weights_init(): 用来初始化模型 model.apply():实现初始化 # coding:utf-8 from torch import nn def weights_init(mod): """设计初始化函数""" classname=mod.__class__.__name__ # 返回传入的module类型 print(classname) if classname.find('Conv')!= -1: #这里的Conv和BatchNnorm是torc.nn里的...
对参数初始化 if type(m) == nn.Linear or type(m) == nn.Conv2d: nn.init.xavier_uniform_(m.weight) net.apply(init_weights) print('training on', device) net.to(device) optimizer = torch.optim.
TRAIN.LR, momentum=cfg.TRAIN.MOMENTUM, weight_decay=cfg.TRAIN.WD, nesterov=cfg.TRAIN.NESTEROV ) 1 2 3 4 5 6 7 5、随机参数初始化 def init_weights(m): if isinstance(m, nn.Conv2d): torch.nn.init.xavier_uniform(m.weight.data) model.apply(init_weights) 1 2 3 4...
nesterov=cfg.TRAIN.NESTEROV )5、随机参数初始化definit_weights(m):ifisinstance(m, nn.Conv2d): torch.nn.init.xavier_uniform(m.weight.data) model.apply(init_weights)
()), #只更新requires_grad=True的参数 lr=cfg.TRAIN.LR, momentum=cfg.TRAIN.MOMENTUM, weight_decay=cfg.TRAIN.WD, nesterov=cfg.TRAIN.NESTEROV ) 5、随机参数初始化 def init_weights(m): if isinstance(m, nn.Conv2d): torch.nn.init.xavier_uniform(m.weight.data) model.apply(init_weights)...
usingtf.contrib.layers.xavier_initializer(seed = 0). You don't need to worry about bias variables as you will soon see that TensorFlow functions take care of the bias. Note also that you will only initialize the weights/filters for the conv2d functions. TensorFlow initializes the layers for...
"init_type": "kaiming", // method can be [normal | xavier| xavier_uniform | kaiming | orthogonal], default is kaiming "module_name": "guided_diffusion", // sr3 | guided_diffusion "norm": true, "unet": { "in_channel": 2, "out_channel": 1, "inner_channel": 32, "channel_mul...
You will initialize weights/filtersW 1 W1W1andW 2 W2W2usingtf.contrib.layers.xavier_initializer(seed = 0). You don’t need to worry about bias variables as you will soon see that TensorFlow functions take care of the bias. Note also that you will only initialize the weights/filters for...
(self.embedding_size) # storage variables for full sort evaluation acceleration self.restore_user_e = None self.restore_item_e = None # parameters initialization self.apply(xavier_uniform_initialization) self.other_parameter_name = ['restore_user_e', 'restore_item_e'] def get_bipartite_inter_...
)5、随机参数初始化definit_weights(m):ifisinstance(m, nn.Conv2d): torch.nn.init.xavier_uniform(m.weight.data) model.apply(init_weights) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. ...