def main(_):
if gfile.Exists(TRAIN_DIR):
gfile.DeleteRecursively(TRAIN_DIR)
gfile.MakeDirs(TRAIN_DIR)
# locally
#train()
print("ps: %s" % (DFLAGS.task_index))
ps_hosts = DFLAGS.ps_hosts.split(",")
worker_hosts = DFLAGS.worker_hosts.split(",")
# Create a cluster from the parameter server and worker hosts.
cluster = tf.train.ClusterSpec({"ps": ps_hosts, "worker": worker_hosts})
# Create and start a server for the local task.
server = tf.train.Server(cluster,
job_name=FLAGS.job_name,
task_index=FLAGS.task_index)
# training data
filename_queue = tf.train.string_input_producer(
["output/data/airquality.csv"])
datas, targets = dataset.mini_batch(filename_queue, BATCH_SIZE)
if DFLAGS.job_name == "ps":
server.join()
elif DFLAGS.job_name == "worker":
# Assigns ops to the local worker by default.
with tf.device(
tf.train.replica_device_setter(
worker_device="/job:worker/task:%d" % DFLAGS.task_index,
cluster=cluster)):
# step num of global
global_step = tf.Variable(0, trainable=False)
# inference
logits = model.inference(datas)
debug_value = model.debug(logits)
# loss graphのoutputとlabelを利用
loss = model.loss(logits, targets)
global_step = tf.Variable(0)
#train_op = tf.train.AdagradOptimizer(0.0001).minimize(
# loss, global_step=global_step)
train_op = op.train(loss, global_step)
saver = tf.train.Saver()
summary_op = tf.merge_all_summaries()
init_op = tf.initialize_all_variables()
# Create a "supervisor", which oversees the training process.
sv = tf.train.Supervisor(is_chief=(FLAGS.task_index == 0),
logdir="/tmp/train_logs",
init_op=init_op,
init_feed_dict=None,
summary_op=summary_op,
saver=saver,
global_step=global_step,
save_model_secs=60)
# The supervisor takes care of session initialization, restoring from
# a checkpoint, and closing when done or an error occurs.
with sv.managed_session(server.target) as sess:
# Loop until the supervisor shuts down or 1000000 steps have completed.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
step = 0
while not sv.should_stop() and step < 1000000:
# Run a training step asynchronously.
# See `tf.train.SyncReplicasOptimizer` for additional details on how to
# perform *synchronous* training.
start_time = time.time()
_, loss_value, predict_value, targets_eval, step = sess.run(
[train_op, loss, debug_value, targets, global_step])
#_, step = sess.run([train_op, global_step])
duration = time.time() - start_time
if step % 100 == 0:
# mini batch size
num_examples_per_step = BATCH_SIZE
# examples num per sec
examples_per_sec = num_examples_per_step / duration
# duration per batch
sec_per_batch = float(duration)
# time, step num, loss, exampeles num per sec, time per batch
format_str = '$s: step %d, loss = %.2f (%.1f examples/sec; %.3f sec/batch)'
print str(datetime.now()) + ': step' + str(
step) + ', loss= ' + str(loss_value) + ' ' + str(
examples_per_sec) + ' examples/sec; ' + str(
sec_per_batch) + ' sec/batch'
print "predict: ", predict_value
print "targets: ", targets_eval
coord.request_stop()
coord.join(threads)
sess.close()
# Ask for all the services to stop.
sv.stop()
def train():
'''
Train CNN_tiny for a number of steps.
'''
with tf.Graph().as_default():
# globalなstep数
global_step = tf.Variable(0, trainable=False)
# 教師データ
#images, labels = data_inputs.distorted_inputs(TF_RECORDS)
# 教師データ
mnist = np.load('./data/mnist_sequence1_sample_5distortions5x5.npz')
trX = mnist['X_train']
trY = mnist['y_train']
# X_valid = mnist_cluttered['X_valid']
# y_valid = mnist_cluttered['y_valid']
teX = mnist['X_test']
teY = mnist['y_test']
trX = trX.reshape(-1, 40, 40, 1)
teX = teX.reshape(-1, 40, 40, 1)
# % turn from dense to one hot representation
trY = dense_to_one_hot(trY, n_classes=10)
# Y_valid = dense_to_one_hot(y_valid, n_classes=10)
teY = dense_to_one_hot(teY, n_classes=10)
print("the number of train data: %d" % (len(trX)))
# create mini_batch
#datas, targets = trX.(trX, trY, BATCH_SIZE)
images = tf.placeholder(tf.float32, [None, 40, 40, 1])
labels = tf.placeholder(tf.float32, [None, 10])
keep_conv = tf.placeholder("float")
keep_hidden = tf.placeholder("float")
# graphのoutput
logits = model.inference(images, keep_conv, keep_hidden)
# loss graphのoutputとlabelを利用
#loss = model.loss(logits, labels)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, labels))
predict_op = tf.argmax(logits, 1)
# 学習オペレーション
train_op = op.train(loss, global_step)
# saver
saver = tf.train.Saver(tf.all_variables())
# サマリー
summary_op = tf.merge_all_summaries()
# 初期化オペレーション
init_op = tf.initialize_all_variables()
# Session
sess = tf.Session(config=tf.ConfigProto(log_device_placement=LOG_DEVICE_PLACEMENT))
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# サマリーのライターを設定
summary_writer = tf.train.SummaryWriter(TRAIN_DIR, graph_def=sess.graph_def)
# max_stepまで繰り返し学習
for step in xrange(MAX_STEPS):
start_time = time.time()
previous_time = start_time
index = 0
for start, end in zip(range(0, len(trX), BATCH_SIZE), range(BATCH_SIZE, len(trX), BATCH_SIZE)):
_, loss_value = sess.run([train_op, loss], feed_dict={images: trX[start:end], labels: trY[start:end], keep_conv: 0.8, keep_hidden: 0.5})
if index % 10 == 0:
end_time = time.time()
duration = end_time - previous_time
num_examples_per_step = BATCH_SIZE * 10 * (step+1)
examples_per_sec = num_examples_per_step / duration
print("%s: %d[epoch]: %d[iteration]: train loss %f: %d[examples/step]: %f[examples/sec]: %f[sec/iteration]" % (datetime.now(), step, index, loss_value, num_examples_per_step, examples_per_sec, duration))
index += 1
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
test_indices = np.arange(len(teX)) # Get A Test Batch
np.random.shuffle(test_indices)
test_indices = test_indices[0:5]
print "="*20
print teY[test_indices]
predict, cost_value = sess.run([predict_op, loss], feed_dict={images: teX[test_indices],
labels: teY[test_indices], keep_conv: 1.0, keep_hidden: 1.0})
print predict
print("test loss: %f" % (cost_value))
print "="*20
previous_time = end_time
index += 1
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
# 1000回ごと
if index % 100 == 0:
pass
summary_str = sess.run(summary_op, feed_dict={images: teX[test_indices],
labels: teY[test_indices], keep_conv: 1.0, keep_hidden: 1.0})
# サマリーに書き込む
summary_writer.add_summary(summary_str, step)
if step % 1 == 0 or (step * 1) == MAX_STEPS:
checkpoint_path = TRAIN_DIR + '/model.ckpt'
saver.save(sess, checkpoint_path, global_step=step)
coord.request_stop()
coord.join(threads)
sess.close()
def train():
'''
Train CNN_tiny for a number of steps.
'''
with tf.Graph().as_default():
# globalなstep数
global_step = tf.Variable(0, trainable=False)
# 教師データ
filename_queue = tf.train.string_input_producer(
["data/airquality.csv"])
datas, targets = load.mini_batch(filename_queue, BATCH_SIZE)
# placeholder
x = tf.placeholder(tf.float32, shape=[None, 5])
y = tf.placeholder(tf.float32, shape=[None, 1])
# graphのoutput
logits = model.inference(x)
debug_value = model.debug(logits)
# loss graphのoutputとlabelを利用
loss = model.loss(logits, y)
# 学習オペレーション
train_op = op.train(loss, global_step)
# saver
saver = tf.train.Saver(tf.all_variables())
# サマリー
summary_op = tf.merge_all_summaries()
# 初期化オペレーション
init_op = tf.initialize_all_variables()
# Session
sess = tf.Session(config=tf.ConfigProto(
log_device_placement=LOG_DEVICE_PLACEMENT))
sess.run(init_op)
print("settion start.")
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# サマリーのライターを設定
summary_writer = tf.train.SummaryWriter(TRAIN_DIR,
graph_def=sess.graph_def)
# model名
model_name = '/model%s.ckpt' % (tdatetime.strftime('%Y%m%d%H%M%S'))
# max_stepまで繰り返し学習
for step in xrange(MAX_STEPS):
start_time = time.time()
a, b = sess.run([datas, targets])
_, loss_value, predict_value = sess.run(
[train_op, loss, debug_value], feed_dict={
x: a,
y: b
})
duration = time.time() - start_time
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
# 100回ごと
if step % 100 == 0:
# stepごとの事例数 = mini batch size
num_examples_per_step = BATCH_SIZE
# 1秒ごとの事例数
examples_per_sec = num_examples_per_step / duration
# バッチごとの時間
sec_per_batch = float(duration)
# time, step数, loss, 1秒で実行できた事例数, バッチあたりの時間
format_str = '$s: step %d, loss = %.2f (%.1f examples/sec; %.3f sec/batch)'
print str(datetime.now()) + ': step' + str(
step) + ', loss= ' + str(loss_value) + ' ' + str(
examples_per_sec) + ' examples/sec; ' + str(
sec_per_batch) + ' sec/batch'
print "x", a
print "ground truth:", b
print "predict: ", predict_value
# 100回ごと
if step % 100 == 0:
pass
#summary_str = sess.run(summary_op)
# サマリーに書き込む
#summary_writer.add_summary(summary_str, step)
if step % 1000 == 0 or (step * 1) == MAX_STEPS:
checkpoint_path = TRAIN_DIR + model_name
saver.save(sess, checkpoint_path, global_step=step)
coord.request_stop()
coord.join(threads)
sess.close()
def train():
'''
Train CNN_tiny for a number of steps.
'''
with tf.Graph().as_default():
# globalなstep数
global_step = tf.Variable(0, trainable=False)
# 教師データ
image_input = ImageInput('./data/101Caltech_shuffles.txt')
print("the number of train data: %d" % (len(image_input.image_paths)))
images = tf.placeholder(tf.float32, [None, 224, 224, 3])
labels = tf.placeholder(tf.float32, [None, 101])
keep_conv = tf.placeholder(tf.float32)
keep_hidden = tf.placeholder(tf.float32)
# graphのoutput
logits, transform_result = model.inference(images, keep_conv,
keep_hidden)
# loss graphのoutputとlabelを利用
# loss = model.loss(logits, labels)
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits, labels))
predict_op = tf.argmax(logits, 1)
# 学習オペレーション
train_op = op.train(loss, global_step)
# サマリー
summary_op = tf.merge_all_summaries()
# 初期化オペレーション
init_op = tf.initialize_all_variables()
# Session
sess = tf.Session(config=tf.ConfigProto(
log_device_placement=LOG_DEVICE_PLACEMENT))
# saver
#saver = tf.train.Saver(tf.all_variables())
sess.run(init_op)
# pretrainと全体を分けて保存
pretrain_params = {}
train_params = {}
for variable in tf.trainable_variables():
variable_name = variable.name
#print("parameter: %s" %(variable_name))
scope, name = variable_name.split("/")
target, _ = name.split(":")
if variable_name.find('spatial_transformer') < 0:
print("pretrain parameter: %s" % (variable_name))
pretrain_params[variable_name] = variable
print("train parameter: %s" % (variable_name))
train_params[variable_name] = variable
saver_cnn = tf.train.Saver(pretrain_params)
saver_transformers = tf.train.Saver(train_params)
# pretrained_model
if FLAGS.fine_tune:
ckpt = tf.train.get_checkpoint_state(PRETRAIN_DIR)
if ckpt and ckpt.model_checkpoint_path:
print("Pretrained Model Loading.")
saver_cnn.restore(sess, ckpt.model_checkpoint_path)
print("Pretrained Model Restored.")
else:
print("No Pretrained Model.")
# pretrained model from another type models.
# # saver
# print type(tf.all_variables())
# for variable in tf.trainable_variables():
# variable_name = variable.name
# variable_value = variable.eval(sess)
# if variable_name.find('softmax_linear') < 0 and variable_name.find('spatial_transformer') < 0:
# print("trained parameter: %s" %(variable_name))
# scope, name = variable_name.split("/")
# target, _ = name.split(":")
# with tf.variable_scope(scope, reuse=True):
# sess.run(tf.get_variable(target).assign(variable_value))
# trained_model = FLAGS.trained_model
# print trained_model
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# サマリーのライターを設定
summary_writer = tf.train.SummaryWriter(TRAIN_DIR,
graph_def=sess.graph_def)
# max_stepまで繰り返し学習
for step in xrange(MAX_STEPS):
start_time = time.time()
previous_time = start_time
index = 0
batches = image_input.get_batches(FLAGS.batch_size)
for batch in batches:
train = batch[0]
label = batch[1]
_, loss_value = sess.run([train_op, loss],
feed_dict={
images: train,
labels: label,
keep_conv: 0.8,
keep_hidden: 0.5
})
if index % 10 == 0:
end_time = time.time()
duration = end_time - previous_time
num_examples_per_step = BATCH_SIZE * 10
examples_per_sec = num_examples_per_step / duration
print(
"%s: %d[epoch]: %d[iteration]: train loss %f: %d[examples/iteration]: %f[examples/sec]: %f[sec/iteration]"
% (datetime.now(), step, index, loss_value,
num_examples_per_step, examples_per_sec, duration))
index += 1
assert not np.isnan(
loss_value), 'Model diverged with loss = NaN'
# test_indices = np.arange(len(teX)) # Get A Test Batch
# np.random.shuffle(test_indices)
# test_indices = test_indices[0:5]
print "=" * 20
testx = train[0:2]
#print testx
testy = label[0:2]
print np.argmax(testy[0])
print np.argmax(testy[1])
output_vec, predict, cost_value = sess.run(
[logits, predict_op, loss],
feed_dict={
images: testx,
labels: testy,
keep_conv: 1.0,
keep_hidden: 1.0
})
print predict
print("test loss: %f" % (cost_value))
print "=" * 20
previous_time = end_time
index += 1
assert not np.isnan(
loss_value), 'Model diverged with loss = NaN'
# 100回ごと
if index % 100 == 0:
pass
summary_str = sess.run(summary_op,
feed_dict={
images: train,
labels: label,
keep_conv: 0.8,
keep_hidden: 0.5
})
# サマリーに書き込む
summary_writer.add_summary(summary_str, step)
if step % 1 == 0 or (step * 1) == MAX_STEPS:
pretrain_checkpoint_path = PRETRAIN_DIR + '/model.ckpt'
train_checkpoint_path = TRAIN_DIR + '/model.ckpt'
saver_cnn.save(sess,
pretrain_checkpoint_path,
global_step=step)
saver_transformers.save(sess,
train_checkpoint_path,
global_step=step)
coord.request_stop()
coord.join(threads)
sess.close()
def train():
'''
Train CNN_tiny for a number of steps.
'''
with tf.Graph().as_default():
# globalなstep数
global_step = tf.Variable(0, trainable=False)
# 教師データ
filename_queue = tf.train.string_input_producer(["data/airquality.csv"])
datas, targets = load.mini_batch(filename_queue, BATCH_SIZE)
# placeholder
x = tf.placeholder(tf.float32, shape=[None, 5])
y = tf.placeholder(tf.float32, shape=[None, 1])
# graphのoutput
logits = model.inference(x)
debug_value = model.debug(logits)
# loss graphのoutputとlabelを利用
loss = model.loss(logits, y)
# 学習オペレーション
train_op = op.train(loss, global_step)
# saver
saver = tf.train.Saver(tf.all_variables())
# サマリー
summary_op = tf.merge_all_summaries()
# 初期化オペレーション
init_op = tf.initialize_all_variables()
# Session
sess = tf.Session(config=tf.ConfigProto(log_device_placement=LOG_DEVICE_PLACEMENT))
sess.run(init_op)
print("settion start.")
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# サマリーのライターを設定
summary_writer = tf.train.SummaryWriter(TRAIN_DIR, graph_def=sess.graph_def)
# model名
model_name = '/model%s.ckpt' % (tdatetime.strftime('%Y%m%d%H%M%S'))
# max_stepまで繰り返し学習
for step in xrange(MAX_STEPS):
start_time = time.time()
a, b = sess.run([datas, targets])
_, loss_value, predict_value = sess.run([train_op, loss, debug_value], feed_dict={x: a, y: b})
duration = time.time() - start_time
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
# 100回ごと
if step % 100 == 0:
# stepごとの事例数 = mini batch size
num_examples_per_step = BATCH_SIZE
# 1秒ごとの事例数
examples_per_sec = num_examples_per_step / duration
# バッチごとの時間
sec_per_batch = float(duration)
# time, step数, loss, 1秒で実行できた事例数, バッチあたりの時間
format_str = '$s: step %d, loss = %.2f (%.1f examples/sec; %.3f sec/batch)'
print str(datetime.now()) + ': step' + str(step) + ', loss= '+ str(loss_value) + ' ' + str(examples_per_sec) + ' examples/sec; ' + str(sec_per_batch) + ' sec/batch'
print "x", a
print "ground truth:", b
print "predict: ", predict_value
# 100回ごと
if step % 100 == 0:
pass
#summary_str = sess.run(summary_op)
# サマリーに書き込む
#summary_writer.add_summary(summary_str, step)
if step % 1000 == 0 or (step * 1) == MAX_STEPS:
checkpoint_path = TRAIN_DIR + model_name
saver.save(sess, checkpoint_path, global_step=step)
coord.request_stop()
coord.join(threads)
sess.close()
def train():
'''
Train CNN_tiny for a number of steps.
'''
with tf.Graph().as_default():
# globalなstep数
global_step = tf.Variable(0, trainable=False)
# 教師データ
images, labels = data_inputs.distorted_inputs(TF_RECORDS)
# graphのoutput
logits = model.inference(images)
# loss graphのoutputとlabelを利用
loss = model.loss(logits, labels)
# 学習オペレーション
train_op = op.train(loss, global_step)
# saver
saver = tf.train.Saver(tf.all_variables())
# サマリー
summary_op = tf.merge_all_summaries()
# 初期化オペレーション
init_op = tf.initialize_all_variables()
# Session
sess = tf.Session(config=tf.ConfigProto(
log_device_placement=LOG_DEVICE_PLACEMENT))
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# サマリーのライターを設定
summary_writer = tf.train.SummaryWriter(TRAIN_DIR,
graph_def=sess.graph_def)
# model名
model_name = '/model%s.ckpt' % (tdatetime.strftime('%Y%m%d%H%M%S'))
# max_stepまで繰り返し学習
for step in xrange(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'
# 100回ごと
if step % 100 == 0:
# stepごとの事例数 = mini batch size
num_examples_per_step = BATCH_SIZE
# 1秒ごとの事例数
examples_per_sec = num_examples_per_step / duration
# バッチごとの時間
sec_per_batch = float(duration)
# time, step数, loss, 1秒で実行できた事例数, バッチあたりの時間
format_str = '$s: step %d, loss = %.2f (%.1f examples/sec; %.3f sec/batch)'
print str(datetime.now()) + ': step' + str(
step) + ', loss= ' + str(loss_value) + ' ' + str(
examples_per_sec) + ' examples/sec; ' + str(
sec_per_batch) + ' sec/batch'
# 100回ごと
if step % 100 == 0:
pass
summary_str = sess.run(summary_op)
# サマリーに書き込む
summary_writer.add_summary(summary_str, step)
if step % 1000 == 0 or (step * 1) == MAX_STEPS:
checkpoint_path = TRAIN_DIR + model_name
saver.save(sess, checkpoint_path, global_step=step)
coord.request_stop()
coord.join(threads)
sess.close()
def train():
'''
Train
'''
with tf.Graph().as_default():
# globalなstep数
global_step = tf.Variable(0, trainable=False)
# NYU Dataset V2 original size(480 x 640 x 3) -> crop -> (460 x 620 x 3)
image_input = ImageInput('./data/nyu_depth_v2_labeled.mat')
print("the number of train data: %d" % (len(image_input.images)))
images = tf.placeholder(tf.float32, [None, FLAGS.crop_size_height, FLAGS.crop_size_width, FLAGS.image_depth])
depths = tf.placeholder(tf.float32, [None, 1, 55, 74])
invalid_depths = tf.placeholder(tf.float32, [None, 1, 55, 74])
keep_conv = tf.placeholder(tf.float32)
keep_hidden = tf.placeholder(tf.float32)
# graphのoutput
if FLAGS.refine_train:
print("refine train.")
logits = model.inference_refine(images, keep_conv, keep_hidden)
else:
print("coarse train.")
logits = model.inference(images, keep_conv, keep_hidden)
# loss graphのoutputとlabelを利用
loss = model.loss(logits, depths, invalid_depths)
# 学習オペレーション
train_op = op.train(loss, global_step)
# サマリー
summary_op = tf.merge_all_summaries()
# 初期化オペレーション
init_op = tf.initialize_all_variables()
# Session
sess = tf.Session(config=tf.ConfigProto(log_device_placement=LOG_DEVICE_PLACEMENT))
# saver
#saver = tf.train.Saver(tf.all_variables())
sess.run(init_op)
# coarseとrefineを分けて保存
coarse_params = {}
refine_params = {}
if FLAGS.refine_train:
for variable in tf.all_variables():
variable_name = variable.name
print("parameter: %s" % (variable_name))
if variable_name.find("/") < 0 or variable_name.count("/") != 1:
print("ignore.")
continue
scope, name = variable_name.split("/")
target, _ = name.split(":")
if variable_name.find('coarse') >= 0:
print("coarse parameter: %s" % (variable_name))
coarse_params[variable_name] = variable
if variable_name.find('fine') >= 0:
print("refine parameter: %s" % (variable_name))
refine_params[variable_name] = variable
else:
for variable in tf.trainable_variables():
variable_name = variable.name
print("parameter: %s" %(variable_name))
if variable_name.find("/") < 0 or variable_name.count("/") != 1:
print("ignore.")
continue
scope, name = variable_name.split("/")
target, _ = name.split(":")
if variable_name.find('coarse') >= 0:
print("coarse parameter: %s" %(variable_name))
coarse_params[variable_name] = variable
if variable_name.find('fine') >= 0:
print("refine parameter: %s" %(variable_name))
refine_params[variable_name] = variable
# define saver
saver_coarse = tf.train.Saver(coarse_params)
saver_refine = tf.train.Saver(refine_params)
# fine tune
if FLAGS.fine_tune:
# load coarse paramteters
coarse_ckpt = tf.train.get_checkpoint_state(COARSE_DIR)
if coarse_ckpt and coarse_ckpt.model_checkpoint_path:
print("Pretrained coarse Model Loading.")
saver_coarse.restore(sess, coarse_ckpt.model_checkpoint_path)
print("Pretrained coarse Model Restored.")
else:
print("No Pretrained coarse Model.")
# load refine parameters
refine_ckpt = tf.train.get_checkpoint_state(REFINE_DIR)
if refine_ckpt and refine_ckpt.model_checkpoint_path:
print("Pretrained refine Model Loading.")
saver_refine.restore(sess, refine_ckpt.model_checkpoint_path)
print("Pretrained refine Model Restored.")
else:
print("No Pretrained refine Model.")
# TODO train coarse or refine (change trainable)
#if not FLAGS.coarse_train:
# for val in coarse_params:
# print val
#if not FLAGS.refine_train:
# for val in coarse_params:
# print val
# train refine
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# debug
# サマリーのライターを設定
#summary_writer = tf.train.SummaryWriter(TRAIN_DIR, graph_def=sess.graph_def)
#batches = image_input.get_batches(FLAGS.batch_size)a
#d = np.asarray(batches[0][0])
#print d.shape
#a = np.asarray(batches[0][1])
#print a.shape
#logits_val, logits_fine_val, loss_value = sess.run([logits, logits_fine, loss], feed_dict={images: batches[0][0], depths: batches[0][1], invalid_depths: batches[0][2], keep_conv: 1.0, keep_hidden: 1.0})
#print len(logits_val[0])
#print len(logits_fine_val[0])
#print loss_value
# max_stepまで繰り返し学習
for step in xrange(MAX_STEPS):
start_time = time.time()
previous_time = start_time
index = 0
batches = image_input.get_batches(FLAGS.batch_size)
vals = image_input.get_validation()
for batch in batches:
train = batch[0]
depth = batch[1]
ignore_depth = batch[2]
_, loss_value = sess.run([train_op, loss], feed_dict={images: train, depths: depth, invalid_depths: ignore_depth, keep_conv: 0.8, keep_hidden: 0.5})
if index % 10 == 0:
end_time = time.time()
duration = end_time - previous_time
num_examples_per_step = BATCH_SIZE * 10
examples_per_sec = num_examples_per_step / duration
print("%s: %d[epoch]: %d[iteration]: train loss %f: %d[examples/iteration]: %f[examples/sec]: %f[sec/iteration]" % (datetime.now(), step, index, loss_value, num_examples_per_step, examples_per_sec, duration))
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
if index % 50 == 0:
output_vec, cost_value = sess.run([logits, loss], feed_dict={images: vals[0], depths: vals[1], invalid_depths: vals[2], keep_conv: 1.0, keep_hidden: 1.0})
print("%s: %d[epoch]: %d[iteration]: validation loss: %f" % (datetime.now(), step, index, cost_value))
if index % 100 == 0:
output_dir = "predicts_%05d_%08d" % (step, index)
print("predicts output: %s" % output_dir)
data_feed_inputs_nyu.output_predict(output_vec, output_dir)
previous_time = end_time
index += 1
# if index % 100 == 0:
# pass
# summary_str = sess.run(summary_op, feed_dict={images: train, labels: label, keep_conv: 0.8, keep_hidden: 0.5})
# # サマリーに書き込む
# summary_writer.add_summary(summary_str, step)
#
if step % 5 == 0 or (step * 1) == MAX_STEPS:
if FLAGS.refine_train:
refine_checkpoint_path = REFINE_DIR + '/model.ckpt'
saver_refine.save(sess, refine_checkpoint_path, global_step=step)
else:
coarse_checkpoint_path = COARSE_DIR + '/model.ckpt'
saver_coarse.save(sess, coarse_checkpoint_path, global_step=step)
coord.request_stop()
coord.join(threads)
sess.close()
def train():
'''
Train CNN_tiny for a number of steps.
'''
with tf.Graph().as_default():
# globalなstep数
global_step = tf.Variable(0, trainable=False)
# 教師データ
#images, labels = data_inputs.distorted_inputs(TF_RECORDS)
# 教師データ
mnist = np.load('./data/mnist_sequence1_sample_5distortions5x5.npz')
trX = mnist['X_train']
trY = mnist['y_train']
# X_valid = mnist_cluttered['X_valid']
# y_valid = mnist_cluttered['y_valid']
teX = mnist['X_test']
teY = mnist['y_test']
trX = trX.reshape(-1, 40, 40, 1)
teX = teX.reshape(-1, 40, 40, 1)
# % turn from dense to one hot representation
trY = dense_to_one_hot(trY, n_classes=10)
trY = trY.reshape(-1, 10)
# Y_valid = dense_to_one_hot(y_valid, n_classes=10)
teY = dense_to_one_hot(teY, n_classes=10)
teY = teY.reshape(-1, 10)
print("the number of train data: %d" % (len(trX)))
# create mini_batch
#datas, targets = trX.(trX, trY, BATCH_SIZE)
images = tf.placeholder(tf.float32, [None, 40, 40, 1])
labels = tf.placeholder(tf.float32, [None, 10])
keep_conv = tf.placeholder(tf.float32)
keep_hidden = tf.placeholder(tf.float32)
# graphのoutput
logits = model.inference(images, keep_conv, keep_hidden)
# loss graphのoutputとlabelを利用
#loss = model.loss(logits, labels)
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits, labels))
predict_op = tf.argmax(logits, 1)
# 学習オペレーション
train_op = op.train(loss, global_step)
# saver
saver = tf.train.Saver(tf.all_variables())
# サマリー
summary_op = tf.merge_all_summaries()
# 初期化オペレーション
init_op = tf.initialize_all_variables()
# Session
sess = tf.Session(config=tf.ConfigProto(
log_device_placement=LOG_DEVICE_PLACEMENT))
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# サマリーのライターを設定
summary_writer = tf.train.SummaryWriter(TRAIN_DIR,
graph_def=sess.graph_def)
# max_stepまで繰り返し学習
for step in xrange(MAX_STEPS):
start_time = time.time()
previous_time = start_time
index = 0
for start, end in zip(range(0, len(trX), BATCH_SIZE),
range(BATCH_SIZE, len(trX), BATCH_SIZE)):
_, loss_value = sess.run(
[train_op, loss],
feed_dict={
images: trX[start:end],
labels: trY[start:end],
keep_conv: 0.8,
keep_hidden: 0.5
})
if index % 10 == 0:
end_time = time.time()
duration = end_time - previous_time
num_examples_per_step = BATCH_SIZE * 10 * (step + 1)
examples_per_sec = num_examples_per_step / duration
print(
"%s: %d[epoch]: %d[iteration]: train loss %f: %d[examples/step]: %f[examples/sec]: %f[sec/iteration]"
% (datetime.now(), step, index, loss_value,
num_examples_per_step, examples_per_sec, duration))
index += 1
assert not np.isnan(
loss_value), 'Model diverged with loss = NaN'
test_indices = np.arange(len(teX)) # Get A Test Batch
np.random.shuffle(test_indices)
test_indices = test_indices[0:5]
print "=" * 20
print teY[test_indices]
predict, cost_value = sess.run(
[predict_op, loss],
feed_dict={
images: teX[test_indices],
labels: teY[test_indices],
keep_conv: 1.0,
keep_hidden: 1.0
})
print predict
print("test loss: %f" % (cost_value))
print "=" * 20
previous_time = end_time
index += 1
assert not np.isnan(
loss_value), 'Model diverged with loss = NaN'
# 1000回ごと
if index % 100 == 0:
pass
summary_str = sess.run(summary_op,
feed_dict={
images: trX[start:end],
labels: trY[start:end],
keep_conv: 0.8,
keep_hidden: 0.5
})
# サマリーに書き込む
summary_writer.add_summary(summary_str, step)
if step % 1 == 0 or (step * 1) == MAX_STEPS:
checkpoint_path = TRAIN_DIR + '/model.ckpt'
saver.save(sess, checkpoint_path, global_step=step)
coord.request_stop()
coord.join(threads)
sess.close()
def train():
'''
Train CNN_tiny for a number of steps.
'''
with tf.Graph().as_default():
# globalなstep数
global_step = tf.Variable(0, trainable=False)
# 教師データ
image_input = ImageInput('./data/101Caltech_shuffles.txt')
print("the number of train data: %d" % (len(image_input.image_paths)))
images = tf.placeholder(tf.float32, [None, 224, 224, 3])
labels = tf.placeholder(tf.float32, [None, 101])
keep_conv = tf.placeholder(tf.float32)
keep_hidden = tf.placeholder(tf.float32)
# graphのoutput
logits, transform_result = model.inference(images, keep_conv, keep_hidden)
# loss graphのoutputとlabelを利用
# loss = model.loss(logits, labels)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, labels))
predict_op = tf.argmax(logits, 1)
# 学習オペレーション
train_op = op.train(loss, global_step)
# サマリー
summary_op = tf.merge_all_summaries()
# 初期化オペレーション
init_op = tf.initialize_all_variables()
# Session
sess = tf.Session(config=tf.ConfigProto(log_device_placement=LOG_DEVICE_PLACEMENT))
# saver
#saver = tf.train.Saver(tf.all_variables())
sess.run(init_op)
# pretrainと全体を分けて保存
pretrain_params = {}
train_params = {}
for variable in tf.trainable_variables():
variable_name = variable.name
#print("parameter: %s" %(variable_name))
scope, name = variable_name.split("/")
target, _ = name.split(":")
if variable_name.find('spatial_transformer') < 0:
print("pretrain parameter: %s" %(variable_name))
pretrain_params[variable_name] = variable
print("train parameter: %s" %(variable_name))
train_params[variable_name] = variable
saver_cnn = tf.train.Saver(pretrain_params)
saver_transformers = tf.train.Saver(train_params)
# pretrained_model
if FLAGS.fine_tune:
ckpt = tf.train.get_checkpoint_state(PRETRAIN_DIR)
if ckpt and ckpt.model_checkpoint_path:
print("Pretrained Model Loading.")
saver_cnn.restore(sess, ckpt.model_checkpoint_path)
print("Pretrained Model Restored.")
else:
print("No Pretrained Model.")
# pretrained model from another type models.
# # saver
# print type(tf.all_variables())
# for variable in tf.trainable_variables():
# variable_name = variable.name
# variable_value = variable.eval(sess)
# if variable_name.find('softmax_linear') < 0 and variable_name.find('spatial_transformer') < 0:
# print("trained parameter: %s" %(variable_name))
# scope, name = variable_name.split("/")
# target, _ = name.split(":")
# with tf.variable_scope(scope, reuse=True):
# sess.run(tf.get_variable(target).assign(variable_value))
# trained_model = FLAGS.trained_model
# print trained_model
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# サマリーのライターを設定
summary_writer = tf.train.SummaryWriter(TRAIN_DIR, graph_def=sess.graph_def)
# max_stepまで繰り返し学習
for step in xrange(MAX_STEPS):
start_time = time.time()
previous_time = start_time
index = 0
batches = image_input.get_batches(FLAGS.batch_size)
for batch in batches:
train = batch[0]
label = batch[1]
_, loss_value = sess.run([train_op, loss], feed_dict={images: train, labels: label, keep_conv: 0.8, keep_hidden: 0.5})
if index % 10 == 0:
end_time = time.time()
duration = end_time - previous_time
num_examples_per_step = BATCH_SIZE * 10
examples_per_sec = num_examples_per_step / duration
print("%s: %d[epoch]: %d[iteration]: train loss %f: %d[examples/iteration]: %f[examples/sec]: %f[sec/iteration]" % (datetime.now(), step, index, loss_value, num_examples_per_step, examples_per_sec, duration))
index += 1
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
# test_indices = np.arange(len(teX)) # Get A Test Batch
# np.random.shuffle(test_indices)
# test_indices = test_indices[0:5]
print "="*20
testx = train[0:2]
#print testx
testy = label[0:2]
print np.argmax(testy[0])
print np.argmax(testy[1])
output_vec, predict, cost_value = sess.run([logits, predict_op, loss], feed_dict={images: testx, labels: testy, keep_conv: 1.0, keep_hidden: 1.0})
print predict
print("test loss: %f" % (cost_value))
print "="*20
previous_time = end_time
index += 1
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
# 100回ごと
if index % 100 == 0:
pass
summary_str = sess.run(summary_op, feed_dict={images: train, labels: label, keep_conv: 0.8, keep_hidden: 0.5})
# サマリーに書き込む
summary_writer.add_summary(summary_str, step)
if step % 1 == 0 or (step * 1) == MAX_STEPS:
pretrain_checkpoint_path = PRETRAIN_DIR + '/model.ckpt'
train_checkpoint_path = TRAIN_DIR + '/model.ckpt'
saver_cnn.save(sess, pretrain_checkpoint_path, global_step=step)
saver_transformers.save(sess, train_checkpoint_path, global_step=step)
coord.request_stop()
coord.join(threads)
sess.close()
def train():
'''
Train CNN_tiny for a number of steps.
'''
with tf.Graph().as_default():
# globalなstep数
global_step = tf.Variable(0, trainable=False)
# 教師データ
images, labels = data_inputs.distorted_inputs(TF_RECORDS)
# graphのoutput
logits = model.inference(images)
# loss graphのoutputとlabelを利用
loss = model.loss(logits, labels)
# 学習オペレーション
train_op = op.train(loss, global_step)
# saver
saver = tf.train.Saver(tf.all_variables())
# サマリー
summary_op = tf.merge_all_summaries()
# 初期化オペレーション
init_op = tf.initialize_all_variables()
# Session
sess = tf.Session(config=tf.ConfigProto(log_device_placement=LOG_DEVICE_PLACEMENT))
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# サマリーのライターを設定
summary_writer = tf.train.SummaryWriter(TRAIN_DIR, graph_def=sess.graph_def)
# model名
model_name = '/model%s.ckpt' % (tdatetime.strftime('%Y%m%d%H%M%S'))
# max_stepまで繰り返し学習
for step in xrange(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'
# 100回ごと
if step % 100 == 0:
# stepごとの事例数 = mini batch size
num_examples_per_step = BATCH_SIZE
# 1秒ごとの事例数
examples_per_sec = num_examples_per_step / duration
# バッチごとの時間
sec_per_batch = float(duration)
# time, step数, loss, 1秒で実行できた事例数, バッチあたりの時間
format_str = '$s: step %d, loss = %.2f (%.1f examples/sec; %.3f sec/batch)'
print str(datetime.now()) + ': step' + str(step) + ', loss= '+ str(loss_value) + ' ' + str(examples_per_sec) + ' examples/sec; ' + str(sec_per_batch) + ' sec/batch'
# 100回ごと
if step % 100 == 0:
pass
summary_str = sess.run(summary_op)
# サマリーに書き込む
summary_writer.add_summary(summary_str, step)
if step % 1000 == 0 or (step * 1) == MAX_STEPS:
checkpoint_path = TRAIN_DIR + model_name
saver.save(sess, checkpoint_path, global_step=step)
coord.request_stop()
coord.join(threads)
sess.close()
def train():
'''
Train
'''
with tf.Graph().as_default():
# globalなstep数
global_step = tf.Variable(0, trainable=False)
# NYU Dataset V2 original size(480 x 640 x 3) -> crop -> (460 x 620 x 3)
image_input = ImageInput('./data/nyu_depth_v2_labeled.mat')
print("the number of train data: %d" % (len(image_input.images)))
images = tf.placeholder(tf.float32, [
None, FLAGS.crop_size_height, FLAGS.crop_size_width,
FLAGS.image_depth
])
depths = tf.placeholder(tf.float32, [None, 1, 55, 74])
invalid_depths = tf.placeholder(tf.float32, [None, 1, 55, 74])
keep_conv = tf.placeholder(tf.float32)
keep_hidden = tf.placeholder(tf.float32)
# graphのoutput
if FLAGS.refine_train:
print("refine train.")
logits = model.inference_refine(images, keep_conv, keep_hidden)
else:
print("coarse train.")
logits = model.inference(images, keep_conv, keep_hidden)
# loss graphのoutputとlabelを利用
loss = model.loss(logits, depths, invalid_depths)
# 学習オペレーション
train_op = op.train(loss, global_step)
# サマリー
summary_op = tf.merge_all_summaries()
# 初期化オペレーション
init_op = tf.initialize_all_variables()
# Session
sess = tf.Session(config=tf.ConfigProto(
log_device_placement=LOG_DEVICE_PLACEMENT))
# saver
#saver = tf.train.Saver(tf.all_variables())
sess.run(init_op)
# coarseとrefineを分けて保存
coarse_params = {}
refine_params = {}
if FLAGS.refine_train:
for variable in tf.all_variables():
variable_name = variable.name
print("parameter: %s" % (variable_name))
if variable_name.find("/") < 0 or variable_name.count(
"/") != 1:
print("ignore.")
continue
scope, name = variable_name.split("/")
target, _ = name.split(":")
if variable_name.find('coarse') >= 0:
print("coarse parameter: %s" % (variable_name))
coarse_params[variable_name] = variable
if variable_name.find('fine') >= 0:
print("refine parameter: %s" % (variable_name))
refine_params[variable_name] = variable
else:
for variable in tf.trainable_variables():
variable_name = variable.name
print("parameter: %s" % (variable_name))
if variable_name.find("/") < 0 or variable_name.count(
"/") != 1:
print("ignore.")
continue
scope, name = variable_name.split("/")
target, _ = name.split(":")
if variable_name.find('coarse') >= 0:
print("coarse parameter: %s" % (variable_name))
coarse_params[variable_name] = variable
if variable_name.find('fine') >= 0:
print("refine parameter: %s" % (variable_name))
refine_params[variable_name] = variable
# define saver
saver_coarse = tf.train.Saver(coarse_params)
saver_refine = tf.train.Saver(refine_params)
# fine tune
if FLAGS.fine_tune:
# load coarse paramteters
coarse_ckpt = tf.train.get_checkpoint_state(COARSE_DIR)
if coarse_ckpt and coarse_ckpt.model_checkpoint_path:
print("Pretrained coarse Model Loading.")
saver_coarse.restore(sess, coarse_ckpt.model_checkpoint_path)
print("Pretrained coarse Model Restored.")
else:
print("No Pretrained coarse Model.")
# load refine parameters
refine_ckpt = tf.train.get_checkpoint_state(REFINE_DIR)
if refine_ckpt and refine_ckpt.model_checkpoint_path:
print("Pretrained refine Model Loading.")
saver_refine.restore(sess, refine_ckpt.model_checkpoint_path)
print("Pretrained refine Model Restored.")
else:
print("No Pretrained refine Model.")
# TODO train coarse or refine (change trainable)
#if not FLAGS.coarse_train:
# for val in coarse_params:
# print val
#if not FLAGS.refine_train:
# for val in coarse_params:
# print val
# train refine
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# debug
# サマリーのライターを設定
#summary_writer = tf.train.SummaryWriter(TRAIN_DIR, graph_def=sess.graph_def)
#batches = image_input.get_batches(FLAGS.batch_size)a
#d = np.asarray(batches[0][0])
#print d.shape
#a = np.asarray(batches[0][1])
#print a.shape
#logits_val, logits_fine_val, loss_value = sess.run([logits, logits_fine, loss], feed_dict={images: batches[0][0], depths: batches[0][1], invalid_depths: batches[0][2], keep_conv: 1.0, keep_hidden: 1.0})
#print len(logits_val[0])
#print len(logits_fine_val[0])
#print loss_value
# max_stepまで繰り返し学習
for step in xrange(MAX_STEPS):
start_time = time.time()
previous_time = start_time
index = 0
batches = image_input.get_batches(FLAGS.batch_size)
vals = image_input.get_validation()
for batch in batches:
train = batch[0]
depth = batch[1]
ignore_depth = batch[2]
_, loss_value = sess.run(
[train_op, loss],
feed_dict={
images: train,
depths: depth,
invalid_depths: ignore_depth,
keep_conv: 0.8,
keep_hidden: 0.5
})
if index % 10 == 0:
end_time = time.time()
duration = end_time - previous_time
num_examples_per_step = BATCH_SIZE * 10
examples_per_sec = num_examples_per_step / duration
print(
"%s: %d[epoch]: %d[iteration]: train loss %f: %d[examples/iteration]: %f[examples/sec]: %f[sec/iteration]"
% (datetime.now(), step, index, loss_value,
num_examples_per_step, examples_per_sec, duration))
assert not np.isnan(
loss_value), 'Model diverged with loss = NaN'
if index % 50 == 0:
output_vec, cost_value = sess.run(
[logits, loss],
feed_dict={
images: vals[0],
depths: vals[1],
invalid_depths: vals[2],
keep_conv: 1.0,
keep_hidden: 1.0
})
print("%s: %d[epoch]: %d[iteration]: validation loss: %f" %
(datetime.now(), step, index, cost_value))
if index % 100 == 0:
output_dir = "predicts_%05d_%08d" % (step, index)
print("predicts output: %s" % output_dir)
data_feed_inputs_nyu.output_predict(
output_vec, output_dir)
previous_time = end_time
index += 1
# if index % 100 == 0:
# pass
# summary_str = sess.run(summary_op, feed_dict={images: train, labels: label, keep_conv: 0.8, keep_hidden: 0.5})
# # サマリーに書き込む
# summary_writer.add_summary(summary_str, step)
#
if step % 5 == 0 or (step * 1) == MAX_STEPS:
if FLAGS.refine_train:
refine_checkpoint_path = REFINE_DIR + '/model.ckpt'
saver_refine.save(sess,
refine_checkpoint_path,
global_step=step)
else:
coarse_checkpoint_path = COARSE_DIR + '/model.ckpt'
saver_coarse.save(sess,
coarse_checkpoint_path,
global_step=step)
coord.request_stop()
coord.join(threads)
sess.close()
