3层网络,隐藏层全连接,顶部Softmax
3层全链接网络加入了学习率衰减,效果依然不好。
max_lr = 0.003
min_lr = 0.0001
decay_speed = 2000.0
learning_rate = min_lr + (max_lr - min_lr)*math.exp(-i/decay_speed)


加入dropout,training pkeep=0.75 , testing pkeep=1.0.出来的是啥呀!


pkeep=0.55 有变化了


减少网络节点数目。
K = 500 → 400 #hindden1的神经元
L = 100 → 50 #隐藏层2的神经元
pkeep:0.55
测试准确率稍微提高了一点,但训练准确度下降。而且,损失曲线仍不对,过拟合。


2层全连接网络:更差
去掉一隐藏层,K = 400
pkeep:0.75


4层全连接网络
K = 400 #hindden1的神经元
L = 50 #隐藏层2的神经元
M = 10 #隐藏层3
max_lr = 0.003
min_lr = 0.0001
pkeep:0.75


网友评论