# 计算网络参数 total = sum([param.nelement() for param in net.parameters()]) # 精确地计算:1MB=1024KB=1048576字节 print('Number of parameter: % .4fM' % (total / 1e6)) 1. 2. 3. 4. 5. 6. 7. 输出: Number of parameter: 21.7977M 1. 参数量方法二: summary的使用:来自于torchinfo...
一定要先定义模型,然后在model后面插入两句代码OK啦~ fromSimNetimportsimNet#导入模型model=simNet()#定义模型total=sum([param.nelement()forparaminmodel.parameters()])#计算总参数量print("Number of parameter:%.6f"%(total))#输出 调用thop模块中的profile包进行计算 这里需要使用包进行计算,调用方式也很简单,...
importtorchfromtorch.nn.functionalimportmse_lossfromtorch.cuda.ampimportautocast,GradScalerx,y=torch.randn(3,100).cuda(),torch.randn(3,5).cuda()# 定义网络输入输出model=torch.nn.Linear(100,5).cuda()# 实例化网络,一个全连接层optimizer=torch.optim.SGD(myNet.parameters(),lr=0.001)# 定义优化...
model=MyModel()num_weights=0num_biases=0forname,paraminmodel.named_parameters():if'weight'inname:num_weights+=param.numel()elif'bias'inname:num_biases+=param.numel()print("Number of weights:",num_weights)print("Number of biases:",num_biases) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. ...
(model.parameters(), 0.5) optimizer.step() total_loss += loss.item() if batch % log_interval == 0 and batch > 0: lr = scheduler.get_last_lr()[0] ms_per_batch = (time.time() - start_time) * 1000 / log_interval cur_loss = total_loss / log_interval ppl = math.exp(cur_...
pyplot as pltfrom matplotlib.ticker import MultipleLocatorLEARNING_RATE = 1e-3EPOCHS = 4STEPS_IN_EPOCH = 8# Set model and optimizermodel = torch.nn.Linear(2, 1)optimizer = torch.optim.SGD(model.parameters(), lr=LEARNING_RATE)# Define your scheduler here as described above# ...# Get ...
(x, 2) x = self.dropout1(x) x = torch.flatten(x, 1) # Move tensor to next device if necessary next_device = next(self.fc1.parameters()).device x = x.to(next_device) x = self.fc1(x) x = F.relu(x) x = self.dropout2(x) x = self.fc2(x) output = F.log_softmax(...
factor =0.5,# The number we multiply learning rate until the milestone.total_iters =8)# The number of steps that the scheduler decays the learning rate 如果起始因子小于1,那么学习率调度器在训练过程中会提高学习率,而不是降低学习率。 4、LinearLR ...
defstep(self):self.base_lrs=[group['initial_lr']forgroupinoptimizer.param_groups]self.last_epoch+=1lrs=self.get_lr()forparam,lrinzip(self.optimizer.param_groups,lrs):param['lr']=lr optimizer2=torch.optim.SGD(model.parameters(),lr=1e-3)scheduler2=CustomLambdaLR(optimizer2,lr_lambda,las...
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,可以使用数据来训练和测试模型,方法是通过网络传递训练数据、计算损失、优化网络权重和...