驻马店网站优化,手机视频网站建设,aws个人免费版,seo和sem是什么意思一 项目展示
下面可以看到验证集可以到了0.9823了#xff0c;实际上#xff0c;在下面的另外一个训练#xff0c;可以得到0.9839#xff0c;我保守的写了0.982
二 项目参数展示
我们先来看看LeNet 5 的结构与参数#xff0c;参数有61#xff0c;706个。 这个是我用…一 项目展示
下面可以看到验证集可以到了0.9823了实际上在下面的另外一个训练可以得到0.9839我保守的写了0.982
二 项目参数展示
我们先来看看LeNet 5 的结构与参数参数有61706个。 这个是我用keras写的可以看到参数只有886个。
项目代码
我们来看一下模型的代码模型由传统卷积改成了可分离卷积 这里padding 主要是为了后面降维方便而设置 这里设置了5层卷积是考虑到如果层数比较少的话感受野比较小增加卷积层从而增加感受野我觉得感受野这时候比模型的复杂度重要 因为mnist的字体大概占图像面积70-85之间
代码的注释也写得比较多可以自行研究。
# 我用的是GPU训练如果训练报错的话可以把SperableConv2D改成Conv2D
# 先训练1个epoch再改成如下的sperableconv2d
def my_network(input_shape(32, 32, 1), classes10):X_input Input(input_shape)X X_input# 这里padding 主要是为了后面降维方便而设置# 这里设置了5层卷积是考虑到如果层数比较少的话感受野比较小增加卷积层从而增加感受野# 因为mnist的字体大概占图像面积70-85之间X ZeroPadding2D((2, 2))(X_input)X Conv2D(4, (3, 3), strides(1, 1), paddingvalid, activationelu, nameconv1)(X)X Conv2D(6, (3, 3), strides(2, 2), paddingvalid, activationelu, nameconv2)(X)X SeparableConv2D(8, (3, 3), strides(2, 2), paddingvalid, activationelu, nameconv3)(X)X SeparableConv2D(10, (3, 3), strides(2, 2), paddingsame, activationelu, nameconv4)(X)X SeparableConv2D(12, (3, 3), strides(2, 2), paddingvalid, activationelu, nameconv5)(X)# 利用浮点卷积做为输出注意激活函数是softmaxX Conv2D(classes, (1, 1), strides(1, 1), paddingsame, activationsoftmax)(X)X keras.layers.Reshape((classes,))(X)model Model(inputsX_input, outputsX, namemy_network)return modelmodel_con my_network(input_shape(28, 28, 1), classes10)# 这里的lr如果用了学习衰减的话就可以不设置了
optimizer keras.optimizers.Adam(lr0.001)
model_con.compile(optimizeroptimizer, losscategorical_crossentropy, metrics[accuracy])
model_con.summary()训练过程
训练的过程用的是Adam优化器利用了学习率衰减的策略先由大的学习加快训练再调整比较小的学习率来训练慢慢训练得出的结果比较稳定。 下面是100个epochs的全部识别的输出其它lr初始值为0.01而不是0.001。
Epoch 1/100
lr change to 0.01
235/235 [] - 2s 7ms/step - loss: 0.1065 - acc: 0.9673 - val_loss: 0.0792 - val_acc: 0.9755
Epoch 2/100
lr change to 0.01
235/235 [] - 1s 6ms/step - loss: 0.0886 - acc: 0.9726 - val_loss: 0.1013 - val_acc: 0.9686
Epoch 3/100
lr change to 0.01
235/235 [] - 1s 6ms/step - loss: 0.0884 - acc: 0.9732 - val_loss: 0.0744 - val_acc: 0.9768
Epoch 4/100
lr change to 0.01
235/235 [] - 2s 6ms/step - loss: 0.0873 - acc: 0.9730 - val_loss: 0.0815 - val_acc: 0.9740
Epoch 5/100
lr change to 0.01
235/235 [] - 1s 6ms/step - loss: 0.0851 - acc: 0.9733 - val_loss: 0.0724 - val_acc: 0.9768
Epoch 6/100
lr change to 0.01
235/235 [] - 2s 6ms/step - loss: 0.0817 - acc: 0.9752 - val_loss: 0.0826 - val_acc: 0.9745
Epoch 7/100
lr change to 0.01
235/235 [] - 1s 6ms/step - loss: 0.0855 - acc: 0.9741 - val_loss: 0.0830 - val_acc: 0.9740
Epoch 8/100
lr change to 0.01
235/235 [] - 2s 6ms/step - loss: 0.0870 - acc: 0.9728 - val_loss: 0.0847 - val_acc: 0.9729
Epoch 9/100
lr change to 0.01
235/235 [] - 2s 7ms/step - loss: 0.0813 - acc: 0.9752 - val_loss: 0.0701 - val_acc: 0.9787
Epoch 10/100
lr change to 0.01
235/235 [] - 2s 7ms/step - loss: 0.0815 - acc: 0.9748 - val_loss: 0.0742 - val_acc: 0.9765
Epoch 11/100
lr change to 0.01
235/235 [] - 2s 7ms/step - loss: 0.0809 - acc: 0.9749 - val_loss: 0.0721 - val_acc: 0.9770
Epoch 12/100
lr change to 0.005
235/235 [] - 1s 6ms/step - loss: 0.0672 - acc: 0.9796 - val_loss: 0.0635 - val_acc: 0.9788
Epoch 13/100
lr change to 0.005
235/235 [] - 2s 6ms/step - loss: 0.0662 - acc: 0.9800 - val_loss: 0.0575 - val_acc: 0.9808
Epoch 14/100
lr change to 0.005
235/235 [] - 2s 6ms/step - loss: 0.0659 - acc: 0.9796 - val_loss: 0.0624 - val_acc: 0.9803
Epoch 15/100
lr change to 0.005
235/235 [] - 1s 6ms/step - loss: 0.0662 - acc: 0.9793 - val_loss: 0.0631 - val_acc: 0.9791
Epoch 16/100
lr change to 0.005
235/235 [] - 2s 6ms/step - loss: 0.0655 - acc: 0.9802 - val_loss: 0.0561 - val_acc: 0.9816
Epoch 17/100
lr change to 0.005
235/235 [] - 2s 7ms/step - loss: 0.0659 - acc: 0.9799 - val_loss: 0.0653 - val_acc: 0.9808
Epoch 18/100
lr change to 0.005
235/235 [] - 2s 6ms/step - loss: 0.0653 - acc: 0.9798 - val_loss: 0.0765 - val_acc: 0.9748
Epoch 19/100
lr change to 0.005
235/235 [] - 2s 7ms/step - loss: 0.0656 - acc: 0.9792 - val_loss: 0.0565 - val_acc: 0.9811
Epoch 20/100
lr change to 0.005
235/235 [] - 2s 7ms/step - loss: 0.0645 - acc: 0.9800 - val_loss: 0.0569 - val_acc: 0.9814
Epoch 21/100
lr change to 0.005
235/235 [] - 2s 7ms/step - loss: 0.0639 - acc: 0.9804 - val_loss: 0.0566 - val_acc: 0.9814
Epoch 22/100
lr change to 0.005
235/235 [] - 2s 7ms/step - loss: 0.0637 - acc: 0.9801 - val_loss: 0.0672 - val_acc: 0.9778
Epoch 23/100
lr change to 0.005
235/235 [] - 2s 7ms/step - loss: 0.0631 - acc: 0.9807 - val_loss: 0.0607 - val_acc: 0.9804
Epoch 24/100
lr change to 0.0025
235/235 [] - 2s 6ms/step - loss: 0.0562 - acc: 0.9825 - val_loss: 0.0587 - val_acc: 0.9804
Epoch 25/100
lr change to 0.0025
235/235 [] - 2s 6ms/step - loss: 0.0550 - acc: 0.9831 - val_loss: 0.0542 - val_acc: 0.9819
Epoch 26/100
lr change to 0.0025
235/235 [] - 2s 7ms/step - loss: 0.0541 - acc: 0.9833 - val_loss: 0.0541 - val_acc: 0.9818
Epoch 27/100
lr change to 0.0025
235/235 [] - 2s 7ms/step - loss: 0.0544 - acc: 0.9829 - val_loss: 0.0545 - val_acc: 0.9825
Epoch 28/100
lr change to 0.0025
235/235 [] - 2s 7ms/step - loss: 0.0548 - acc: 0.9829 - val_loss: 0.0514 - val_acc: 0.9827
Epoch 29/100
lr change to 0.0025
235/235 [] - 2s 7ms/step - loss: 0.0536 - acc: 0.9833 - val_loss: 0.0587 - val_acc: 0.9805
Epoch 30/100
lr change to 0.0025
235/235 [] - 2s 7ms/step - loss: 0.0534 - acc: 0.9831 - val_loss: 0.0683 - val_acc: 0.9783
Epoch 31/100
lr change to 0.0025
235/235 [] - 2s 7ms/step - loss: 0.0535 - acc: 0.9839 - val_loss: 0.0517 - val_acc: 0.9831
Epoch 32/100
lr change to 0.0025
235/235 [] - 2s 7ms/step - loss: 0.0537 - acc: 0.9831 - val_loss: 0.0524 - val_acc: 0.9821
Epoch 33/100
lr change to 0.0025
235/235 [] - 2s 8ms/step - loss: 0.0533 - acc: 0.9833 - val_loss: 0.0543 - val_acc: 0.9820
Epoch 34/100
lr change to 0.0025
235/235 [] - 2s 8ms/step - loss: 0.0527 - acc: 0.9836 - val_loss: 0.0529 - val_acc: 0.9814
Epoch 35/100
lr change to 0.0025
235/235 [] - 2s 7ms/step - loss: 0.0534 - acc: 0.9835 - val_loss: 0.0558 - val_acc: 0.9814
Epoch 36/100
lr change to 0.00125
235/235 [] - 2s 7ms/step - loss: 0.0487 - acc: 0.9852 - val_loss: 0.0523 - val_acc: 0.9823
Epoch 37/100
lr change to 0.00125
235/235 [] - 2s 7ms/step - loss: 0.0478 - acc: 0.9854 - val_loss: 0.0500 - val_acc: 0.9837
Epoch 38/100
lr change to 0.00125
235/235 [] - 2s 7ms/step - loss: 0.0482 - acc: 0.9851 - val_loss: 0.0532 - val_acc: 0.9823
Epoch 39/100
lr change to 0.00125
235/235 [] - 2s 7ms/step - loss: 0.0480 - acc: 0.9849 - val_loss: 0.0509 - val_acc: 0.9832
Epoch 40/100
lr change to 0.00125
235/235 [] - 2s 7ms/step - loss: 0.0477 - acc: 0.9850 - val_loss: 0.0509 - val_acc: 0.9824
Epoch 41/100
lr change to 0.00125
235/235 [] - 2s 7ms/step - loss: 0.0481 - acc: 0.9850 - val_loss: 0.0509 - val_acc: 0.9831
Epoch 42/100
lr change to 0.00125
235/235 [] - 2s 7ms/step - loss: 0.0482 - acc: 0.9847 - val_loss: 0.0511 - val_acc: 0.9827
Epoch 43/100
lr change to 0.00125
235/235 [] - 2s 7ms/step - loss: 0.0480 - acc: 0.9851 - val_loss: 0.0513 - val_acc: 0.9838
Epoch 44/100
lr change to 0.00125
235/235 [] - 2s 7ms/step - loss: 0.0474 - acc: 0.9850 - val_loss: 0.0530 - val_acc: 0.9831
Epoch 45/100
lr change to 0.00125
235/235 [] - 2s 7ms/step - loss: 0.0476 - acc: 0.9852 - val_loss: 0.0513 - val_acc: 0.9831
Epoch 46/100
lr change to 0.00125
235/235 [] - 2s 7ms/step - loss: 0.0470 - acc: 0.9855 - val_loss: 0.0554 - val_acc: 0.9817
Epoch 47/100
lr change to 0.00125
235/235 [] - 2s 7ms/step - loss: 0.0471 - acc: 0.9850 - val_loss: 0.0527 - val_acc: 0.9817
Epoch 48/100
lr change to 0.000625
235/235 [] - 2s 7ms/step - loss: 0.0451 - acc: 0.9857 - val_loss: 0.0506 - val_acc: 0.9828
Epoch 49/100
lr change to 0.000625
235/235 [] - 2s 7ms/step - loss: 0.0448 - acc: 0.9861 - val_loss: 0.0501 - val_acc: 0.9838
Epoch 50/100
lr change to 0.000625
235/235 [] - 2s 7ms/step - loss: 0.0448 - acc: 0.9859 - val_loss: 0.0490 - val_acc: 0.9829
Epoch 51/100
lr change to 0.000625
235/235 [] - 2s 7ms/step - loss: 0.0447 - acc: 0.9865 - val_loss: 0.0501 - val_acc: 0.9834
Epoch 52/100
lr change to 0.000625
235/235 [] - 2s 7ms/step - loss: 0.0444 - acc: 0.9864 - val_loss: 0.0488 - val_acc: 0.9826
Epoch 53/100
lr change to 0.000625
235/235 [] - 2s 7ms/step - loss: 0.0446 - acc: 0.9860 - val_loss: 0.0507 - val_acc: 0.9826
Epoch 54/100
lr change to 0.000625
235/235 [] - 2s 7ms/step - loss: 0.0446 - acc: 0.9856 - val_loss: 0.0511 - val_acc: 0.9827
Epoch 55/100
lr change to 0.000625
235/235 [] - 2s 7ms/step - loss: 0.0446 - acc: 0.9862 - val_loss: 0.0506 - val_acc: 0.9830
Epoch 56/100
lr change to 0.000625
235/235 [] - 2s 7ms/step - loss: 0.0446 - acc: 0.9858 - val_loss: 0.0500 - val_acc: 0.9830
Epoch 57/100
lr change to 0.000625
235/235 [] - 2s 7ms/step - loss: 0.0443 - acc: 0.9861 - val_loss: 0.0517 - val_acc: 0.9831
Epoch 58/100
lr change to 0.000625
235/235 [] - 2s 7ms/step - loss: 0.0442 - acc: 0.9860 - val_loss: 0.0507 - val_acc: 0.9828
Epoch 59/100
lr change to 0.000625
235/235 [] - 2s 7ms/step - loss: 0.0445 - acc: 0.9864 - val_loss: 0.0504 - val_acc: 0.9833
Epoch 60/100
lr change to 0.0003125
235/235 [] - 2s 7ms/step - loss: 0.0430 - acc: 0.9868 - val_loss: 0.0499 - val_acc: 0.9825
Epoch 61/100
lr change to 0.0003125
235/235 [] - 2s 7ms/step - loss: 0.0429 - acc: 0.9865 - val_loss: 0.0507 - val_acc: 0.9834
Epoch 62/100
lr change to 0.0003125
235/235 [] - 2s 7ms/step - loss: 0.0430 - acc: 0.9866 - val_loss: 0.0494 - val_acc: 0.9830
Epoch 63/100
lr change to 0.0003125
235/235 [] - 2s 7ms/step - loss: 0.0432 - acc: 0.9865 - val_loss: 0.0501 - val_acc: 0.9836
Epoch 64/100
lr change to 0.0003125
235/235 [] - 2s 7ms/step - loss: 0.0426 - acc: 0.9866 - val_loss: 0.0518 - val_acc: 0.9830
Epoch 65/100
lr change to 0.0003125
235/235 [] - 2s 7ms/step - loss: 0.0428 - acc: 0.9865 - val_loss: 0.0500 - val_acc: 0.9824
Epoch 66/100
lr change to 0.0003125
235/235 [] - 2s 7ms/step - loss: 0.0427 - acc: 0.9866 - val_loss: 0.0506 - val_acc: 0.9824
Epoch 67/100
lr change to 0.0003125
235/235 [] - 2s 7ms/step - loss: 0.0430 - acc: 0.9866 - val_loss: 0.0504 - val_acc: 0.9826
Epoch 68/100
lr change to 0.0003125
235/235 [] - 2s 7ms/step - loss: 0.0427 - acc: 0.9868 - val_loss: 0.0501 - val_acc: 0.9832
Epoch 69/100
lr change to 0.0003125
235/235 [] - 2s 7ms/step - loss: 0.0425 - acc: 0.9866 - val_loss: 0.0505 - val_acc: 0.9823
Epoch 70/100
lr change to 0.0003125
235/235 [] - 2s 7ms/step - loss: 0.0427 - acc: 0.9869 - val_loss: 0.0505 - val_acc: 0.9839
Epoch 71/100
lr change to 0.0003125
235/235 [] - 2s 7ms/step - loss: 0.0432 - acc: 0.9868 - val_loss: 0.0494 - val_acc: 0.9838
Epoch 72/100
lr change to 0.00015625
235/235 [] - 2s 7ms/step - loss: 0.0419 - acc: 0.9870 - val_loss: 0.0493 - val_acc: 0.9833
Epoch 73/100
lr change to 0.00015625
235/235 [] - 2s 7ms/step - loss: 0.0419 - acc: 0.9871 - val_loss: 0.0498 - val_acc: 0.9835
Epoch 74/100
lr change to 0.00015625
235/235 [] - 2s 7ms/step - loss: 0.0419 - acc: 0.9871 - val_loss: 0.0496 - val_acc: 0.9831
Epoch 75/100
lr change to 0.00015625
235/235 [] - 2s 7ms/step - loss: 0.0419 - acc: 0.9868 - val_loss: 0.0499 - val_acc: 0.9834
Epoch 76/100
lr change to 0.00015625
235/235 [] - 2s 7ms/step - loss: 0.0419 - acc: 0.9868 - val_loss: 0.0496 - val_acc: 0.9835
Epoch 77/100
lr change to 0.00015625
235/235 [] - 2s 7ms/step - loss: 0.0420 - acc: 0.9870 - val_loss: 0.0500 - val_acc: 0.9834
Epoch 78/100
lr change to 0.00015625
235/235 [] - 2s 7ms/step - loss: 0.0419 - acc: 0.9871 - val_loss: 0.0496 - val_acc: 0.9833
Epoch 79/100
lr change to 0.00015625
235/235 [] - 2s 7ms/step - loss: 0.0419 - acc: 0.9868 - val_loss: 0.0497 - val_acc: 0.9835
Epoch 80/100
lr change to 0.00015625
235/235 [] - 2s 7ms/step - loss: 0.0419 - acc: 0.9869 - val_loss: 0.0499 - val_acc: 0.9835
Epoch 81/100
lr change to 0.00015625
235/235 [] - 2s 7ms/step - loss: 0.0420 - acc: 0.9866 - val_loss: 0.0499 - val_acc: 0.9830
Epoch 82/100
lr change to 0.00015625
235/235 [] - 2s 7ms/step - loss: 0.0418 - acc: 0.9870 - val_loss: 0.0503 - val_acc: 0.9839
Epoch 83/100
lr change to 0.00015625
235/235 [] - 2s 7ms/step - loss: 0.0420 - acc: 0.9868 - val_loss: 0.0494 - val_acc: 0.9832
Epoch 84/100
lr change to 7.8125e-05
235/235 [] - 2s 7ms/step - loss: 0.0414 - acc: 0.9873 - val_loss: 0.0499 - val_acc: 0.9837
Epoch 85/100
lr change to 7.8125e-05
235/235 [] - 2s 7ms/step - loss: 0.0416 - acc: 0.9871 - val_loss: 0.0496 - val_acc: 0.9833
Epoch 86/100
lr change to 7.8125e-05
235/235 [] - 2s 7ms/step - loss: 0.0414 - acc: 0.9872 - val_loss: 0.0501 - val_acc: 0.9837
Epoch 87/100
lr change to 7.8125e-05
235/235 [] - 2s 7ms/step - loss: 0.0415 - acc: 0.9871 - val_loss: 0.0502 - val_acc: 0.9836
Epoch 88/100
lr change to 7.8125e-05
235/235 [] - 2s 7ms/step - loss: 0.0415 - acc: 0.9871 - val_loss: 0.0501 - val_acc: 0.9834
Epoch 89/100
lr change to 7.8125e-05
235/235 [] - 2s 7ms/step - loss: 0.0414 - acc: 0.9870 - val_loss: 0.0496 - val_acc: 0.9835
Epoch 90/100
lr change to 7.8125e-05
235/235 [] - 2s 7ms/step - loss: 0.0414 - acc: 0.9872 - val_loss: 0.0498 - val_acc: 0.9837
Epoch 91/100
lr change to 7.8125e-05
235/235 [] - 2s 7ms/step - loss: 0.0416 - acc: 0.9870 - val_loss: 0.0502 - val_acc: 0.9839
Epoch 92/100
lr change to 7.8125e-05
235/235 [] - 2s 7ms/step - loss: 0.0414 - acc: 0.9872 - val_loss: 0.0503 - val_acc: 0.9834
Epoch 93/100
lr change to 7.8125e-05
235/235 [] - 2s 7ms/step - loss: 0.0415 - acc: 0.9870 - val_loss: 0.0500 - val_acc: 0.9836
Epoch 94/100
lr change to 7.8125e-05
235/235 [] - 2s 7ms/step - loss: 0.0415 - acc: 0.9869 - val_loss: 0.0499 - val_acc: 0.9832
Epoch 95/100
lr change to 7.8125e-05
235/235 [] - 2s 7ms/step - loss: 0.0415 - acc: 0.9871 - val_loss: 0.0502 - val_acc: 0.9836
Epoch 96/100
lr change to 6.25e-05
235/235 [] - 2s 7ms/step - loss: 0.0413 - acc: 0.9871 - val_loss: 0.0501 - val_acc: 0.9838
Epoch 97/100
lr change to 6.25e-05
235/235 [] - 2s 7ms/step - loss: 0.0413 - acc: 0.9872 - val_loss: 0.0501 - val_acc: 0.9837
Epoch 98/100
lr change to 6.25e-05
235/235 [] - 2s 7ms/step - loss: 0.0413 - acc: 0.9872 - val_loss: 0.0503 - val_acc: 0.9834
Epoch 99/100
lr change to 6.25e-05
235/235 [] - 2s 7ms/step - loss: 0.0413 - acc: 0.9870 - val_loss: 0.0499 - val_acc: 0.9833
Epoch 100/100
lr change to 6.25e-05
235/235 [] - 2s 7ms/step - loss: 0.0417 - acc: 0.9870 - val_loss: 0.0500 - val_acc: 0.9834总结
通过此次认识到了感受野有时候比模型的复杂度更重要还有学习率衰减策略也对模型有比较大的影响。 就是你用CPU训练这么少的参数也会很快的。
下面附上本项目的代码的Jupyter notebook。 链接https://pan.baidu.com/s/1EEzfRDD_PAgSeS999Eq9-Q 提取码3131 如果你觉得好请给我个star如有问题也可以与我联系。
