def evaluate():
"""Eval CIFAR-10 for a number of steps."""
with tf.Graph().as_default() as g:
# 导入cifar100中的测试数据
eval_data = FLAGS.eval_data == 'test'
images, labels = cifar100.single_inputs(eval_data=eval_data)
# 通过卷积神经网络得到结果，此处为构建模型，并未真正计算
logits = cifar100.inference(images)
# 每个logits元素前k个最大值是否包含labels的正确结果
top_k_op = tf.nn.in_top_k(logits, labels, 1)
# 恢复学习完成后的滑动平均变量
variable_averages = tf.train.ExponentialMovingAverage(
cifar100.MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
# 将保存的影子变量值直接赋予当前变量，等待注释
saver = tf.train.Saver(variables_to_restore)
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(FLAGS.eval_dir, g)
while True:
eval_once(saver, summary_writer, top_k_op, summary_op, logits)
if FLAGS.run_once:
break
time.sleep(FLAGS.eval_interval_secs)
def evaluate_one():
"""导出一张图片的标签"""
with tf.Graph().as_default() as g:
eval_data = FLAGS.eval_data == 'test'
images, labels = cifar100.inputs(eval_data=eval_data)
# 通过卷积神经网络得到结果
logits = cifar100.inference(images)
# 恢复学习完成后的滑动平均变量
variable_averages = tf.train.ExponentialMovingAverage(
cifar100.MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
# 将保存的影子变量值直接赋予当前变量，等待注释
saver = tf.train.Saver(variables_to_restore)
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
# 加载检查点数据
saver.restore(sess, ckpt.model_checkpoint_path)
print("load successfully")
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
else:
# 加载未成功
print('No checkpoint file found')
return
final_label = sess.run(logits)
print(final_label)
def evaluate():
"""Eval CIFAR-100 for a number of steps."""
with tf.Graph().as_default() as g:
# Get images and labels for CIFAR-100.
eval_data = FLAGS.eval_data == 'test'
images, labels = cifar100.inputs(eval_data=eval_data)
# Build a Graph that computes the logits predictions from the
# inference model.
logits = cifar100.inference(images)
# Calculate predictions.
top_k_op = tf.nn.in_top_k(logits, labels, 1)
# Restore the moving average version of the learned variables for eval.
variable_averages = tf.train.ExponentialMovingAverage(
cifar100.MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(FLAGS.eval_dir, g)
while True:
eval_once(saver, summary_writer, top_k_op, summary_op)
if FLAGS.run_once:
break
time.sleep(FLAGS.eval_interval_secs)
def tower_loss(scope, images, labels):
"""Calculate the total loss on a single tower running the CIFAR model.
Args:
scope: unique prefix string identifying the CIFAR tower, e.g. 'tower_0'
images: Images. 4D tensor of shape [batch_size, height, width, 3].
labels: Labels. 1D tensor of shape [batch_size].
Returns:
Tensor of shape [] containing the total loss for a batch of data
"""
# Build inference Graph.
logits = cifar100.inference(images)
# Build the portion of the Graph calculating the losses. Note that we will
# assemble the total_loss using a custom function below.
_ = cifar100.loss(logits, labels)
# Assemble all of the losses for the current tower only.
losses = tf.get_collection('losses', scope)
# Calculate the total loss for the current tower.
total_loss = tf.add_n(losses, name='total_loss')
# Attach a scalar summary to all individual losses and the total loss; do the
# same for the averaged version of the losses.
for l in losses + [total_loss]:
# Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training
# session. This helps the clarity of presentation on tensorboard.
loss_name = re.sub('%s_[0-9]*/' % cifar100.TOWER_NAME, '', l.op.name)
tf.summary.scalar(loss_name, l)
return total_loss
def evaluate():
with tf.Graph().as_default() as g:
global_step = tf.Variable(0, trainable=False)
images, labels = cifar100.eval_inputs()
logits = cifar100.inference(images)
top_k_op = tf.nn.in_top_k(logits, labels, 3)
saver = tf.train.Saver(tf.all_variables())
while True:
eval_once(saver, top_k_op)
if FLAGS.run_once:
break
time.sleep(FLAGS.eval_interval_secs)
def train():
with tf.Graph().as_default():
global_step = tf.Variable(0, trainable=False)
images, labels = cifar100.distorted_inputs()
logits = cifar100.inference(images)
loss = cifar100.loss(logits, labels)
train_op = cifar100.train(loss, global_step)
saver = tf.train.Saver(tf.all_variables())
init = tf.initialize_all_variables()
sess = tf.Session(config=tf.ConfigProto(
log_device_placement=FLAGS.log_device_placement))
sess.run(init)
tf.train.start_queue_runners(sess=sess)
for step in xrange(FLAGS.max_steps):
start_time = time.time()
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
assert not np.isnan(loss_value)
if step % 10 == 0:
num_examples_per_step = FLAGS.batch_size
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = (
'%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
print(format_str % (datetime.now(), step, loss_value,
examples_per_sec, sec_per_batch))
# Save the model checkpoint periodically.
if step % 1000 == 0 or (step + 1) == FLAGS.max_steps:
checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
def train():
"""Train CIFAR-100 for a number of steps."""
with tf.Graph().as_default():
global_step = tf.contrib.framework.get_or_create_global_step()
# Get images and labels for CIFAR-100.
# Force input pipeline to CPU:0 to avoid operations sometimes ending up on
# GPU and resulting in a slow down.
with tf.device('/cpu:0'):
images, labels = cifar100.distorted_inputs()
# Build a Graph that computes the logits predictions from the
# inference model.
logits = cifar100.inference(images)
# Calculate loss.
loss = cifar100.loss(logits, labels)
# Build a Graph that trains the model with one batch of examples and
# updates the model parameters.
train_op = cifar100.train(loss, global_step)
class _LoggerHook(tf.train.SessionRunHook):
"""Logs loss and runtime."""
def begin(self):
self._step = -1
self._start_time = time.time()
def before_run(self, run_context):
self._step += 1
return tf.train.SessionRunArgs(loss) # Asks for loss value.
def after_run(self, run_context, run_values):
if self._step % FLAGS.log_frequency == 0:
current_time = time.time()
duration = current_time - self._start_time
self._start_time = current_time
loss_value = run_values.results
self._last_loss = loss_value
examples_per_sec = FLAGS.log_frequency * FLAGS.batch_size / duration
sec_per_batch = float(duration / FLAGS.log_frequency)
format_str = (
'%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
print(format_str % (datetime.now(), self._step, loss_value,
examples_per_sec, sec_per_batch))
def last_loss(self):
return self._last_loss
loghook = _LoggerHook()
with tf.train.MonitoredTrainingSession(
checkpoint_dir=FLAGS.train_dir,
hooks=[
tf.train.StopAtStepHook(last_step=FLAGS.max_steps),
tf.train.NanTensorHook(loss), loghook
],
config=tf.ConfigProto(log_device_placement=FLAGS.
log_device_placement)) as mon_sess:
t1 = time.time()
while not mon_sess.should_stop():
mon_sess.run(train_op)
t2 = time.time()
print('spent %f seconds to train %d step' %
(t2 - t1, FLAGS.max_steps))
print('spent %f seconds to train %d step' %
(t2 - t1, FLAGS.max_steps))
print('last loss value: %.2f ' % loghook.last_loss())
def train():
"""训练cifar100"""
with tf.Graph().as_default():
global_step = tf.train.get_or_create_global_step()
with tf.device('/cpu:0'):
images, labels = cifar100.destorted_inputs()
# 建立模型，并获取得到的结果logits，用于与labels求交叉熵
logits = cifar100.inference(images)
# 计算损失
loss = cifar100.loss(logits, labels)
train_op = cifar100.train(loss, global_step)
class _LoggerHook(tf.train.SessionRunHook):
"""打印损失和运行状态"""
def begin(self):
self._step = -1
self._start_time = time.time()
def before_run(self, run_context):
self._step += 1
return tf.train.SessionRunArgs(loss)
def after_run(self, run_context, run_values):
if self._step % FLAGS.log_frequency == 0:
current_time = time.time()
duration = current_time - self._start_time
self._start_time = current_time
loss_value = run_values.results
examples_per_sec = FLAGS.log_frequency * FLAGS.batch_size / duration
sec_per_batch = float(duration / FLAGS.log_frequency)
format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
print(format_str % (datetime.now(), self._step, loss_value,
examples_per_sec, sec_per_batch))
config = tf.ConfigProto(log_device_placement=FLAGS.log_device_placement)
config.gpu_options.allocator_type = 'BFC' # 使用BFC算法
config.gpu_options.per_process_gpu_memory_fraction = 0.5 # 程序最多只能占用指定gpu50%的显存
config.gpu_options.allow_growth = True # 程序按需申请内存
# MonitoredTrainingSession是一个方便的tensorflow会话初始化/恢复器,
# 也可用于分布式训练
with tf.train.MonitoredTrainingSession(
# 加载保存的训练状态的目录，如为空则设为保存目录
checkpoint_dir=FLAGS.train_dir,
# 保存间隔
save_checkpoint_secs=None,
save_checkpoint_steps=10000,
# 可选的SessionRunHook对象列表
# StopAtStepHook表示停止步数
# NanTensorHook表示当loss为None时返回异常并停止训练
hooks=[tf.train.StopAtStepHook(last_step=FLAGS.max_steps),
tf.train.NanTensorHook(loss),
_LoggerHook()],
config=config) as mon_sess:
while not mon_sess.should_stop():
mon_sess.run(train_op)
def train():
"""Train CIFAR-10 for a number of steps."""
#lanhin
#Construct the cluster and start the server
ps_spec = FLAGS.ps_hosts.split(",")
worker_spec = FLAGS.worker_hosts.split(",")
# Get the number of workers.
num_workers = len(worker_spec)
cluster = tf.train.ClusterSpec({"ps": ps_spec, "worker": worker_spec})
server = tf.train.Server(cluster,
job_name=FLAGS.job_name,
task_index=FLAGS.task_index)
if FLAGS.job_name == "ps":
server.join()
# only worker will do train()
is_chief = False
if FLAGS.task_index == 0:
is_chief = True
#lanhin end
#with tf.Graph().as_default():
# Use comment to choose which way of tf.device() you want to use
#with tf.Graph().as_default(), tf.device(tf.train.replica_device_setter(
# worker_device="/job:worker/task:%d" % FLAGS.task_index,
# cluster=cluster)):
with tf.device("job:worker/task:%d" % FLAGS.task_index):
global_step = tf.contrib.framework.get_or_create_global_step()
# Get images and labels for CIFAR-10.
# Force input pipeline to CPU:0 to avoid operations sometimes ending up on
# GPU and resulting in a slow down.
#with tf.device('/cpu:0'):
#images, labels = cifar10.distorted_inputs()
(x_train, y_train_orl), (x_test, y_test_orl) = dset.cifar100.load_data(
label_mode='fine')
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train, x_test = normalize(x_train, x_test)
y_train_orl = y_train_orl.astype('int32')
y_test_orl = y_test_orl.astype('int32')
y_train_flt = y_train_orl.ravel()
y_test_flt = y_test_orl.ravel()
x = tf.placeholder(tf.float32, shape=(FLAGS.batch_size, 32, 32, 3))
y = tf.placeholder(tf.int32, shape=(FLAGS.batch_size, ))
# Build a Graph that computes the logits predictions from the
# inference model.
#logits, local_var_list = cifar10.inference(images)
logits, local_var_list = cifar100.inference(x)
# Calculate loss.
#loss = cifar10.loss(logits, labels)
loss = cifar100.loss(logits, y)
# Build a Graph that trains the model with one batch of examples and
# updates the model parameters.
train_op = cifar100.train(loss, global_step)
# the temp var part, for performance testing
tmp_var_list = []
var_index = 0
for var in local_var_list:
var_index += 1
tmp_var_list.append(
tf.Variable(tf.zeros(var.shape),
name="tmp_var" + str(var_index)))
# the non chief workers get local var init_op here
if not is_chief:
init_op = tf.global_variables_initializer()
else:
init_op = None
# start global variables region
global_var_list = []
with tf.device("/job:ps/replica:0/task:0/cpu:0"):
# barrier var
finished = tf.get_variable("worker_finished", [],
tf.int32,
tf.zeros_initializer(tf.int32),
trainable=False)
with finished.graph.colocate_with(finished):
finish_op = finished.assign_add(1, use_locking=True)
var_index = 0
for var in local_var_list:
var_index += 1
global_var_list.append(
tf.Variable(tf.zeros(var.shape),
name="glo_var" + str(var_index)))
def assign_global_vars(): # assign local vars' values to global vars
return [
gvar.assign(lvar)
for (gvar, lvar) in zip(global_var_list, local_var_list)
]
def assign_local_vars(): # assign global vars' values to local vars
return [
lvar.assign(gvar)
for (gvar, lvar) in zip(global_var_list, local_var_list)
]
def assign_tmp_vars(): # assign local vars' values to tmp vars
return [
tvar.assign(lvar)
for (tvar, lvar) in zip(tmp_var_list, local_var_list)
]
def assign_local_vars_from_tmp(
): # assign tmp vars' values to local vars
return [
lvar.assign(tvar)
for (tvar, lvar) in zip(tmp_var_list, local_var_list)
]
def update_before_train(alpha, w, global_w):
varib = alpha * (w - global_w)
gvar_op = global_w.assign(global_w + varib)
return gvar_op, varib
def update_after_train(w, vab):
return w.assign(w - vab)
assign_list_local = assign_local_vars()
assign_list_global = assign_global_vars()
assign_list_loc2tmp = assign_tmp_vars()
assign_list_tmp2loc = assign_local_vars_from_tmp()
before_op_tuple_list = []
after_op_tuple_list = []
vbholder_list = []
for (gvar, lvar) in zip(global_var_list, local_var_list):
before_op_tuple_list.append(
(update_before_train(alpha, lvar, gvar)))
for var in local_var_list:
vbholder_list.append(tf.placeholder("float", var.shape))
after_op_tuple_list.append(
(update_after_train(var,
vbholder_list[-1]), vbholder_list[-1]))
# the chief worker get global var init op here
if is_chief:
init_op = tf.global_variables_initializer()
# global variables region end
#lanhin start
sv = tf.train.Supervisor(
is_chief=True, #is_chief,
logdir=FLAGS.train_dir,
init_op=init_op,
#local_init_op=loc_init_op,
recovery_wait_secs=1)
#global_step=global_step)
sess_config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=FLAGS.log_device_placement,
device_filters=[
"/job:ps", "/job:worker/task:%d" % FLAGS.task_index
])
# The chief worker (task_index==0) session will prepare the session,
# while the remaining workers will wait for the preparation to complete.
if is_chief:
print("Worker %d: Initializing session..." % FLAGS.task_index)
else:
print("Worker %d: Waiting for session to be initialized..." %
FLAGS.task_index)
sess = sv.prepare_or_wait_for_session(server.target,
config=sess_config)
if is_chief:
sess.run(assign_list_global)
barrier_finished = sess.run(finish_op)
print("barrier_finished:", barrier_finished)
else:
barrier_finished = sess.run(finish_op)
print("barrier_finished:", barrier_finished)
while barrier_finished < num_workers:
time.sleep(1)
barrier_finished = sess.run(finished)
sess.run(assign_list_local)
print("Worker %d: Session initialization complete." % FLAGS.task_index)
# lanhin end
#sess = tf.Session()
#sess.run(init_op)
#tf.train.start_queue_runners(sess)
f = open('tl_dist.json', 'w')
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
time_begin = time.time()
# while not mon_sess.should_stop():
# mon_sess.run(train_op)
for step in range(FLAGS.max_steps):
offset = (step * FLAGS.batch_size) % (EPOCH_SIZE -
FLAGS.batch_size)
x_data = x_train[offset:(offset + FLAGS.batch_size), ...]
y_data_flt = y_train_flt[offset:(offset + FLAGS.batch_size)]
if step % FLAGS.log_frequency == 0:
time_step = time.time()
steps_time = time_step - time_begin
print("step:", step, " steps time:", steps_time, end=' ')
sess.run(assign_list_loc2tmp)
sess.run(assign_list_local)
predt(sess, x_test, y_test_flt, logits, x, y)
sess.run(assign_list_tmp2loc)
time_begin = time.time()
if step % FLAGS.tau == 0 and step > 0: # update global weights
thevarib_list = []
for i in range(0, len(before_op_tuple_list)):
(gvar_op, varib) = before_op_tuple_list[i]
_, thevarib = sess.run([gvar_op, varib])
thevarib_list.append(thevarib)
sess.run(train_op, feed_dict={x: x_data, y: y_data_flt})
for i in range(0, len(after_op_tuple_list)):
(lvar_op, thevaribHolder) = after_op_tuple_list[i]
sess.run(lvar_op,
feed_dict={thevaribHolder: thevarib_list[i]})
else:
sess.run(train_op, feed_dict={
x: x_data,
y: y_data_flt
}) #, options=run_options, run_metadata=run_metadata)
#tl = timeline.Timeline(run_metadata.step_stats)
#ctf = tl.generate_chrome_trace_format()
#f.write(ctf)
time_end = time.time()
training_time = time_end - time_begin
print("Training elapsed time: %f s" % training_time)
f.close()
sess.run(assign_list_local)
predt(sess, x_test, y_test_flt, logits, x, y)
def train():
"""Train CIFAR-100 for a number of steps."""
output = open('output_data/output_' + str(time.time()) + '.txt', 'w')
with tf.Graph().as_default():
global_step = tf.train.get_or_create_global_step()
# Get images and labels for CIFAR-100.
# Force input pipeline to CPU:0 to avoid operations sometimes ending up on
# GPU and resulting in a slow down.
with tf.device('/cpu:0'):
images, labels = cifar100.distorted_inputs()
# Build a Graph that computes the logits predictions from the
# inference model.
logitsA,logitsB = cifar100.inference(images)
# Calculate loss.
lossA = cifar100.loss(logitsA, labels)
lossB = cifar100.loss(logitsB, labels)
# Build a Graph that trains the model with one batch of examples and
# updates the model parameters.
train_opA = cifar100.train(lossA, global_step)
train_opB = cifar100.train(lossB, global_step)
class _LoggerHook(tf.train.SessionRunHook):
"""Logs loss and runtime."""
def begin(self):
self._step = -1
self._start_time = time.time()
def before_run(self, run_context):
self._step += 1
return tf.train.SessionRunArgs(lossA) # Asks for loss value.
def after_run(self, run_context, run_values):
if self._step % FLAGS.log_frequency == 0:
current_time = time.time()
duration = current_time - self._start_time
self._start_time = current_time
loss_value = run_values.results
examples_per_sec = FLAGS.log_frequency * FLAGS.batch_size / duration
sec_per_batch = float(duration / FLAGS.log_frequency)
format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
print(format_str % (datetime.now(), self._step, loss_value,
examples_per_sec, sec_per_batch))
print((str(self._step) + '\t' +
str(loss_value) + '\n'), file=output)
with tf.train.MonitoredTrainingSession(
checkpoint_dir=FLAGS.train_dir,
hooks=[tf.train.StopAtStepHook(last_step=FLAGS.max_steps),
tf.train.NanTensorHook(lossA),
tf.train.NanTensorHook(lossB),
_LoggerHook()],
config=tf.ConfigProto(
log_device_placement=FLAGS.log_device_placement)) as mon_sess:
file_writer = tf.summary.FileWriter('tb-logs/', mon_sess.graph)
while not mon_sess.should_stop():
print("stepA")
mon_sess.run(train_opA)
print("stepB")
mon_sess.run(train_opB)
output.close()
def train():
print('FLAGS.data_dir: %s' % FLAGS.data_dir)
ps_hosts = FLAGS.ps_hosts.split(",")
worker_hosts = FLAGS.worker_hosts.split(",")
# Create a cluster from the parameter server and worker hosts.
cluster = tf.train.ClusterSpec({"ps": ps_hosts, "worker": worker_hosts})
server = tf.train.Server(cluster,
job_name=FLAGS.job_name,
task_index=FLAGS.task_index)
if FLAGS.job_name == 'ps':
server.join()
is_chief = (FLAGS.task_index == 0)
with tf.device(
tf.train.replica_device_setter(
worker_device="/job:worker/task:%d" % FLAGS.task_index,
ps_device="/job:ps/task:0",
cluster=cluster)):
global_step = tf.get_variable('global_step', [],
initializer=tf.constant_initializer(0),
trainable=False)
# Get images and labels for CIFAR-100.
images, labels = cifar100.distorted_inputs()
num_workers = len(worker_hosts)
num_replicas_to_aggregate = num_workers
logits = cifar100.inference(images)
# Calculate loss.
loss = cifar100.loss(logits, labels)
# Retain the summaries from the chief.
# Calculate the learning rate schedule.
num_batches_per_epoch = (cifar100.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN /
FLAGS.batch_size)
decay_steps = int(num_batches_per_epoch *
cifar100.NUM_EPOCHS_PER_DECAY)
# Decay the learning rate exponentially based on the number of steps.
lr = tf.train.exponential_decay(cifar100.INITIAL_LEARNING_RATE,
global_step,
decay_steps,
cifar100.LEARNING_RATE_DECAY_FACTOR,
staircase=True)
if is_chief:
summaries = tf.get_collection(tf.GraphKeys.SUMMARIES)
# Add a summary to track the learning rate.
summaries.append(tf.summary.scalar('learning_rate', lr))
# Create an optimizer that performs gradient descent.
opt = tf.train.GradientDescentOptimizer(lr)
opt = tf.train.SyncReplicasOptimizer(
opt,
replicas_to_aggregate=num_replicas_to_aggregate,
total_num_replicas=num_workers,
#use_locking=True)
use_locking=False)
# Calculate the gradients for the batch
grads = opt.compute_gradients(loss)
# Add histograms for gradients at the chief worker.
if is_chief:
for grad, var in grads:
if grad is not None:
summaries.append(
tf.summary.histogram(var.op.name + '/gradients', grad))
# apply gradients to variable
train_op = opt.apply_gradients(grads, global_step=global_step)
# Add histograms for trainable variables.
if is_chief:
for var in tf.trainable_variables():
summaries.append(tf.summary.histogram(var.op.name, var))
#variable_averages = tf.train.ExponentialMovingAverage(
# cifar100.MOVING_AVERAGE_DECAY, global_step)
#variables_averages_op = variable_averages.apply(tf.trainable_variables())
#train_op = tf.group(train_op, variables_averages_op)
if is_chief:
#Build the summary operation at the chief worker
summary_op = tf.summary.merge(summaries)
chief_queue_runner = opt.get_chief_queue_runner()
init_token_op = opt.get_init_tokens_op()
# Build an initialization operation to run below.
init_op = tf.global_variables_initializer()
# Create a saver.
saver = tf.train.Saver(tf.global_variables())
sv = tf.train.Supervisor(is_chief=is_chief,
global_step=global_step,
init_op=init_op)
sess_config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=FLAGS.log_device_placement)
with sv.prepare_or_wait_for_session(server.target,
config=sess_config) as sess:
# Start running operations on the Graph. allow_soft_placement must be set to
# True to build towers on GPU, as some of the ops do not have GPU
# implementations.
# start sync queue runner and run the init token op at the chief worker
queue_runners = tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS)
sv.start_queue_runners(sess, queue_runners)
if is_chief:
sv.start_queue_runners(sess, [chief_queue_runner])
sess.run(init_token_op)
#open the summary writer
summary_writer = tf.summary.FileWriter(FLAGS.train_dir, sess.graph)
t1 = time.time()
for step in xrange(FLAGS.max_steps):
start_time = time.time()
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
if step % 10 == 0:
num_examples_per_step = FLAGS.batch_size * num_workers
examples_per_sec = num_examples_per_step / duration
sec_per_batch = duration / num_workers
format_str = (
'%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
print(format_str % (datetime.now(), step, loss_value,
examples_per_sec, sec_per_batch))
if step % 100 == 0:
if is_chief:
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)
# Save the model checkpoint periodically.
if step % 1000 == 0 or (step + 1) == FLAGS.max_steps:
if is_chief:
checkpoint_path = os.path.join(FLAGS.train_dir,
'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
t2 = time.time()
print('spent %f seconds to train %d step' % (t2 - t1, FLAGS.max_steps))
logger.info('spent %f seconds to train %d step' %
(t2 - t1, FLAGS.max_steps))
logger.info('last loss value: %.2f ' % loss_value)
def evaluate_images(images): # 执行验证
logits = cifar100.inference(images)
load_trained_model(logits=logits)
def evaluate_images(images): # 执行验证
logits = cifar100.inference(images)
return logits
