Comments (8)
尝试降低学习率
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计划自行搭建网络模型,动态调整学习率
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初步采用动态学习率,约提升9%的准确率
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平衡了样本数量,在dataloader平衡易受到随机取样的波动,故在损失函数处平衡,目前模型没有太大的改善
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准备以一种较为规律的方式改变学习率,而不是被动改变,计划逐层解冻迁移学习层数
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计划尝试累计梯度,以解决显存不足的情况
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累计梯度意义不大,采用colab16GB显存没有必要累计,不仅训练较慢而且准确率并没有提升
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通过动态学习率以及解冻模型提升约8%的准确率,目前存在过拟合的问题,且数据增强难以解决
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