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( (...
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...
AI代码解释 classLLTM(torch.nn.Module):def__init__(self,input_features,state_size):super(LLTM,self).__init__()self.input_features=input_features self.state_size=state_size #3*state_sizeforinput gate,output gate and candidate cell gate.# input_features+state_size because we will multiplyw...
删除那些不能加载预训练的k,假设你现在报错的模型是classifier.1.weight和 classifier.1.bias,那就可以改为pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict and k not in ['classifier.1.weight', 'classifier.1.bias']}。
'num_class':10, # 类数,与 multisoftmax 并用 'gamma':0.05, # 在树的叶子节点下一个分区的最小损失,越大算法模型越保守 。[0:] 'max_depth':12, # 构建树的深度 [1:] #'lambda':450, # L2 正则项权重 'subsample':0.4, # 采样训练数据,设置为0.5,随机选择一般的数据实例 (0:1] ...
class Optimizer(object):def state_dict(self):return {'state': packed_state, 'param_groups': param_groups,}def load_state_dict(self, state_dict): pytorch特性:张量梯度不自动清零 下面我们学习一下优化器具体使用方法: (1)构建可学习参数和学习率 ...
class MLP(nn.Module): def __init__(self): super(MLP, self).__init__() self.fc1 = nn.Linear(10, 20) self.fc2 = nn.Linear(20, 1) self.fc3 = nn.Linear(20, 1) # 共享参数 self.fc1.weight = self.fc2.weight self.fc1.bias = self.fc2.bias def forward(self, x): x = ...
更新网络的权重,通常使用一个简单的更新规则:weight = weight - learning_rate * gradient 定义网络 定义一个网络: import torch import torch.nn as nn import torch.nn.functional as F class Net(nn.Module): def __init__(self): super(Net, self).__init__() ...
class weight_pool(nn.Module): def __init__(self, in_features): super(weight_pool, self).__init__() self.in_features = in_features self.weight = nn.Parameter(torch.Tensor(self.in_features)) self.reset_parameters() def reset_parameters(self): ...
class DeformConv2d(nn.Module):def init(self, inchannels, outchannels, kernel_size, stride=1, padding=0, dilation=1, groups=1, deformable_groups=1):super(DeformConv2d, self).__init()self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels, kernel_size, kernel_size))self.bias ...