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)