def main(_):
bst_acc = 0.0
with tf.train.MonitoredTrainingSession() as sess:
sess.run(init_op)
start_time = time.time()
for tn_batch in range(tn_num_batch):
tn_image_np, tn_label_np = cifar.next_batch(sess)
feed_dict = {
tn_dis.image_ph: tn_image_np,
tn_dis.hard_label_ph: tn_label_np,
}
sess.run(tn_dis.pre_train, feed_dict=feed_dict)
if (tn_batch + 1) % eval_interval != 0 and (tn_batch +
1) != tn_num_batch:
continue
acc = cifar.compute_acc(sess, vd_dis)
bst_acc = max(acc, bst_acc)
end_time = time.time()
duration = end_time - start_time
avg_time = duration / (tn_batch + 1)
print('#batch=%d acc=%.4f time=%.4fs/batch est=%.4fh' %
(tn_batch + 1, bst_acc, avg_time,
avg_time * tn_num_batch / 3600))
if acc < bst_acc:
continue
tn_dis.saver.save(utils.get_session(sess), flags.dis_model_ckpt)
print('#cifar=%d final=%.4f' % (flags.train_size, bst_acc))
def main(_):
bst_acc = 0.0
writer = tf.summary.FileWriter(config.logs_dir, graph=tf.get_default_graph())
with tf.train.MonitoredTrainingSession() as sess:
sess.run(init_op)
start = time.time()
for tn_batch in range(tn_num_batch):
tn_image_np, tn_label_np = mnist.train.next_batch(flags.batch_size)
feed_dict = {
tn_dis.image_ph:tn_image_np,
tn_dis.hard_label_ph:tn_label_np,
}
_, summary = sess.run([tn_dis.pre_update, summary_op], feed_dict=feed_dict)
writer.add_summary(summary, tn_batch)
if (tn_batch + 1) % eval_interval != 0:
continue
feed_dict = {
vd_dis.image_ph:mnist.test.images,
vd_dis.hard_label_ph:mnist.test.labels,
}
acc = sess.run(vd_dis.accuracy, feed_dict=feed_dict)
bst_acc = max(acc, bst_acc)
tot_time = time.time() - start
global_step = sess.run(tn_dis.global_step)
avg_time = (tot_time / global_step) * (mnist.train.num_examples / flags.batch_size)
print('#%08d curacc=%.4f curbst=%.4f tot=%.0fs avg=%.2fs/epoch' %
(tn_batch, acc, bst_acc, tot_time, avg_time))
if acc < bst_acc:
continue
tn_dis.saver.save(utils.get_session(sess), flags.dis_model_ckpt)
tot_time = time.time() - start
print('#mnist=%d bstacc=%.4f et=%.0fs' % (tn_size, bst_acc, tot_time))
def main(_):
best_prec = 0.0
writer = tf.summary.FileWriter(config.logs_dir,
graph=tf.get_default_graph())
with tf.train.MonitoredTrainingSession() as sess:
sess.run(init_op)
start = time.time()
for tn_batch in range(tn_num_batch):
tn_image_np, _, tn_label_np = yfccdata.next_batch(flags, sess)
feed_dict = {
tn_gen.image_ph: tn_image_np,
tn_gen.hard_label_ph: tn_label_np
}
_, summary = sess.run([tn_gen.pre_update, summary_op],
feed_dict=feed_dict)
writer.add_summary(summary, tn_batch)
if (tn_batch + 1) % eval_interval != 0:
continue
prec = yfcceval.compute_prec(flags, sess, vd_gen)
best_prec = max(prec, best_prec)
tot_time = time.time() - start
global_step = sess.run(tn_gen.global_step)
avg_time = (tot_time / global_step) * (tn_size / flags.batch_size)
print('#%08d prec@%d=%.4f best=%.4f tot=%.0fs avg=%.2fs/epoch' %
(global_step, flags.cutoff, prec, best_prec, tot_time,
avg_time))
if prec < best_prec:
continue
tn_gen.saver.save(utils.get_session(sess), flags.gen_model_ckpt)
tot_time = time.time() - start
print('best@%d=%.4f et=%.0fs' % (flags.cutoff, best_prec, tot_time))
def main(_):
bst_acc = 0.0
acc_list = []
writer = tf.summary.FileWriter(config.logs_dir, graph=tf.get_default_graph())
with tf.train.MonitoredTrainingSession() as sess:
sess.run(init_op)
start = time.time()
for tn_batch in range(tn_num_batch):
tn_image_np, tn_label_np = mnist.train.next_batch(flags.batch_size)
feed_dict = {tn_gen.image_ph:tn_image_np, tn_gen.hard_label_ph:tn_label_np}
_, summary = sess.run([tn_gen.pre_update, summary_op], feed_dict=feed_dict)
writer.add_summary(summary, tn_batch)
if flags.log_accuracy:
feed_dict = {
vd_gen.image_ph:mnist.test.images,
vd_gen.hard_label_ph:mnist.test.labels,
}
acc = sess.run(vd_gen.accuracy, feed_dict=feed_dict)
acc_list.append(acc)
if (tn_batch + 1) % eval_interval != 0:
continue
else:
if (tn_batch + 1) % eval_interval != 0:
continue
feed_dict = {
vd_gen.image_ph:mnist.test.images,
vd_gen.hard_label_ph:mnist.test.labels,
}
acc = sess.run(vd_gen.accuracy, feed_dict=feed_dict)
bst_acc = max(acc, bst_acc)
tot_time = time.time() - start
global_step = sess.run(tn_gen.global_step)
avg_time = (tot_time / global_step) * (tn_size / flags.batch_size)
print('#%08d curacc=%.4f curbst=%.4f tot=%.0fs avg=%.2fs/epoch' %
(tn_batch, acc, bst_acc, tot_time, avg_time))
if acc < bst_acc:
continue
tn_gen.saver.save(utils.get_session(sess), flags.gen_model_ckpt)
tot_time = time.time() - start
print('#mnist=%d bstacc=%.4f et=%.0fs' % (tn_size, bst_acc, tot_time))
if flags.log_accuracy:
utils.create_pardir(flags.all_learning_curve_p)
pickle.dump(acc_list, open(flags.all_learning_curve_p, 'wb'))