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在这里插入图片描述 以上，如果有总结的不到位的地方欢迎指出。 一些清晰的总结和体会 最近看了sklearn用户指南里的 交叉验证：评估估计器性能，有一些更明确的体会记录. 在机器学习中，epoch 数量是指整个训练集通过模型的次数。一个epoch意味着训练数据集中的每个样本都有机会更新内部模型参数。 epoch由一个或多个batch组成。 选择合适的 epoch 数. 最后的结果还是可以的，train和val的acc都是保持上升的，但前面val的acc在波动，可能是在训练集上 过拟合 了。 可以尝试加些正则项（比如l1、l2）或者使用dropout；另外设置 早停机.
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变化趋势分析： train loss 不断下降，test loss不断下降，说明网络仍在学习;（最好的） train loss 不断下降，test loss趋于不变，说明网络过拟合； train loss 趋于不变，test loss不断下降，说. La diferencia es que el código sin jquery es de una sola línea, mientras que con jquery son muchas líneas, y es un código rebuscado que intenta funcionar en todos los.
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