ConvE 模型实例

本文主要是介绍ConvE 模型实例，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

问题代码

# 创建 ConvE 模型实例
args = {"entity_dim": 200,"relation_dim": 200,"time_dim": 200,"emb_2D_d1": 10,"emb_2D_d2": 20,"num_out_channels": 32,"kernel_size": 3,"hidden_dropout_rate": 0.3,"feat_dropout_rate": 0.3
}
num_entities = 1000  # 假设实体数量为1000model = ConvE(args, num_entities)
print(model)

问题：

AttributeError: ‘dict’ object has no attribute ‘entity_dim’

解决方案：

问题可能是因为传递的参数 args 是一个字典，但模型的初始化方法期望的第一个参数是一个自定义的对象，而不是字典。
为了解决这个问题，可以创建一个包含所有参数的自定义类，并将其传递给模型的初始化方法。这样可以确保模型能够正确地访问各个参数。

代码

class Args:def __init__(self, entity_dim, relation_dim, time_dim, emb_2D_d1, emb_2D_d2,num_out_channels, kernel_size, hidden_dropout_rate, feat_dropout_rate):self.entity_dim = entity_dimself.relation_dim = relation_dimself.time_dim = time_dimself.emb_2D_d1 = emb_2D_d1self.emb_2D_d2 = emb_2D_d2self.num_out_channels = num_out_channelsself.kernel_size = kernel_sizeself.hidden_dropout_rate = hidden_dropout_rateself.feat_dropout_rate = feat_dropout_rate# 创建 Args 实例
args = Args(entity_dim=200, relation_dim=200, time_dim=200, emb_2D_d1=10, emb_2D_d2=20,num_out_channels=32, kernel_size=3, hidden_dropout_rate=0.3, feat_dropout_rate=0.3)# 创建 ConvE 模型实例
model = ConvE(args, num_entities)
print(model)

模型结构

ConvE((HiddenDropout): Dropout(p=0.3, inplace=False)(FeatureDropout): Dropout(p=0.3, inplace=False)(conv1): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))(bn0): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(bn2): BatchNorm1d(200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(fc): Linear(in_features=16128, out_features=200, bias=True)
)

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