def get_parameter_number_details(net): trainable_num_details = {name: p.numel() for name, p in net.named_parameters() if p.requires_grad} return {'Trainable': trainable_num_details} model = DCN(...) print(get_parameter_number(model)) print(get_parameter_number_details(model)) 模型参...
for name, param in self.named_parameters(): yield param 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 他主要是引用另一个类内成员函数named_parameters(),实现对所有参数的索引包装,生成迭代器,下面看另一个函数: def named_parameters(self, memo=None, prefix=''): r"""Returns an i...
fromptflopsimportget_model_complexity_info 导入ptflops macs,params=get_model_complexity_info(model1,(3,352,352),as_strings=True,print_per_layer_stat=False,verbose=True)print('{:<30}{:<8}'.format('Computational complexity: ',macs))print('{:<30}{:<8}'.format('Number of parameters: ',...
初始化学习率"""optimizer=torch.optim.SGD(model.parameters(),lr=1e-3)"""设置lr策略"""lr_lambda=lambda epoch:1.0ifepoch<10elsenp.math.exp(0.1*(10-epoch))scheduler=LambdaLR(optimizer=optimizer,lr_lambda=lr_lambda)lr_history=scheduler_lr(optimizer,scheduler)...
macs, params= get_model_complexity_info(net, (3, 224, 224), as_strings=True, print_per_layer_stat=True, verbose=True)print('{:<30} {:<8}'.format('Computational complexity:', macs))print('{:<30} {:<8}'.format('Number of parameters:', params))...
所以,对DL最本质的理解就是,它就是一个model架子+一堆参数,复杂如chatGPT的DL模型也是,参数就是模型对于数据的理解,就像人脑对于数据和概念的理解。而我们的任务就是找到最优模型,然后确定参数。 激活函数(Activation Function)是一种添加到人工神经网络中的函数,旨在帮助网络学习数据中的复杂模式。 类似于人类大脑中...
specifies the name this value will take on.targetis similarly the name of the argument.argsholds either: 1) nothing, or 2) a single argument denoting the default parameter of the function input.kwargsis don’t-care. Placeholders correspond to the function parameters (e.g.x) in the graph ...
一、问题现象(附报错日志上下文): 运行bash examples/baichuan2/pretrain_baichuan2_ptd_13B.sh时报错 /root/.local/conda/envs/baichuan2/lib/python3.8/site-packages/torch/distributed/launch.py:181: FutureWarning: The...
() self.weight = nn.Parameter(torch.randn((dim1, dim2), requires_grad=True)) self.bias = nn.Parameter(torch.zeros(dim2, requires_grad=True)) def forward(self, x: torch.Tensor): return F.relu(x @ self.weight + self.bias) net = model1(4, 2) for i in net.parameters(): ...
importtorch.optimasopt learning_rate =0.001optimizer = opt.Adam(model.parameters(), lr=learning_rate) 提示 有关PyTorch 中可用优化器的详细信息,请参阅 PyTorch 文档中的算法。 创建训练和测试函数 定义网络并为其准备数据后,可以通过通过网络传递训练数据、计算损失、优化网络权重和偏差以及验证网络的性能,来训...