def forward(self, inputs, targets): ce_loss = F.cross_entropy(inputs, targets, reduction='none') weights = torch.ones_like(targets).float() for class_idx, weight in enumerate(self.class_weights): weights[targets == class_idx] = weight weighted_loss = ce_loss * weights return torch....
pytorch交叉熵损失函数的weight参数的使⽤⾸先 必须将权重也转为Tensor的cuda格式；然后 将该class_weight作为交叉熵函数对应参数的输⼊值。class_weight = torch.FloatTensor([0.13859937, 0.5821059, 0.63871904, 2.30220396, 7.1588294, 0]).cuda()补充：关于pytorch的CrossEntropyLoss的weight参数 ⾸先...
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( (...
resampled_ds = resampled_ds.batch(BATCH_SIZE).prefetch(2) 设置类别权重,建立一个classweight字典,并且在模型中设置class_weight即可: # Scaling by total/2 helps keep the loss to a similar magnitude. # The sum of the weights of all examples stays the same. weight_for_0 = (1 / neg)*(total...
tanh_gain=nn.init.calculate_gain('tanh')nn.init.xavier_uniform_(m.weight.data,gain=tanh_gain) 这里面用到了一个函数nn.init.calculate_gain(nonlinearity, param=None)这个函数的作用是计算激活函数的「方差变化尺度」,怎么理解这个方差变化尺度呢?其实就是输入数据的方差除以经过激活函数之后的输出数据的方差...
在模型拟合过程中处理类不平衡占比的一种方法是对少数类的错误预测赋予更大的惩罚。通过scikit-learn,调整这种惩罚非常方便,只需将class_weight参数设置为class_weight='balanced',大多数分类器都实现了这个功能。 处理类不平衡的其他常用策略包括增加少数类别的样本、减少多数类别的样本以及生成合成训练样本。可惜并没有...
weight_decay:L2正则化系数 nesterov:是否采用NAG梯度下降方法,布尔变量 常用优化器: optim.SGD:随机梯度下降法 optim.Adagrad:自适应学习率梯度下降法 optim.RMSprop:Adagrad的改进 optim.Adadelta:Adagrad的改进 optim.Adam:RMSprop结合Momentum optim.Adamax:Adam增加学习率上限 ...
fromtorch.utils.dataimportWeightedRandomSamplerimporttorch.nn.functionalasF# 假设我们有一个不平衡的数据集imbalanced_labels = torch.tensor([0,0,0,0,1,1,2])class_sample_count = torch.tensor([(imbalanced_labels == t).sum()fortinto...
class MyNet(nn.Module): def __init__(self): super(MyNet, self).__init__() self.wpool = weight_pool((1,2)) def forward(self, x): x = self.wpool(x) return x 下面展示前10个epoch的loss曲线图,可以看到2个epoch时网络已经接近收敛。此时学习到的参数为1.0324和0.9731,非常接近我们最理想...
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() ...