Xrdmatch #1234
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Xrdmatch #1234
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数据和模型链接为通过网盘分享的文件：saved_models_ppsci等2个文件 |
mkdocs.yml
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| # setup_commands: | ||
| # - import sys | ||
| # - sys.path.append("../") | ||
| paths: [../ppsci] | ||
| selection: | ||
| new_path_syntax: true | ||
| # selection: | ||
| # new_path_syntax: true |
ppsci/arch/vgg.py
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| x = self.features(x) | ||
| x = paddle.flatten(x, 1) | ||
| if self.classifier is None: | ||
| # 动态推断flatten后的特征维度 |
ppsci/arch/vgg.py
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| def vgg(config="vgg16", pretrained=False, pretrained_path=None, **kwargs): | ||
| model = VGG(config=config, **kwargs) | ||
| return model |
docs/zh/examples/xrdmatch.md
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| ### 环境要求 | ||
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| ```bash | ||
| pip install paddlepaddle ppsci numpy pandas scikit-learn tqdm |
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这个案例只需要使用cpu的paddle运行吗？不需要装-gpu版的？
Contributor
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由于这个后续涉及项目后面进行了一步模型集成学习，从而保留了较多的模型，但是这一步不是文章中的关键，从而舍弃了。由于训练数据较小，采取cpu即可，从而我直接设置的cpu，不过改成cuda是可以运行的。 |
Contributor
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参考其他案例 整理一下文档，提供运行的命令 |
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