definitialize(self):forminself.modules():ifisinstance(m,nn.Linear):nn.init.normal_(m.weight.data)# normal:mean=0,std=1# 用一下网络 layer_nums=100neural_nums=256batch_size=16net=MLP(neural_nums,layer_nums)net.initialize()inputs=torch.randn((batch_size,neural_nums))# normal:mean=0,std...
人们常常将各种初始化方法定义为一个initialize_weights()的函数并在模型初始后进行使用。 123456789101112131415 def initialize_weights(self): for m in self.modules(): # 判断是否属于Conv2d if isinstance(m, nn.Conv2d): torch.nn.init.xavier_normal_(m.weight.data) # 判断是否有偏置 if m.bias is no...
(2)forward函数:模型模块Modulelist构建好后,拼接子模块在forward中实现前向传播,只需要利用for循环依次从linear中获取每个全连接层,对全连接层实现前向传播,就可以返回输出值x。 (3)initialize初始化:对每一个模块进行for循环判断是否为线性层linear,如果是,采用标准正态分布(0均值、1标准差)对权值WW进行初始化。
net =MLP(neural_nums,layer_nums) net.initialize() inputs = torch.randn((batch_size,neural_nums)) # normal: mean=0, std=1 output = net(inputs) print(output) 输出为: tensor([[nan,nan,nan, ...,nan,nan,nan], [nan,nan,nan, ...,nan,nan,nan], [nan,nan,nan, ...,nan,nan,n...
# 初始化网络结构def initialize_weights(self):for m in self.modules():if isinstance(m, nn.Conv2d):# 采用了何凯明的初始化方法nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')elif isinstance(m, nn.BatchNorm2d):# 常数初始化: 使值为常数nn.init.constant_(m.weight, ...
SimpleElasticAgent._initialize_workers: 先调用_rendezvous等待至少min 个node加入,然后调用_start_workers接口在当前node上启动worker process while loop monitor worker:while循环,监控上一步启动process的状态 若process group的状态为succeeded:调用_exit_barrier接口等待所有node上agent相应并退出 ...
() RuntimeError: Initialize:torch_npu/csrc/core/npu/sys_ctrl/npu_sys_ctrl.cpp:120 NPU error, error code is 507008 [Error]: Failed to obtain the SOC version. Rectify the fault based on the error information in the ascend log. EE1001: The argument is invalid.Reason: rtGetDevMsg ...
# Initialize bias to a small constant valuenn.init.constant_(self.out_proj.bias, 1.0) self.S6 = S6(seq_len, 2*d_model, state_size, device) # Add 1D convolution with kernel size 3self.conv = nn.Conv1d(seq_len, seq_len, kernel_...
""" Initialize the distributed environment. """ os.environ['MASTER_ADDR'] = '162.128.0.22' os.environ['MASTER_PORT'] = '29555' dist.init_process_group(backend, rank=rank, world_size=size) torch.cuda.manual_seed(1) fn(rank, size) ...
在混合精度训练上,Apex 的封装十分优雅。直接使用amp.initialize包装模型和优化器,apex 就会自动帮助我们管理模型参数和优化器的精度了,根据精度需求不同可以传入其他配置参数。from apex import amp model, optimizer = amp.initialize(model, optimizer, opt_level='O1') ...