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( (...
我使用的数据集是一个具有大量0的二进制分类数据集。我决定尝试使用PyTorch的交叉熵损失的weight参数.通过sklearn.utils.class_weight.compute_class_weight计算权重,得到[0.58479532, 3.44827586]的权重值。当我将这个class_weights张量添加到损失的weight参数中时(即, 浏览6提问于2022-05-27得票数 0 1回答 PyTorch中...
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) ...
# --- 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_...
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=<...
weight– 形状为 (out_channels x in_channels/groups x kH x kW) 的滤波器 bias– 可选的偏置,形状为 (out_channels). 默认值: None stride– 卷积核的步长. 可以是单个数字, 也可以是一个元组 (sH, sW). 默认值: 1 padding– 输入两端隐式零填充的个数. 可以是单个数字, 也可以是一个元组 (pad...
model_weight_path="./weights/resnet18-f37072fd.pth"net.load_state_dict(torch.load(model_weight...
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...
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) ...