【论文阅读】AlexNet:ImageNet Classification with Deep Covolutional Network
网络结构 使用ReLU非线性层 作者在网络结构中使用了ReLU作为非线性激活层来代替当时比较常用的tanh或sigmoid，加速了网络的训练速度，对此作者也做了对比实验并可视化了结果。 可以看到同样是下降到0.25的loss rate，ReLU（实现）比tanh（虚线）少用了六倍的时间。 局部响应归一化（LRN） 由于ReLU不像tanh或sigmoid一样，输出有一定范围，所以需要对其输出做归一
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相关文章
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