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()
