GCN Note

企业开发 2023-04-06 18:42:15 阅读次数: 0

1.1 Base Layer

class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = GCNConv(2, 64)
        self.conv2 = GCNConv(64, 256)
        self.conv3 = GCNConv(256, 512)
        self.linear = torch.nn.Linear(512, 512)
        self.linear2 = torch.nn.Linear(512, 10)

    def forward(self, data):
        x, edge_index = data.x, data.edge_index
        x = self.conv1(x, edge_index)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x = self.conv2(x, edge_index)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x = self.conv3(x, edge_index)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x, _ = scatter_max(x, data.batch, dim=0)
        action_mean = self.linear(x)
        x = self.linear2(action_mean)
        return x

Output

epoch: 1 loss: 1.311  Test Accuracy: 76.31 %%  Test Loss: 0.714           
epoch: 2 loss: 0.592  Test Accuracy: 87.32 %%  Test Loss: 0.398           
epoch: 3 loss: 0.414  Test Accuracy: 88.37 %%  Test Loss: 0.371           
epoch: 4 loss: 0.350  Test Accuracy: 89.79 %%  Test Loss: 0.326           
epoch: 5 loss: 0.308  Test Accuracy: 92.77 %%  Test Loss: 0.235           
epoch: 6 loss: 0.282  Test Accuracy: 92.79 %%  Test Loss: 0.243           
epoch: 7 loss: 0.262  Test Accuracy: 93.78 %%  Test Loss: 0.209           
epoch: 8 loss: 0.250  Test Accuracy: 93.91 %%  Test Loss: 0.203           
epoch: 9 loss: 0.240  Test Accuracy: 94.03 %%  Test Loss: 0.199           
epoch: 10 loss: 0.226  Test Accuracy: 93.88 %%  Test Loss: 0.196           
epoch: 11 loss: 0.227  Test Accuracy: 93.72 %%  Test Loss: 0.199           
epoch: 12 loss: 0.218  Test Accuracy: 94.05 %%  Test Loss: 0.204           
epoch: 13 loss: 0.200  Test Accuracy: 95.10 %%  Test Loss: 0.168           
epoch: 14 loss: 0.206  Test Accuracy: 94.79 %%  Test Loss: 0.169           
epoch: 15 loss: 0.196  Test Accuracy: 93.98 %%  Test Loss: 0.191           
epoch: 16 loss: 0.190  Test Accuracy: 95.16 %%  Test Loss: 0.158           
epoch: 17 loss: 0.194  Test Accuracy: 95.03 %%  Test Loss: 0.165           
epoch: 18 loss: 0.189  Test Accuracy: 93.57 %%  Test Loss: 0.209           
epoch: 19 loss: 0.181  Test Accuracy: 95.27 %%  Test Loss: 0.158           
epoch: 20 loss: 0.183  Test Accuracy: 95.68 %%  Test Loss: 0.148           
epoch: 21 loss: 0.183  Test Accuracy: 94.40 %%  Test Loss: 0.179           
epoch: 22 loss: 0.181  Test Accuracy: 94.76 %%  Test Loss: 0.180           
epoch: 23 loss: 0.175  Test Accuracy: 94.42 %%  Test Loss: 0.180           
epoch: 24 loss: 0.175  Test Accuracy: 95.25 %%  Test Loss: 0.158           
epoch: 25 loss: 0.165  Test Accuracy: 94.72 %%  Test Loss: 0.175           
epoch: 26 loss: 0.166  Test Accuracy: 94.91 %%  Test Loss: 0.173           
epoch: 27 loss: 0.164  Test Accuracy: 94.91 %%  Test Loss: 0.157           
epoch: 28 loss: 0.164  Test Accuracy: 95.60 %%  Test Loss: 0.145           
epoch: 29 loss: 0.169  Test Accuracy: 93.82 %%  Test Loss: 0.213           
epoch: 30 loss: 0.163  Test Accuracy: 95.81 %%  Test Loss: 0.139

1.2 Change channel

class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = GCNConv(2, 16)
        self.conv2 = GCNConv(16, 128)
        self.conv3 = GCNConv(128, 512)
        self.linear = torch.nn.Linear(512, 512)
        self.linear2 = torch.nn.Linear(512, 10)

    def forward(self, data):
        x, edge_index = data.x, data.edge_index
        x = self.conv1(x, edge_index)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x = self.conv2(x, edge_index)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x = self.conv3(x, edge_index)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x, _ = scatter_max(x, data.batch, dim=0)
        action_mean = self.linear(x)
        x = self.linear2(action_mean)
        return x

Output

epoch: 1 loss: 1.472  Test Accuracy: 65.24 %%  Test Loss: 1.006           
epoch: 2 loss: 0.881  Test Accuracy: 78.00 %%  Test Loss: 0.671           
epoch: 3 loss: 0.671  Test Accuracy: 82.65 %%  Test Loss: 0.546           
epoch: 4 loss: 0.555  Test Accuracy: 85.86 %%  Test Loss: 0.447           
epoch: 5 loss: 0.486  Test Accuracy: 86.88 %%  Test Loss: 0.418           
epoch: 6 loss: 0.434  Test Accuracy: 87.71 %%  Test Loss: 0.384           
epoch: 7 loss: 0.415  Test Accuracy: 87.27 %%  Test Loss: 0.379           
epoch: 8 loss: 0.394  Test Accuracy: 89.62 %%  Test Loss: 0.328           
epoch: 9 loss: 0.374  Test Accuracy: 90.03 %%  Test Loss: 0.314           
epoch: 10 loss: 0.360  Test Accuracy: 89.25 %%  Test Loss: 0.329           
epoch: 11 loss: 0.341  Test Accuracy: 90.24 %%  Test Loss: 0.300           
epoch: 12 loss: 0.339  Test Accuracy: 91.68 %%  Test Loss: 0.270           
epoch: 13 loss: 0.310  Test Accuracy: 91.38 %%  Test Loss: 0.277           
epoch: 14 loss: 0.308  Test Accuracy: 88.32 %%  Test Loss: 0.352           
epoch: 15 loss: 0.299  Test Accuracy: 91.20 %%  Test Loss: 0.278           
epoch: 16 loss: 0.297  Test Accuracy: 90.05 %%  Test Loss: 0.303           
epoch: 17 loss: 0.280  Test Accuracy: 92.57 %%  Test Loss: 0.240           
epoch: 18 loss: 0.281  Test Accuracy: 92.48 %%  Test Loss: 0.246           
epoch: 19 loss: 0.271  Test Accuracy: 92.20 %%  Test Loss: 0.243           
epoch: 20 loss: 0.271  Test Accuracy: 93.02 %%  Test Loss: 0.217           
epoch: 21 loss: 0.264  Test Accuracy: 92.10 %%  Test Loss: 0.257           
epoch: 22 loss: 0.262  Test Accuracy: 92.76 %%  Test Loss: 0.226           
epoch: 23 loss: 0.264  Test Accuracy: 92.85 %%  Test Loss: 0.222           
epoch: 24 loss: 0.259  Test Accuracy: 93.21 %%  Test Loss: 0.219           
epoch: 25 loss: 0.249  Test Accuracy: 92.30 %%  Test Loss: 0.254           
epoch: 26 loss: 0.250  Test Accuracy: 92.40 %%  Test Loss: 0.241           
epoch: 27 loss: 0.246  Test Accuracy: 93.26 %%  Test Loss: 0.225           
epoch: 28 loss: 0.242  Test Accuracy: 93.48 %%  Test Loss: 0.216           
epoch: 29 loss: 0.242  Test Accuracy: 93.39 %%  Test Loss: 0.215           
epoch: 30 loss: 0.244  Test Accuracy: 92.83 %%  Test Loss: 0.227

1.3 Change Linear

class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = GCNConv(2, 64)
        self.conv2 = GCNConv(64, 256)
        self.conv3 = GCNConv(256, 1024)
        self.linear = torch.nn.Linear(1024, 512)
        self.linear2 = torch.nn.Linear(512, 10)

    def forward(self, data):
        x, edge_index = data.x, data.edge_index
        x = self.conv1(x, edge_index)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x = self.conv2(x, edge_index)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x = self.conv3(x, edge_index)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x, _ = scatter_max(x, data.batch, dim=0)
        action_mean = self.linear(x)
        x = self.linear2(action_mean)
        return x

Output

epoch: 1 loss: 1.250  Test Accuracy: 78.13 %%  Test Loss: 0.663           
epoch: 2 loss: 0.586  Test Accuracy: 86.19 %%  Test Loss: 0.432           
epoch: 3 loss: 0.414  Test Accuracy: 90.19 %%  Test Loss: 0.321           
epoch: 4 loss: 0.337  Test Accuracy: 92.22 %%  Test Loss: 0.258           
epoch: 5 loss: 0.295  Test Accuracy: 91.20 %%  Test Loss: 0.273           
epoch: 6 loss: 0.271  Test Accuracy: 93.45 %%  Test Loss: 0.215           
epoch: 7 loss: 0.256  Test Accuracy: 92.12 %%  Test Loss: 0.262           
epoch: 8 loss: 0.242  Test Accuracy: 93.78 %%  Test Loss: 0.204           
epoch: 9 loss: 0.233  Test Accuracy: 94.60 %%  Test Loss: 0.185           
epoch: 10 loss: 0.225  Test Accuracy: 93.80 %%  Test Loss: 0.213           
epoch: 11 loss: 0.226  Test Accuracy: 91.60 %%  Test Loss: 0.262           
epoch: 12 loss: 0.217  Test Accuracy: 94.44 %%  Test Loss: 0.179           
epoch: 13 loss: 0.212  Test Accuracy: 94.99 %%  Test Loss: 0.179           
epoch: 14 loss: 0.206  Test Accuracy: 94.28 %%  Test Loss: 0.193           
epoch: 15 loss: 0.205  Test Accuracy: 93.70 %%  Test Loss: 0.200           
epoch: 16 loss: 0.192  Test Accuracy: 94.47 %%  Test Loss: 0.186           
epoch: 17 loss: 0.192  Test Accuracy: 95.10 %%  Test Loss: 0.161           
epoch: 18 loss: 0.187  Test Accuracy: 93.66 %%  Test Loss: 0.210           
epoch: 19 loss: 0.186  Test Accuracy: 94.01 %%  Test Loss: 0.200           
epoch: 20 loss: 0.184  Test Accuracy: 95.57 %%  Test Loss: 0.150           
epoch: 21 loss: 0.190  Test Accuracy: 95.52 %%  Test Loss: 0.150           
epoch: 22 loss: 0.173  Test Accuracy: 94.39 %%  Test Loss: 0.178           
epoch: 23 loss: 0.178  Test Accuracy: 94.41 %%  Test Loss: 0.190           
epoch: 24 loss: 0.170  Test Accuracy: 94.87 %%  Test Loss: 0.170           
epoch: 25 loss: 0.177  Test Accuracy: 95.66 %%  Test Loss: 0.143           
epoch: 26 loss: 0.167  Test Accuracy: 94.76 %%  Test Loss: 0.174           
epoch: 27 loss: 0.168  Test Accuracy: 95.22 %%  Test Loss: 0.147           
epoch: 28 loss: 0.168  Test Accuracy: 96.14 %%  Test Loss: 0.129           
epoch: 29 loss: 0.165  Test Accuracy: 95.70 %%  Test Loss: 0.143           
epoch: 30 loss: 0.160  Test Accuracy: 95.86 %%  Test Loss: 0.140

1.4 Add Layer

class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = GCNConv(2, 16)
        self.conv2 = GCNConv(16, 64)
        self.conv3 = GCNConv(64, 256)
        self.conv4 = GCNConv(256, 512)
        self.linear = torch.nn.Linear(512, 512)
        self.linear2 = torch.nn.Linear(512, 10)

    def forward(self, data):
        x, edge_index = data.x, data.edge_index
        x = self.conv1(x, edge_index)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x = self.conv2(x, edge_index)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x = self.conv3(x, edge_index)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x = self.conv4(x, edge_index)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x, _ = scatter_max(x, data.batch, dim=0)
        action_mean = self.linear(x)
        x = self.linear2(action_mean)
        return x

Output

epoch: 1 loss: 1.427  Test Accuracy: 71.44 %%  Test Loss: 0.855           
epoch: 2 loss: 0.723  Test Accuracy: 80.81 %%  Test Loss: 0.577           
epoch: 3 loss: 0.506  Test Accuracy: 86.76 %%  Test Loss: 0.436           
epoch: 4 loss: 0.427  Test Accuracy: 89.07 %%  Test Loss: 0.352           
epoch: 5 loss: 0.389  Test Accuracy: 90.06 %%  Test Loss: 0.318           
epoch: 6 loss: 0.361  Test Accuracy: 89.33 %%  Test Loss: 0.336           
epoch: 7 loss: 0.336  Test Accuracy: 90.64 %%  Test Loss: 0.318           
epoch: 8 loss: 0.321  Test Accuracy: 90.05 %%  Test Loss: 0.316           
epoch: 9 loss: 0.313  Test Accuracy: 91.52 %%  Test Loss: 0.265           
epoch: 10 loss: 0.299  Test Accuracy: 90.85 %%  Test Loss: 0.303           
epoch: 11 loss: 0.287  Test Accuracy: 92.03 %%  Test Loss: 0.258           
epoch: 12 loss: 0.279  Test Accuracy: 91.43 %%  Test Loss: 0.271           
epoch: 13 loss: 0.276  Test Accuracy: 92.56 %%  Test Loss: 0.238           
epoch: 14 loss: 0.268  Test Accuracy: 92.25 %%  Test Loss: 0.253           
epoch: 15 loss: 0.261  Test Accuracy: 92.45 %%  Test Loss: 0.248           
epoch: 16 loss: 0.254  Test Accuracy: 92.98 %%  Test Loss: 0.218           
epoch: 17 loss: 0.246  Test Accuracy: 93.73 %%  Test Loss: 0.203           
epoch: 18 loss: 0.244  Test Accuracy: 92.39 %%  Test Loss: 0.241           
epoch: 19 loss: 0.243  Test Accuracy: 93.30 %%  Test Loss: 0.216           
epoch: 20 loss: 0.236  Test Accuracy: 93.71 %%  Test Loss: 0.204           
epoch: 21 loss: 0.235  Test Accuracy: 93.94 %%  Test Loss: 0.195           
epoch: 22 loss: 0.228  Test Accuracy: 93.81 %%  Test Loss: 0.196           
epoch: 23 loss: 0.229  Test Accuracy: 93.58 %%  Test Loss: 0.206           
epoch: 24 loss: 0.225  Test Accuracy: 93.66 %%  Test Loss: 0.206           
epoch: 25 loss: 0.222  Test Accuracy: 94.21 %%  Test Loss: 0.187           
epoch: 26 loss: 0.219  Test Accuracy: 92.57 %%  Test Loss: 0.244           
epoch: 27 loss: 0.223  Test Accuracy: 94.35 %%  Test Loss: 0.182           
epoch: 28 loss: 0.210  Test Accuracy: 93.73 %%  Test Loss: 0.202           
epoch: 29 loss: 0.212  Test Accuracy: 94.18 %%  Test Loss: 0.187           
epoch: 30 loss: 0.208  Test Accuracy: 94.16 %%  Test Loss: 0.187

1.5 Dec Layer

class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = GCNConv(2, 16)
        self.conv2 = GCNConv(16, 64)
        self.conv3 = GCNConv(64, 256)
        self.conv4 = GCNConv(256, 512)
        self.linear = torch.nn.Linear(512, 512)
        self.linear2 = torch.nn.Linear(512, 10)

    def forward(self, data):
        x, edge_index = data.x, data.edge_index
        x = self.conv1(x, edge_index)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x = self.conv2(x, edge_index)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x = self.conv3(x, edge_index)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x = self.conv4(x, edge_index)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x, _ = scatter_max(x, data.batch, dim=0)
        action_mean = self.linear(x)
        x = self.linear2(action_mean)
        return x

Output

epoch: 1 loss: 1.294  Test Accuracy: 73.58 %%  Test Loss: 0.827           
epoch: 2 loss: 0.765  Test Accuracy: 80.89 %%  Test Loss: 0.593           
epoch: 3 loss: 0.600  Test Accuracy: 84.10 %%  Test Loss: 0.498           
epoch: 4 loss: 0.532  Test Accuracy: 85.91 %%  Test Loss: 0.436           
epoch: 5 loss: 0.474  Test Accuracy: 86.62 %%  Test Loss: 0.429           
epoch: 6 loss: 0.428  Test Accuracy: 88.02 %%  Test Loss: 0.378           
epoch: 7 loss: 0.389  Test Accuracy: 90.22 %%  Test Loss: 0.316           
epoch: 8 loss: 0.370  Test Accuracy: 89.78 %%  Test Loss: 0.332           
epoch: 9 loss: 0.345  Test Accuracy: 91.19 %%  Test Loss: 0.281           
epoch: 10 loss: 0.325  Test Accuracy: 89.05 %%  Test Loss: 0.340           
epoch: 11 loss: 0.307  Test Accuracy: 92.12 %%  Test Loss: 0.261           
epoch: 12 loss: 0.294  Test Accuracy: 92.69 %%  Test Loss: 0.231           
epoch: 13 loss: 0.274  Test Accuracy: 91.27 %%  Test Loss: 0.297           
epoch: 14 loss: 0.272  Test Accuracy: 93.50 %%  Test Loss: 0.215           
epoch: 15 loss: 0.264  Test Accuracy: 92.89 %%  Test Loss: 0.238           
epoch: 16 loss: 0.257  Test Accuracy: 93.26 %%  Test Loss: 0.215           
epoch: 17 loss: 0.249  Test Accuracy: 91.77 %%  Test Loss: 0.269           
epoch: 18 loss: 0.251  Test Accuracy: 92.26 %%  Test Loss: 0.248           
epoch: 19 loss: 0.246  Test Accuracy: 92.76 %%  Test Loss: 0.231           
epoch: 20 loss: 0.243  Test Accuracy: 92.68 %%  Test Loss: 0.230           
epoch: 21 loss: 0.237  Test Accuracy: 93.94 %%  Test Loss: 0.194           
epoch: 22 loss: 0.229  Test Accuracy: 93.74 %%  Test Loss: 0.205           
epoch: 23 loss: 0.230  Test Accuracy: 91.84 %%  Test Loss: 0.262           
epoch: 24 loss: 0.228  Test Accuracy: 93.90 %%  Test Loss: 0.190           
epoch: 25 loss: 0.221  Test Accuracy: 94.03 %%  Test Loss: 0.195           
epoch: 26 loss: 0.218  Test Accuracy: 94.27 %%  Test Loss: 0.194           
epoch: 27 loss: 0.217  Test Accuracy: 94.02 %%  Test Loss: 0.189           
epoch: 28 loss: 0.221  Test Accuracy: 94.17 %%  Test Loss: 0.187           
epoch: 29 loss: 0.215  Test Accuracy: 94.27 %%  Test Loss: 0.188           
epoch: 30 loss: 0.212  Test Accuracy: 94.44 %%  Test Loss: 0.190

猜你喜欢

转载自blog.csdn.net/weixin_45084253/article/details/128042956
GCN
今日推荐
开源日报 | Chrome内置Gemini的意义不在于Gemini；中国AI追随之路的五大误区；ECharts创始人“下海”养鱼；谷歌I/O开发者大会什么都有，只是没有惊喜
微软回应中国区AI团队“打包赴美”传闻
基于大语言模型的开源知识库问答系统 MaxKB GitHub Star 数量突破 5,000 个！
美国拟限制 AI 大模型出口中国和俄罗斯
苹果将与 OpenAI 达成协议，将 ChatGPT 应用于 iPhone
openKylin 社区生态委员会第六次会议圆满召开
阿里云正式发布通义千问 2.5
Python 3.13 发布首个 Beta：实验性自由线程模式和 JIT、改进交互式解释器
Stack Overflow 拿我的代码去训练 AI 大模型，还封了我的账号
Pop!_OS 的 COSMIC 桌面完成 App Store 上架工作
《2024 年一季度互联网投融资运行情况》研究报告
报告：Django 仍然是 74% 开发者的首选
周排行
laravle中orm简单的增删改查
文本分类 特征选取之CHI开方检验
Spark核心编程-WordCount
大数据开发实战系列之电信客服(1)
读书笔记 - 把时间当作朋友 by 李笑来
python 笔记--if else
SpringBoot/Mybatis/Druid, 多数据源MultiDataSource配置思路
排序三个整数
redis集群搭建【2】-Windows中Redis集群搭建
STM32F030驱动TM1650点亮4联数码管
每日归档
更多
2024-05-16(6)
2024-05-15(24)
2024-05-14(0)
2024-05-13(18)
2024-05-12(0)
2024-05-11(38)
2024-05-10(38)
2024-05-09(35)
2024-05-08(42)
2024-05-07(14)

代码天地 © 2018 codetd.com

境外服务器   最新电视剧2022