to(device) # 定义损失函数和优化器 criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9) # 训练网络 for epoch in range(10): running_loss = 0.0 for i, data in enumerate(trainlo
If you have parameters in your model, which should be saved and restored in the state_dict, but not trained by the optimizer, you should register them as buffers.Buffers won’t be returned in model.parameters(), so that the optimizer won’t have a change to update them. 模型中需要保存...
input = data.type(dtype).to(device) label = label.type(dtype).to(device) output = model(input) loss = loss_func(output, label) # 反向传播 optimizer_orig.zero_grad() loss.backward() optimizer_orig.step() print("Outside: input size", input.size() ,"output_size", output.size()) w...
self).__init__()self.linear=nn.Linear(3,1)defforward(self,x):returnself.linear(x)# 实例化模型并移动到设备model=SimpleModel().to(device)# 定义损失函数
to(device) learning_rate = 0.01 optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate) # 设置网络的训练参数 total_train_step = 0 total_test_step = 0 epoch = 10 # 添加tensorboard writer = SummaryWriter("logs") start_time = time.time() for i in range(epoch): print("--...
其中optimizer不需要转换 首先定义 1device = t.device('cuda:0') 将model和criterion to(device) 1#cuda2model =model.to(device)3criterion = criterion.to(device) 再将43行的inputs、target,46行的outputs to(device)到GPU上训练 1deftrain(epoch):2running_loss = 0.03forbatch_idx, datainenumerate(tr...
to(device).long() output = model(spec_mag) # 计算损失值 los = loss(output, label) optimizer.zero_grad() los.backward() optimizer.step() # 计算准确率 output = torch.nn.functional.softmax(output) output = output.data.cpu().numpy() output = np.argmax(output, axis=1) label = label...
这篇文章演示如何将训练好的pytorch模型部署到安卓设备上。我也是刚开始学安卓,代码写的简单。 环境:pytorch版本:1.10.0 # 模型转化 pytorch_android支持的模型是.pt模型,我们训练出来的模型是.pth。所以需要转化才可以用。 先看官网上给的转化方式: importtorchimporttorchvisionfromtorch.utils.mobile_optimizerimportop...
to(device) cnn torch.nn.CrossEntropyLoss对输出概率介于0和1之间的分类模型进行分类。 训练模型 代码语言:javascript 代码运行次数:0 运行 AI代码解释 # 超参数:Hyper Parameters learning_rate = 0.001 train_losses = [] val_losses = [] # Loss function and Optimizer criterion = nn.CrossEntropyLoss()...
FSDP Warning: When using FSDP, it is efficient and recommended to call prepare for the model before creating the optimizer 即使如此，我们还是推荐用户在使用 FSDP 时用以下方式显式准备模型和优化器:model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", return_dict=True)+ model ...