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()
net_name = "d40_SE" path = "" def get_minibatch(p, size): data = [] labels = [] for i in range(size): (img, label) = p.get() data.append(img) labels.append(label) return {"data": np.array(data).astype(np.float32), "label":np.array(labels)} if __name__ == '__main__': parser = argparse.ArgumentParser() os.system("rm -r tbdata/") tb = TB("tbdata/") with TrainingEnv(name = "lyy.{}.test".format(net_name), part_count = 2, custom_parser = parser) as env: args = parser.parse_args() num_GPU = len(args.devices.split(',')) minibatch_size *= num_GPU net = make_network(minibatch_size = minibatch_size) preloss = net.loss_var net.loss_var = WeightDecay(net.loss_var, {"*conv*": 1e-4, "*fc*": 1e-4, "*bnaff*:k": 1e-4, "*offset*":1e-4}) train_func = env.make_func_from_loss_var(net.loss_var, "train", train_state = True) lr = 0.1 * num_GPU optimizer = Momentum(lr, 0.9) optimizer(train_func)
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()