本文整理汇总了Python中config.cfg.epoch方法的典型用法代码示例。如果您正苦于以下问题：Python cfg.epoch方法的具体用法？Python cfg.epoch怎么用？Python cfg.epoch使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类config.cfg的用法示例。
在下文中一共展示了cfg.epoch方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: from config import cfg [as 别名]
# 或者: from config.cfg import epoch [as 别名]
def main(_):
# get dataset info
result = create_image_lists(cfg.images)
max_iters = len(result["train"]) * cfg.epoch // cfg.batch_size
tf.logging.info('Loading Graph...')
model = DFN(max_iters, batch_size=cfg.batch_size, init_lr=cfg.init_lr, power=cfg.power, momentum=cfg.momentum, stddev=cfg.stddev, regularization_scale=cfg.regularization_scale, alpha=cfg.alpha, gamma=cfg.gamma, fl_weight=cfg.fl_weight)
tf.logging.info('Graph loaded.')
if cfg.is_training:
if not tf.gfile.Exists(cfg.logdir):
tf.gfile.MakeDirs(cfg.logdir)
if not tf.gfile.Exists(cfg.models):
tf.gfile.MakeDirs(cfg.models)
if os.path.exists(cfg.log):
os.remove(cfg.log)
fd = open(cfg.log, "a")
tf.logging.info('Start training...')
fd.write('Start training...\n')
train(result, model, cfg.logdir, cfg.train_sum_freq, cfg.val_sum_freq, cfg.save_freq, cfg.models, fd)
tf.logging.info('Training done.')
fd.write('Training done.')
fd.close()
else:
if not tf.gfile.Exists(cfg.test_outputs):
tf.gfile.MakeDirs(cfg.test_outputs)
tf.logging.info('Start testing...')
test(result, model, cfg.models, cfg.test_outputs)
tf.logging.info('Testing done.') 示例2: train
# 需要导入模块: from config import cfg [as 别名]
# 或者: from config.cfg import epoch [as 别名]
def train(model, supervisor, num_label):
trX, trY, num_tr_batch, valX, valY, num_val_batch = load_data(cfg.dataset, cfg.batch_size, is_training=True)
Y = valY[:num_val_batch * cfg.batch_size].reshape((-1, 1))
fd_train_acc, fd_loss, fd_val_acc = save_to()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with supervisor.managed_session(config=config) as sess:
print("\nNote: all of results will be saved to directory: " + cfg.results)
for epoch in range(cfg.epoch):
print('Training for epoch ' + str(epoch) + '/' + str(cfg.epoch) + ':')
if supervisor.should_stop():
print('supervisor stoped!')
break
for step in tqdm(range(num_tr_batch), total=num_tr_batch, ncols=70, leave=False, unit='b'):
start = step * cfg.batch_size
end = start + cfg.batch_size
global_step = epoch * num_tr_batch + step
if global_step % cfg.train_sum_freq == 0:
_, loss, train_acc, summary_str = sess.run([model.train_op, model.total_loss, model.accuracy, model.train_summary])
assert not np.isnan(loss), 'Something wrong! loss is nan...'
supervisor.summary_writer.add_summary(summary_str, global_step)
fd_loss.write(str(global_step) + ',' + str(loss) + "\n")
fd_loss.flush()
fd_train_acc.write(str(global_step) + ',' + str(train_acc / cfg.batch_size) + "\n")
fd_train_acc.flush()
else:
sess.run(model.train_op)
if cfg.val_sum_freq != 0 and (global_step) % cfg.val_sum_freq == 0:
val_acc = 0
for i in range(num_val_batch):
start = i * cfg.batch_size
end = start + cfg.batch_size
acc = sess.run(model.accuracy, {model.X: valX[start:end], model.labels: valY[start:end]})
val_acc += acc
val_acc = val_acc / (cfg.batch_size * num_val_batch)
fd_val_acc.write(str(global_step) + ',' + str(val_acc) + '\n')
fd_val_acc.flush()
if (epoch + 1) % cfg.save_freq == 0:
supervisor.saver.save(sess, cfg.logdir + '/model_epoch_%04d_step_%02d' % (epoch, global_step))
fd_val_acc.close()
fd_train_acc.close()
fd_loss.close() 示例3: train
# 需要导入模块: from config import cfg [as 别名]
# 或者: from config.cfg import epoch [as 别名]
def train(model, supervisor, num_label):
trX, trY, num_tr_batch, valX, valY, num_val_batch = load_data(cfg.dataset, cfg.batch_size, is_training=True)
Y = valY[:num_val_batch * cfg.batch_size].reshape((-1, 1))
fd_train_acc, fd_loss, fd_val_acc = save_to()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with supervisor.managed_session(config=config) as sess:
print("\nNote: all of results will be saved to directory: " + cfg.results)
for epoch in range(cfg.epoch):
print("Training for epoch %d/%d:" % (epoch, cfg.epoch))
if supervisor.should_stop():
print('supervisor stoped!')
break
for step in tqdm(range(num_tr_batch), total=num_tr_batch, ncols=70, leave=False, unit='b'):
start = step * cfg.batch_size
end = start + cfg.batch_size
global_step = epoch * num_tr_batch + step
if global_step % cfg.train_sum_freq == 0:
_, loss, train_acc, summary_str = sess.run([model.train_op, model.total_loss, model.accuracy, model.train_summary])
assert not np.isnan(loss), 'Something wrong! loss is nan...'
supervisor.summary_writer.add_summary(summary_str, global_step)
fd_loss.write(str(global_step) + ',' + str(loss) + "\n")
fd_loss.flush()
fd_train_acc.write(str(global_step) + ',' + str(train_acc / cfg.batch_size) + "\n")
fd_train_acc.flush()
else:
sess.run(model.train_op)
if cfg.val_sum_freq != 0 and (global_step) % cfg.val_sum_freq == 0:
val_acc = 0
for i in range(num_val_batch):
start = i * cfg.batch_size
end = start + cfg.batch_size
acc = sess.run(model.accuracy, {model.X: valX[start:end], model.labels: valY[start:end]})
val_acc += acc
val_acc = val_acc / (cfg.batch_size * num_val_batch)
fd_val_acc.write(str(global_step) + ',' + str(val_acc) + '\n')
fd_val_acc.flush()
if (epoch + 1) % cfg.save_freq == 0:
supervisor.saver.save(sess, cfg.logdir + '/model_epoch_%04d_step_%02d' % (epoch, global_step))
fd_val_acc.close()
fd_train_acc.close()
fd_loss.close()