import time
for i in range(15):
  time.sleep(0.5) # 这里为了查看输出变化，实际使用不需要sleep
  print('\r', i, end='')
  # print('\r', 15-i, end='') # 从两位变一位会有问题 

import time
for i in range(15):
  time.sleep(0.5)
  print('\r', "{:d}".format(15-i), end='')

import time

# 设置epochs和batch的数量
epochs = 5
n_batch = 15
# 这里是测试自己设置的loss和acc
train_loss = 1.2345
train_acc = 0.345678
test_loss = 1.1234
test_acc = 0.235467

for i in range(epochs):
  for j in range(n_batch):
    time.sleep(0.5)
    train_loss -= 0.001
    train_acc += 0.0001

    print("\rEpoch: {:d} batch: {:d} loss: {:.4f} acc: {:.4f} | {:.2%}"
       .format(i+1, j+1, train_loss, train_acc, (j+1)*1.0/n_batch), end='')
       
  test_loss -= 0.001
  test_acc += 0.0001
  
  print("\rEpoch: {:d}/{:d} train_loss: {:.4f} test_loss: {:.4f} train_acc: {:.4f} test_acc: {:.4f}"
   .format(i+1, 5, train_loss, test_loss, train_acc, test_acc), end='\n')

import time
from tqdm import tqdm, trange

for i in tqdm(range(100)):
  time.sleep(0.05)
  # do something

# for i in trange(100):
#   time.sleep(0.05)
#   # do something

import time
from tqdm import tqdm
pbar = tqdm(["a", "b", "c", "d", "e"])
for char in pbar:
  time.sleep(0.8)
  pbar.set_description("Processing %s" % char)
  # pbar.set_description("Processing {0}".format(char))