def evaluate():
"""Eval network for a number of steps."""
with tf.Graph().as_default():
# Get images and labels for network.
eval_data = FLAGS.eval_data == 'test'
print(eval_data)
print("evaluating model...")
images, labels = network.inputs(eval_data=eval_data)
# Build a Graph that computes the logits predictions from the
# inference model.
logits = network.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(
network.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.merge_all_summaries()
graph_def = tf.get_default_graph().as_graph_def()
summary_writer = tf.train.SummaryWriter(FLAGS.eval_dir,
graph_def=graph_def)
while True:
eval_once(saver, summary_writer, top_k_op, summary_op)
if FLAGS.run_once:
break
time.sleep(FLAGS.eval_interval_secs)
def test(test_dir, checkpoint_dir='./checkpoint/'):
# predict the result
test_images = os.listdir(test_dir)
features = tf.placeholder("float32", shape=[None, IMAGE_SIZE, IMAGE_SIZE, IMAGE_CHANNEL], name="features")
labels = tf.placeholder("float32", [None], name="labels")
one_hot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=80)
train_step, cross_entropy, logits, keep_prob = network.inference(features, one_hot_labels)
values, indices = tf.nn.top_k(logits, 3)
with tf.Session() as sess:
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
print('Restore the model from checkpoint %s' % ckpt.model_checkpoint_path)
# Restores from checkpoint
saver.restore(sess, ckpt.model_checkpoint_path)
start_step = int(ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1])
else:
raise Exception('no checkpoint find')
result = []
for test_image in test_images:
temp_dict = {}
x = scene_input.img_resize(os.path.join(test_dir, test_image), IMAGE_SIZE)
predictions = np.squeeze(sess.run(indices, feed_dict={features: np.expand_dims(x, axis=0), keep_prob: 1}), axis=0)
temp_dict['image_id'] = test_image
temp_dict['label_id'] = predictions.tolist()
result.append(temp_dict)
print('image %s is %d,%d,%d' % (test_image, predictions[0], predictions[1], predictions[2]))
with open('submit.json', 'w') as f:
json.dump(result, f)
print('write result json, num is %d' % len(result))
def train():
"""Train network for a number of steps."""
with tf.Graph().as_default():
global_step = tf.Variable(0, trainable=False)
# Get images and labels for network.
images, labels = network.distorted_inputs()
# Build a Graph that computes the logits predictions from the
# inference model.
logits = network.inference(images)
# Calculate loss.
loss = network.loss(logits, labels)
# Build a Graph that trains the model with one batch of examples and
# updates the model parameters.
train_op = network.train(loss, global_step)
# Create a saver.
saver = tf.train.Saver(tf.all_variables())
# Build the summary operation based on the TF collection of Summaries.
#summary_op = tf.merge_all_summaries()
# Build an initialization operation to run below.
init = tf.initialize_all_variables()
# Start running operations on the Graph.
sess = tf.Session(config=tf.ConfigProto(
log_device_placement=FLAGS.log_device_placement))
sess.run(init)
# Start the queue runners.
tf.train.start_queue_runners(sess=sess)
#summary_writer = tf.train.SummaryWriter(FLAGS.train_dir,
# graph_def=sess.graph_def)
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_input_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))
def test():
with tf.Graph().as_default() as g:
images, labels = dataset.process_inputs("testing")
logits = network.inference(images)
top_k_op = tf.nn.in_top_k(logits, labels, 1)
variable_averages = tf.train.ExponentialMovingAverage(
arg_parsing.MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(FLAGS.model_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
else:
raise ValueError("No checkpoint file found")
coord = tf.train.Coordinator()
try:
threads = []
for qr in tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS):
threads.extend(qr.create_threads(sess, coord=coord, daemon=True, start=True))
num_iter = int(math.ceil(FLAGS.num_examples / FLAGS.batch_size))
true_count = 0
total_sample_count = num_iter * FLAGS.batch_size
step = 0
while step < num_iter and not coord.should_stop():
predictions = sess.run([top_k_op])
true_count += np.sum(predictions)
step += 1
precision = true_count / total_sample_count
print('%s: precision @ 1 = %.3f' % (datetime.now(), precision))
except Exception as e:
coord.request_stop(e)
coord.request_stop()
coord.join(threads, stop_grace_period_secs=10)
def _build_training_graph(images, labels, num_classes, reuse_variables=None):
with tf.variable_scope(tf.get_variable_scope(), reuse=reuse_variables):
logits, features = \
network.inference(images, num_classes, for_training=True, feature_name=FLAGS.feature_name)
losses = [network.loss_ce(logits, labels)]
regularization_losses = tf.get_collection(
tf.GraphKeys.REGULARIZATION_LOSSES)
total_loss = tf.add_n(losses + regularization_losses, name='total_loss')
loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
loss_averages_op = loss_averages.apply(losses + [total_loss])
for l in losses + [total_loss]:
loss_name = re.sub('%s_[0-9]*/' % network.TOWER_NAME, '', l.op.name)
tf.summary.scalar(loss_name + ' (raw)', l)
tf.summary.scalar(loss_name, loss_averages.average(l))
with tf.control_dependencies([loss_averages_op]):
total_loss = tf.identity(total_loss)
return total_loss, logits
def train(train_dir, annotations, max_step, checkpoint_dir='./checkpoint/'):
# train the model
scene_data = scene_input.scene_data_fn(train_dir, annotations)
features = tf.placeholder("float32", shape=[None, IMAGE_SIZE, IMAGE_SIZE, IMAGE_CHANNEL], name="features")
labels = tf.placeholder("float32", [None], name="labels")
one_hot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=80)
train_step, cross_entropy, logits, keep_prob = network.inference(features, one_hot_labels)
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_labels, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
with tf.Session() as sess:
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
print('Restore the model from checkpoint %s' % ckpt.model_checkpoint_path)
# Restores from checkpoint
saver.restore(sess, ckpt.model_checkpoint_path)
start_step = int(ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1])
else:
sess.run(tf.global_variables_initializer())
start_step = 0
print('start training from new state')
logger = scene_input.train_log(LOGNAME)
for step in range(start_step, start_step + max_step):
start_time = time.time()
x, y = scene_data.next_batch(BATCH_SIZE, IMAGE_SIZE)
sess.run(train_step, feed_dict={features: x, labels: y, keep_prob: 0.5})
if step % 50 == 0:
train_accuracy = sess.run(accuracy, feed_dict={features: x, labels: y, keep_prob: 1})
train_loss = sess.run(cross_entropy, feed_dict={features: x, labels: y, keep_prob: 1})
duration = time.time() - start_time
logger.info("step %d: training accuracy %g, loss is %g (%0.3f sec)" % (step, train_accuracy, train_loss, duration))
if step % 1000 == 1:
saver.save(sess, CHECKFILE, global_step=step)
print('writing checkpoint at step %s' % step)
def evaluate():
"""Eval CIFAR-10 for a number of steps."""
with tf.Graph().as_default():
float_image, label = tfrecord.eval_data_read(
tfrecord_path=FLAGS.eval_data)
images, labels = tfrecord.create_batch(float_image,
label,
count_num=FLAGS.num_examples)
logits = network.inference(images)
# tf.nn.in_top_k:计算预测的结果和实际结果的是否相等,返回bool类型的张量
top_k_op = tf.nn.in_top_k(logits, labels, 1)
variable_averages = tf.train.ExponentialMovingAverage(
network.MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
summary_op = tf.summary.merge_all()
graph_def = tf.get_default_graph().as_graph_def()
summary_writer = tf.summary.FileWriter(FLAGS.eval_dir,
graph_def=graph_def)
while True:
eval_once(saver, summary_writer, top_k_op, summary_op)
if FLAGS.run_once:
break
time.sleep(FLAGS.eval_interval_secs)
def main(argv=None):
keep_probability = tf.placeholder(tf.float32, name="keep_probabilty")
X = tf.placeholder(tf.float32, [4, sys_param.N_USER])
Y = tf.placeholder(tf.int32, [None, 2 * sys_param.N_USER])
# initial UL & DL deadline in time slot
class deadline:
True_UL_deadline = np.zeros(shape=[
sys_param.N_USER,
])
for i in range(sys_param.N_USER):
True_UL_deadline[i] = random.randint(5 * i + 1, 5 * i + 6)
Expected_UL_deadline = np.zeros(shape=True_UL_deadline.shape)
True_DL_deadline = np.zeros(shape=[
sys_param.N_USER,
])
for i in range(sys_param.N_USER):
True_DL_deadline[i] = random.randint(5 * i + 1, 5 * i + 6)
# initial battery level
class bat_level:
#Expected_bat_level = np.random.randint(low=0, high=40, size=20)
True_bat_level = sys_param.Tx_power * np.ones(shape=[
sys_param.N_USER,
])
Expected_bat_level = np.zeros(shape=True_bat_level.shape)
# initial penalty
class penalty:
UL_deadline_penalty = np.zeros(shape=(1, 20), dtype=int)
DL_deadline_penalty = np.zeros(shape=(1, 20), dtype=int)
UL_bat_level_penalty = np.zeros(shape=(1, 20), dtype=int)
count = np.zeros(shape=(3, 20), dtype=int)
h_5, selection, var_dict = network.inference(keep_probability,
FLAGS.logs_dir, FLAGS.weight,
X, sys_param.N_USER)
# loss function
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=h_5, labels=Y))
trainable_var = tf.trainable_variables()
train_op = train(loss, trainable_var)
print("Session Open")
sess = tf.Session()
print("Weight Initialization")
sess.run(tf.global_variables_initializer())
if FLAGS.mode == "train":
print("Start Training")
# load channel information
mat = scipy.io.loadmat('/home/mukim/Desktop/EH/H_H_hermitian.mat')
channel = mat['H_H_hermitian']
train_data_size = channel.shape[0]
total_penalty = np.zeros([1, 40])
cumul_expected_penalty = np.zeros([1, 40])
save_loss = np.zeros([MAX_ITERATION, train_data_size / 100])
save_penalty_count = np.zeros([MAX_ITERATION, 3, sys_param.N_USER])
for itr in xrange(MAX_ITERATION):
print("Process: %d iteration, Current time: %s" %
(itr + 1, datetime.datetime.now()))
penalty.count = np.zeros(shape=(3, 20), dtype=int)
for train_count in xrange(train_data_size):
# Make training dataset
UL_d_p = np.reshape(penalty.UL_deadline_penalty,
newshape=[
sys_param.N_USER,
])
DL_d_p = np.reshape(penalty.DL_deadline_penalty,
newshape=[
sys_param.N_USER,
])
UL_b_p = np.reshape(penalty.UL_bat_level_penalty,
newshape=[
sys_param.N_USER,
])
channel_info = np.zeros(shape=[
sys_param.N_USER,
])
for i in range(sys_param.N_USER):
ind = np.argsort(channel[train_count, :])[i]
channel_info[
ind] = i # the largest channel gain means sys_param.N-1, the smallest channel gain means 0
# input = np.array([channel[train_count,:],deadline.Expected_UL_deadline,deadline.True_DL_deadline,bat_level.Expected_bat_level])
input = np.array([channel_info, UL_d_p, DL_d_p, UL_b_p])
input = np.reshape(input, newshape=(4, sys_param.N_USER))
if (train_count % 1000) < 2 * sys_param.N_USER:
nd_array_selection = np.zeros(
shape=[1, 2 * sys_param.N_USER])
nd_array_selection[0][train_count % 1000] = 1
nd_array_selection = np.reshape(
nd_array_selection, newshape=[2, sys_param.N_USER])
deadline, bat_level, penalty, Expected_label, Expected_total_penalty = \
info_update(sys_param,deadline,bat_level,penalty,nd_array_selection,input)
else:
feed_dict = {keep_probability: 0.7, X: input}
nd_array_selection = sess.run(selection,
feed_dict=feed_dict)
deadline, bat_level, penalty, Expected_label, Expected_total_penalty = \
info_update(sys_param, deadline, bat_level, penalty, nd_array_selection, input)
feed_dict = {
keep_probability: 0.7,
X: input,
Y: Expected_label
}
sess.run(train_op, feed_dict=feed_dict)
if train_count % (train_data_size - 1) == 0:
if (itr + 1) % 10 == 0:
weight_dict_ = sess.run(var_dict,
feed_dict=feed_dict)
np.save(
"/home/mukim/Desktop/EH/weight" + "_" +
str(itr + 1) + ".npy", weight_dict_)
print("Weight saved!")
if train_count % 100 == 0:
train_loss, weight_dict_ = sess.run(
[loss, var_dict], feed_dict=feed_dict)
#print("-----------------Penalty count-------------------")
#print (penalty.count)
#print("------------True battery level----------------")
#print (bat_level.True_bat_level)
#print("------------Expected battery level----------------")
#print(bat_level.Expected_bat_level)
#current_penalty = cal_penalty(penalty)
#total_penalty = total_penalty + current_penalty
#cumul_expected_penalty = cumul_expected_penalty+Expected_total_penalty
#print (current_penalty)
#print("--------------EXPECTATION---------------")
#print (Expected_total_penalty)
save_loss[itr][train_count / 100] = train_loss
print("Time: %s, Round: %d, Batch: %d, Train_loss:%g" %
(datetime.datetime.now(), itr + 1, train_count,
train_loss))
save_penalty_count[itr, :, :] = penalty.count
np.save('/home/mukim/Desktop/EH/h7_channelinfo_loss.npy',
save_loss)
np.save('/home/mukim/Desktop/EH/h7_channelinfo_penalty_count.npy',
save_penalty_count)
elif FLAGS.mode == "test":
print("To be continue...")
TOTAL_STEPS = int(os.environ['TOTAL_STEPS'] or 1000)
LEARNING_RATE = float(os.environ['LEARNING_RATE'] or 0.1)
RESTORE = ((os.environ['RESTORE'] or '') == 'true') or False
learning_rate_value = LEARNING_RATE
session_config = tf.ConfigProto(log_device_placement=True)
session_config.gpu_options.allow_growth = True
# this is required if want to use GPU as device.
# see: https://github.com/tensorflow/tensorflow/issues/2292
session_config.allow_soft_placement = True
if __name__ == "__main__":
with tf.Graph().as_default() as g, tf.device(USE_DEVICE):
# inference()
input, deep_features = network.inference()
labels, logits, cross_entropy = network.loss(deep_features)
centroid_loss, centroids, spread = network.center_loss(
deep_features, labels)
# combine the two losses
_lambda = tf.placeholder(dtype=tf.float32)
total_loss = cross_entropy + _lambda / 2. * centroid_loss
learning_rate, train, global_step = network.training(total_loss)
eval = network.evaluation(logits, labels)
init = tf.initialize_all_variables()
with tf.Session(config=session_config) as sess, \
h5py.File(DUMP_FILE, 'a', libver='latest', swmr=True) as h5_file:
def train(networkmodel, MODEL_SAVE_PATH, MODEL_NAME):
# with tf.device('/gpu:0'):
with tf.device('/cpu:0'):
train_start = time.time()
# 生成训练数据含标签
x, y_ = readdata.get_batch(train=True,
batch_size=BATCH_SIZE,
num_epochs=None)
# 生成测试数据含标签
text_x, text_y = readdata.get_batch(train=False,
batch_size=BATCH_SIZE,
num_epochs=None)
# 神经网络模型
if networkmodel:
# 调整神经网络输入为一维，-1代表未知数量
x = tf.reshape(x, [-1, x.shape[1] * x.shape[2] * x.shape[3]])
# 训练部分输出
y = network.inference(x, avg_class=None, reuse=False, lamada=None)
else:
# 卷积模型
# 训练部分输入、输出tensor
y = cnn.inference(x, False, False, regularizer=None)
# 初始化，从0开始，每batch一次，增加1
global_step = tf.Variable(0, trainable=False)
# 神经网络模型
if networkmodel:
# 测试数据转化为一维，适应神经网络输入
text_x = tf.reshape(
text_x,
[-1, text_x.shape[1] * text_x.shape[2] * text_x.shape[3]])
# 测试输出
average_y = network.inference(text_x,
avg_class=None,
reuse=True,
lamada=None)
else:
# 卷积网络模型测试输入、输出
average_y = cnn.inference(text_x, True, False, regularizer=None)
# 对每个batch数据结果求均值，cross_entropy是一种信息熵方法，能够预测模型对真实概率分布估计的准确程度
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=y, labels=tf.argmax(y_, 1))
# 求平均值
cross_entropy_mean = tf.reduce_mean(cross_entropy)
# 损失函数
loss = cross_entropy_mean
# 训练操作，GradientDescentOptimizer为梯度下降算法的优化器，学习率LEARNING_RATE，minimize为最小化损失函数操作
train_step = tf.train.GradientDescentOptimizer(LEARNING_RATE).minimize(
loss, global_step=global_step)
# 设计计算图
with tf.control_dependencies([train_step]):
train_op = tf.no_op(name='train')
# 预测数字类别是否为正确类别，tf.argmax找出真实类别
correct_prediction = tf.equal(tf.argmax(average_y, 1),
tf.argmax(text_y, 1))
# tf.reduce_mean求平均值
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# 初始化tf持久化类
saver = tf.train.Saver()
# 初始化会话，并开始训练
with tf.Session() as sess:
# 初始化模型的参数
sess.run(tf.local_variables_initializer())
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator() # 创建一个协调器，管理线程
threads = tf.train.start_queue_runners(sess=sess,
coord=coord) # 启动所有队列线程
# 迭代的训练神经网络
for i in range(TRAINING_STEPS):
start_time = time.time()
_, loss_value, step = sess.run([train_op, loss,
global_step]) # 设置好整个图后，启动计算
end_time = time.time()
print('Training elapsed each step time:%f s' %
(end_time - start_time))
# 打印训练损失
if (i + 1) % 10 == 0:
print(
"After %d training step(s), loss on training batch is %g."
% (step, loss_value))
# 打印验证准确率
if (i + 1) % 100 == 0:
validate_acc = sess.run(accuracy) # 设置好整个图后，启动计算accuracy
print(
"After %d training step(s),validation accuracy using average model is %g."
% (step, validate_acc))
a = os.path.join(MODEL_SAVE_PATH, MODEL_NAME)
saver.save(sess,
os.path.join(MODEL_SAVE_PATH, MODEL_NAME),
global_step=global_step) # 保存模型
train_end = time.time()
print('Training elapsed total time:%f s' %
(train_end - train_start))
coord.request_stop() # 要求所有线程停止
coord.join(threads)
def train():
"""Train CIFAR-10 for a number of steps."""
with tf.Graph().as_default():
gloabl_step = tf.train.get_or_create_global_step()
# Get images and lables for CIFAR-10
# Force input pipelines to CPU:0 to avoid operations sometimes ending up on GPU and resultign in a slow down.
with tf.device('/cpu:0'):
images, labels = network.distorted_inputs()
# Build a Graph that computes the logits predictions from the inference model.
logits = network.inference(images)
# print(logits.get_shape())
# print(labels.get_shape())
# os.system('pause')
# Calcute loss.
loss = network.loss(logits, labels)
# Buid a Graph that trains the model with one batch of examples and updates the model parameters.
train_op = network.train(loss, gloabl_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
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 exmples/sec: %.3f sec/batch"
)
print(format_str % (datetime.now(), self._step, loss_value,
examples_per_sec, sec_per_batch))
with tf.train.MonitoredTrainingSession(
checkpoint_dir=FLAGS.train_dir,
hooks=[
tf.train.StopAtStepHook(last_step=FLAGS.max_steps),
tf.train.NanTensorHook(loss),
_LoggerHook()
],
config=tf.ConfigProto(log_device_placement=FLAGS.
log_device_placement)) as mon_sess:
print(
'********************************Sussessfully creating session'
)
while not mon_sess.should_stop():
mon_sess.run(train_op)
def train(networkmodel, MODEL_SAVE_PATH, MODEL_NAME):
if FLAGS.job_name is None or FLAGS.job_name == '':
raise ValueError('Must specify an explicit job_name !')
else:
print('job_name : %s' % FLAGS.job_name)
if FLAGS.task_index is None or FLAGS.task_index == '':
raise ValueError('Must specify an explicit task_index!')
else:
print('task_index : %d' % FLAGS.task_index)
ps_spec = FLAGS.ps_hosts.split(',')
worker_spec = FLAGS.worker_hosts.split(',')
# 创建集群
# num_worker = 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()
is_chief = (FLAGS.task_index == 0)
# worker_device = '/job:worker/task%d/cpu:0' % FLAGS.task_index
with tf.device(tf.train.replica_device_setter(cluster=cluster)):
# 生成训练数据含标签
x, y_ = readdata.get_batch(train=True,
batch_size=BATCH_SIZE,
num_epochs=None)
# 生成测试数据含标签
text_x, text_y = readdata.get_batch(train=False,
batch_size=BATCH_SIZE,
num_epochs=50)
# 神经网络模型
if networkmodel:
# 调整神经网络输入为一维，-1代表未知数量
x = tf.reshape(x, [-1, x.shape[1] * x.shape[2] * x.shape[3]])
# 训练部分输出
y = network.inference(x, avg_class=None, reuse=False, lamada=None)
else:
# 卷积模型
# 训练部分输入、输出tensor
y = cnn.inference(x, False, False, regularizer=None)
# 初始化，从0开始，每batch一次，增加1,创建纪录全局训练步数变量
global_step = tf.Variable(0, name='global_step', trainable=False)
# 神经网络模型
if networkmodel:
# 测试数据转化为一维，适应神经网络输入
text_x = tf.reshape(
text_x,
[-1, text_x.shape[1] * text_x.shape[2] * text_x.shape[3]])
# 测试输出
average_y = network.inference(text_x,
avg_class=None,
reuse=True,
lamada=None)
else:
# 卷积网络模型测试输入、输出
average_y = cnn.inference(text_x, True, False, regularizer=None)
# 对每个batch数据结果求均值，cross_entropy是一种信息熵方法，能够预测模型对真实概率分布估计的准确程度
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=y, labels=tf.argmax(y_, 1))
# 求损失函数
loss = tf.reduce_mean(cross_entropy)
# 训练操作，GradientDescentOptimizer为梯度下降算法的优化器，学习率LEARNING_RATE，minimize为最小化损失函数操作
train_step = tf.train.GradientDescentOptimizer(LEARNING_RATE).minimize(
loss, global_step=global_step)
# 预测数字类别是否为正确类别，tf.argmax找出真实类别
correct_prediction = tf.equal(tf.argmax(average_y, 1),
tf.argmax(text_y, 1))
# tf.reduce_mean求平均值
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# # 设计计算图
# with tf.control_dependencies([train_step]):
# train_op = tf.no_op(name='train')
# 生成本地的参数初始化操作init_op
init_op = tf.global_variables_initializer()
train_dir = tempfile.mkdtemp()
sv = tf.train.Supervisor(is_chief=is_chief,
logdir=train_dir,
init_op=init_op,
recovery_wait_secs=1,
global_step=global_step)
if is_chief:
print('Worker %d: Initailizing session...' % FLAGS.task_index)
else:
print('Worker %d: Waiting for session to be initaialized...' %
FLAGS.task_index)
sess = sv.prepare_or_wait_for_session(server.target)
print('Worker %d: Session initialization complete.' %
FLAGS.task_index)
time_begin = time.time()
print('Traing begins @ %f' % time_begin)
local_step = 0
for i in range(TRAINING_STEPS):
coord = tf.train.Coordinator() # 创建一个协调器，管理线程
threads = tf.train.start_queue_runners(sess=sess,
coord=coord) # 启动所有队列线程
_, step, loss_value = sess.run([train_step, global_step, loss])
local_step += 1
now = time.time()
print('%f: Worker %d: traing step %d dome (global step:%d)' %
(now, FLAGS.task_index, local_step, step))
# 打印验证准确率
if (i + 1) % 100 == 0:
validate_acc = sess.run(accuracy) # 设置好整个图后，启动计算accuracy
print(
"After %d training step(s),validation accuracy using average model is %g."
% (step, validate_acc))
coord.request_stop() # 要求所有线程停止
coord.join(threads)
time_end = time.time()
print('Training ends @ %f' % time_end)
train_time = time_end - time_begin
print('Training elapsed time:%f s' % train_time)
sess.close()
def train():
total_epoch, total_iter = 100, 0
best_loss, init_lr = 1e10, 5e-5
batch_size, image_h, image_w = 8, 512, 512
image = tf.placeholder(tf.float32, [None, image_h, image_w, 3])
label = tf.placeholder(tf.float32, [None, image_h, image_w, 3])
lr = tf.placeholder(tf.float32)
pred = inference(image, width=0.75, is_training=True)
c_loss = color_loss(pred, label)
s_loss = smoothness_loss(pred)
r_loss = reconstruct_loss(pred, label)
total_loss = 1e-2*c_loss + 1e2*s_loss + r_loss
#total_loss = c_loss + r_loss
all_vars = tf.trainable_variables()
backbone_vars = [var for var in all_vars if 'backbone' in var.name]
train_psnr = cal_psnr(pred, label)
tf.summary.scalar('loss', total_loss)
tf.summary.scalar('color_loss', c_loss)
tf.summary.scalar('smoothness_loss', s_loss)
tf.summary.scalar('reconstruct_loss', r_loss)
tf.summary.scalar('psnr', train_psnr)
optimizer = tf.train.AdamOptimizer(learning_rate=lr)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
train_op = optimizer.minimize(total_loss)
train_op = tf.group([train_op, update_ops])
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
'''
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.75)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
'''
train_writer = tf.summary.FileWriter('train_log', sess.graph)
summary_op = tf.summary.merge_all()
saver = tf.train.Saver()
with tf.device('/device:GPU:0'):
sess.run(tf.global_variables_initializer())
weight = np.load('mobilenetv2_075.npy', allow_pickle=True)
assign_ops = []
for var, para in zip(backbone_vars, weight):
assign_ops.append(var.assign(para))
sess.run(assign_ops)
data_dir = 'data_loacation_in_your_computer'
dataloader = get_train_loader((image_h, image_w), batch_size, data_dir)
for epoch in range(total_epoch):
for batch in tqdm(dataloader):
total_iter += 1
_, train_info, loss = sess.run([train_op, summary_op, total_loss],
feed_dict={image: batch[0],
label: batch[1],
lr: init_lr})
train_writer.add_summary(train_info, total_iter)
if np.mod(total_iter, 20) == 0:
print('{}th epoch, {}th iter, loss: {}'.format(epoch, total_iter, loss))
if loss < best_loss:
best_loss = loss
saver.save(sess, 'saved_models/model', global_step=total_iter)
TOTAL_STEPS = int(os.environ['TOTAL_STEPS'] or 1000)
LEARNING_RATE = float(os.environ['LEARNING_RATE'] or 0.1)
RESTORE = ((os.environ['RESTORE'] or '') == 'true') or False
learning_rate_value = LEARNING_RATE
session_config = tf.ConfigProto(log_device_placement=True)
session_config.gpu_options.allow_growth = True
# this is required if want to use GPU as device.
# see: https://github.com/tensorflow/tensorflow/issues/2292
session_config.allow_soft_placement = True
if __name__ == "__main__":
with tf.Graph().as_default() as g, tf.device(USE_DEVICE):
# inference()
input, deep_features = network.inference()
labels, logits, cross_entropy = network.loss(deep_features)
centroid_loss, centroids, spread = network.center_loss(deep_features, labels)
# combine the two losses
_lambda = tf.placeholder(dtype=tf.float32)
total_loss = cross_entropy + _lambda / 2. * centroid_loss
learning_rate, train, global_step = network.training(total_loss)
eval = network.evaluation(logits, labels)
init = tf.initialize_all_variables()
with tf.Session(config=session_config) as sess, \
h5py.File(DUMP_FILE, 'a', libver='latest', swmr=True) as h5_file:
# Merge all the summaries and write them out to /tmp/mnist_logs (by default)
def main(_):
with tf.Graph().as_default():
images, labels = utils.prepare_testdata(FLAGS.dataset_dir, FLAGS.batch_size)
logits, _ = network.inference(images, FLAGS.num_classes, for_training=False, feature_name=FLAGS.feature_name)
top_1_op = tf.nn.in_top_k(logits, labels, 1)
top_5_op = tf.nn.in_top_k(logits, labels, 5)
var_averages = tf.train.ExponentialMovingAverage(FLAGS.ema_decay)
var_to_restore = var_averages.variables_to_restore()
saver = tf.train.Saver(var_to_restore)
ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_path)
model_checkpoint_path = ckpt.model_checkpoint_path
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
saver.restore(sess, model_checkpoint_path)
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
print('Successfully loaded model from %s at step=%s.' %
(model_checkpoint_path, global_step))
coord = tf.train.Coordinator()
try:
threads = []
for qr in tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS):
threads.extend(qr.create_threads(sess, coord=coord, daemon=True, start=True))
num_iter = int(math.ceil(FLAGS.num_examples / FLAGS.batch_size))
print('num_iter = ' + str(num_iter))
# Counts the number of correct predictions.
count_top_1 = count_top_5 = 0.0
total_sample_count = num_iter * FLAGS.batch_size
step = 0
print('%s: starting evaluation on (%s).' % (datetime.now(), 'test'))
start_time = time.time()
while step < num_iter and not coord.should_stop():
top_1, top_5 = sess.run([top_1_op, top_5_op])
count_top_1 += np.sum(top_1)
count_top_5 += np.sum(top_5)
step += 1
# print progress every 20 batches
if step % 20 == 0:
duration = time.time() - start_time
sec_per_batch = duration / 20.0
examples_per_sec = FLAGS.batch_size / sec_per_batch
print('%s: [%d batches out of %d] (%.1f examples/sec; %.3f sec/batch)'
% (datetime.now(), step, num_iter, examples_per_sec, sec_per_batch))
start_time = time.time()
# Compute precision @ 1. (accuracy) and print results
precision_at_1 = count_top_1 / total_sample_count
recall_at_5 = count_top_5 / total_sample_count
print('%s: precision @ 1 = %.4f recall @ 5 = %.4f [%d examples]' %
(datetime.now(), precision_at_1, recall_at_5, total_sample_count))
# save results into a txt file
file_path = FLAGS.eval_dir+FLAGS.save_txt
text_file = open(file_path, 'a')
text_file.write(FLAGS.checkpoint_path)
text_file.write('\n')
text_file.write('%s: precision @ 1 = %.4f recall @ 5 = %.4f' %
(datetime.now(), precision_at_1, recall_at_5))
text_file.write('\n')
text_file.close()
except Exception as e: # pylint: disable=broad-except
coord.request_stop(e)
coord.request_stop()
coord.join(threads, stop_grace_period_secs=10)
def main():
args = get_parser().parse_args()
observation_length = 17
action_length = 6
# Read the expert rollouts from disk.
observations, actions = load_data(args.rollouts_file)
print("observations shape = " + str(observations.shape))
print("actions shape = " + str(actions.shape))
# Make sure our files exist!
assert (os.path.exists(os.path.dirname(os.path.abspath(args.stats_file))))
# Load the expert.
print("Loading and building expert policy.")
policy_fn = load_policy.load_policy(args.expert_policy_file)
print("Expert policy loaded and built.")
# Assemble the network.
opl = tf.placeholder(tf.float32,
shape=(None, observation_length),
name="observations")
apl = tf.placeholder(tf.float32,
shape=(None, action_length),
name="actions")
logits = network.inference(opl, observation_length, args.hidden1,
args.hidden2, action_length)
errors, loss = network.loss(logits, apl)
global_step, train_op = network.training(loss, args.learning_rate)
with tf.Session() as sess:
# Initialize the network.
tf_util.initialize()
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(args.checkpoint_dir))
env = gym.make("Walker2d-v1")
max_steps = env.spec.timestep_limit
avg_returns = []
stddev_returns = []
observations = list(observations)
actions = list(actions)
for iteration in range(args.num_iterations):
obs = np.array(observations)
acts = np.array(actions)
assert (obs.shape[0] == acts.shape[0])
# Train the network.
if iteration != 0:
num_batches = int(obs.shape[0] / args.batch_size)
for step in range(args.training_steps):
i = step % num_batches
if i == 0:
p = np.random.permutation(obs.shape[0])
obs = obs[p]
acts = acts[p]
start = int(i * args.batch_size)
stop = int((i + 1) * args.batch_size)
feed_dict = {opl: obs[start:stop], apl: acts[start:stop]}
_, loss_value, step_value = sess.run(
[train_op, loss, global_step], feed_dict=feed_dict)
if step % 100 == 0:
loss_value = sess.run(loss,
feed_dict={
opl: obs,
apl: acts
})
msg = "Iteration {}; step {}; loss = {}".format(
iteration, step_value, loss_value)
print(msg)
# Generate new rollouts.
rewards = []
for i in range(args.num_rollouts):
print("Iteration {}; rollout {}".format(iteration, i))
obs = env.reset()
done = False
steps = 0
totalr = 0
while not done:
expert_action = policy_fn(obs[None, :])
observations.append(obs)
actions.append(expert_action[0])
action = sess.run(logits, feed_dict={opl: obs[None, :]})
obs, r, done, _ = env.step(action)
totalr += r
steps += 1
if steps >= max_steps:
break
rewards.append(totalr)
print("Iteration {}; average return {}".format(
iteration, np.mean(rewards)))
print("Iteration {}; stddev return {}".format(
iteration, np.std(rewards)))
avg_returns.append(np.mean(rewards))
stddev_returns.append(np.std(rewards))
with open(args.stats_file, "w") as f:
stats = {
"mean_return": avg_returns,
"stddev_returns": stddev_returns
}
json.dump(stats, f, indent=4)
def main():
########################################## USER INPUT ##############################################################
# Training parameters:
if len(sys.argv) >= 8:
IMAGE_NAME = sys.argv[1] # IMAGE_NAME = '1'
NETWORK_NAME = sys.argv[2] # 'unet', 'deep_decoder'
LOSS_NAME = sys.argv[
3] # 'mse', 'l1', 'mse_l1', 'mse_with_tv_reg', 'mse_with_edge_reg'
OPTIMIZER_TYPE = sys.argv[4] # 'sgd', 'adam'
LEARNING_RATE = float(sys.argv[5])
NUM_ITERATIONS = int(sys.argv[6])
ITERATIONS_TO_SAVE = int(sys.argv[7])
if len(sys.argv) == 11:
w_h = float(sys.argv[8])
w_v = float(sys.argv[9])
w_mse = float(sys.argv[10])
else:
w_h = None
w_v = None
w_mse = None
else:
print('Not enough input parameters.')
return
####################################################################################################################
# Load images:
RAW_FILENAME = os.path.join('Raw', '{}_Raw Image.tif'.format(IMAGE_NAME))
AVERAGED_FILENAME = os.path.join(
'Averaged', '{}_Averaged Image.tif'.format(IMAGE_NAME))
try:
input_image = hf.get_training_image(RAW_FILENAME)
except:
print("Error loading {}".format(RAW_FILENAME))
return
try:
ground_truth = hf.get_training_image(AVERAGED_FILENAME)
except:
print("Error loading {}".format(AVERAGED_FILENAME))
return
# Validate settings:
VALID_NETWORK_NAMES = ["unet", "deep_decoder"]
VALID_OPTIMIZER_TYPES = ["sgd", "adam"]
VALID_LOSS_NAMES = [
"mse", "l1", "mse_l1", "mse_with_tv_reg", "mse_with_edge_reg"
]
if not (NETWORK_NAME in VALID_NETWORK_NAMES):
print("Error: {} network does not exist.".format(NETWORK_NAME))
return
if not (OPTIMIZER_TYPE in VALID_OPTIMIZER_TYPES):
print("Error: {} optimizer does not exist.".format(OPTIMIZER_TYPE))
return
if not (LOSS_NAME in VALID_LOSS_NAMES):
print("Error: {} loss does not exist.".format(LOSS_NAME))
return
# Create folder to save results:
SAVE_FOLDER = os.path.join('./results', IMAGE_NAME)
count = 0
CHECK_FOLDER = SAVE_FOLDER
while os.path.exists(CHECK_FOLDER):
count += 1
CHECK_FOLDER = '{}({})'.format(SAVE_FOLDER, count)
SAVE_FOLDER = CHECK_FOLDER
os.mkdir(SAVE_FOLDER)
WRITE_FILENAME = os.path.join(SAVE_FOLDER, 'metrics.txt')
with open(WRITE_FILENAME, 'a') as wf:
wf.write(
'PARAMETERS\nNetwork: {}\nLoss: {}\nOptimizer: {}\nLearning rate: {}\nNumber of iterations: {}'
.format(NETWORK_NAME, LOSS_NAME, OPTIMIZER_TYPE, LEARNING_RATE,
NUM_ITERATIONS))
wf.write('\n\nw_h: {}\nw_v: {}\nw_mse: {}'.format(w_h, w_v, w_mse))
wf.write('\n\nIteration\tLoss\tSNR\tCNR\tSSIM')
# Get input noise:
if NETWORK_NAME == "unet":
input_noise = hf.get_noise_matrix(input_image.shape[1],
input_image.shape[2], 32)
elif NETWORK_NAME == "deep_decoder":
input_noise = hf.get_noise_matrix(input_image.shape[1] / (2**4),
input_image.shape[2] / (2**4), 64)
# Save inputs:
save_filename = os.path.join(SAVE_FOLDER, 'input_image.tif')
imsave(save_filename, input_image[0, :, :, 0], cmap='gray')
save_filename = os.path.join(SAVE_FOLDER, 'ground_truth.tif')
imsave(save_filename, ground_truth[0, :, :, 0], cmap='gray')
# Calculate initial metrics:
snr_i = hf.calculate_metrics(ground_truth, input_image, 'snr', IMAGE_NAME)
cnr_i = hf.calculate_metrics(ground_truth, input_image, 'cnr', IMAGE_NAME)
ssim_i = hf.calculate_metrics(ground_truth, input_image, 'ssim',
IMAGE_NAME)
with open(WRITE_FILENAME, 'a') as wf:
wf.write('\ninput_image\tN/A\t{}\t{}\t{}'.format(snr_i, cnr_i, ssim_i))
# Placeholders:
z = tf.placeholder(tf.float32, shape=[1, None, None,
input_noise.shape[3]]) # input noise
x = tf.placeholder(tf.float32, shape=[1, None, None, 1]) # input image
# Network:
y = network.inference(NETWORK_NAME,
z,
height=input_noise.shape[1],
width=input_noise.shape[2],
channels=input_noise.shape[3])
if LOSS_NAME == "mse_with_edge_reg" or LOSS_NAME == "mse_with_tv_reg":
loss, mse, edge_h, edge_v = network.loss(y, x, LOSS_NAME, w_h, w_v,
w_mse)
else:
loss = network.loss(y, x, LOSS_NAME)
# Update moving mean and variance for batch normalization (if required):
if NETWORK_NAME == "deep_decoder":
update_op = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
# Create different optimizers here:
if OPTIMIZER_TYPE == "sgd":
train_op = tf.train.GradientDescentOptimizer(
learning_rate=LEARNING_RATE).minimize(loss)
elif OPTIMIZER_TYPE == "adam":
train_op = tf.train.AdamOptimizer(
learning_rate=LEARNING_RATE).minimize(loss)
# Start session:
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# Keep track of metrics:
track_iter = []
track_loss = []
track_snr = []
track_cnr = []
track_ssim = []
for i in range(NUM_ITERATIONS + 1):
if NETWORK_NAME == "unet":
if LOSS_NAME == "mse_with_edge_reg" or LOSS_NAME == "mse_with_tv_reg":
_, output_image, loss_i, mse_i, edge_h_i, edge_v_i = sess.run(
[train_op, y, loss, mse, edge_h, edge_v],
feed_dict={
z: input_noise,
x: input_image
})
else:
_, output_image, loss_i = sess.run([train_op, y, loss],
feed_dict={
z: input_noise,
x: input_image
})
elif NETWORK_NAME == "deep_decoder":
if LOSS_NAME == "mse_with_edge_reg" or LOSS_NAME == "mse_with_tv_reg":
_, _, output_image, loss_i, mse_i, edge_h_i, edge_v_i = sess.run(
[update_op, train_op, y, loss, mse, edge_h, edge_v],
feed_dict={
z: input_noise,
x: input_image
})
else:
_, _, output_image, loss_i = sess.run(
[update_op, train_op, y, loss],
feed_dict={
z: input_noise,
x: input_image
})
if i % ITERATIONS_TO_SAVE == 0:
# Save image:
save_filename = os.path.join(SAVE_FOLDER,
'iteration_{}.tif'.format(i))
imsave(save_filename, output_image[0, :, :, 0], cmap='gray')
# Calculate metrics:
snr_i = hf.calculate_metrics(ground_truth, output_image, 'snr',
IMAGE_NAME)
cnr_i = hf.calculate_metrics(ground_truth, output_image, 'cnr',
IMAGE_NAME)
ssim_i = hf.calculate_metrics(ground_truth, output_image,
'ssim', IMAGE_NAME)
with open(WRITE_FILENAME, 'a') as wf:
wf.write('\n{}\t{}\t{}\t{}\t{}'.format(
i, loss_i, snr_i, cnr_i, ssim_i))
# Display:
if LOSS_NAME == "mse_with_edge_reg" or LOSS_NAME == "mse_with_tv_reg":
print(
'Iteration {}/{}\t| Loss: {}\tSNR: {}\tCNR: {}\tSSIM: {}\tMSE: {}\tEdge_h: {}\tEdge_v: {}'
.format(i, NUM_ITERATIONS, loss_i, snr_i, cnr_i,
ssim_i, mse_i, edge_h_i, edge_v_i))
else:
print(
'Iteration {}/{}\t| Loss: {}\tSNR: {}\tCNR: {}\tSSIM: {}'
.format(i, NUM_ITERATIONS, loss_i, snr_i, cnr_i,
ssim_i))
# Track:
track_iter.append(i)
track_loss.append(loss_i)
track_snr.append(snr_i)
track_cnr.append(cnr_i)
track_ssim.append(ssim_i)
# Plot:
hf.plot_metrics(track_iter, track_loss, 'loss',
os.path.join(SAVE_FOLDER, 'loss.tif'))
hf.plot_metrics(track_iter, track_snr, 'snr',
os.path.join(SAVE_FOLDER, 'snr.tif'))
hf.plot_metrics(track_iter, track_cnr, 'cnr',
os.path.join(SAVE_FOLDER, 'cnr.tif'))
hf.plot_metrics(track_iter, track_ssim, 'ssim',
os.path.join(SAVE_FOLDER, 'ssim.tif'))
print('Completed.')
import os
sys.path.append(os.path.abspath(os.path.join(
os.path.dirname(__file__),
os.path.pardir,
'tracker')))
import network
# Load tensorflow
tf.Graph().as_default()
batchSize = 1
delta = 1
imagePlaceholder = tf.placeholder(tf.float32, shape=(batchSize * delta * 2, 227, 227, 3))
labelsPlaceholder = tf.placeholder(tf.float32, shape=(batchSize * delta, 4))
learningRate = tf.placeholder(tf.float32)
tfOutputs = network.inference(imagePlaceholder, num_unrolls=delta, train=True)
tfLossFull, tfLoss = network.loss(tfOutputs, labelsPlaceholder)
train_op = network.training(tfLossFull, learningRate)
summary = tf.summary.merge_all()
init = tf.global_variables_initializer()
saver = tf.train.Saver()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
summary_writer = tf.summary.FileWriter('logs/train/caffe_copy', sess.graph)
ops = []
with sess.as_default():
sess.run(init)
import caffe
caffe.set_mode_cpu()
if not convolutional:
x = tf.placeholder(tf.float32, shape=[None, img_size_flat], name='x')
else:
x = tf.placeholder(tf.float32,
shape=[None, img_shape[0], img_shape[1], num_channels],
name='x')
x_image = tf.reshape(x, [-1, img_size, img_size, num_channels])
tf.summary.image('input', x_image, 3)
y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')
y_true_cls = tf.argmax(y_true, dimension=1)
"""
Inference (Forward Pass)
"""
logits, features = inference(x_image, num_classes=num_classes)
y_probs = tf.nn.softmax(logits)
tf.summary.histogram('probs', y_probs)
y_pred = tf.argmax(logits, dimension=1)
"""
Restore variables
"""
saver = tf.train.Saver()
saver.restore(session, tf.train.latest_checkpoint('./saved_models'))
"""
Data set Configuration
"""
def run_training():
"""Train network for a number of epochs."""
# Tell TensorFlow that the model will be built into the default Graph.
with tf.Graph().as_default():
with tf.name_scope('input'):
# Input data, pin to CPU because rest of pipeline is CPU-only
with tf.device('/cpu:0'):
input_data = tf.constant(training_data)
input_labels = tf.constant(training_labels)
input, label = tf.train.slice_input_producer(
[input_data, input_labels], num_epochs=FLAGS.num_epochs)
label = tf.cast(label, tf.int32)
input, labels = tf.train.batch([input, label],
batch_size=FLAGS.batch_size)
# Build a Graph that computes predictions from the inference model.
logits = network.inference(input, FLAGS.hidden1, FLAGS.hidden2)
# Add to the Graph the Ops for loss calculation.
loss = network.loss(logits, labels)
# Add to the Graph the Ops that calculate and apply gradients.
train_op = network.training(loss, FLAGS.learning_rate)
# Add the Op to compare the logits to the labels during evaluation.
eval_correct = network.evaluation(logits, labels)
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.summary.merge_all()
# Create a saver for writing training checkpoints.
saver = tf.train.Saver()
# Create the op for initializing variables.
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
# Create a session for running Ops on the Graph.
sess = tf.Session()
# Run the Op to initialize the variables.
sess.run(init_op)
# Instantiate a SummaryWriter to output summaries and the Graph.
summary_writer = tf.train.SummaryWriter(FLAGS.train_dir,
sess.graph)
# Start input enqueue threads.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# And then after everything is built, start the training loop.
for ep in xrange(FLAGS.num_epochs):
for step in xrange(FLAGS.max_steps):
start_time = time.time()
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
# Write the summaries and print an overview fairly often.
if loss_value - 0.0 <= 0.00001:
print(
'Loss value: %.4f, done training for %d epochs, %d steps.'
% (loss_value, ep, ep * FLAGS.max_steps + step))
return
if step % 100 == 0:
# Print status to stdout.
print('Epochs %d: loss = %.4f (%.3f sec)' %
(ep, loss_value, duration))
# Update the events file.
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)
# Save a checkpoint periodically.
if (step + 1) % 1000 == 0 or (step + 1) == FLAGS.max_steps:
print('Saving')
saver.save(sess, FLAGS.train_dir, global_step=step)
print('Train shape: ', x_train.shape)
print('Test shape: ', x_test.shape)
print(x_test[0:3, :])
x = tf.placeholder(tf.float32, [None, predict_slide])
y = tf.placeholder(tf.float32, [None])
# Create train & test data pipeline
total_train_data = x_train.shape[0]
batch_size = 256
epochs = 10
learn_rate = 0.05
total_batch = int(np.floor(total_train_data / batch_size)) + 1
predict_y = net.inference(x, reuse=tf.AUTO_REUSE)
predict_y = tf.squeeze(predict_y) #remove size 1 dimension
mse = tf.losses.mean_squared_error(labels=y, predictions=predict_y)
#mse = tf.reduce_mean(tf.squared_difference(predict_y, y))
with tf.variable_scope('opt', reuse=tf.AUTO_REUSE):
train_op = tf.train.AdamOptimizer(learn_rate).minimize(mse)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
#print(tf.global_variables())
print('Train data size: ', x_train.shape[0])
print('Total batch:', total_batch)
print('Start training...')
print(x_test[0:3, :])
loss_curve = []
BATCH_SIZE = 100
LEARNING_RATE_BASE = 0.01
LEARNING_RATE_DECAY = 0.99
REGULARIZATION_RATE = 0.0001
TRAINING_STEPS = 30000
MOVING_AVERAGE_DECAY = 0.99
MODEL_SAVE_PATH = "net/"
MODEL_NAME = "model"
# 定义输入输出placeholder。
x = tf.placeholder(tf.float32, [None, network.INPUT_NODE], name='x-input')
y_ = tf.placeholder(tf.float32, [None, network.OUTPUT_NODE], name='y-input')
# 建立模型
regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)
y = network.inference(x, regularizer)
# 定义滑动平均操作。
global_step = tf.Variable(0, trainable=False)
variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY,
global_step)
variables_averages_op = variable_averages.apply(tf.trainable_variables())
# 定义损失函数
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=y, labels=y_)
cross_entropy_mean = tf.reduce_mean(cross_entropy)
loss = cross_entropy_mean + tf.add_n(tf.get_collection('losses'))
# 定义优化函数
learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE,
global_step,
1,
LEARNING_RATE_DECAY,
staircase=True)