CS231n Neural Networks Part 3: Learning and Evaluation Babysitting the learning process
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1.Loss function
如果是mini-batch，那么loss一般是抖动下降的，对于不同的lr有着不同的loss下降的曲线。
2.Train/Val accuracy
不同的acc曲线对应着不同的含义，比如说一般来说epoch上升，acc也会上升，但是如果过拟合了，acc在val会下降。
3.Ratio of weights：updates
如果将grad和weights变成向量，那么两个向量的长度的比例可以看作是ratio，其中ratio是1e-3左右。
4.Activation/Gradient distributions per layer
每一层的max min 直方图分布等，convNetJs上面有demo
5.First-layer Visualizations
可以看第一层的特征，第一层一般是纹理特征，如果学习正确，会是一些线条来提取线条的分布用的。
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