trainA.tfrecords'.format(dataset_parser.dataset_name),
batch_size=flags.batch_size,
shuffle_size=None)
val_a_dataset = dataset_parser.tfrecord_get_dataset(
name='{}_valA.tfrecords'.format(dataset_parser.dataset_name),
batch_size=flags.batch_size,
need_flip=(flags.mode == 'train'))
# DatasetB
training_b_dataset = dataset_parser.tfrecord_get_dataset(
name='{}_trainB.tfrecords'.format(dataset_parser.dataset_name),
batch_size=flags.batch_size,
shuffle_size=None)
val_b_dataset = dataset_parser.tfrecord_get_dataset(
name='{}_valB.tfrecords'.format(dataset_parser.dataset_name),
batch_size=flags.batch_size,
need_flip=(flags.mode == 'train'))
# A feed-able iterator
with tf.name_scope('RealA'):
handle_a = tf.placeholder(tf.string, shape=[])
iterator_a = tf.contrib.data.Iterator.from_string_handle(
handle_a, training_a_dataset.output_types,
training_a_dataset.output_shapes)
real_a, real_a_name, real_a_shape = iterator_a.get_next()
with tf.name_scope('RealB'):
handle_b = tf.placeholder(tf.string, shape=[])
iterator_b = tf.contrib.data.Iterator.from_string_handle(
handle_b, training_b_dataset.output_types,
training_b_dataset.output_shapes)
real_b, real_b_name, real_b_shape = iterator_b.get_next()
with tf.name_scope('InitialA_op'):
training_a_iterator = training_a_dataset.make_initializable_iterator(
)
validation_a_iterator = val_a_dataset.make_initializable_iterator(
)
with tf.name_scope('InitialB_op'):
training_b_iterator = training_b_dataset.make_initializable_iterator(
)
validation_b_iterator = val_b_dataset.make_initializable_iterator(
)
"""
Network (Computes predictions from the inference model)
"""
with tf.name_scope('Network'):
# Input
global_step = tf.Variable(0,
trainable=False,
name='global_step',
dtype=tf.int32)
global_step_update_op = tf.assign_add(global_step,
1,
name='global_step_update_op')
# mean_rgb = tf.constant((123.68, 116.78, 103.94), dtype=tf.float32)
fake_b_pool = tf.placeholder(tf.float32,
shape=[
None, flags.image_height,
flags.image_width, flags.c_in_dim
],
name='fake_B_pool')
image_linear_shape = tf.constant(
flags.image_height * flags.image_width * flags.c_in_dim,
dtype=tf.int32,
name='image_linear_shape')
# A -> B
'''
with tf.name_scope('Generator'):
with slim.arg_scope(vgg.vgg_arg_scope()):
net, end_points = vgg.vgg_16(real_a - mean_rgb, num_classes=1, is_training=True,
spatial_squeeze=False)
print(net)
return
with tf.variable_scope('Generator_A2B'):
pred = tf.layers.conv2d(tf.nn.relu(net), 1, 1, 1)
pred_upscale = tf.image.resize_bilinear(pred, (flags.image_height, flags.image_width),
name='up_scale')
segment_a = tf.nn.sigmoid(pred_upscale, name='segment_a')
# sigmoid cross entropy Loss
with tf.name_scope('loss_gen_a2b'):
loss_gen_a2b = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=pred_upscale, labels=real_b/255.0, name='sigmoid'), name='mean')
'''
# A -> B
# adjusted_a = tf.zeros_like(real_a, tf.float32, name='mask', optimize=True)
# adjusted_a = high_light(real_a, name='high_light')
# adjusted_a = tf.layers.average_pooling2d(real_a, 7, strides=1, padding='same', name='adjusted_a')
adjusted_a = gaussian_blur(real_a, name='adjusted_a')
logits_a = generator_resnet(real_a,
flags,
False,
name="Generator_A2B")
segment_a = tf.nn.tanh(logits_a, name='segment_a')
logits_a_ori = tf.image.resize_bilinear(
logits_a, (real_a_shape[0][0], real_b_shape[0][1]),
name='logits_a_ori')
segment_a_ori = tf.nn.tanh(logits_a_ori, name='segment_a_ori')
with tf.variable_scope('Fake_B'):
foreground = tf.multiply(real_a, segment_a, name='foreground')
background = tf.multiply(adjusted_a, (1 - segment_a),
name='background')
fake_b_logits = tf.add(foreground,
background,
name='fake_b_logits')
fake_b = tf.clip_by_value(fake_b_logits, 0, 255, name='fake_b')
#
fake_b_f = tf.reshape(fake_b, [-1, image_linear_shape],
name='fake_b_f')
fake_b_pool_f = tf.reshape(fake_b_pool, [-1, image_linear_shape],
name='fake_b_pool_f')
real_b_f = tf.reshape(real_b, [-1, image_linear_shape],
name='real_b_f')
dis_fake_b = discriminator_se_wgangp(fake_b_f,
flags,
reuse=False,
name="Discriminator_B")
dis_fake_b_pool = discriminator_se_wgangp(fake_b_pool_f,
flags,
reuse=True,
name="Discriminator_B")
dis_real_b = discriminator_se_wgangp(real_b_f,
flags,
reuse=True,
name="Discriminator_B")
# WGAN Loss
with tf.name_scope('loss_gen_a2b'):
loss_gen_a2b = -tf.reduce_mean(dis_fake_b)
with tf.name_scope('loss_dis_b'):
loss_dis_b_adv_real = -tf.reduce_mean(dis_real_b)
loss_dis_b_adv_fake = tf.reduce_mean(dis_fake_b_pool)
loss_dis_b = tf.reduce_mean(dis_fake_b_pool) - tf.reduce_mean(
dis_real_b)
with tf.name_scope('wgan-gp'):
alpha = tf.random_uniform(shape=[flags.batch_size, 1],
minval=0.,
maxval=1.)
differences = fake_b_pool_f - real_b_f
interpolates = real_b_f + (alpha * differences)
gradients = tf.gradients(
discriminator_se_wgangp(interpolates,
flags,
reuse=True,
name="Discriminator_B"),
[interpolates])[0]
slopes = tf.sqrt(
tf.reduce_sum(tf.square(gradients),
reduction_indices=[1]))
gradient_penalty = tf.reduce_mean((slopes - 1.)**2)
loss_dis_b += flags.lambda_gp * gradient_penalty
# Optimizer
'''
trainable_var_resnet = tf.get_collection(
key=tf.GraphKeys.TRAINABLE_VARIABLES, scope='vgg_16')
trainable_var_gen_a2b = tf.get_collection(
key=tf.GraphKeys.TRAINABLE_VARIABLES, scope='Generator_A2B') + trainable_var_resnet
slim.model_analyzer.analyze_vars(trainable_var_gen_a2b, print_info=True)
'''
trainable_var_gen_a2b = tf.get_collection(
key=tf.GraphKeys.TRAINABLE_VARIABLES, scope='Generator_A2B')
trainable_var_dis_b = tf.get_collection(
key=tf.GraphKeys.TRAINABLE_VARIABLES, scope='Discriminator_B')
with tf.name_scope('learning_rate_decay'):
decay = tf.maximum(
0.,
1. -
(tf.cast(global_step, tf.float32) / flags.training_iter),
name='decay')
learning_rate = tf.multiply(flags.learning_rate,
decay,
name='learning_rate')
train_op_gen_a2b = train_op(loss_gen_a2b,
learning_rate,
flags,
trainable_var_gen_a2b,
name='gen_a2b')
train_op_dis_b = train_op(loss_dis_b,
learning_rate,
flags,
trainable_var_dis_b,
name='dis_b')
saver = tf.train.Saver(max_to_keep=2)
# Graph Logs
with tf.name_scope('GEN_a2b'):
tf.summary.scalar("loss/gen_a2b/all", loss_gen_a2b)
with tf.name_scope('DIS_b'):
tf.summary.scalar("loss/dis_b/all", loss_dis_b)
tf.summary.scalar("loss/dis_b/adv_real", loss_dis_b_adv_real)
tf.summary.scalar("loss/dis_b/adv_fake", loss_dis_b_adv_fake)
summary_op = tf.summary.merge_all()
"""
Session
"""
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
with tf.Session(config=tfconfig) as sess:
with tf.name_scope('Initial'):
ckpt = tf.train.get_checkpoint_state(
dataset_parser.checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
print("Model restored: {}".format(
ckpt.model_checkpoint_path))
saver.restore(sess, ckpt.model_checkpoint_path)
else:
print("No Model found.")
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
sess.run(init_op)
# init_fn = slim.assign_from_checkpoint_fn('./pretrained/vgg_16.ckpt',
# slim.get_model_variables('vgg_16'))
# init_fn(sess)
summary_writer = tf.summary.FileWriter(dataset_parser.logs_dir,
sess.graph)
"""
Training Mode
"""
if flags.mode == 'train':
print('Training mode! Batch size:{:d}'.format(
flags.batch_size))
with tf.variable_scope('Input_port'):
training_a_handle = sess.run(
training_a_iterator.string_handle())
training_b_handle = sess.run(
training_b_iterator.string_handle())
# val_a_handle = sess.run(validation_a_iterator.string_handle())
# val_b_handle = sess.run(validation_b_iterator.string_handle())
image_pool_a, image_pool_b = ImagePool(
flags.pool_size), ImagePool(flags.pool_size)
print('Start Training!')
start_time = time.time()
sess.run([
training_a_iterator.initializer,
training_b_iterator.initializer
])
feed_dict_train = {
handle_a: training_a_handle,
handle_b: training_b_handle
}
# feed_dict_valid = {is_training: False}
global_step_sess = sess.run(global_step)
while global_step_sess < flags.training_iter:
try:
# Update gen_A2B, gen_B2A
_, fake_b_sess = sess.run([train_op_gen_a2b, fake_b],
feed_dict=feed_dict_train)
# _, loss_gen_a2b_sess = sess.run([train_op_gen_a2b, loss_gen_a2b], feed_dict=feed_dict_train)
# Update dis_B, dis_A
fake_b_pool_query = image_pool_b.query(fake_b_sess)
_ = sess.run(train_op_dis_b,
feed_dict={
fake_b_pool: fake_b_pool_query,
handle_b: training_b_handle
})
sess.run(global_step_update_op)
global_step_sess, learning_rate_sess = sess.run(
[global_step, learning_rate])
print(
'global step:[{:d}/{:d}], learning rate:{:f}, time:{:4.4f}'
.format(global_step_sess, flags.training_iter,
learning_rate_sess,
time.time() - start_time))
# Logging the events
if global_step_sess % flags.log_freq == 1:
print('Logging the events')
summary_op_sess = sess.run(summary_op,
feed_dict={
handle_a:
training_a_handle,
handle_b:
training_b_handle,
fake_b_pool:
fake_b_pool_query
})
summary_writer.add_summary(summary_op_sess,
global_step_sess)
# summary_writer.flush()
# Observe training situation (For debugging.)
if flags.debug and global_step_sess % flags.observe_freq == 1:
real_a_sess, real_b_sess, adjusted_a_sess, segment_a_sess, fake_b_sess, \
real_a_name_sess, real_b_name_sess = \
sess.run([real_a, real_b, adjusted_a, segment_a, fake_b,
real_a_name, real_b_name],
feed_dict={handle_a: training_a_handle, handle_b: training_b_handle})
print('Logging training images.')
dataset_parser.visualize_data(
real_a=real_a_sess,
real_b=real_b_sess,
adjusted_a=adjusted_a_sess,
segment_a=segment_a_sess,
fake_b=fake_b_sess,
shape=(1, 1),
global_step=global_step_sess,
logs_dir=dataset_parser.logs_image_train_dir,
real_a_name=real_a_name_sess[0].decode(),
real_b_name=real_b_name_sess[0].decode())
"""
Saving the checkpoint
"""
if global_step_sess % flags.save_freq == 0:
print('Saving model...')
saver.save(sess,
dataset_parser.checkpoint_dir +
'/model.ckpt',
global_step=global_step_sess)
except tf.errors.OutOfRangeError:
print(
'----------------One epochs finished!----------------'
)
sess.run([
training_a_iterator.initializer,
training_b_iterator.initializer
])
elif flags.mode == 'test':
from PIL import Image
import numpy as np
print('Start Testing!')
'''
with tf.variable_scope('Input_port'):
val_a_handle = sess.run(validation_a_iterator.string_handle())
val_b_handle = sess.run(validation_b_iterator.string_handle())
sess.run([validation_a_iterator.initializer, validation_b_iterator.initializer])
'''
with tf.variable_scope('Input_port'):
val_a_handle = sess.run(
training_a_iterator.string_handle())
val_b_handle = sess.run(
training_b_iterator.string_handle())
sess.run([
training_a_iterator.initializer,
training_b_iterator.initializer
])
feed_dict_test = {
handle_a: val_a_handle,
handle_b: val_b_handle
}
image_idx = 0
while True:
try:
segment_a_ori_sess, real_a_name_sess = \
sess.run([segment_a_ori, real_a_name], feed_dict=feed_dict_test)
segment_a_ori_sess = np.squeeze(
segment_a_ori_sess) * 255
x_png = Image.fromarray(
segment_a_ori_sess.astype(np.uint8))
x_png.save('{}/{}.png'.format(
dataset_parser.logs_image_val_dir,
real_a_name_sess[0].decode()),
format='PNG')
print(image_idx)
image_idx += 1
except tf.errors.OutOfRangeError:
print(
'----------------One epochs finished!----------------'
)
break
def batch_inputs(self, dataset, train):
"""Contruct batches of training or evaluation examples from the image input_data.
Args:
dataset: instance of Dataset class specifying the input_data.
See input_data.py for details.
batch_size: integer
train: boolean
num_preprocess_threads: integer, total number of preprocessing threads
num_readers: integer, number of parallel readers
Returns:
images: 4-D float Tensor of a batch of images
labels: 1-D integer Tensor of [batch_size].
Raises:
ValueError: if data is not found
"""
with tf.name_scope('batch_processing'):
data_files = dataset.data_files()
if data_files is None:
raise ValueError('No data files found for this input_data')
# Create filename_queue
if train:
filename_queue = tf.train.string_input_producer(data_files,
shuffle=True,
capacity=16)
else:
filename_queue = tf.train.string_input_producer(data_files,
shuffle=False,
capacity=1)
# Approximate number of examples per shard.
examples_per_shard = 1024
# Size the random shuffle queue to balance between good global
# mixing (more examples) and memory use (fewer examples).
# 1 image uses 299*299*3*4 bytes = 1MB
# The default input_queue_memory_factor is 16 implying a shuffling queue
# size: examples_per_shard * 16 * 1MB = 17.6GB
min_queue_examples = examples_per_shard * self.input_queue_memory_factor
if train:
examples_queue = tf.RandomShuffleQueue(
capacity=min_queue_examples + 3 * self.batch_size,
min_after_dequeue=min_queue_examples,
dtypes=[tf.string])
else:
examples_queue = tf.FIFOQueue(capacity=examples_per_shard +
3 * self.batch_size,
dtypes=[tf.string])
# Create multiple readers to populate the queue of examples.
if self.num_readers > 1:
enqueue_ops = []
for _ in range(self.num_readers):
reader = dataset.reader()
_, value = reader.read(filename_queue)
enqueue_ops.append(examples_queue.enqueue([value]))
tf.train.queue_runner.add_queue_runner(
tf.train.queue_runner.QueueRunner(examples_queue,
enqueue_ops))
example_serialized = examples_queue.dequeue()
else:
reader = dataset.reader()
_, example_serialized = reader.read(filename_queue)
pos_queue = None
neg_queue = None
if self.batch_size < 2:
pos_queue = tf.RandomShuffleQueue(
name="pos-queue",
capacity=10,
min_after_dequeue=5,
dtypes=[tf.float32, tf.float32, tf.string])
neg_queue = tf.RandomShuffleQueue(
name="neg-queue",
capacity=10,
min_after_dequeue=5,
dtypes=[tf.float32, tf.float32, tf.string])
pos_queue_enq = []
neg_queue_enq = []
with tf.name_scope('split-merge'):
if train and self.ensure_posneg_balance:
images_and_masks = []
for thread_id in range(self.num_preprocess_threads):
# Parse a serialized Example proto to extract the image and metadata.
image_buffer, mask_buffer, img_name_ = self.parse_example_proto(
example_serialized)
image_ = self.image_preprocessing(
image_buffer,
img_size=(self.input_size[0], self.input_size[1]),
num_channels=self.input_size[2])
mask_ = self.image_preprocessing(
mask_buffer,
img_size=(self.mask_size[0], self.mask_size[1]),
num_channels=self.mask_size[2])
image_ = tf.expand_dims(image_, 0)
mask_ = tf.expand_dims(mask_, 0)
img_name_ = tf.expand_dims(img_name_, 0)
img_shape = tf.TensorShape([
image_.shape[1], image_.shape[2], image_.shape[3]
])
mask_shape = tf.TensorShape(
[mask_.shape[1], mask_.shape[2], mask_.shape[3]])
img_name_shape = tf.TensorShape([])
# initialize pos/neg queues with proper shape size on first
if pos_queue is None or neg_queue is None:
pos_queue = tf.RandomShuffleQueue(
name="pos-queue",
capacity=10,
min_after_dequeue=5,
dtypes=[tf.float32, tf.float32, tf.string],
shapes=[img_shape, mask_shape, img_name_shape])
neg_queue = tf.RandomShuffleQueue(
name="neg-queue",
capacity=10,
min_after_dequeue=5,
dtypes=[tf.float32, tf.float32, tf.string],
shapes=[img_shape, mask_shape, img_name_shape])
is_pos = tf.squeeze(
tf.reduce_sum(mask_, [1, 2], keep_dims=False))
neg_mask = tf.less_equal(is_pos, 0)
pos_idx = tf.reshape(
tf.where([tf.logical_not(neg_mask)]), [-1])
neg_idx = tf.reshape(tf.where([neg_mask]), [-1])
pos_data = [
tf.gather(image_, pos_idx),
tf.gather(mask_, pos_idx),
tf.gather(img_name_, pos_idx)
]
neg_data = [
tf.gather(image_, neg_idx),
tf.gather(mask_, neg_idx),
tf.gather(img_name_, neg_idx)
]
pos_queue_enq.append(pos_queue.enqueue_many(pos_data))
neg_queue_enq.append(neg_queue.enqueue_many(neg_data))
tf.train.queue_runner.add_queue_runner(
tf.train.queue_runner.QueueRunner(
pos_queue, pos_queue_enq))
tf.train.queue_runner.add_queue_runner(
tf.train.queue_runner.QueueRunner(
neg_queue, neg_queue_enq))
if self.batch_size >= 2:
if self.batch_size % 2 != 0:
raise Exception(
"'batch_size' mod 2 != 0 ! only even batch sizes supported at the moment"
)
num_deque = int(self.batch_size / 2)
pos_data = pos_queue.dequeue_many(num_deque)
neg_data = neg_queue.dequeue_many(num_deque)
concat_data = [
tf.concat([pos_data[0], neg_data[0]],
axis=0,
name='Concat-img'),
tf.concat([pos_data[1], neg_data[1]],
axis=0,
name='Concat-mask'),
tf.concat([pos_data[2], neg_data[2]],
axis=0,
name='Concat-img-name')
]
# randomly permute within batch size (is this even necessary ??)
idx = tf.Variable(range(0, self.batch_size),
trainable=False,
dtype=tf.int32)
idx = tf.random_shuffle(idx)
images = tf.gather(concat_data[0], idx)
masks = tf.gather(concat_data[1], idx)
img_names = tf.gather(concat_data[2], idx)
else:
# positive only
#images, masks, img_names = pos_queue.dequeue()
# negative only
#images, masks, img_names = neg_queue.dequeue()
# mix 50/50
counter = tf.Variable(initial_value=0,
trainable=False,
dtype=tf.int32)
counter = tf.assign_add(counter, 1)
condition_term = tf.equal(tf.mod(counter, 2),
tf.constant(0))
images, masks, img_names = tf.cond(
condition_term, lambda: pos_queue.dequeue(),
lambda: neg_queue.dequeue())
if self.use_random_rotation:
images.set_shape(
tensor_shape.as_shape([None, None, 1]))
masks.set_shape(
tensor_shape.as_shape([None, None, 1]))
# randomly rotate image by 90 degrees
rot_factor = tf.random_uniform([1],
minval=0,
maxval=3,
dtype=tf.int32)
rot_factor = tf.gather(rot_factor, 0)
images = tf.image.rot90(images, k=rot_factor)
masks = tf.image.rot90(masks, k=rot_factor)
images = tf.expand_dims(images, axis=0)
masks = tf.expand_dims(masks, axis=0)
img_names = tf.expand_dims(img_names, axis=0)
else:
# Parse a serialized Example proto to extract the image and metadata.
image_buffer, mask_buffer, img_names = self.parse_example_proto(
example_serialized)
images = self.image_preprocessing(
image_buffer,
img_size=(self.input_size[0], self.input_size[1]),
num_channels=self.input_size[2])
masks = self.image_preprocessing(
mask_buffer,
img_size=(self.mask_size[0], self.mask_size[1]),
num_channels=1)
images = tf.expand_dims(images, axis=0)
masks = tf.expand_dims(masks, axis=0)
img_names = tf.expand_dims(img_names, axis=0)
# Reshape images into these desired dimensions.
images = tf.cast(images, tf.float32)
masks = tf.cast(masks, tf.float32)
images.set_shape(
tensor_shape.as_shape(
[self.batch_size, None, None, self.input_size[2]]))
masks.set_shape(
tensor_shape.as_shape([
self.batch_size, self.input_size[0], self.input_size[1],
self.mask_size[2]
]))
# Display the training images in the visualizer.
tf.summary.image('images', images)
tf.summary.image('masks', masks)
return images, masks, img_names
def __init__(self, name, make_model,
devices=get_available_gpus(),
master_device=None,
TrainerClass=SampleBasedTrainer,
sess=None, *args, verbose=False, **kwargs):
""" A wrapper-class that performs batch-parallel training with some trainer. """
self.name = name
self.sess = sess = sess or tf.get_default_session() or tf.InteractiveSession()
self.master_device = master_device = master_device or next(iter(devices))
assert master_device in devices
self.verbose = verbose
class Worker(TrainerClass):
def get_optimizer(self, *args, **kwargs):
""" Worker does not update weights by itself. use sgd to avoid wasting memory """
return tf.train.GradientDescentOptimizer(learning_rate=0)
with tf.variable_scope(name):
self.workers_by_device = {}
for i, device in enumerate(devices):
with tf.device(device), tf.variable_scope('worker_%i' % i):
model = make_model()
if device == master_device:
worker = TrainerClass(model, *args, **kwargs)
else:
worker = Worker(model, *args, **kwargs)
self.workers_by_device[device] = worker
if verbose:
print("Created model {} weights and worker on device {}"
"".format(model.name, device))
self.master_model = self.workers_by_device[master_device].model
self.master_worker = self.workers_by_device[self.master_device]
assert isinstance(self.master_worker, TrainerClass)
# step 1: send main model's weights to all worker replicas
self.scatter_weights = []
for device, worker in self.workers_by_device.items():
if worker == self.master_worker:
continue
self.scatter_weights.extend(map(tf.assign,
worker.optimized_variables,
self.master_worker.optimized_variables))
# step 2: compute grads and counters at all workers
self.gather_grads, self.gather_counters = [], []
for device, worker in self.workers_by_device.items():
if worker == self.master_worker:
continue
self.gather_grads.extend(
map(tf.assign_add, self.master_worker.accumulated_grads, worker.accumulated_grads)
)
self.gather_grads.append(
tf.assign_add(self.master_worker.accumulated_num_batches, worker.accumulated_num_batches)
)
master_counters_flat = [self.master_worker.accumulated_counters[name]
for name in sorted(self.master_worker.accumulated_counters.keys())]
worker_counters_flat = [worker.accumulated_counters[name]
for name in sorted(self.master_worker.accumulated_counters.keys())]
self.gather_counters.extend(
map(tf.assign_add, master_counters_flat, worker_counters_flat)
)
# step 3: perform gradient step and reset all accumulated values
self.reset_slave_grads = [
worker.reset_gradients for worker in self.workers_by_device.values()
if worker != self.master_worker
]
self.reset_slave_counters = [
worker.reset_counters for worker in self.workers_by_device.values()
if worker != self.master_worker
]
def model(features, labels, mode):
tc = constant_tensors()
tc_1d1_goals_f, tc_home_points_i, tc_away_points_i, calc_poisson_prob, p_tendency_mask_f, p_gdiff_mask_f, p_fulltime_index_matrix = tc
with tf.variable_scope("Model"):
logits1H = buildGraph("1H", features, columns, mode)
t_is_home_bool = tf.equal(features["Where"], "Home")
predictions1H = create_predictions(logits1H, t_is_home_bool, tc)
if mode == tf.estimator.ModeKeys.TRAIN:
logits2H = buildGraph(
"2H", features, columns, mode,
tf.stack([features["T1_GHT"], features["T2_GHT"]], axis=1))
else:
logits2H = buildGraph("2H", features, columns, mode,
predictions1H["pred"])
predictions2H = create_predictions(logits2H, t_is_home_bool, tc)
predictions = combine1H2H(predictions1H, predictions2H,
t_is_home_bool, tc_home_points_i,
tc_away_points_i,
p_fulltime_index_matrix,
calc_poisson_prob)
logits2H_alt = buildGraph("2H", features, columns, mode,
predictions1H["alt_pred"])
if mode == tf.estimator.ModeKeys.PREDICT:
# Build alternative prediction with 2nd-most likely outcome of 1H
predictions2H_alt = create_predictions(logits2H_alt,
t_is_home_bool, tc)
predictions_alt = combine1H2H(predictions1H, predictions2H_alt,
t_is_home_bool, tc_home_points_i,
tc_away_points_i,
p_fulltime_index_matrix,
calc_poisson_prob)
for key, value in predictions_alt.items():
predictions['Alt_' + key] = value
export_outputs = {
"predictions":
tf.estimator.export.RegressionOutput(predictions["p_marg_1"])
}
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode,
predictions=predictions,
export_outputs=export_outputs)
with tf.variable_scope("Evaluation"):
t_is_home_loss_bool = (t_is_home_bool & tf.less(
features["T1_GFT"], features["T2_GFT"])) | (
tf.logical_not(t_is_home_bool)
& tf.greater(features["T1_GFT"], features["T2_GFT"]))
t_is_home_win_bool = (t_is_home_bool & tf.greater(
features["T1_GFT"], features["T2_GFT"])) | (
tf.logical_not(t_is_home_bool)
& tf.less(features["T1_GFT"], features["T2_GFT"]))
eval_metric_ops_1H, loss_1H = create_losses_and_metrics_HH(
"1H", predictions1H, features["T1_GHT"], features["T2_GHT"],
t_is_home_bool, mode, tc, t_is_home_win_bool,
t_is_home_loss_bool)
eval_metric_ops_2H, loss_2H = create_losses_and_metrics_HH(
"2H", predictions2H, features["T1_GFT"] - features["T1_GHT"],
features["T2_GFT"] - features["T2_GHT"], t_is_home_bool, mode,
tc, t_is_home_win_bool, t_is_home_loss_bool)
eval_metric_ops = eval_metric_ops_1H
eval_metric_ops.update(eval_metric_ops_2H)
loss = loss_1H + loss_2H
# softpoints
gs = tf.minimum(features["T1_GFT"], 6)
gc = tf.minimum(features["T2_GFT"], 6)
achievable_points_mask = tf.where(
t_is_home_bool, tf.gather(tc_home_points_i, gs * 7 + gc),
tf.gather(tc_away_points_i, gs * 7 + gc))
pt_softpoints = tf.reduce_sum(predictions["p_pred_12"] *
achievable_points_mask,
axis=1)
eval_metric_ops["pt_softpoints"] = tf.metrics.mean(pt_softpoints)
loss -= tf.reduce_mean(pt_softpoints)
result_metrics = create_result_metrics(predictions["pred"][:, 0],
predictions["pred"][:, 1],
features["T1_GFT"],
features["T2_GFT"],
t_is_home_bool)
eval_metric_ops.update(result_metrics)
# loss -= eval_metric_ops["z_points"][1]
for key, value in eval_metric_ops.items():
tf.summary.scalar(key, value[1])
if mode == tf.estimator.ModeKeys.EVAL:
return tf.estimator.EstimatorSpec(mode=mode,
predictions=predictions,
loss=loss,
eval_metric_ops=eval_metric_ops)
global_step = tf.train.get_global_step()
#optimizer = tf.train.GradientDescentOptimizer(1e-4)
learning_rate = 1e-2
print("Learning rate = {}".format(learning_rate))
optimizer = tf.train.AdamOptimizer(learning_rate)
train = tf.group(optimizer.minimize(loss),
tf.assign_add(global_step, 1))
summary_op = tf.summary.merge_all()
summary_hook = tf.train.SummarySaverHook(save_steps=100,
output_dir=model_dir +
"/train",
scaffold=None,
summary_op=summary_op)
return tf.estimator.EstimatorSpec(mode=mode,
predictions=predictions,
loss=loss,
train_op=train,
eval_metric_ops=eval_metric_ops,
training_hooks=[summary_hook])
def train(name,
hparams,
multi_gpu=False,
n_models=1,
train_completeness_threshold=0.01,
seed=None,
logdir='data/logs',
max_epoch=100,
patience=2,
train_sampling=1.0,
eval_sampling=1.0,
eval_memsize=5,
gpu=0,
gpu_allow_growth=False,
save_best_model=False,
forward_split=False,
write_summaries=False,
verbose=False,
asgd_decay=None,
tqdm=True,
side_split=True,
max_steps=None,
save_from_step=None,
do_eval=True,
predict_window=63,
back_offset=0):
eval_k = int(round(2621 * eval_memsize / n_models))
eval_batch_size = int(
eval_k /
(hparams.rnn_depth *
hparams.encoder_rnn_layers)) # 128 -> 1024, 256->512, 512->256
eval_pct = 0.2
batch_size = hparams.batch_size
train_window = hparams.train_window
tf.reset_default_graph()
if seed:
tf.set_random_seed(seed)
with tf.device("/cpu:0"):
inp = VarFeeder.read_vars("data/vars")
if side_split:
splitter = Splitter(ucdoc_features(inp),
inp.page_map,
3,
train_sampling=train_sampling,
test_sampling=eval_sampling,
seed=seed)
else:
splitter = FakeSplitter(ucdoc_features(inp),
3,
seed=seed,
test_sampling=eval_sampling)
real_train_pages = splitter.splits[0].train_size
real_eval_pages = splitter.splits[0].test_size
items_per_eval = real_eval_pages * eval_pct
eval_batches = int(np.ceil(items_per_eval / eval_batch_size))
steps_per_epoch = real_train_pages // batch_size
eval_every_step = int(round(steps_per_epoch * eval_pct))
# eval_every_step = int(round(items_per_eval * train_sampling / batch_size))
global_step = tf.train.get_or_create_global_step()
inc_step = tf.assign_add(global_step, 1)
all_models: List[ModelTrainerV2] = []
def create_model(scope, index, prefix, seed):
with tf.variable_scope('input') as inp_scope:
with tf.device("/cpu:0"):
split = splitter.splits[index]
pipe = InputPipe(
inp,
features=split.train_set,
n_pages=split.train_size,
# mode=ModelMode.TRAIN, batch_size=batch_size, n_epoch=None, verbose=verbose,
mode=ModelMode.TRAIN_SKIP_PREDICT,
batch_size=batch_size,
n_epoch=None,
verbose=verbose,
train_completeness_threshold=train_completeness_threshold,
predict_completeness_threshold=train_completeness_threshold,
train_window=train_window,
predict_window=predict_window,
rand_seed=seed,
train_skip_first=hparams.train_skip_first,
back_offset=back_offset)
inp_scope.reuse_variables()
if side_split:
side_eval_pipe = InputPipe(
inp,
features=split.test_set,
n_pages=split.test_size,
mode=ModelMode.EVAL,
batch_size=eval_batch_size,
n_epoch=None,
verbose=verbose,
predict_window=predict_window,
train_completeness_threshold=0.01,
predict_completeness_threshold=0,
train_window=train_window,
rand_seed=seed,
runs_in_burst=eval_batches,
back_offset=predict_window *
(2 if forward_split else 1))
else:
side_eval_pipe = None
if forward_split:
forward_eval_pipe = InputPipe(
inp,
features=split.test_set,
n_pages=split.test_size,
mode=ModelMode.EVAL,
batch_size=eval_batch_size,
n_epoch=None,
verbose=verbose,
predict_window=predict_window,
train_completeness_threshold=0.01,
predict_completeness_threshold=0,
train_window=train_window,
rand_seed=seed,
runs_in_burst=eval_batches,
back_offset=predict_window)
else:
forward_eval_pipe = None
avg_sgd = asgd_decay is not None
#asgd_decay = 0.99 if avg_sgd else None
train_model = Model(pipe,
hparams,
is_train=True,
graph_prefix=prefix,
asgd_decay=asgd_decay,
seed=seed)
scope.reuse_variables()
eval_stages = []
if side_split:
side_eval_model = Model(
side_eval_pipe,
hparams,
is_train=False,
#loss_mask=np.concatenate([np.zeros(50, dtype=np.float32), np.ones(10, dtype=np.float32)]),
seed=seed)
eval_stages.append((Stage.EVAL_SIDE, side_eval_model))
if avg_sgd:
eval_stages.append((Stage.EVAL_SIDE_EMA, side_eval_model))
if forward_split:
forward_eval_model = Model(forward_eval_pipe,
hparams,
is_train=False,
seed=seed)
eval_stages.append((Stage.EVAL_FRWD, forward_eval_model))
if avg_sgd:
eval_stages.append((Stage.EVAL_FRWD_EMA, forward_eval_model))
if write_summaries:
summ_path = f"{logdir}/{name}_{index}"
if os.path.exists(summ_path):
shutil.rmtree(summ_path)
summ_writer = tf.summary.FileWriter(
summ_path) # , graph=tf.get_default_graph()
else:
summ_writer = None
if do_eval and forward_split:
stop_metric = lambda metrics: metrics[Stage.EVAL_FRWD]['SMAPE'
].avg_epoch
else:
stop_metric = None
return ModelTrainerV2(train_model,
eval_stages,
index,
patience=patience,
stop_metric=stop_metric,
summary_writer=summ_writer)
if n_models == 1:
with tf.device(f"/gpu:{gpu}"):
scope = tf.get_variable_scope()
all_models = [create_model(scope, 0, None, seed=seed)]
else:
for i in range(n_models):
device = f"/gpu:{i}" if multi_gpu else f"/gpu:{gpu}"
with tf.device(device):
prefix = f"m_{i}"
with tf.variable_scope(prefix) as scope:
all_models.append(
create_model(scope, i, prefix=prefix, seed=seed + i))
trainer = MultiModelTrainer(all_models, inc_step)
if save_best_model or save_from_step:
saver_path = f'data/cpt/{name}'
if os.path.exists(saver_path):
shutil.rmtree(saver_path)
os.makedirs(saver_path)
saver = tf.train.Saver(max_to_keep=10, name='train_saver')
else:
saver = None
avg_sgd = asgd_decay is not None
if avg_sgd:
from itertools import chain
def ema_vars(model):
ema = model.train_model.ema
return {
ema.average_name(v): v
for v in model.train_model.ema._averages
}
ema_names = dict(
chain(*[ema_vars(model).items() for model in all_models]))
#ema_names = all_models[0].train_model.ema.variables_to_restore()
ema_loader = tf.train.Saver(var_list=ema_names,
max_to_keep=1,
name='ema_loader')
ema_saver = tf.train.Saver(max_to_keep=1, name='ema_saver')
else:
ema_loader = None
init = tf.global_variables_initializer()
if forward_split and do_eval:
eval_smape = trainer.metric(Stage.EVAL_FRWD, 'SMAPE')
eval_mae = trainer.metric(Stage.EVAL_FRWD, 'MAE')
else:
eval_smape = DummyMetric()
eval_mae = DummyMetric()
if side_split and do_eval:
eval_mae_side = trainer.metric(Stage.EVAL_SIDE, 'MAE')
eval_smape_side = trainer.metric(Stage.EVAL_SIDE, 'SMAPE')
else:
eval_mae_side = DummyMetric()
eval_smape_side = DummyMetric()
train_smape = trainer.metric(Stage.TRAIN, 'SMAPE')
train_mae = trainer.metric(Stage.TRAIN, 'MAE')
grad_norm = trainer.metric(Stage.TRAIN, 'GrNorm')
eval_stages = []
ema_eval_stages = []
if forward_split and do_eval:
eval_stages.append(Stage.EVAL_FRWD)
ema_eval_stages.append(Stage.EVAL_FRWD_EMA)
if side_split and do_eval:
eval_stages.append(Stage.EVAL_SIDE)
ema_eval_stages.append(Stage.EVAL_SIDE_EMA)
# gpu_options=tf.GPUOptions(allow_growth=False),
with tf.Session(
config=tf.ConfigProto(allow_soft_placement=True,
gpu_options=tf.GPUOptions(
allow_growth=gpu_allow_growth))) as sess:
sess.run(init)
# pipe.load_vars(sess)
inp.restore(sess)
for model in all_models:
model.init(sess)
# if beholder:
# visualizer = Beholder(session=sess, logdir=summ_path)
step = 0
prev_top = np.inf
best_smape = np.inf
# Contains best value (first item) and subsequent values
best_epoch_smape = []
for epoch in range(max_epoch):
# n_steps = pusher.n_pages // batch_size
if tqdm:
#tqr = trange(steps_per_epoch, desc="%2d" % (epoch + 1), leave=False)
tqr = trange(steps_per_epoch,
desc="%2d" % (epoch + 1),
leave=False,
file=logging.root.handlers[0].stream)
else:
tqr = range(steps_per_epoch)
for _ in tqr:
try:
step = trainer.train_step(sess, epoch)
pred, time_y, true_y, true_x, time_x, page_ix, norm_mean, norm_std, lagged_ix = sess.run(
[
trainer.trainers[0].train_model.predictions,
trainer.trainers[0].train_model.inp.time_y,
trainer.trainers[0].train_model.inp.true_y,
trainer.trainers[0].train_model.inp.true_x,
trainer.trainers[0].train_model.inp.time_x,
trainer.trainers[0].train_model.inp.page_ix,
trainer.trainers[0].train_model.inp.norm_mean,
trainer.trainers[0].train_model.inp.norm_std,
trainer.trainers[0].train_model.inp.lagged_x
])
#sess.run(trainer.trainers[0].train_model.inp.inp.hits)
#inp = all_models[0].train_model.inp.inp,
pred_exp = np.round(np.expm1(pred))
true_exp = np.expm1(true_y)
error_exp = np.mean(
np.abs(true_exp - pred_exp) / (true_exp))
error = np.mean(np.abs(true_y - pred) / (true_y))
# page_ix = sess.run([trainer.trainers[0].train_model.inp.page_ix])[0][0]
# true_x = sess.run([trainer.trainers[0].train_model.inp.true_x])[0][0]
last_error = error_exp
epsilon = 0.1 # Smoothing factor, helps SMAPE to be well-behaved near zero
true_o = np.expm1(true_y)
pred_o = np.expm1(pred)
summ = np.maximum(np.abs(true_o) + epsilon, 0.5 + epsilon)
smape = np.mean(np.abs(pred_o - true_o) / summ)
except tf.errors.OutOfRangeError:
break
# if beholder:
# if step % 5 == 0:
# noinspection PyUnboundLocalVariable
# visualizer.update()
if step % eval_every_step == 0:
if eval_stages:
trainer.eval_step(sess,
epoch,
step,
eval_batches,
stages=eval_stages)
if save_best_model and epoch > 0 and eval_smape.last < best_smape:
best_smape = eval_smape.last
saver.save(sess,
f'data/cpt/{name}/cpt',
global_step=step)
if save_from_step and step >= save_from_step:
saver.save(sess,
f'data/cpt/{name}/cpt',
global_step=step)
if avg_sgd and ema_eval_stages:
ema_saver.save(sess,
'data/cpt_tmp/ema',
write_meta_graph=False)
# restore ema-backed vars
ema_loader.restore(sess, 'data/cpt_tmp/ema')
trainer.eval_step(sess,
epoch,
step,
eval_batches,
stages=ema_eval_stages)
# restore normal vars
ema_saver.restore(sess, 'data/cpt_tmp/ema')
MAE = "%.3f/%.3f/%.3f" % (eval_mae.last, eval_mae_side.last,
train_mae.last)
improvement = '↑' if eval_smape.improved else ' '
SMAPE = "%s%.3f/%.3f/%.3f" % (improvement, eval_smape.last,
eval_smape_side.last,
train_smape.last)
if tqdm:
tqr.set_postfix(gr=grad_norm.last, MAE=MAE, SMAPE=SMAPE)
if not trainer.has_active() or (max_steps
and step > max_steps):
break
if tqdm:
tqr.close()
trainer.end_epoch()
if not best_epoch_smape or eval_smape.avg_epoch < best_epoch_smape[
0]:
best_epoch_smape = [eval_smape.avg_epoch]
else:
best_epoch_smape.append(eval_smape.avg_epoch)
current_top = eval_smape.top
if prev_top > current_top:
prev_top = current_top
has_best_indicator = '↑'
else:
has_best_indicator = ' '
status = "%2d: Best top SMAPE=%.3f%s (%s)" % (
epoch + 1, current_top, has_best_indicator, ",".join(
["%.3f" % m.top for m in eval_smape.metrics]))
if trainer.has_active():
status += ", frwd/side best MAE=%.3f/%.3f, SMAPE=%.3f/%.3f; avg MAE=%.3f/%.3f, SMAPE=%.3f/%.3f, %d am ,Error=%3f " % \
(eval_mae.best_epoch, eval_mae_side.best_epoch, eval_smape.best_epoch, eval_smape_side.best_epoch,
eval_mae.avg_epoch, eval_mae_side.avg_epoch, eval_smape.avg_epoch, eval_smape_side.avg_epoch,
trainer.has_active(), last_error)
log.info(status)
else:
log.info(status)
log.info("Early stopping!")
break
if max_steps and step > max_steps:
log.info("Max steps calculated")
break
sys.stderr.flush()
# noinspection PyUnboundLocalVariable
return np.mean(best_epoch_smape, dtype=np.float64)
def train():
with tf.Graph().as_default(), tf.device('/cpu:0'):
tf.gfile.Copy(FLAGS.input_previous_model_path + "/" +
FLAGS.tree_index_file,
FLAGS.output_model_path + "/" + FLAGS.tree_index_file,
overwrite=True)
global_step = tf.train.get_or_create_global_step()
inc_step = tf.assign_add(global_step, 1)
#Training setting
train_input_pipe = InputPipe([
FLAGS.input_training_data_path + "/" + i
for i in tf.gfile.ListDirectory(FLAGS.input_training_data_path)
], FLAGS.batch_size, FLAGS.num_epochs, 5, "", True)
#auc_eval_pipe = InputPipe(FLAGS.input_validation_data_path + "/label_data.txt", FLAGS.eval_batch_size,1,3,"0,1",False) if FLAGS.auc_evaluation else None
auc_eval_pipe = InputPipe(FLAGS.input_validation_data_path,
FLAGS.eval_batch_size, 1, 3, "",
True) if FLAGS.auc_evaluation else None
#bleu_eval_pipe = InputPipe(FLAGS.input_validation_data_path + "/bleu_data.txt", FLAGS.eval_batch_size,1,2,"0",False) if FLAGS.bleu_evaluation else None
model = TreeModel()
trainer = SingleboxTrainer(model, inc_step, train_input_pipe,
auc_eval_pipe, None)
summary_op = tf.summary.merge_all()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
saver = tf.train.Saver(max_to_keep=FLAGS.max_model_to_keep,
name='model_saver')
with tf.Session(config=config) as session:
summ_writer = tf.summary.FileWriter(FLAGS.log_dir, session.graph)
#Load Pretrain
session.run(tf.local_variables_initializer())
session.run(tf.global_variables_initializer())
session.run(tf.tables_initializer())
session.run(train_input_pipe.iterator.initializer)
ckpt = tf.train.get_checkpoint_state(
FLAGS.input_previous_model_path)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(session, ckpt.model_checkpoint_path)
print("Load Model From ", ckpt.model_checkpoint_path)
else:
print("No Initial Model Found.")
trainer.start_time = time.time()
while True:
try:
_, avg_loss, total_weight, step, summary = session.run(
trainer.train_ops() + [summary_op])
#print(step)
if step % FLAGS.log_frequency == 1:
summ_writer.add_summary(summary, step)
trainer.print_log(total_weight, step, avg_loss)
if step % FLAGS.checkpoint_frequency == 1:
if FLAGS.auc_evaluation:
trainer.eval(step, session, 'auc')
if FLAGS.bleu_evaluation:
trainer.eval(step, session, 'bleu')
if trainer.improved():
saver.save(session,
FLAGS.output_model_path + "/tree_model",
global_step=step)
elif trainer.early_stop():
print("\nEarly stop")
break
except tf.errors.OutOfRangeError:
print("End of training.")
break
if not trainer.early_stop():
saver.save(session,
FLAGS.output_model_path + "/" + "tree_model_final",
global_step=step)
h0 = sample_prob(h0_prob)
h1 = h0
for step in range(gibbs_sampling_steps):
v1_prob = tf.nn.sigmoid(
tf.matmul(h1, tf.transpose(w1)) + tf.transpose(vb1))
v1 = sample_prob(v1_prob)
h1_prob = tf.nn.sigmoid(tf.matmul(v1, w1) + tf.transpose(hb1))
h1 = sample_prob(h1_prob)
w1_positive_grad = tf.matmul(tf.transpose(X1), h0_prob)
w1_negative_grad = tf.matmul(tf.transpose(v1_prob), h1_prob)
dw1 = (w1_positive_grad - w1_negative_grad) / tf.to_float(tf.shape(X1)[0])
update_w1 = tf.assign_add(w1, alpha * dw1)
update_vb1 = tf.assign_add(vb1, alpha * tf.reduce_mean(X1 - v1, 0))
update_hb1 = tf.assign_add(hb1, alpha * tf.reduce_mean(h0 - h1, 0))
out1 = (update_w1, update_vb1, update_hb1)
v1_prob = tf.nn.sigmoid(
tf.matmul(h1, tf.transpose(w1)) + tf.transpose(vb1))
v1 = sample_prob(v1_prob)
err1 = X1 - v1_prob
err_sum1 = tf.reduce_mean(err1 * err1)
initialize1 = tf.global_variables_initializer()
batch_size = 100
def custom_loss(self, y_true, y_pred):
mask_shape = tf.shape(y_true)[:4]
cell_x = tf.to_float(tf.reshape(tf.tile(tf.range(self.grid_w), [self.grid_h]), (1, self.grid_h, self.grid_w, 1, 1)))
cell_y = tf.transpose(cell_x, (0,2,1,3,4))
cell_grid = tf.tile(tf.concat([cell_x,cell_y], -1), [self.batch_size, 1, 1, self.nb_box, 1])
coord_mask = tf.zeros(mask_shape)
conf_mask = tf.zeros(mask_shape)
class_mask = tf.zeros(mask_shape)
seen = tf.Variable(0.)
total_recall = tf.Variable(0.)
"""
Adjust prediction
"""
### adjust x and y
pred_box_xy = tf.sigmoid(y_pred[..., :2]) + cell_grid
### adjust w and h
pred_box_wh = tf.exp(y_pred[..., 2:4]) * np.reshape(self.anchors, [1,1,1,self.nb_box,2])
### adjust confidence
pred_box_conf = tf.sigmoid(y_pred[..., 4])
### adjust class probabilities
pred_box_class = y_pred[..., 5:]
"""
Adjust ground truth
"""
### adjust x and y
true_box_xy = y_true[..., 0:2] # relative position to the containing cell
### adjust w and h
true_box_wh = y_true[..., 2:4] # number of cells accross, horizontally and vertically
### adjust confidence
true_wh_half = true_box_wh / 2.
true_mins = true_box_xy - true_wh_half
true_maxes = true_box_xy + true_wh_half
pred_wh_half = pred_box_wh / 2.
pred_mins = pred_box_xy - pred_wh_half
pred_maxes = pred_box_xy + pred_wh_half
intersect_mins = tf.maximum(pred_mins, true_mins)
intersect_maxes = tf.minimum(pred_maxes, true_maxes)
intersect_wh = tf.maximum(intersect_maxes - intersect_mins, 0.)
intersect_areas = intersect_wh[..., 0] * intersect_wh[..., 1]
true_areas = true_box_wh[..., 0] * true_box_wh[..., 1]
pred_areas = pred_box_wh[..., 0] * pred_box_wh[..., 1]
union_areas = pred_areas + true_areas - intersect_areas
iou_scores = tf.truediv(intersect_areas, union_areas)
true_box_conf = iou_scores * y_true[..., 4]
### adjust class probabilities
true_box_class = tf.argmax(y_true[..., 5:], -1)
"""
Determine the masks
"""
### coordinate mask: simply the position of the ground truth boxes (the predictors)
coord_mask = tf.expand_dims(y_true[..., 4], axis=-1) * self.coord_scale
### confidence mask: penelize predictors + penalize boxes with low IOU
# penalize the confidence of the boxes, which have IOU with some ground truth box < 0.6
true_xy = self.true_boxes[..., 0:2]
true_wh = self.true_boxes[..., 2:4]
true_wh_half = true_wh / 2.
true_mins = true_xy - true_wh_half
true_maxes = true_xy + true_wh_half
pred_xy = tf.expand_dims(pred_box_xy, 4)
pred_wh = tf.expand_dims(pred_box_wh, 4)
pred_wh_half = pred_wh / 2.
pred_mins = pred_xy - pred_wh_half
pred_maxes = pred_xy + pred_wh_half
intersect_mins = tf.maximum(pred_mins, true_mins)
intersect_maxes = tf.minimum(pred_maxes, true_maxes)
intersect_wh = tf.maximum(intersect_maxes - intersect_mins, 0.)
intersect_areas = intersect_wh[..., 0] * intersect_wh[..., 1]
true_areas = true_wh[..., 0] * true_wh[..., 1]
pred_areas = pred_wh[..., 0] * pred_wh[..., 1]
union_areas = pred_areas + true_areas - intersect_areas
iou_scores = tf.truediv(intersect_areas, union_areas)
best_ious = tf.reduce_max(iou_scores, axis=4)
conf_mask = conf_mask + tf.to_float(best_ious < 0.6) * (1 - y_true[..., 4]) * self.no_object_scale
# penalize the confidence of the boxes, which are reponsible for corresponding ground truth box
conf_mask = conf_mask + y_true[..., 4] * self.object_scale
### class mask: simply the position of the ground truth boxes (the predictors)
class_mask = y_true[..., 4] * tf.gather(self.class_wt, true_box_class) * self.class_scale
"""
Warm-up training
"""
no_boxes_mask = tf.to_float(coord_mask < self.coord_scale/2.)
seen = tf.assign_add(seen, 1.)
true_box_xy, true_box_wh, coord_mask = tf.cond(tf.less(seen, self.warmup_batches+1),
lambda: [true_box_xy + (0.5 + cell_grid) * no_boxes_mask,
true_box_wh + tf.ones_like(true_box_wh) * \
np.reshape(self.anchors, [1,1,1,self.nb_box,2]) * \
no_boxes_mask,
tf.ones_like(coord_mask)],
lambda: [true_box_xy,
true_box_wh,
coord_mask])
"""
Finalize the loss
"""
nb_coord_box = tf.reduce_sum(tf.to_float(coord_mask > 0.0))
nb_conf_box = tf.reduce_sum(tf.to_float(conf_mask > 0.0))
nb_class_box = tf.reduce_sum(tf.to_float(class_mask > 0.0))
loss_xy = tf.reduce_sum(tf.square(true_box_xy-pred_box_xy) * coord_mask) / (nb_coord_box + 1e-6) / 2.
loss_wh = tf.reduce_sum(tf.square(true_box_wh-pred_box_wh) * coord_mask) / (nb_coord_box + 1e-6) / 2.
loss_conf = tf.reduce_sum(tf.square(true_box_conf-pred_box_conf) * conf_mask) / (nb_conf_box + 1e-6) / 2.
loss_class = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=true_box_class, logits=pred_box_class)
loss_class = tf.reduce_sum(loss_class * class_mask) / (nb_class_box + 1e-6)
loss = tf.cond(tf.less(seen, self.warmup_batches+1),
lambda: loss_xy + loss_wh + loss_conf + loss_class + 10,
lambda: loss_xy + loss_wh + loss_conf + loss_class)
if self.debug:
nb_true_box = tf.reduce_sum(y_true[..., 4])
nb_pred_box = tf.reduce_sum(tf.to_float(true_box_conf > 0.5) * tf.to_float(pred_box_conf > 0.3))
current_recall = nb_pred_box/(nb_true_box + 1e-6)
total_recall = tf.assign_add(total_recall, current_recall)
loss = tf.Print(loss, [loss_xy], message='Loss XY \t', summarize=1000)
loss = tf.Print(loss, [loss_wh], message='Loss WH \t', summarize=1000)
loss = tf.Print(loss, [loss_conf], message='Loss Conf \t', summarize=1000)
loss = tf.Print(loss, [loss_class], message='Loss Class \t', summarize=1000)
loss = tf.Print(loss, [loss], message='Total Loss \t', summarize=1000)
loss = tf.Print(loss, [current_recall], message='Current Recall \t', summarize=1000)
loss = tf.Print(loss, [total_recall/seen], message='Average Recall \t', summarize=1000)
return loss
def batch_norm_log_diff(input_,
dim,
name,
train=True,
epsilon=1e-8,
decay=.1,
axes=[0],
reuse=None,
bn_lag=DEFAULT_BN_LAG):
"""Batch normalization with corresponding log determinant Jacobian."""
if reuse is None:
reuse = not train
# create variables
with tf.variable_scope(name) as scope:
if reuse:
scope.reuse_variables()
var = variable_on_cpu("var", [dim],
tf.constant_initializer(1.),
trainable=False)
mean = variable_on_cpu("mean", [dim],
tf.constant_initializer(0.),
trainable=False)
step = variable_on_cpu("step", [],
tf.constant_initializer(0.),
trainable=False)
# choose the appropriate moments
if train:
used_mean, used_var = tf.nn.moments(input_, axes, name="batch_norm")
cur_mean, cur_var = used_mean, used_var
if bn_lag > 0.:
used_var = stable_var(input_=input_, mean=used_mean, axes=axes)
cur_var = used_var
used_mean -= (1 - bn_lag) * (used_mean - tf.stop_gradient(mean))
used_mean /= (1. - bn_lag**(step + 1))
used_var -= (1 - bn_lag) * (used_var - tf.stop_gradient(var))
used_var /= (1. - bn_lag**(step + 1))
else:
used_mean, used_var = mean, var
cur_mean, cur_var = used_mean, used_var
# update variables
if train:
with tf.name_scope(name, "AssignMovingAvg", [mean, cur_mean, decay]):
with ops.colocate_with(mean):
new_mean = tf.assign_sub(
mean,
tf.check_numerics(decay * (mean - cur_mean),
"NaN in moving mean."))
with tf.name_scope(name, "AssignMovingAvg", [var, cur_var, decay]):
with ops.colocate_with(var):
new_var = tf.assign_sub(
var,
tf.check_numerics(decay * (var - cur_var),
"NaN in moving variance."))
with tf.name_scope(name, "IncrementTime", [step]):
with ops.colocate_with(step):
new_step = tf.assign_add(step, 1.)
used_var += 0. * new_mean * new_var * new_step
used_var += epsilon
return used_mean, used_var
N = settings["NumOptions"]
offset = 0
v_select = []
for sample in range(N):
v_select.append(v_g[:, sample + offset])
if sample + offset >= dim:
continue
if np.iscomplex(w_g[sample + offset]):
offset += 1
from networks.network import Network
#Creating High level policy
with tf.device(args.processor):
global_step = tf.Variable(0, trainable=False, name='global_step')
global_step_next = tf.assign_add(global_step, 1)
network = Network(settings["NetworkConfig"], N, netConfigOverride)
Method = GetFunction(settings["Method"])
net = Method(network,
sess,
scope="net",
stateShape=dFeatures,
actionSize=N,
HPs=settings["NetworkHPs"],
nTrajs=nTrajs)
#Creating Auxilary Functions for logging and saving.
writer = tf.summary.FileWriter(LOG_PATH, graph=sess.graph)
saver = tf.train.Saver(max_to_keep=3, var_list=net.getVars + [global_step])
net.InitializeVariablesFromFile(saver, MODEL_PATH_)
InitializeVariables(
batch_size = 50
batch_label = np.ones([batch_size, n, 1])
x = tf.placeholder(tf.float32, [None, n_his + 1, n, 1])
keep_prob = tf.placeholder(tf.float32)
is_training = tf.placeholder(tf.bool)
train_loss = STGCN(x, n, n_his, Ks, Kt, keep_prob, is_training)
copy_loss = tf.add_n(tf.get_collection('copy_loss'))
global_step = tf.Variable(0, trainable=False)
n_sample = np.shape(xtr)[0]
n_batch = int(n_sample / float(batch_size))
lr = tf.train.exponential_decay(
1e-2, global_step, decay_steps=5 * n_batch, decay_rate=0.7, staircase=True)
step_op = tf.assign_add(global_step, 1)
with tf.control_dependencies([step_op]):
train_op = tf.train.RMSPropOptimizer(lr).minimize(train_loss)
saver = tf.train.Saver()
sess = tf.Session()
sess.run(tf.global_variables_initializer())
min_va_mape9 = min_va_mape6 = min_va_mape3 = 0.4
min_va_mse9 = min_va_mse6 = min_va_mse3 = 1e5
min_va_mae9 = min_va_mae6 = min_va_mae3 = 1e5
min_mape9 = min_mape6 = min_mape3 = 0.4
min_mse9 = min_mse6 = min_mse3 = 1e5
min_mae9 = min_mae6 = min_mae3 = 1e5
flag3 = flag6 = flag9 = False
def batch_norm(input_,
dim,
name,
scale=True,
train=True,
epsilon=1e-8,
decay=.1,
axes=[0],
bn_lag=DEFAULT_BN_LAG):
"""Batch normalization."""
# create variables
with tf.variable_scope(name):
var = variable_on_cpu("var", [dim],
tf.constant_initializer(1.),
trainable=False)
mean = variable_on_cpu("mean", [dim],
tf.constant_initializer(0.),
trainable=False)
step = variable_on_cpu("step", [],
tf.constant_initializer(0.),
trainable=False)
if scale:
gamma = variable_on_cpu("gamma", [dim],
tf.constant_initializer(1.))
beta = variable_on_cpu("beta", [dim], tf.constant_initializer(0.))
# choose the appropriate moments
if train:
used_mean, used_var = tf.nn.moments(input_, axes, name="batch_norm")
cur_mean, cur_var = used_mean, used_var
if bn_lag > 0.:
used_mean -= (1. - bn_lag) * (used_mean - tf.stop_gradient(mean))
used_var -= (1 - bn_lag) * (used_var - tf.stop_gradient(var))
used_mean /= (1. - bn_lag**(step + 1))
used_var /= (1. - bn_lag**(step + 1))
else:
used_mean, used_var = mean, var
cur_mean, cur_var = used_mean, used_var
# normalize
res = (input_ - used_mean) / tf.sqrt(used_var + epsilon)
# de-normalize
if scale:
res *= gamma
res += beta
# update variables
if train:
with tf.name_scope(name, "AssignMovingAvg", [mean, cur_mean, decay]):
with ops.colocate_with(mean):
new_mean = tf.assign_sub(
mean,
tf.check_numerics(decay * (mean - cur_mean),
"NaN in moving mean."))
with tf.name_scope(name, "AssignMovingAvg", [var, cur_var, decay]):
with ops.colocate_with(var):
new_var = tf.assign_sub(
var,
tf.check_numerics(decay * (var - cur_var),
"NaN in moving variance."))
with tf.name_scope(name, "IncrementTime", [step]):
with ops.colocate_with(step):
new_step = tf.assign_add(step, 1.)
res += 0. * new_mean * new_var * new_step
return res
#try finally
import tensorflow as tf
array_to_outof = tf.constant([1, 2, 3, 4, 5, 6, 7, 8])
counter = tf.Variable(0)
x = tf.Variable(1.)
w = tf.Variable(2.)
op = tf.multiply(w, x)
count_op = tf.assign_add(counter, 1)
coord = tf.train.Coordinator()
sess = tf.Session()
with sess.as_default():
sess.run(tf.global_variables_initializer())
"""Start Training"""
threads = tf.train.start_queue_runners(coord=coord)
try:
while not coord.should_stop():
print('in while-loop:')
op_result_ = sess.run([op])
print(op_result_)
counter_ = sess.run(count_op)
print('counter_:', counter_)
array_to_outof[counter_]
# except tf.errors.OutOfRangeError:
# print('except')
except ValueError:
def __init__(self):
self.anchor_per_scale = cfg.YOLO.ANCHOR_PER_SCALE
self.classes = utils.read_class_names(cfg.YOLO.CLASSES)
self.num_classes = len(self.classes)
self.learn_rate_init = cfg.TRAIN.LEARN_RATE_INIT
self.learn_rate_end = cfg.TRAIN.LEARN_RATE_END
self.first_stage_epochs = cfg.TRAIN.FISRT_STAGE_EPOCHS
self.second_stage_epochs = cfg.TRAIN.SECOND_STAGE_EPOCHS
self.warmup_periods = cfg.TRAIN.WARMUP_EPOCHS
self.initial_weight = cfg.TRAIN.INITIAL_WEIGHT
self.time = time.strftime('%Y-%m-%d-%H-%M-%S',
time.localtime(time.time()))
self.moving_ave_decay = cfg.YOLO.MOVING_AVE_DECAY
self.max_bbox_per_scale = 150
self.train_logdir = "./data/log/train"
self.trainset = Dataset('train')
self.testset = Dataset('test')
self.steps_per_period = len(self.trainset)
config = tf.ConfigProto()
config.allow_soft_placement = True
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
with tf.name_scope('define_input'):
self.input_data = tf.placeholder(dtype=tf.float32,
name='input_data')
self.label_sbbox = tf.placeholder(dtype=tf.float32,
name='label_sbbox')
self.label_mbbox = tf.placeholder(dtype=tf.float32,
name='label_mbbox')
self.label_lbbox = tf.placeholder(dtype=tf.float32,
name='label_lbbox')
self.true_sbboxes = tf.placeholder(dtype=tf.float32,
name='sbboxes')
self.true_mbboxes = tf.placeholder(dtype=tf.float32,
name='mbboxes')
self.true_lbboxes = tf.placeholder(dtype=tf.float32,
name='lbboxes')
self.trainable = tf.placeholder(dtype=tf.bool, name='training')
with tf.name_scope("define_loss"):
self.model = YOLOV3(self.input_data, self.trainable)
self.net_var = tf.global_variables()
self.giou_loss, self.conf_loss, self.prob_loss = self.model.compute_loss(
self.label_sbbox, self.label_mbbox, self.label_lbbox,
self.true_sbboxes, self.true_mbboxes, self.true_lbboxes)
self.loss = self.giou_loss + self.conf_loss + self.prob_loss
with tf.name_scope('learn_rate'):
self.global_step = tf.Variable(1.0,
dtype=tf.float64,
trainable=False,
name='global_step')
warmup_steps = tf.constant(self.warmup_periods *
self.steps_per_period,
dtype=tf.float64,
name='warmup_steps')
train_steps = tf.constant(
(self.first_stage_epochs + self.second_stage_epochs) *
self.steps_per_period,
dtype=tf.float64,
name='train_steps')
self.learn_rate = tf.cond(
pred=self.global_step < warmup_steps,
true_fn=lambda: self.global_step / warmup_steps * self.
learn_rate_init,
false_fn=lambda: self.learn_rate_end + 0.5 *
(self.learn_rate_init - self.learn_rate_end) * (1 + tf.cos(
(self.global_step - warmup_steps) /
(train_steps - warmup_steps) * np.pi)))
global_step_update = tf.assign_add(self.global_step, 1.0)
with tf.name_scope("define_weight_decay"):
moving_ave = tf.train.ExponentialMovingAverage(
self.moving_ave_decay).apply(tf.trainable_variables())
with tf.name_scope("define_first_stage_train"):
self.first_stage_trainable_var_list = []
for var in tf.trainable_variables():
var_name = var.op.name
var_name_mess = str(var_name).split('/')
if var_name_mess[0] in [
'conv_sbbox', 'conv_mbbox', 'conv_lbbox'
]:
self.first_stage_trainable_var_list.append(var)
first_stage_optimizer = tf.train.AdamOptimizer(
self.learn_rate).minimize(
self.loss, var_list=self.first_stage_trainable_var_list)
with tf.control_dependencies(
tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
with tf.control_dependencies(
[first_stage_optimizer, global_step_update]):
with tf.control_dependencies([moving_ave]):
self.train_op_with_frozen_variables = tf.no_op()
with tf.name_scope("define_second_stage_train"):
second_stage_trainable_var_list = tf.trainable_variables()
second_stage_optimizer = tf.train.AdamOptimizer(
self.learn_rate).minimize(
self.loss, var_list=second_stage_trainable_var_list)
with tf.control_dependencies(
tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
with tf.control_dependencies(
[second_stage_optimizer, global_step_update]):
with tf.control_dependencies([moving_ave]):
self.train_op_with_all_variables = tf.no_op()
with tf.name_scope('loader_and_saver'):
self.loader = tf.train.Saver(self.net_var)
self.saver = tf.train.Saver(tf.global_variables(), max_to_keep=10)
with tf.name_scope('summary'):
tf.summary.scalar("learn_rate", self.learn_rate)
tf.summary.scalar("giou_loss", self.giou_loss)
tf.summary.scalar("conf_loss", self.conf_loss)
tf.summary.scalar("prob_loss", self.prob_loss)
tf.summary.scalar("total_loss", self.loss)
logdir = "./data/log/"
if os.path.exists(logdir): shutil.rmtree(logdir)
os.mkdir(logdir)
self.write_op = tf.summary.merge_all()
self.summary_writer = tf.summary.FileWriter(logdir,
graph=self.sess.graph)
def call(self, x):
input_image, y_pred, y_true, true_boxes = x
# adjust the shape of the y_predict [batch, grid_h, grid_w, 3, 4+1+nb_class]
y_pred = tf.reshape(
y_pred,
tf.concat([tf.shape(y_pred)[:3],
tf.constant([3, -1])], axis=0))
# initialize the masks
object_mask = tf.expand_dims(y_true[..., 4], 4)
# the variable to keep track of number of batches processed
batch_seen = tf.Variable(0.)
# compute grid factor and net factor
grid_h = tf.shape(y_true)[1]
grid_w = tf.shape(y_true)[2]
grid_factor = tf.reshape(tf.cast([grid_w, grid_h], tf.float32),
[1, 1, 1, 1, 2])
net_h = tf.shape(input_image)[1]
net_w = tf.shape(input_image)[2]
net_factor = tf.reshape(tf.cast([net_w, net_h], tf.float32),
[1, 1, 1, 1, 2])
"""
Adjust prediction
"""
pred_box_xy = (self.cell_grid[:, :grid_h, :grid_w, :, :] +
tf.sigmoid(y_pred[..., :2])) # sigma(t_xy) + c_xy
pred_box_wh = y_pred[..., 2:4] # t_wh
pred_box_conf = tf.expand_dims(tf.sigmoid(y_pred[..., 4]),
4) # adjust confidence
pred_box_class = tf.sigmoid(y_pred[...,
5:]) # adjust class probabilities
"""
Adjust ground truth
"""
true_box_xy = y_true[..., 0:2] # (sigma(t_xy) + c_xy)
true_box_wh = y_true[..., 2:4] # t_wh
true_box_conf = tf.expand_dims(y_true[..., 4], 4)
true_box_class = y_true[..., 5:]
"""
Compare each predicted box to all true boxes
"""
# initially, drag all objectness of all boxes to 0
conf_delta = pred_box_conf - 0
# then, ignore the boxes which have good overlap with some true box
true_xy = true_boxes[..., 0:2] / grid_factor
true_wh = true_boxes[..., 2:4] / net_factor
true_wh_half = true_wh / 2.
true_mins = true_xy - true_wh_half
true_maxes = true_xy + true_wh_half
pred_xy = tf.expand_dims(pred_box_xy / grid_factor, 4)
pred_wh = tf.expand_dims(
tf.exp(pred_box_wh) * self.anchors / net_factor, 4)
pred_wh_half = pred_wh / 2.
pred_mins = pred_xy - pred_wh_half
pred_maxes = pred_xy + pred_wh_half
intersect_mins = tf.maximum(pred_mins, true_mins)
intersect_maxes = tf.minimum(pred_maxes, true_maxes)
intersect_wh = tf.maximum(intersect_maxes - intersect_mins, 0.)
intersect_areas = intersect_wh[..., 0] * intersect_wh[..., 1]
true_areas = true_wh[..., 0] * true_wh[..., 1]
pred_areas = pred_wh[..., 0] * pred_wh[..., 1]
union_areas = pred_areas + true_areas - intersect_areas
iou_scores = tf.truediv(intersect_areas, union_areas)
best_ious = tf.reduce_max(iou_scores, axis=4)
conf_delta *= tf.expand_dims(
tf.to_float(best_ious < self.ignore_thresh), 4)
"""
Compute some online statistics
"""
true_xy = true_box_xy / grid_factor
true_wh = tf.exp(true_box_wh) * self.anchors / net_factor
true_wh_half = true_wh / 2.
true_mins = true_xy - true_wh_half
true_maxes = true_xy + true_wh_half
pred_xy = pred_box_xy / grid_factor
pred_wh = tf.exp(pred_box_wh) * self.anchors / net_factor
pred_wh_half = pred_wh / 2.
pred_mins = pred_xy - pred_wh_half
pred_maxes = pred_xy + pred_wh_half
intersect_mins = tf.maximum(pred_mins, true_mins)
intersect_maxes = tf.minimum(pred_maxes, true_maxes)
intersect_wh = tf.maximum(intersect_maxes - intersect_mins, 0.)
intersect_areas = intersect_wh[..., 0] * intersect_wh[..., 1]
true_areas = true_wh[..., 0] * true_wh[..., 1]
pred_areas = pred_wh[..., 0] * pred_wh[..., 1]
union_areas = pred_areas + true_areas - intersect_areas
iou_scores = tf.truediv(intersect_areas, union_areas)
iou_scores = object_mask * tf.expand_dims(iou_scores, 4)
count = tf.reduce_sum(object_mask)
count_noobj = tf.reduce_sum(1 - object_mask)
detect_mask = tf.to_float(pred_box_conf * object_mask >= 0.5)
class_mask = tf.expand_dims(
tf.to_float(
tf.equal(tf.argmax(pred_box_class, -1),
tf.argmax(true_box_class, -1))), 4)
recall50 = tf.to_float(iou_scores >= 0.5) * detect_mask
recall75 = tf.to_float(iou_scores >= 0.75) * detect_mask
recall50_c = tf.reduce_sum(recall50 * class_mask) / (count + 1e-3)
recall75_c = tf.reduce_sum(recall75 * class_mask) / (count + 1e-3)
recall50 = tf.reduce_sum(recall50) / (count + 1e-3)
recall75 = tf.reduce_sum(recall75) / (count + 1e-3)
avg_iou = tf.reduce_sum(iou_scores) / (count + 1e-3)
avg_obj = tf.reduce_sum(pred_box_conf * object_mask) / (count + 1e-3)
avg_noobj = tf.reduce_sum(pred_box_conf *
(1 - object_mask)) / (count_noobj + 1e-3)
avg_cat = tf.reduce_sum(
pred_box_class * true_box_class) / (count + 1e-3)
"""
Warm-up training
"""
batch_seen = tf.assign_add(batch_seen, 1.)
true_box_xy, true_box_wh, xywh_mask = tf.cond(
tf.less(batch_seen, self.warmup_batches + 1), lambda: [
true_box_xy +
(0.5 + self.cell_grid[:, :grid_h, :grid_w, :, :]) *
(1 - object_mask), true_box_wh + tf.zeros_like(true_box_wh) *
(1 - object_mask),
tf.ones_like(object_mask)
], lambda: [true_box_xy, true_box_wh, object_mask])
"""
Compare each true box to all anchor boxes
"""
xywh_scale = tf.exp(true_box_wh) * self.anchors / net_factor
xywh_scale = tf.expand_dims(
2 - xywh_scale[..., 0] * xywh_scale[..., 1],
axis=4) # the smaller the box, the bigger the scale
xy_delta = xywh_mask * (pred_box_xy - true_box_xy) * xywh_scale
wh_delta = xywh_mask * (pred_box_wh - true_box_wh) * xywh_scale
conf_delta = object_mask * (pred_box_conf - true_box_conf) * 5 + (
1 - object_mask) * conf_delta
class_delta = object_mask * (pred_box_class - true_box_class)
loss = tf.reduce_sum(tf.square(xy_delta), list(range(1,5))) + \
tf.reduce_sum(tf.square(wh_delta), list(range(1,5))) + \
tf.reduce_sum(tf.square(conf_delta), list(range(1,5))) + \
tf.reduce_sum(tf.square(class_delta), list(range(1,5)))
loss = tf.cond(
tf.less(batch_seen, self.warmup_batches +
1), # add 10 to the loss if this is the warmup stage
lambda: loss + 10,
lambda: loss)
# loss = tf.Print(loss, [grid_h, avg_obj], message='avg_obj \t\t', summarize=1000)
# loss = tf.Print(loss, [grid_h, avg_noobj], message='avg_noobj \t\t', summarize=1000)
# loss = tf.Print(loss, [grid_h, avg_iou], message='avg_iou \t\t', summarize=1000)
# loss = tf.Print(loss, [grid_h, avg_cat], message='avg_cat \t\t', summarize=1000)
# loss = tf.Print(loss, [grid_h, recall50], message='recall50 \t', summarize=1000)
# loss = tf.Print(loss, [grid_h, recall75], message='recall75 \t', summarize=1000)
# loss = tf.Print(loss, [grid_h, recall50_c], message='recall50_cat \t', summarize=1000)
# loss = tf.Print(loss, [grid_h, recall75_c], message='recall75_Cat \t', summarize=1000)
# loss = tf.Print(loss, [grid_h, count], message='count \t', summarize=1000)
# loss = tf.Print(loss, [grid_h, tf.reduce_sum(loss)], message='loss: \t', summarize=1000)
return loss * self.scale
# add tensor to tensorboard
ns_image_tb = tf.summary.image(name='ns_image',
tensor=tf.reshape(ns, shape=[1, 520, 600, 1]))
ns_mean_tb = tf.summary.scalar(name='ns_mean',
tensor=tf.reduce_mean(tf.reduce_mean(ns)))
ns_hist_tb = tf.summary.histogram(name='ns_hist', values=ns)
zs_squre = tf.multiply(zs, zs)
zs_add = tf.add(zs_squre, xs)
zs_abs = tf.abs(zs_add)
zs_less = tf.math.less(zs_abs, 4)
zs_cast = tf.cast(zs_less, tf.float32)
#
step = tf.group(tf.assign(zs, zs_add), tf.assign_add(ns, zs_cast), name='step')
#
sess = tf.Session()
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
tbs = tf.summary.merge_all()
writer = tf.summary.FileWriter(logdir='./tensorboard')
writer.add_graph(tf.get_default_graph())
for i in range(200):
_, tbs_, cast_ = sess.run([step, tbs, zs_cast])
writer.add_summary(tbs_, global_step=i)
saver.save(sess, save_path='./model/mandelbrot')
train_step = tf.train.AdagradOptimizer(
learning_rate=opt.hyper.learning_rate / 10).minimize(
total_loss, var_list=all_var)
elif opt.optimizer == 4:
train_step = tf.train.ProximalAdagradOptimizer(learning_rate=opt.hyper.learning_rate/10). \
minimize(total_loss, var_list=all_var)
elif opt.optimizer == 5:
train_step = tf.train.RMSPropOptimizer(
learning_rate=opt.hyper.learning_rate / 10).minimize(
total_loss, var_list=all_var)
elif opt.optimizer == 6:
train_step = tf.train.FtrlOptimizer(
learning_rate=opt.hyper.learning_rate / 10).minimize(
total_loss, var_list=all_var)
inc_global_step = tf.assign_add(global_step, 1, name='increment')
raw_grads = tf.gradients(total_loss, all_var)
grads = list(zip(raw_grads, tf.trainable_variables()))
for g, v in grads:
summary.gradient_summaries(g, v, opt)
################################################################################################
################################################################################################
# Set up checkpoints and data
################################################################################################
saver = tf.compat.v1.train.Saver(max_to_keep=opt.max_to_keep_checkpoints)
# Automatic restore model, or force train from scratch
def _finish(self, update_ops, name_scope):
with tf.control_dependencies(update_ops):
return tf.assign_add(self._step_count, 1)
def train(train_imgs,train_labels,val_imgs,val_labels):
has_train=True
TB_LOG_DIR=os.path.join('..','model')
ckpt = tf.train.get_checkpoint_state(TB_LOG_DIR)
if not ckpt and not ckpt.model_checkpoint_path:
has_train=False
if has_train==False:
#dataset param
EPOCHS=150
SHUFFLE_SZ=1000
BATCH_SZ=200
#model param
OUTPUT_CNS=[24,48,96,192,1024]
CLASS_NUM=200
WEIGHT_DECAY=4e-5
#training param
WARM_UP_LR=0.002
LEARNING_RATE=0.5
LEARNING_RATE_DECAY=0.95
TOTAL_STEPS=EPOCHS*100000//BATCH_SZ
LEARNING_RATE_STEPS=TOTAL_STEPS//100
MOMENTUM=0.9
#display
DISPLAY_STEP=TOTAL_STEPS//100
TB_LOG_DIR=os.path.join('..','model')
#validation
VAL_SZ=10000
else:
#dataset param
EPOCHS=50
SHUFFLE_SZ=1000
BATCH_SZ=200
#model param
OUTPUT_CNS=[24,48,96,192,1024]
CLASS_NUM=200
WEIGHT_DECAY=4e-5
#training param
WARM_UP_LR=0.0005
LEARNING_RATE=0.0005
LEARNING_RATE_DECAY=0.9
TOTAL_STEPS=EPOCHS*100000//BATCH_SZ
LEARNING_RATE_STEPS=TOTAL_STEPS//100
MOMENTUM=0.9
#display
DISPLAY_STEP=TOTAL_STEPS//100
TB_LOG_DIR=os.path.join('..','model')
#validation
VAL_SZ=10000
imgpaths=tf.convert_to_tensor(train_imgs)
labels=tf.convert_to_tensor(train_labels)
valimgpaths=tf.convert_to_tensor(val_imgs)
vallabels=tf.convert_to_tensor(val_labels)
#sess=tf.Session()
def _parse_function(imgpath,label):
img=tf.read_file(imgpath)
img_decoded=tf.image.decode_jpeg(img,3)
img_decoded.set_shape([64,64,3])
img_decoded=tf.cast(img_decoded,dtype=tf.float32)
return img_decoded,label
dataset=tf.data.Dataset.from_tensor_slices((imgpaths,labels)).map(_parse_function)
dataset=dataset.shuffle(buffer_size=SHUFFLE_SZ)
dataset=dataset.repeat(EPOCHS)
dataset=dataset.batch(BATCH_SZ)
iterator=dataset.make_initializable_iterator()
batch_imgs,batch_labels=iterator.get_next()
valset=tf.data.Dataset.from_tensor_slices((valimgpaths,vallabels)).map(_parse_function)
valset=valset.batch(VAL_SZ)
valiterator=dataset.make_initializable_iterator()
valbatch_imgs,valbatch_labels=valiterator.get_next()
#dimgs,dlabels=sess.run([batch_imgs,batch_labels])
initial=tf.variance_scaling_initializer()
regular=tf.contrib.layers.l2_regularizer(1.0)
logits=model(batch_imgs,OUTPUT_CNS,CLASS_NUM,True,regular,initial)
with tf.name_scope('loss'):
loss=tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,labels=batch_labels))
reg=tf.losses.get_regularization_loss()
loss+=WEIGHT_DECAY*reg
with tf.name_scope('train'):
global_step=tf.get_variable('step',shape=[],trainable=False,
initializer=tf.zeros_initializer(dtype=tf.int64))
def get_lr(global_step,total_step,base_lr,warm_up_lr):
warm_up_total_step=total_step//20
transition_total_step=warm_up_total_step
remain_total_step=total_step-warm_up_total_step-transition_total_step
transition_dlrt=tf.convert_to_tensor((1.0*base_lr-warm_up_lr)/transition_total_step,dtype=tf.float32)
base_lrt=tf.convert_to_tensor(base_lr,dtype=tf.float32)
warm_up_lrt=tf.convert_to_tensor(warm_up_lr,dtype=tf.float32)
warm_up_total_step=tf.convert_to_tensor(warm_up_total_step,dtype=tf.float32)
transition_total_step=tf.convert_to_tensor(transition_total_step,dtype=tf.float32)
remain_total_step=tf.convert_to_tensor(remain_total_step,dtype=tf.float32)
transition_lr=(tf.cast(global_step,tf.float32)-warm_up_total_step)*transition_dlrt+warm_up_lrt
remain_lr=tf.train.exponential_decay(base_lrt,tf.cast(global_step,tf.float32)-warm_up_total_step-transition_total_step,
remain_total_step//120 ,LEARNING_RATE_DECAY)
lr=tf.case({tf.less(global_step,warm_up_total_step): lambda:warm_up_lrt,
tf.greater(global_step,transition_total_step+warm_up_total_step): lambda:remain_lr},
default=lambda:transition_lr,exclusive=True)
return lr
if has_train==False:
learning_rate=get_lr(global_step,TOTAL_STEPS,LEARNING_RATE,WARM_UP_LR)
else:
learning_rate=tf.train.exponential_decay(LEARNING_RATE,global_step,LEARNING_RATE_STEPS,LEARNING_RATE_DECAY)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op=tf.train.MomentumOptimizer(learning_rate=learning_rate,
momentum=MOMENTUM).minimize(loss)
with tf.control_dependencies([train_op]):
global_step_update=tf.assign_add(global_step,1)
if has_train==False:
init=tf.global_variables_initializer()
with tf.name_scope('batch_train_accuracy'):
logits_train=model(batch_imgs,OUTPUT_CNS,CLASS_NUM,False,regular,initial)
correct_pred_train=tf.equal(tf.cast(tf.argmax(logits_train,1),dtype=tf.int32),batch_labels)
accuracy_train=tf.reduce_mean(tf.cast(correct_pred_train,tf.float32))
with tf.name_scope('val_accuracy'):
logits_val=model(valbatch_imgs,OUTPUT_CNS,CLASS_NUM,False,regular,initial)
correct_pred_val=tf.equal(tf.cast(tf.argmax(logits_val,1),dtype=tf.int32),valbatch_labels)
accuracy_val=tf.reduce_mean(tf.cast(correct_pred_val,tf.float32))
sess=tf.Session()
if has_train==False:
sess.run(init)
else:
saver=tf.train.Saver()
saver.restore(sess,ckpt.model_checkpoint_path)
sess.run(iterator.initializer)
tf.summary.scalar('loss',loss)
tf.summary.scalar('batch_train_accuracy',accuracy_train)
tf.summary.scalar('val_accuracy',accuracy_val)
tf.summary.scalar('learning_rate',learning_rate)
tb_merge_summary_op=tf.summary.merge_all()
summary_writer=tf.summary.FileWriter(os.path.join(TB_LOG_DIR,'tensorboard'),graph=sess.graph)
saver=tf.train.Saver()
sess.run(tf.assign(global_step,0.0))
for step in range(1,TOTAL_STEPS+1):
try:
#_,print_step=sess.run(train_op)
sess.run(global_step_update)
except tf.errors.OutOfRangeError:
break
if step%DISPLAY_STEP==0 or step==1:
sess.run(valiterator.initializer)
l,acct,accv,lr,summary_str=sess.run([loss,accuracy_train,accuracy_val,learning_rate,tb_merge_summary_op])
summary_writer.add_summary(summary_str,step)
print("epoch {:d} steps {:d}: loss={:.4f}, accuracy_batch_train={:.4f}, accuracy_val={:.4f}, learning_rate={:.5f}".format(
step//(TOTAL_STEPS//EPOCHS),step,l,acct,accv,lr))
summary_writer.close()
saver.save(sess,os.path.join(TB_LOG_DIR,'model_1.ckpt'))
def _assign_moving_average(orig_val, new_val, momentum, name):
with tf.name_scope(name):
scaled_diff = (1 - momentum) * (new_val - orig_val)
return tf.assign_add(orig_val, scaled_diff)
def __init__(self, debug=False, **kwargs):
super(Learner, self).__init__(**kwargs)
with self.graph.as_default(), tf.device(self.device):
# initialize predictive model, if either:
# * you want to use the predictive model to "undo delay"
# * you want a predictive model to help you explore
# note: self.predict is perhaps a misnomer.
if self.predict or (self.train_model or self.explore_scale):
self._init_model(**kwargs)
if self.train_policy:
self._init_policy(**kwargs)
# build computation graph
# to train the the policy, you have to train the critic. (self.train_policy and
# self.train_critic might both be false, if we're only training the predictive
# model)
if self.train_policy or self.train_critic:
print("Creating critic.")
self.critic = Critic(self.core.output_size, **kwargs)
# experience = trajectory. usually a list of SimpleStateAction's.
self.experience = ct.inputCType(
ssbm.SimpleStateAction, [None, self.config.experience_length],
"experience")
# instantaneous rewards for all but the last state
self.experience['reward'] = tf.placeholder(
tf.float32, [None, self.config.experience_length - 1],
name='experience/reward')
# manipulating time along the first axis is much more efficient
experience = util.deepMap(tf.transpose, self.experience)
# initial state for recurrent networks
self.experience['initial'] = tuple(
tf.placeholder(tf.float32, [None, size],
name='experience/initial/%d' % i)
for i, size in enumerate(self.core.hidden_size))
experience['initial'] = self.experience['initial']
states = self.embedGame(experience['state'])
prev_actions = self.embedAction(experience['prev_action'])
combined = tf.concat(axis=2, values=[states, prev_actions])
actions = self.embedAction(experience['action'])
memory = self.config.memory
delay = self.config.delay
length = self.config.experience_length - memory
history = [combined[i:i + length] for i in range(memory + 1)]
inputs = tf.concat(axis=-1, values=history)
if self.core.recurrent:
def f(prev, current_input):
_, prev_state = prev
return self.core(current_input, prev_state)
batch_size = tf.shape(self.experience['reward'])[0]
dummy_output = tf.zeros(
tf.stack([batch_size,
tf.constant(self.core.output_size)]))
scan_fn = tf.scan if self.dynamic else tfl.scan
core_outputs, hidden_states = scan_fn(
f, inputs, (dummy_output, experience['initial']))
else:
core_outputs, hidden_states = self.core(
inputs, experience['initial'])
actions = actions[memory:]
rewards = experience['reward'][memory:]
print("Creating train ops")
train_ops = []
losses = []
loss_vars = []
if self.train_model or self.predict:
model_loss, predicted_core_outputs = self.model.train(
history, core_outputs, hidden_states, actions,
experience['state'])
if self.train_model:
#train_ops.append(train_model)
losses.append(model_loss)
loss_vars.extend(self.model.getVariables())
if self.train_policy:
if self.predict:
predict_steps = self.model.predict_steps
actor_inputs = predicted_core_outputs
else:
predict_steps = 0
actor_inputs = core_outputs
delay_length = length - delay
actor_inputs = actor_inputs[:delay_length]
# delayed_actions is a D+1-P length list of shape [T-M-D, B] tensors
# The valid state indices are [M+P, T+P-D)
# Element i corresponds to the i'th queued up action: 0 is the action about to be taken, D-P was the action chosen on this frame.
delayed_actions = []
for i in range(predict_steps, delay + 1):
delayed_actions.append(actions[i:i + delay_length])
train_probs, train_log_probs, entropy = self.policy.train_probs(
actor_inputs, delayed_actions)
behavior_probs = experience['prob'][
memory +
delay:] # these are the actions we can compute probabilities for
prob_ratios = tf.minimum(train_probs / behavior_probs, 1.)
self.kls = -tf.reduce_mean(tf.log(prob_ratios), 0)
self.kls = tf.check_numerics(self.kls, 'kl')
kl = tf.reduce_mean(self.kls)
tf.summary.scalar('kl', kl)
else:
prob_ratios = tf.ones_like() # todo
if self.explore_scale:
if self.evolve_explore_scale:
self.explore_scale = tf.Variable(self.explore_scale,
trainable=False,
name='explore_scale')
self.evo_variables.append(
('explore_scale', self.explore_scale, relative(1.5)))
distances, _ = self.model.distances(history,
core_outputs,
hidden_states,
actions,
experience['state'],
predict_steps=1)
distances = tf.add_n(list(util.deepValues(
distances))) # sum over different state components
explore_rewards = self.explore_scale * distances[0]
explore_rewards = tf.stop_gradient(explore_rewards)
tfl.stats(explore_rewards, 'explore_rewards')
rewards += explore_rewards
# build the critic (which you'll also need to train the policy)
if self.train_policy or self.train_critic:
shifted_core_outputs = core_outputs[:
delay_length] if self.unshift_critic else core_outputs[
delay:]
critic_loss, targets, advantages = self.critic(
shifted_core_outputs, rewards[delay:], prob_ratios[:-1])
if self.train_critic:
losses.append(critic_loss)
loss_vars.extend(self.critic.variables)
if self.train_policy:
policy_loss = self.policy.train(train_log_probs[:-1],
advantages, entropy[:-1])
losses.append(policy_loss)
loss_vars.extend(self.policy.getVariables())
if self.evolve_learning_rate:
self.learning_rate = tf.Variable(self.learning_rate,
trainable=False,
name='learning_rate')
self.evo_variables.append(
('learning_rate', self.learning_rate, relative(1.5)))
total_loss = tf.add_n(losses)
with tf.variable_scope('train'):
optimizer = tf.train.AdamOptimizer(self.learning_rate)
gvs = optimizer.compute_gradients(total_loss)
# gvs = [(tf.check_numerics(g, v.name), v) for g, v in gvs]
gs, vs = zip(*gvs)
norms = tf.stack([tf.norm(g) for g in gs])
max_norm = tf.reduce_max(norms)
tf.summary.scalar('max_grad_norm', max_norm)
capped_gs = [
tf.clip_by_norm(g, self.clip_max_grad) for g in gs
]
train_op = optimizer.apply_gradients(zip(capped_gs, vs))
train_ops.append(train_op)
print("Created train op(s)")
avg_reward, _ = tfl.stats(experience['reward'], 'reward')
misc_ops = []
if not self.dynamic:
misc_ops.append(tf.add_check_numerics_ops())
if self.pop_id >= 0:
self.reward = tf.Variable(0.,
trainable=False,
name='avg_reward')
tf.summary.scalar('avg_reward', self.reward)
new_reward = (1. - self.reward_decay
) * self.reward + self.reward_decay * avg_reward
misc_ops.append(tf.assign(self.reward, new_reward))
self.mutators = []
for name, evo_variable, mutator in self.evo_variables:
tf.summary.scalar(name, evo_variable, family='evolution')
self.mutators.append(
tf.assign(evo_variable, mutator(evo_variable)))
self.summarize = tf.summary.merge_all()
misc_ops.append(tf.assign_add(self.global_step, 1))
self.misc = tf.group(*misc_ops)
self.train_ops = tf.group(*train_ops)
print("Creating summary writer at logs/%s." % self.name)
#self.writer = tf.summary.FileWriter('logs/' + self.name)#, self.graph)
self.writer = tf.summary.FileWriter(self.path)
self._finalize_setup()
import tensorflow as tf
q = tf.FIFOQueue(1000, "float")
counter = tf.Variable(0.0)
increment_op = tf.assign_add(counter, tf.constant(1.0))
enqueue_op = q.enqueue([counter])
qr = tf.train.QueueRunner(q, enqueue_ops=[increment_op, enqueue_op] * 1)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
#coordinator协调器，可以看作一种信号量，用来做同步
coord = tf.train.Coordinator()
enqueue_threads = qr.create_threads(sess, coord=coord, start=True)
coord.request_stop() #通知其他线程关闭
#主线程
for i in range(0, 10):
try:
print(sess.run(q.dequeue()))
except tf.errors.OutOfRangeError:
break
coord.join(enqueue_threads) #直到其他线程结束才退出
def __init__(
self,
training_pipeline: InputPipeline,
cv_pipeline: InputPipeline,
network_factory: NetworkFactory,
objective_factory: ObjectiveFactory,
training_options: TrainingOptions,
learning_rate: float,
beta1: float = 0.9,
beta2: float = 0.999):
"""
Create a new network trainer
:param training_pipeline: Input pipeline used for training
:param cv_pipeline: Input pipeline used for cross-validation
:param network_factory: Factory to create training and evaluation networks
:param objective_factory: Factory to create generator and discriminator losses
:param training_options: Options controlling the training process
:param learning_rate: Learning rate to use in the Adam optimizer
:param beta1: Beta1 to use in the Adam optimizer
:param beta2: Beta2 to use in the Adam optimizer
"""
self._training_options = training_options
self._restored_iteration = None
# Create input pipelines
with use_cpu():
self._training_pipeline = training_pipeline
self._train_x, self._train_y, _ = training_pipeline.create_pipeline()
self._cv_pipeline = cv_pipeline
self._cv_x, self._cv_y, _ = self._cv_pipeline.create_pipeline()
# Create training graph
with tf.name_scope("training"):
# Create networks
self._generator = network_factory.create_generator(self._train_x, use_gpu=self._training_options.use_gpu,
data_format=self._training_options.data_format)
self._discriminator_generated = network_factory.create_discriminator(
self._train_x, self._generator, use_gpu=self._training_options.use_gpu,
data_format=self._training_options.data_format)
self._discriminator_real = network_factory.create_discriminator(
self._train_x, self._train_y, reuse=True, use_gpu=self._training_options.use_gpu,
data_format=self._training_options.data_format)
# Create losses
self._generator_loss, generator_summary = objective_factory.create_generator_loss(
self._train_x, self._train_y,
self._generator, self._discriminator_generated, use_gpu=self._training_options.use_gpu,
data_format=self._training_options.data_format)
self._discriminator_loss, discriminator_summary = objective_factory.create_discriminator_loss(
self._train_x, self._train_y,
self._generator, self._discriminator_generated, self._discriminator_real,
use_gpu=self._training_options.use_gpu,
data_format=self._training_options.data_format)
with tf.device(select_device(self._training_options.use_gpu)):
# Create optimizers
trainable_variables = tf.trainable_variables()
variables_discriminator = [var for var in trainable_variables if var.name.startswith("discriminator")]
variables_generator = [var for var in trainable_variables if var.name.startswith("generator")]
self._optimizer_generator = tf.train.AdamOptimizer(learning_rate, beta1, beta2, name="adam_generator")
self._optimizer_discriminator = tf.train.AdamOptimizer(learning_rate, beta1, beta2, name="adam_discriminator")
self._op_generator = self._optimizer_generator.minimize(self._generator_loss, var_list=variables_generator)
self._op_discriminator = self._optimizer_discriminator.minimize(self._discriminator_loss, var_list=variables_discriminator)
with use_cpu():
# Iteration counter
self._global_step = tf.Variable(0, trainable=False, name="global_step", dtype=tf.int64)
self._step_op = tf.assign_add(self._global_step, 1)
# Create summary operation
accuracy, precision, recall, f1_score, specificity, jaccard_similarity = _create_summaries(self._generator, self._train_y)
summary_operations = [
tf.summary.scalar("accuracy", accuracy),
tf.summary.scalar("precision", precision),
tf.summary.scalar("recall", recall),
tf.summary.scalar("f1_score", f1_score),
tf.summary.scalar("specificity", specificity),
tf.summary.scalar("jaccard_similarity", jaccard_similarity)
]
self._train_saver = tf.train.Saver(keep_checkpoint_every_n_hours=1)
# Merge summaries
self._train_summary = tf.summary.merge(summary_operations + generator_summary + discriminator_summary)
self._train_summary_writer = tf.summary.FileWriter(
os.path.join(self._training_options.summary_directory, "training"), graph=tf.get_default_graph())
# Create CV graph
with tf.name_scope("cv"):
# Create networks
generator = network_factory.create_generator(
self._cv_x, reuse=True, use_gpu=self._training_options.use_gpu,
data_format=self._training_options.data_format)
discriminator_generated = network_factory.create_discriminator(
self._cv_x, generator, reuse=True, use_gpu=self._training_options.use_gpu,
data_format=self._training_options.data_format)
discriminator_real = network_factory.create_discriminator(
self._cv_x, self._cv_y, reuse=True, use_gpu=self._training_options.use_gpu,
data_format=self._training_options.data_format)
# Create losses
_, generator_summary = objective_factory.create_generator_loss(
self._cv_x, self._cv_y, generator, discriminator_generated, use_gpu=self._training_options.use_gpu,
data_format=self._training_options.data_format)
_, discriminator_summary = objective_factory.create_discriminator_loss(
self._cv_x, self._cv_y, generator, discriminator_generated, discriminator_real,
use_gpu=self._training_options.use_gpu,
data_format=self._training_options.data_format)
# Create other summary options
accuracy, precision, recall, f1_score, specificity, jaccard_similarity = _create_summaries(generator, self._cv_y)
# Create summary operation
summary_operations = [
tf.summary.scalar("accuracy", accuracy),
tf.summary.scalar("precision", precision),
tf.summary.scalar("recall", recall),
tf.summary.scalar("f1_score", f1_score),
tf.summary.scalar("specificity", specificity),
tf.summary.scalar("jaccard_similarity", jaccard_similarity)
]
with use_cpu():
# Concatenated images
self._concatenated_images_op = _create_concatenated_images(
self._cv_x,
self._cv_y,
generator,
self._cv_pipeline.color_converter,
self._training_options.data_format
)
# Merge summaries
self._cv_summary = tf.summary.merge(summary_operations + generator_summary + discriminator_summary)
self._cv_summary_writer = tf.summary.FileWriter(
os.path.join(self._training_options.summary_directory, "cv"))
def testAccumulator(self):
# testAccumulator compares
# - explicit averaging of independently computed var_grads1 and
# var_grads2,
# - Accumulator(SGD) optimizer effectively doing this over 2 steps.
np.random.seed(12345)
np_input1 = np.random.normal(0.1, 0.5, [2, 4, 3])
np.random.seed(12346)
np_input2 = np.random.normal(0.1, 0.5, [2, 4, 3])
with self.session(use_gpu=True, graph=tf.Graph()) as sess:
tf.set_random_seed(123456)
params = layers.ProjectionLayer.Params()
params.name = 'proj'
params.dtype = tf.float64
params.input_dim = 3
params.output_dim = 2
params.params_init = py_utils.WeightInit.Gaussian(0.01, 123456)
params.is_eval = False
params.batch_norm = False
proj_layer = layers.ProjectionLayer(params)
inputs1 = tf.placeholder(shape=[2, 4, 3], dtype=tf.float64)
in_padding1 = tf.zeros([2, 4, 1], dtype=tf.float64)
inputs2 = tf.placeholder(shape=[2, 4, 3], dtype=tf.float64)
in_padding2 = tf.zeros([2, 4, 1], dtype=tf.float64)
output1 = proj_layer.FPropDefaultTheta(inputs1, in_padding1)
output2 = proj_layer.FPropDefaultTheta(inputs2, in_padding2)
loss1 = tf.reduce_sum(output1)
loss2 = tf.reduce_sum(output2)
var_grads1 = py_utils.ComputeGradients(loss1, proj_layer.vars)
var_grads2 = py_utils.ComputeGradients(loss2, proj_layer.vars)
op = optimizer.SGD.Params()
opt = op.Instantiate()
lr = 1e-1
with tf.control_dependencies([loss1, loss2]):
var_update_op1 = opt.Apply(
lr, py_utils.ApplyGradMultiplier(var_grads1, 1. / 2.))
with tf.control_dependencies([var_update_op1]):
var_update_op2 = opt.Apply(
lr, py_utils.ApplyGradMultiplier(var_grads2, 1. / 2.))
sess.run(tf.global_variables_initializer())
vars1 = sess.run(proj_layer.vars.Flatten())
loss1_1, grads1_1, loss1_2, grads1_2 = sess.run(
[loss1, var_grads1, loss2, var_grads2],
feed_dict={
inputs1: np_input1,
inputs2: np_input2,
})
sess.run(
[var_update_op2], feed_dict={
inputs1: np_input1,
inputs2: np_input2,
})
vars1_1 = sess.run(proj_layer.vars.Flatten())
with self.session(use_gpu=True, graph=tf.Graph()) as sess:
tf.set_random_seed(123456)
params = layers.ProjectionLayer.Params()
params.name = 'proj'
params.dtype = tf.float64
params.input_dim = 3
params.output_dim = 2
params.params_init = py_utils.WeightInit.Gaussian(0.01, 123456)
params.is_eval = False
params.batch_norm = False
proj_layer = layers.ProjectionLayer(params)
in_padding1 = tf.zeros([2, 4, 1], dtype=tf.float64)
inputs1 = tf.placeholder(shape=[2, 4, 3], dtype=tf.float64)
output1 = proj_layer.FPropDefaultTheta(inputs1, in_padding1)
loss = tf.reduce_sum(output1)
var_grads = py_utils.ComputeGradients(loss, proj_layer.vars)
op = optimizer.Accumulator.Params().Set(
accum_steps=2, dtype=tf.float64, optimizer_tpl=optimizer.SGD.Params())
opt = op.Instantiate()
lr = 1e-1
var_update_op = opt.Apply(lr, var_grads)
increment_global_step_op = tf.assign_add(
py_utils.GetOrCreateGlobalStepVar(), 1)
sess.run(tf.global_variables_initializer())
vars2 = sess.run(proj_layer.vars.Flatten())
loss2_1, grads2_1 = sess.run(
[loss, var_grads], feed_dict={
inputs1: np_input1,
})
loss2_2, grads2_2 = sess.run(
[loss, var_grads], feed_dict={
inputs1: np_input2,
})
acc_0 = sess.run(
[v for v in tf.global_variables() if 'grad_accumulator' in v.name])[0]
sess.run(
[var_update_op], feed_dict={
inputs1: np_input1,
})
acc_1 = sess.run(
[v for v in tf.global_variables() if 'grad_accumulator' in v.name])[0]
vars2_intermediate = sess.run(proj_layer.vars.Flatten())
sess.run(increment_global_step_op)
sess.run(
[var_update_op], feed_dict={
inputs1: np_input2,
})
acc_2 = sess.run(
[v for v in tf.global_variables() if 'grad_accumulator' in v.name])[0]
vars2_1 = sess.run(proj_layer.vars.Flatten())
self.assertAllClose(vars1, vars2)
self.assertAllClose(acc_0, np.zeros_like(acc_0))
self.assertAllClose(acc_1, grads2_1['w'][1])
self.assertAllClose(acc_2, np.zeros_like(acc_0))
self.assertAllClose(loss1_1, loss2_1)
self.assertAllClose(loss1_2, loss2_2)
self.assertAllClose(grads1_1, grads2_1)
self.assertAllClose(grads1_2, grads2_2)
self.assertAllClose(vars1, vars2_intermediate)
self.assertAllClose(vars2[0], grads2_1['w'][0])
self.assertAllClose(vars2[0], grads2_2['w'][0])
self.assertAllClose(
vars1[0] - 0.5 * lr * (grads1_1['w'][1] + grads1_2['w'][1]), vars1_1[0])
self.assertAllClose(
vars2[0] - 0.5 * lr * (grads2_1['w'][1] + grads2_2['w'][1]), vars2_1[0])
self.assertAllClose(vars2, vars2_intermediate)
self.assertAllClose(vars1_1, vars2_1)
def train(model=None):
assert FLAGS.train_dir, 'train_dir must be given'
global_step = tf.Variable(0, trainable=False)
add_global = tf.assign_add(global_step, 1)
loss_op, train_op, acc_op = model.build_train_graph(global_step)
dev_acc_op, dev_num_op, dev_init_op = model.build_dev_graph()
test_acc_op, test_num_op, test_init_op = model.build_test_graph()
train_ckpt_dir = FLAGS.train_dir + '/train_ckpt'
os.makedirs(train_ckpt_dir, exist_ok=True)
sum_writer = tf.summary.FileWriter(str(train_ckpt_dir),
graph=tf.get_default_graph())
best_dev_acc = 0.0
final_acc = 0.0
saver = tf.train.Saver(max_to_keep=1)
init = tf.global_variables_initializer()
with tf.Session(config=utils.get_config()) as sess:
tf.set_random_seed(FLAGS.random_seed)
np.random.seed(FLAGS.random_seed)
sess.run(init)
for _ in itertools.count(1):
this_global_step = sess.run(add_global)
if this_global_step >= FLAGS.max_steps + 1:
break
_, loss, acc = sess.run([train_op, loss_op, acc_op])
if this_global_step != 0 and this_global_step % FLAGS.test_steps == 0:
number = 0
accuracy = 0.0
while True:
try:
acc, num = sess.run([dev_acc_op, dev_num_op])
number += num
accuracy += acc * num
except tf.errors.OutOfRangeError:
break
accuracy /= number
print('At step %d. dev num=%d acc=%f.' %
(this_global_step, number, accuracy))
if accuracy > best_dev_acc:
best_dev_acc = accuracy
print("best acc=%f At step %d." %
(best_dev_acc, this_global_step))
test_accuracy = 0.
test_number = 0
while True:
try:
test_acc, test_num = sess.run(
[test_acc_op, test_num_op])
test_number += test_num
test_accuracy += test_acc * test_num
except tf.errors.OutOfRangeError:
break
test_accuracy /= test_number
print('test num=%d acc=%f.' % (test_number, test_accuracy))
final_acc = test_accuracy
sess.run(test_init_op)
save_checkpoint(saver, sess, FLAGS.train_dir,
this_global_step)
summary = tf.Summary()
summary.value.add(tag='test_acc', simple_value=accuracy)
summary.value.add(tag='best_dev_acc',
simple_value=best_dev_acc)
sum_writer.add_summary(summary, this_global_step)
sess.run(dev_init_op)
sum_writer.close()
print('Accuracy of test set is %f .' % final_acc)
def pathint_stabilization(self, adam_optimizer):
""" Synaptic stabilization via the Zenke method """
# Set up method
optimizer_task = tf.train.GradientDescentOptimizer(learning_rate=1.0)
small_omega_var = {}
small_omega_var_div = {}
reset_small_omega_ops = []
update_small_omega_ops = []
update_big_omega_ops = []
# If using reinforcement learning, update rewards
if par['training_method'] == 'RL':
self.previous_reward = tf.Variable(-tf.ones([]), trainable=False)
self.current_reward = tf.Variable(-tf.ones([]), trainable=False)
reward_stacked = tf.stack(self.reward, axis=0)
current_reward = tf.reduce_mean(
tf.reduce_sum(reward_stacked, axis=0))
self.update_current_reward = tf.assign(self.current_reward,
current_reward)
self.update_previous_reward = tf.assign(self.previous_reward,
self.current_reward)
# Iterate over variables in the model
for var in tf.trainable_variables():
# Reset the small omega vars
small_omega_var[var.op.name] = tf.Variable(tf.zeros(
var.get_shape()),
trainable=False)
small_omega_var_div[var.op.name] = tf.Variable(tf.zeros(
var.get_shape()),
trainable=False)
reset_small_omega_ops.append(
tf.assign(small_omega_var[var.op.name],
small_omega_var[var.op.name] * 0.0))
reset_small_omega_ops.append(
tf.assign(small_omega_var_div[var.op.name],
small_omega_var_div[var.op.name] * 0.0))
# Update the big omega vars based on the training method
if par['training_method'] == 'RL':
update_big_omega_ops.append(tf.assign_add( self.big_omega_var[var.op.name], tf.div(tf.abs(small_omega_var[var.op.name]), \
(par['omega_xi'] + small_omega_var_div[var.op.name]))))
elif par['training_method'] == 'SL':
update_big_omega_ops.append(tf.assign_add( self.big_omega_var[var.op.name], tf.div(tf.nn.relu(small_omega_var[var.op.name]), \
(par['omega_xi'] + small_omega_var_div[var.op.name]**2))))
# After each task is complete, call update_big_omega and reset_small_omega
self.update_big_omega = tf.group(*update_big_omega_ops)
# Reset_small_omega also makes a backup of the final weights, used as hook in the auxiliary loss
self.reset_small_omega = tf.group(*reset_small_omega_ops)
# This is called every batch
self.delta_grads = adam_optimizer.return_delta_grads()
self.gradients = optimizer_task.compute_gradients(self.pol_loss)
# Update the samll omegas using the gradients
for (grad, var) in self.gradients:
if par['training_method'] == 'RL':
delta_reward = self.current_reward - self.previous_reward
update_small_omega_ops.append(
tf.assign_add(small_omega_var[var.op.name],
self.delta_grads[var.op.name] *
delta_reward))
update_small_omega_ops.append(
tf.assign_add(
small_omega_var_div[var.op.name],
tf.abs(self.delta_grads[var.op.name] * delta_reward)))
elif par['training_method'] == 'SL':
update_small_omega_ops.append(
tf.assign_add(small_omega_var[var.op.name],
-self.delta_grads[var.op.name] * grad))
update_small_omega_ops.append(
tf.assign_add(small_omega_var_div[var.op.name],
self.delta_grads[var.op.name]))
# Make update group
self.update_small_omega = tf.group(
*update_small_omega_ops) # 1) update small_omega after each train!
def accu_global_ms(self):
accu = list()
for grad, ms in zip(self._grads, self._global_ms):
accu.append(tf.assign_add(ms, tf.square(grad)))
inc_op = tf.assign_add(self._batches_num, tf.ones([]))
return [accu, inc_op]
