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.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)
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)
import sys
sys.path.append("/home/liuyanyi02/CIFAR/latest_tools")
from th import TensorboardHelper as TB
import os
tb = TB(os.getcwd())
for t in range(100):
tb.add_scalar("tmp", t)
tb.tick()
tb.flush()
