class_weights = torch.tensor([1 / i for i in df_agg_classes["proportion"].values], dtype=torch.float) model = MLP() criterion = torch.nn.CrossEntropyLoss(weight=class_weights) optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4) 最终的结构如下: >>> MLP( (...
AI代码解释 default_dynamic_qconfig=QConfigDynamic(activation=default_dynamic_quant_observer,weight=default_weight_observer)default_dynamic_quant_observer=PlaceholderObserver.with_args(dtype=torch.float,compute_dtype=torch.quint8)default_weight_observer=MinMaxObserver.with_args(dtype=torch.qint8,qscheme=torch...
# --- compute by handidx = 0input_1 = inputs.detach().numpy()[idx] # [1, 2]target_1 = target.numpy()[idx] # [0]# 第一项x_class = input_1[target_1]# 第二项sigma_exp_x = np.sum(list(map(np.exp, input_1)))log_sigma_exp_x = np.log(sigma_exp_x)# 输出lossloss_...
weight=default_weight_observer) default_dynamic_quant_observer = PlaceholderObserver.with_args(dtype=torch.float, compute_dtype=torch.quint8) default_weight_observer = MinMaxObserver.with_args(dtype=torch.qint8, qscheme=torch.per_tensor_symmetric) ...
fromtorch.distributed.optim import ZeroRedundancyOptimizerifargs.enable_zero_optim:print('=> using ZeroRedundancyOptimizer')optimizer = torch.distributed.optim.ZeroRedundancyOptimizer(model.parameters(),optimizer_class=torch.optim.SGD,lr=args.lr,momentum=args.momentum,weight_decay=args.weight_decay)else:...
class MyLoss(torch.nn.Moudle):def __init__(self):super(MyLoss, self).__init__() def forward(self, x, y):loss = torch.mean((x - y) ** 2)return loss 标签平滑(label smoothing) 写一个label_smoothing.py的文件,然后在训练代码里引用,用LSR代替交叉熵损...
总之,你可以看出来,每个op的输入都需要经过self.weight_fake_quant来处理下,输出又都需要经过self.activation_post_process来处理下,这两个都是FakeQuantize的实例,只是里面包含的observer不一样。以Conv2d为例: #conv2d weight=functools.partial(<class 'torch.quantization.fake_quantize.FakeQuantize'>, observer=<...
fromtorch.optimimportAdam# Define the loss function with Classification Cross-Entropy loss and an optimizer with Adam optimizerloss_fn = nn.CrossEntropyLoss() optimizer = Adam(model.parameters(), lr=0.001, weight_decay=0.0001) 使用训练数据训练模型。
1报错内容:RuntimeError: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same版本: 1.0.0 with python 3.6.1原因:有部分变量未加载进入显存,注意,在如下情况 class model(nn.Module): def __init__(self): ...
importtorchimporttorch.nnasnn# 自定义损失函数类classCustomLoss(nn.Module): def __init__(self,weight): super(CustomLoss,self).__init__() self.weight = weight def forward(self,predictions,targets): loss = torch.mean((predictions-targets) ** 2) ...