def evaluate():
with tf.Graph().as_default():
# testデータのロード
images, labels = data_inputs.inputs(
'data/train_kirin_norm_32.tfrecords')
logits = model.inference(images)
top_k_op = tf.nn.in_top_k(logits, labels, 1)
variable_averages = tf.train.ExponentialMovingAverage(
FLAGS.moving_average_decay)
variables_to_restore = {}
for v in tf.trainable_variables():
if v in tf.trainable_variables():
restore_name = variable_averages.average_name(v)
else:
restore_name = v.op.name
variables_to_restore[restore_name] = v
saver = tf.train.Saver(variables_to_restore)
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 evaluate():
with tf.Graph().as_default():
# testデータのロード
images, labels = data_inputs.inputs("data/train_kirin_norm_32.tfrecords")
logits = model.inference(images)
top_k_op = tf.nn.in_top_k(logits, labels, 1)
variable_averages = tf.train.ExponentialMovingAverage(FLAGS.moving_average_decay)
variables_to_restore = {}
for v in tf.trainable_variables():
if v in tf.trainable_variables():
restore_name = variable_averages.average_name(v)
else:
restore_name = v.op.name
variables_to_restore[restore_name] = v
saver = tf.train.Saver(variables_to_restore)
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 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)
# 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'
# 10回ごと
if step % 10 == 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 = '$s: step %d, loss = %.2f (%.1f examples/sec; %.3f sec/batch)'
print str(datetime.now()) + ': step' + str(step) + ', loss= '+ str(loss_value) + ' ' + str(examples_per_sec) + ' examples/sec; ' + str(sec_per_batch) + ' sec/batch'
# 100回ごと
if step % 100 == 0:
pass
summary_str = sess.run(summary_op)
# サマリーに書き込む
summary_writer.add_summary(summary_str, step)
if step % 1000 == 0 or (step * 1) == MAX_STEPS:
checkpoint_path = TRAIN_DIR + '/model.ckpt'
saver.save(sess, checkpoint_path, global_step=step)
coord.request_stop()
coord.join(threads)
sess.close()
def train():
'''
Train CNN_tiny for a number of steps.
'''
with tf.Graph().as_default():
# globalなstep数
global_step = tf.Variable(0, trainable=False)
# 教師データ
images, labels = data_inputs.distorted_inputs(TF_RECORDS)
# graphのoutput
logits = model.inference(images)
# loss graphのoutputとlabelを利用
loss = model.loss(logits, labels)
# 学習オペレーション
train_op = op.train(loss, global_step)
# saver
saver = tf.train.Saver(tf.all_variables())
# サマリー
summary_op = tf.merge_all_summaries()
# 初期化オペレーション
init_op = tf.initialize_all_variables()
# Session
sess = tf.Session(config=tf.ConfigProto(
log_device_placement=LOG_DEVICE_PLACEMENT))
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# サマリーのライターを設定
summary_writer = tf.train.SummaryWriter(TRAIN_DIR,
graph_def=sess.graph_def)
# max_stepまで繰り返し学習
for step in xrange(MAX_STEPS):
start_time = time.time()
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
# 10回ごと
if step % 10 == 0:
# stepごとの事例数 = mini batch size
num_examples_per_step = BATCH_SIZE
# 1秒ごとの事例数
examples_per_sec = num_examples_per_step / duration
# バッチごとの時間
sec_per_batch = float(duration)
# time, step数, loss, 1秒で実行できた事例数, バッチあたりの時間
format_str = '$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.ckpt'
saver.save(sess, checkpoint_path, global_step=step)
coord.request_stop()
coord.join(threads)
sess.close()