def Draw():
for net_name in ["p120", "r120", "d120", "d40", "r40"]:
tb = TB(net_name + "_tbdata/")
grad_lis = pickle.load(open(net_name + "_grad.data", "rb"))
for i in grad_lis:
tb.tick()
tb.add_scalar("loged_grad", np.log(i))
tb.add_scalar("grad", i)
tb.flush()
tb.add_scalar("loss", loss)
tb.add_scalar("traing_acc", acc)
print("Minibatch = {}, Loss = {}, Acc = {}".format(i, loss, acc))
#Learning Rate Adjusting
if i == ORI_IT // 2 or i == ORI_IT // 4 * 3:
optimizer.learning_rate /= 10
if i == ORI_IT:
optimizer.learning_rate = 1e-5
if i % (EPOCH_NUM) == 0:
epoch += 1
acc = C.test(valid_func)
his_test.append([i, acc])
print("Epoch = {}, Acc = {}, Max_acc = {}".format(epoch, acc, max_acc))
b = time.time()
b = b + (b - a) / i * (TOT_IT - i)
print("Expected finish time {}".format(time.asctime(time.localtime(b))))
tb.add_scalar("test_acc", acc)
if acc > max_acc and i > ORI_IT:
max_acc = acc
env.save_checkpoint(path + "{}.data".format(net_name))
print("**************************")
import pickle
with open("hisloss.data", "wb") as f:
pickle.dump(his, f)
with open("histest.data", "wb") as f:
pickle.dump(his_test, f)
tb.flush()
