def train(self, niter, piter, siter, save_dir=None, asset_dir=None):
self.logger.info("Model training starts")
final_iter = self.start_iter + niter
max_accuracy = -1
max_acc_iter = -1
for iter_ in tqdm_range(self.start_iter, final_iter):
# train_idx = (iter_ - self.start_iter)//piter
train_idx = iter_ // piter
self.run_batch(train_idx)
if (iter_ + 1) % siter == 0 or iter_ + 1 == final_iter:
include_discrete = False if train_idx < self.args.ntime else True
accuracy = self.evaluate(include_discrete=include_discrete)
self.latent_traversal_gif(path=asset_dir +
'{}.gif'.format(iter_ + 1),
include_discrete=include_discrete)
if max_accuracy == -1 or max_accuracy < accuracy:
self.save(iter_, save_dir)
self.logger.info("Save process")
max_accuracy = accuracy
max_acc_iter = iter_
print('max_accuracy:', max_accuracy)
self.logger.info('max_accuracy: ' + str(max_accuracy))
self.logger.info('max_acc_iter: ' + str(max_acc_iter))
self.logger.info("Model training ends")
def train(self, niter, piter, siter, save_dir=None, asset_dir=None):
self.logger.info("Model training starts")
final_iter = self.start_iter + niter
max_accuracy = -1
for iter_ in tqdm_range(self.start_iter, final_iter):
train_idx = (iter_ - self.start_iter) // piter
self.run_batch(train_idx)
if (iter_ + 1) % siter == 0 or iter_ + 1 == final_iter:
accuracy = self.evaluate()
self.latent_traversal_gif(path=asset_dir +
'{}.gif'.format(iter_ + 1))
if max_accuracy == -1 or max_accuracy < accuracy:
self.save(iter_, save_dir)
self.logger.info("Save process")
max_accuracy = accuracy
self.logger.info("Model training ends")
def train(self, niter, piter, siter, save_dir=None, asset_dir=None):
self.logger.info("Model training starts")
current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
train_log_dir = os.path.join(asset_dir, current_time, 'train')
# test_log_dir = os.path.join(asset_dir, current_time, '/test')
train_summary_writer = tf.summary.FileWriter(train_log_dir,
self.sess.graph)
final_iter = self.start_iter + niter
max_accuracy = -1
max_acc_iter = -1
for iter_ in tqdm_range(self.start_iter, final_iter):
train_idx = (iter_ - self.start_iter) // piter
summary = self.run_batch(train_idx)
train_summary_writer.add_summary(summary, iter_)
if (iter_ + 1) % siter == 0 or iter_ + 1 == final_iter:
if self.args.use_discrete:
include_discrete = False if train_idx < self.args.ntime else True
else:
include_discrete = False
accuracy = self.evaluate(include_discrete=include_discrete)
self.latent_traversal_gif(path=asset_dir +
'{}.gif'.format(iter_ + 1),
include_discrete=include_discrete)
if max_accuracy == -1 or max_accuracy < accuracy:
self.save(iter_, save_dir)
self.logger.info("Save process")
max_accuracy = accuracy
max_acc_iter = iter_
self.logger.info('max_accuracy: ' + str(max_accuracy))
self.logger.info('max_acc_iter: ' + str(max_acc_iter))
with open(os.path.join(asset_dir, 'acc.txt'), 'a') as f:
f.write('iter: '+str(iter_) + ', acc: ' + str(accuracy) + \
'; max_iter:' + str(max_acc_iter) + \
', max_acc:' + str(max_accuracy))
self.logger.info("Model training ends")