def run_eval():
with tf.Graph().as_default(), tf.device('/gpu:0'):
images, labels = captcha.inputs(train=False, batch_size=FLAGS.batch_size)
logits = captcha.inference(images, keep_prob=1)
eval_correct = captcha.evaluation(logits, labels)
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(FLAGS.checkpoint_dir))
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
num_iter = int(math.ceil(FLAGS.num_examples / FLAGS.batch_size))
true_count = 0
total_true_count = 0
total_sample_count = num_iter * FLAGS.batch_size
step = 0
print('>> loop: %d, total_sample_count: %d' % (num_iter, total_sample_count))
while step < num_iter and not coord.should_stop():
true_count = sess.run(eval_correct)
total_true_count += true_count
precision = true_count / FLAGS.batch_size
print('>> %s Step %d: true/total: %d/%d precision @ 1 = %.3f'
%(datetime.now(), step, true_count, FLAGS.batch_size, precision))
step += 1
precision = total_true_count / total_sample_count
print('>> %s true/total: %d/%d precision @ 1 = %.3f'
%(datetime.now(), total_true_count, total_sample_count, precision))
except Exception as e:
coord.request_stop(e)
finally:
coord.request_stop()
coord.join(threads)
sess.close()
def tower_loss(scope, keep_prob):
images, labels = captcha.inputs(train=True, batch_size=FLAGS.batch_size)
logits = captcha.inference(images, keep_prob)
_ = captcha.loss(logits, labels)
losses = tf.get_collection('losses', scope)
total_loss = tf.add_n(losses, name='total_loss')
return total_loss
def run_train():
"""Train CAPTCHA for a number of steps."""
with tf.Graph().as_default():
images, labels = captcha.inputs(train=True, batch_size=192)
logits = captcha.inference(images, keep_prob=0.5)
loss = captcha.loss(logits, labels)
train_op = captcha.training(loss)
saver = tf.compat.v1.train.Saver()
init_op = tf.group(tf.compat.v1.global_variables_initializer(),
tf.compat.v1.local_variables_initializer())
sess = tf.compat.v1.Session()
#sess.run(init_op)
try:
#saver.restore(sess, FLAGS.checkpoint)
saver.restore(sess,
tf.train.latest_checkpoint(FLAGS.checkpoint_dir))
except Exception as e:
print(e)
sess.run(init_op)
#exit()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
_loss = 100000
step = 0
while not coord.should_stop():
start_time = time.time()
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
if step % 10 == 0:
print('>> Step %d run_train: loss = %f (%.4f sec)' %
(step, loss_value, duration))
if _loss > loss_value:
print('>> %s STEP %d LOSS %f SPAN %.4f' %
(datetime.now(), step, loss_value, duration))
saver.save(sess, FLAGS.checkpoint, global_step=step)
_loss = loss_value
#open('learning.log', 'a').write(step.__str__()+'\t'+loss_value.__str__()+'\n')
step += 1
except Exception as e:
#print('>> %s STEP:' % (datetime.now()))
saver.save(sess, FLAGS.checkpoint, global_step=step)
coord.request_stop(e)
finally:
coord.request_stop()
coord.join(threads)
sess.close()
def run_train():
"""Train CAPTCHA for a number of steps."""
with tf.Graph().as_default():
images, labels = captcha.inputs(
train=True, batch_size=FLAGS.batch_size)
logits = captcha.inference(images, keep_prob=1)
loss = captcha.loss(logits, labels)
train_op = captcha.training(loss)
saver = tf.train.Saver(tf.global_variables())
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
with tf.Session() as sess:
sess.run(init_op)
writer = tf.summary.FileWriter('./logs/', sess.graph)
# load DATA
if hasCheckpoint():
saver.restore(
sess, tf.train.latest_checkpoint('./captcha_train'))
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
step = 0
while not coord.should_stop():
start_time = time.time()
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
if step % 10 == 0:
print('>> Step %d run_train: loss = %.2f (%.3f sec)' % (step, loss_value,
duration))
if step % 100 == 0:
print('>> %s Saving in %s' %
(datetime.now(), FLAGS.checkpoint))
saver.save(sess, FLAGS.checkpoint, global_step=step)
step += 1
except Exception as e:
print('>> %s Saving in %s' %
(datetime.now(), FLAGS.checkpoint))
saver.save(sess, FLAGS.checkpoint, global_step=step)
coord.request_stop(e)
finally:
coord.request_stop()
coord.join(threads)
writer.close()
def run_train():
"""Train CAPTCHA for a number of steps."""
with tf.Graph().as_default():
images, labels = captcha.inputs(train=True,
batch_size=FLAGS.batch_size)
logits = captcha.inference(images, keep_prob=0.5)
loss = captcha.loss(logits, labels)
train_op = captcha.training(loss)
saver = tf.train.Saver(tf.global_variables())
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
sess = tf.Session()
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
checkpoint_path = os.path.join(FLAGS.model_dir, 'checkpoint')
print("checkpoint_path: %s" % checkpoint_path)
try:
step = 0
while not coord.should_stop():
start_time = time.time()
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
if step % 10 == 0:
print('>> Step %d run_train: loss = %.2f (%.3f sec)' %
(step, loss_value, duration))
if step % 100 == 0:
print('>> %s Saving in %s' %
(datetime.now(), checkpoint_path))
saver.save(sess, checkpoint_path, global_step=step)
step += 1
except Exception as e:
print('>> %s Saving in %s' % (datetime.now(), checkpoint_path))
saver.save(sess, checkpoint_path, global_step=step)
coord.request_stop(e)
finally:
coord.request_stop()
coord.join(threads)
sess.close()
def run_train():
"""Train CAPTCHA for a number of steps."""
with tf.Graph().as_default():
images, labels = captcha.inputs(train=True,
batch_size=FLAGS.batch_size)
test_images, test_labels = captcha.inputs(train=False,
batch_size=FLAGS.batch_size)
logits = captcha.inference(images, keep_prob=0.7, is_training=True)
test_logits = captcha.inference(test_images,
keep_prob=1,
is_training=False)
loss = captcha.loss(logits, labels)
correct = captcha.evaluation(logits, labels)
test_correct = captcha.evaluation(test_logits, test_labels)
eval_correct = correct / FLAGS.batch_size
tf.summary.scalar('precision', eval_correct)
tf.summary.scalar('loss', loss)
tf.summary.image('image', images, 10)
summary = tf.summary.merge_all()
train_op = captcha.training(loss)
saver = tf.train.Saver(tf.global_variables())
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
with tf.Session() as sess:
sess.run(init_op)
summary_writer = tf.summary.FileWriter(FLAGS.train_dir, sess.graph)
saver.restore(sess, tf.train.latest_checkpoint(FLAGS.train_dir))
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
step = 0
while not coord.should_stop():
start_time = time.time()
_, loss_value, test_co, train_co = sess.run(
[train_op, loss, test_correct, correct])
result = sess.run(summary) #merged也是需要run的
summary_writer.add_summary(result, step)
summary_writer.flush()
duration = time.time() - start_time
if step % 100 == 0:
print(
'>> Step %d run_train: loss = %.2f , train_correct= %d, test_correct= %d (%.3f sec)'
% (step, loss_value, train_co, test_co, duration))
with open('test.txt', 'a') as f:
f.write(str(test_co) + '\n')
if step % 300 == 0:
print('>> %s Saving in %s' %
(datetime.now(), FLAGS.checkpoint))
saver.save(sess, FLAGS.checkpoint, global_step=step)
step += 1
if step > 200000:
break
except Exception as e:
print('>> %s Saving in %s' %
(datetime.now(), FLAGS.checkpoint))
saver.save(sess, FLAGS.checkpoint, global_step=step)
coord.request_stop(e)
finally:
coord.request_stop()
coord.join(threads)
def run_train():
"""Train CAPTCHA for a number of steps."""
with tf.Graph().as_default():
images, labels = captcha.inputs(train=True,
batch_size=FLAGS.batch_size)
test_images, test_labels = captcha.inputs(train=False,
batch_size=FLAGS.batch_size)
logits = captcha.inference(images, keep_prob=0.75, is_training=True)
test_logits = captcha.inference(test_images,
keep_prob=1,
is_training=False)
loss = captcha.loss(logits, labels)
test_correct = captcha.evaluation(test_logits, test_labels) #test
correct = captcha.evaluation(logits, labels) #train
train_precision = correct / FLAGS.batch_size
tf.summary.scalar('loss', loss)
tf.summary.scalar('train_precision', train_precision)
tf.summary.image('images', images, 10)
summary = tf.summary.merge_all()
train_op = captcha.training(loss)
saver = tf.train.Saver()
# init_op = tf.group(tf.global_variables_initializer(),
# tf.local_variables_initializer())
sess = tf.Session()
summary_writer = tf.summary.FileWriter(FLAGS.train_dir, sess.graph)
# sess.run(init_op)
saver.restore(sess, tf.train.latest_checkpoint(FLAGS.checkpoint_dir))
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
step = 120140
while not coord.should_stop():
start_time = time.time()
_, loss_value, test_value, train_value = sess.run(
[train_op, loss, test_correct, correct])
summary_str = sess.run(summary)
summary_writer.add_summary(summary_str, step)
summary_writer.flush()
duration = time.time() - start_time
step += 1
if step % 10 == 0:
print(
'>> Step %d run_train: loss = %.2f, test = %.2f, train = %.2f (%.3f sec)'
%
(step, loss_value, test_value, train_value, duration))
#-------------------------------
if step % 100 == 0:
print('>> %s Saving in %s' %
(datetime.now(), FLAGS.checkpoint))
saver.save(sess, FLAGS.checkpoint, global_step=step)
print(images.shape.as_list(), labels.shape.as_list())
if step > 2000000:
break
except KeyboardInterrupt:
print('INTERRUPTED')
coord.request_stop()
except Exception as e:
coord.request_stop(e)
finally:
saver.save(sess, FLAGS.checkpoint, global_step=step)
print('Model saved in file :%s' % FLAGS.checkpoint)
coord.request_stop()
coord.join(threads)
sess.close()