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Trasfer_learning_example_paddle2.0rc_flowerdataset #939
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Trasfer_learning_example_paddle2.0rc_flowerdataset #939
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{ |
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文件路径错了,应该在 paddle2.0_docs 下面
"collapsed": false | ||
}, | ||
"outputs": [], | ||
"source": [ |
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"collapsed": false | ||
}, | ||
"outputs": [], | ||
"source": [ |
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上面的这一部分文案,也可以简短的写一下哈,大意可以围绕着使用飞桨框架预训练模型,通过迁移学习来完成图像分类,最好不要直接套用高层API的那篇文章,不太好~~
"### 3 模型训练\n", | ||
"\n", | ||
"\n", | ||
"过去常常困扰深度学习开发者的一个问题是,模型训练的代码过于复杂,常常要写好多步骤,才能正确的使程序运行起来,冗长的代码使许多开发者望而却步。\n", |
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这一段同理
"collapsed": false | ||
}, | ||
"source": [ | ||
"其实,这里也会用到上文提到的自定义Callback,我们只需要自定义与loss相关的Callback,然后保存loss信息,最后将其转化为图片即可。具体的实现过程如下所示:" |
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这里以及下面的一段代码,没有用到,可以删除哈
"collapsed": false | ||
}, | ||
"source": [ | ||
"#### 3.3 模型验证" |
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验证完了 最好写一段总结什么的,就是通过这个项目实现了什么任务,大概总结一下就好
使用paddle2.0高层API完成迁移学习任务,使用的数据集为API中封装的鲜花数据集,通过雨哥的指导,对标签值做了预处理。迭代了两次。使用预训练模型resnet-18.