【Python机器学习】实验14 手写体卷积神经网络2-阿里云开发者社区

11. 读取测试集的图片预测值（神经网络的输出为10）

#导入模型
model1=torch.load('./model-cifar10.pth')

pre_result=torch.zeros(len(test_dataset),10)
for i in range(len(test_dataset)):
    pre_result[i,:]=model1(torch.reshape(test_dataset[i][0],(-1,1,28,28)))
pre_result

tensor([[-1.8005, -0.1725,  1.4765,  ..., 13.5399, -0.6261,  3.8320],
        [ 4.3233,  7.6017, 16.5872,  ..., -0.0560,  5.2066, -7.0792],
        [-2.1821,  9.3779,  0.7749,  ...,  1.8749,  1.6951, -2.9422],
        ...,
        [-5.5755, -2.2075, -9.2250,  ..., -0.2451,  3.3703,  1.2951],
        [ 0.5491, -7.7018, -5.8288,  ..., -7.6734,  9.3946, -1.9603],
        [ 3.5516, -8.2659, -0.5965,  ..., -8.5934,  1.0133, -2.2048]],
       grad_fn=<CopySlices>)

pre_result.shape

torch.Size([10000, 10])

pre_result[:5]

tensor([[-1.8005e+00, -1.7254e-01,  1.4765e+00,  3.0824e+00, -2.5454e+00,
         -7.6911e-01, -1.2368e+01,  1.3540e+01, -6.2614e-01,  3.8320e+00],
        [ 4.3233e+00,  7.6017e+00,  1.6587e+01,  3.6477e+00, -6.6674e+00,
         -6.0579e+00, -1.5660e+00, -5.5983e-02,  5.2066e+00, -7.0792e+00],
        [-2.1821e+00,  9.3779e+00,  7.7487e-01, -3.0049e+00,  1.3374e+00,
         -1.6613e+00,  8.8544e-01,  1.8749e+00,  1.6951e+00, -2.9422e+00],
        [ 1.3456e+01, -9.9020e+00,  2.8586e+00, -2.2105e+00, -1.8515e+00,
          1.7651e-03,  4.7584e+00, -1.3772e+00, -2.2127e+00,  1.5543e+00],
        [-2.9573e+00, -2.5707e+00, -3.5142e+00, -1.7487e+00,  1.2020e+01,
         -8.8355e-01, -1.0698e+00,  6.3823e-01, -3.5542e-01,  3.6258e+00]],
       grad_fn=<SliceBackward0>)

#显示这10000张图片的标签
label_10000=[test_dataset[i][1] for i in range(10000)]
label_10000

[7,
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import numpy as np
pre_10000=pre_result.detach()
pre_10000

tensor([[-1.8005, -0.1725,  1.4765,  ..., 13.5399, -0.6261,  3.8320],
        [ 4.3233,  7.6017, 16.5872,  ..., -0.0560,  5.2066, -7.0792],
        [-2.1821,  9.3779,  0.7749,  ...,  1.8749,  1.6951, -2.9422],
        ...,
        [-5.5755, -2.2075, -9.2250,  ..., -0.2451,  3.3703,  1.2951],
        [ 0.5491, -7.7018, -5.8288,  ..., -7.6734,  9.3946, -1.9603],
        [ 3.5516, -8.2659, -0.5965,  ..., -8.5934,  1.0133, -2.2048]])

pre_10000=np.array(pre_10000)
pre_10000

array([[-1.8004757 , -0.17253768,  1.4764961 , ..., 13.539932  ,
        -0.6261405 ,  3.832048  ],
       [ 4.323273  ,  7.601658  , 16.587166  , ..., -0.05598306,
         5.20656   , -7.0792093 ],
       [-2.1820781 ,  9.377863  ,  0.7748679 , ...,  1.8749483 ,
         1.6950815 , -2.9421623 ],
       ...,
       [-5.575542  , -2.2075167 , -9.225033  , ..., -0.24509335,
         3.3702612 ,  1.2950805 ],
       [ 0.5491407 , -7.7017508 , -5.8287773 , ..., -7.6733685 ,
         9.39456   , -1.9602803 ],
       [ 3.5516088 , -8.265893  , -0.59651583, ..., -8.593432  ,
         1.0132635 , -2.2048213 ]], dtype=float32)

12. 采用pandas可视化数据

import pandas as pd 
table=pd.DataFrame(zip(pre_10000,label_10000))
table

	0	1
0	[-1.8004757, -0.17253768, 1.4764961, 3.0824265...	7
1	[4.323273, 7.601658, 16.587166, 3.6476722, -6....	2

2	[-2.1820781, 9.377863, 0.7748679, -3.0049446, ...	1
3	[13.455704, -9.902006, 2.8586285, -2.2104588, ...	0

4	[-2.9572597, -2.5707455, -3.5142026, -1.748683...	4
...	...	...
9995	[-2.5784128, 10.5256405, 23.895123, 8.827512, ...	2

9996	[-2.773907, 0.56169015, 1.6811254, 15.230703, ...	3
9997	[-5.575542, -2.2075167, -9.225033, -5.60418, 1...	4

9998

[0.5491407, -7.7017508, -5.8287773, 2.2394006,...

9999

[3.5516088, -8.265893, -0.59651583, -4.034732,...

10000 rows × 2 columns

table[0].values

array([array([ -1.8004757 ,  -0.17253768,   1.4764961 ,   3.0824265 ,
               -2.545419  ,  -0.76911056, -12.368087  ,  13.539932  ,
               -0.6261405 ,   3.832048  ], dtype=float32)            ,
       array([ 4.323273  ,  7.601658  , 16.587166  ,  3.6476722 , -6.6673512 ,
              -6.05786   , -1.5660243 , -0.05598306,  5.20656   , -7.0792093 ],
             dtype=float32)                                                    ,
       array([-2.1820781,  9.377863 ,  0.7748679, -3.0049446,  1.3374403,
              -1.6612737,  0.8854448,  1.8749483,  1.6950815, -2.9421623],
             dtype=float32)                                               ,
       ...,
       array([-5.575542  , -2.2075167 , -9.225033  , -5.60418   , 17.216341  ,
               2.8671436 ,  1.0113716 , -0.24509335,  3.3702612 ,  1.2950805 ],
             dtype=float32)                                                    ,
       array([ 0.5491407, -7.7017508, -5.8287773,  2.2394006, -7.533697 ,
              13.003905 ,  6.1807218, -7.6733685,  9.39456  , -1.9602803],
             dtype=float32)                                               ,
       array([ 3.5516088 , -8.265893  , -0.59651583, -4.034732  ,  1.3853229 ,
               6.1974382 , 16.321545  , -8.593432  ,  1.0132635 , -2.2048213 ],
             dtype=float32)                                                    ],
      dtype=object)

table["pred"]=[np.argmax(table[0][i]) for i in range(table.shape[0])]
table

	0	1	pred
0	[-1.8004757, -0.17253768, 1.4764961, 3.0824265...	7	7
1	[4.323273, 7.601658, 16.587166, 3.6476722, -6....	2	2

2	[-2.1820781, 9.377863, 0.7748679, -3.0049446, ...	1	1
3	[13.455704, -9.902006, 2.8586285, -2.2104588, ...	0	0

4	[-2.9572597, -2.5707455, -3.5142026, -1.748683...	4	4
...	...	...	...
9995	[-2.5784128, 10.5256405, 23.895123, 8.827512, ...	2	2

9996	[-2.773907, 0.56169015, 1.6811254, 15.230703, ...	3	3
9997	[-5.575542, -2.2075167, -9.225033, -5.60418, 1...	4	4

9998	[0.5491407, -7.7017508, -5.8287773, 2.2394006,...	5	5
9999	[3.5516088, -8.265893, -0.59651583, -4.034732,...	6	6

10000 rows × 3 columns

13. 对预测错误的样本点进行可视化

mismatch=table[table[1]!=table["pred"]]

mismatch

	0	1	pred
247	[-0.28747877, 1.9184055, 8.627771, -3.1354206,...	4	2
340	[-5.550468, 1.6552217, -0.96347404, 9.110174, ...	5	3

449	[-6.0154114, -3.7659, -2.7571707, 14.220249, -...	3	5
582	[-1.4626387, 1.3258317, 10.138913, 5.996572, -...	8	2

659	[-3.1300178, 8.830592, 8.781635, 5.6512327, -3...	2	1
...	...	...	...
9768	[2.6190603, -5.539648, 3.0145228, 4.8416886, -...	2	3

9770	[7.0385275, -9.72994, 0.03886398, -0.3356622, ...	5	6
9792	[-0.84618676, -0.038114145, -4.388391, 0.12577...	4	9

9904	[1.6193992, -7.525599, 2.833153, 3.7744582, -2...	2	8
9982	[0.8662107, -7.932593, -0.3750058, 1.9749051, ...	5	6

158 rows × 3 columns

from matplotlib import pyplot as plt
plt.scatter(mismatch[1],mismatch["pred"])

<matplotlib.collections.PathCollection at 0x217dc403490>

14. 看看错误样本被预测为哪些数据

mismatch[mismatch[1]==8].sort_values("pred").index

Int64Index([4807, 2896,  582, 6625, 7220, 3871, 4123, 1878, 1319, 2179, 4601,
            4956, 3023, 9280, 8408, 6765, 4497, 1530,  947],
           dtype='int64')

table.iloc[4500,:]

0       [-4.9380565, 6.2523484, -1.2272537, 0.32682633...
1                                                       9
pred                                                    1
Name: 4500, dtype: object

idx_lst=mismatch[mismatch[1]==8].sort_values("pred").index.values
idx_lst,len(idx_lst)

(array([4807, 2896,  582, 6625, 7220, 3871, 4123, 1878, 1319, 2179, 4601,
        4956, 3023, 9280, 8408, 6765, 4497, 1530,  947], dtype=int64),
 19)

mismatch[mismatch[1]==8].sort_values("pred")

	0	1	pred

4807	[5.3192024, -4.2546616, 3.6083155, 3.8956034, ...	8	0
2896	[7.4840407, -8.972937, 0.9461607, 1.6278361, -...	8	0

582	[-1.4626387, 1.3258317, 10.138913, 5.996572, -...	8	2
6625	[-5.413072, 2.7984824, 6.0430045, 2.3938487, 0...	8	2

7220	[-3.1443837, -3.4629154, 4.8560658, 12.752452,...	8	3
3871	[0.1749076, -5.8143945, 3.083826, 8.113558, -5...	8	3

4123	[-3.8682778, -2.290763, 6.1067047, 10.920237, ...	8	3
1878	[-2.8437655, -2.4290323, 3.1861248, 9.739316, ...	8	3

1319	[3.583813, -6.279593, -0.21310738, 7.2746606, ...	8	3
2179	[-0.57300043, -3.8434098, 8.02766, 12.139142, ...	8	3

4601	[-9.5640745, -2.1305811, -5.2161045, 2.3105593...	8	4
4956	[-7.5286517, -4.080871, -6.850239, -2.9094412,...	8	4

3023	[-2.6319933, -11.065216, -1.3231966, 0.0415189...	8	5
9280	[-1.9706918, -11.544259, -0.51283014, 3.955923...	8	5

8408	[1.0573181, -3.7079592, 0.34973174, -0.3489528...	8	6
6765	[2.8831, -2.6855779, 0.39529848, -1.855415, -2...	8	6

4497	[-4.830113, -0.28656, 4.911254, 4.4041815, -2....	8	7
1530	[-4.4495664, -2.5381584, 5.4418654, 9.994939, ...	8	7

947

[-2.8835857, -8.3713045, -1.5150836, 3.1263702...

import numpy as np
img=np.stack(list(test_dataset[idx_lst[i]][0][0] for i in range(5)),axis=1).reshape(28,28*5)
plt.imshow(img)

<matplotlib.image.AxesImage at 0x217dc28e9d0>

#显示3行
import numpy as np
img30=np.stack(
    tuple(np.stack(
            tuple(test_dataset[idx_lst[i+j*5]][0][0] for i in range(5)),
        axis=1).reshape(28,28*5) for j in range(3)),axis=0).reshape(28*3,28*5)
plt.imshow(img30)
plt.axis('off')

(-0.5, 139.5, 83.5, -0.5)

arr2=table.iloc[idx_lst[:30],2].values
arr2

array([0, 0, 2, 2, 3, 3, 3, 3, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 9],
      dtype=int64)

【Python机器学习】实验14 手写体卷积神经网络2

11. 读取测试集的图片预测值（神经网络的输出为10）

12. 采用pandas可视化数据

13. 对预测错误的样本点进行可视化

14. 看看错误样本被预测为哪些数据

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【Python机器学习】实验14 手写体卷积神经网络2

11. 读取测试集的图片预测值（神经网络的输出为10）

12. 采用pandas可视化数据

13. 对预测错误的样本点进行可视化

14. 看看错误样本被预测为哪些数据

热门文章

最新文章

相关课程

相关电子书

相关实验场景

推荐镜像