我目前正在使用PyTorch来训练神经网络。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, ...
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( (...
value) for index, value in enumerate(item_compute) if (value>=22734 or value<0)] # print("***bug: ", bug) # print("***max(item_compute): ", max(item_compute)) # print("***min(item_compute): ", min(item_compute)) # print("***item_embed: ", len(item_embed)) item...
# --- 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=<...
edge_weight (Tensor, optional): 一维向量, 表示边的权重 fill_value (float, optional): 权重默认填充值 (默认值为 1) num_nodes (int, optional): 图中的节点数量 :rtype: (:class:`LongTensor`, :class:`Tensor`) """ # edge_index (2,E) 存储边 # edge_weight (E) 边的个数 [1,1,.....
# --- compute by hand # flag = 0 flag = 1 if flag: idx = 0 input_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(...
batch_size = 64 Din = 31 Dout = 33 weight = torch.randn(Dout, Din) print(f"weight shape = {weight.shape}") bias = torch.randn(Dout) x = torch.randn(batch_size, Din) compute_batch_jacobian = vmap(jacrev(predict, argnums=2), in_dims=(None, None, 0)) batch_jacobian0 = compu...
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...
classMaskedConv2d(nn.Conv2d):def__init__(self, mask_type, *args, **kwargs):super(MaskedConv2d, self).__init__(*args, **kwargs)assertmask_typein('A','B') self.register_buffer('mask', self.weight.data.clone()) _, _, kH, kW = self.weight.size() ...