print('pred:', data.printtag(tf.argmax(pred[0]).eval()))
step += 1
else:
loss_total += loss
acc_total += acc
step += 1
#帮global_step(用来调节学习率指数下降的)加一
model.global_step += 1
#print(model.global_step.eval())
#输出
if step % display_step == 0:
writer.add_summary(summary, step)
#print('free memory= '+str(int(getMem()/1000000))+"GB, Iter= " + str(step+1) + ", Average Loss= " + \
print( "Iter= " + str(step+1) + ", Average Loss= " + \
"{:.6f}".format(loss_total/display_step) + ", Average Accuracy= " + \
"{:.2f}%".format(100*acc_total/display_step)," Elapsed time: ", elapsed(time.time() - start_time))
if False: # testflag==False and acc_total>max_acc_total:
max_acc_total = acc_total
print(
'saved to: ',
saver.save(session,
saving_path3,
global_step=step))
# start_time=time.time()
acc_total = 0
loss_total = 0
#保存
if step % saving_step == 0:
print(
'saved to: ',
"Test w/o decorator syntax."
import datetime
import time
import os
from elapsed import elapsed
def test(a, b="unkwnown"):
print a, b
time.sleep(5)
test = elapsed(test)
test("hello", os.getenv("USER"))