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 iterator over module parameters, yielding both the name of the parameter as well as the parameter itself Yields: (string,...
models.resnet50() inputs = torch.randn(1, 3, 224, 224) # 获取模型总计算量 total_ops, total_params = torch.nn.utils.profile(model, inputs, verbose=True) print(f"Total operations: {total_ops}") print(f"Total parameters: {total_params}") 计算图计算图是描述模型中张量运算的图形结构,...
train_acc,train_loss=test_model(model,train_dataloader) val_acc,val_loss=test_model(model,val_dataloader)#Checkmemory usage. handle=nvidia_smi.nvmlDeviceGetHandleByIndex(0) info=nvidia_smi.nvmlDeviceGetMemoryInfo(handle) memory_used=info.usedmemory_used=(memory_used/1024)/1024print(f"Epoch={ep...
1、model.named_parameters(),迭代打印model.named_parameters()将会打印每一次迭代元素的名字和param forname, paraminnet.named_parameters():print(name,param.requires_grad) param.requires_grad=False#conv_1_3x3.weight False bn_1.weight False bn_1.bias False ...
有了model,只需要截取latent layer,就得到了每个cell的topic的component,后面还可以调取每个topic的贡献feature。 所以,autoencoder的整体建模都是非常明确且简单的。 多品品这一页的教程,结合自己跑代码的经验:https://mira-multiome.readthedocs.io/en/latest/notebooks/tutorial_topic_model_tuning_full.html ...
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 ...
to(device)num_epochs=10#Lossloss_func = nn.CrossEntropyLoss()# Optimizer # optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=1e-5)optimizer = optim.SGD(params=model.parameters(), lr=0.001, momentum=0.9)#Fitting the model.model = train_with_grad_checkpointing(model, ...
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = CaptchaRecognizer().to(device) optimizer = torch.optim.Adam(model.parameters(), lr=0.001) loss_fn = nn.CrossEntropyLoss() for epoch in range(10): model.train() total_loss = 0 for images, labels in trai...
importtorchmodel=torch.nn.Linear(D_in,D_out).cuda()optimizer=torch.optim.SGD(model.parameters(),lr=1e-3)model,optimizer=amp.initialize(model,optimizer,opt_level="O2")forimg,labelindataloader:out=model(img)loss=LOSS(out,label)# loss.backward()withamp.scale_loss(loss,optimizer)asscaled_loss...
(m.parameters(), lr=0.1) loss_fn = lambda out, tgt: torch.pow(tgt-out, 2).mean() for epoch in range(n_epochs): x = torch.rand(10,2,24,24) out = m(x) loss = loss_fn(out, torch.rand_like(out)) opt.zero_grad() loss.backward() opt.step() """Convert""" m.eval() ...