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()
ORI_IT = 64000
BN_IT = 10000
TOT_IT = ORI_IT + BN_IT
C = CIFAR_test()
his = []
his_test = []
import time
with control(io = [tr_p]):
with control(io = [va_p]):
a = time.time()
while i <= TOT_IT:
i += 1
tb.tick()
token1 = time.time()
data = get_minibatch(tr_p, minibatch_size)
time_data = time.time() - token1
token2 = time.time()
out = train_func(data = data['data'], label = data["label"])
time_train = time.time() - token2
if time_data > (time_train + time_data) * 0.2:
print("Loading data may spends too much time {}".format(time_data / (time_train + time_data)))
loss = out["pre_loss"]
pred = np.array(out["outputs"]).argmax(axis = 1)
acc = (pred == np.array(data["label"])).mean()
his.append([loss, acc])
tb.add_scalar("loss", loss)
