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DL之Attention：基于ClutteredMNIST手写数字图片数据集分别利用CNN_Init、ST_CNN算法(CNN+SpatialTransformer)实现多分类预测

2022-07-24 132
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简介： DL之Attention：基于ClutteredMNIST手写数字图片数据集分别利用CNN_Init、ST_CNN算法(CNN+SpatialTransformer)实现多分类预测

目录

基于ClutteredMNIST手写数字图片数据集分别利用CNN_Init、ST_CNN算法(CNN+SpatialTransformer)实现多分类预测

数据特征工程

T1、CNN_Init start

输出结果

核心代码

T2、ST_CNN start

核心代码

 

 

 

相关文章

DL之Attention：基于ClutteredMNIST手写数字图片数据集分别利用CNN_Init、ST_CNN算法(CNN+SpatialTransformer)实现多分类预测

DL之Attention：基于ClutteredMNIST手写数字图片数据集分别利用CNN_Init、ST_CNN算法(CNN+SpatialTransformer)实现多分类预测实现

 

基于ClutteredMNIST手写数字图片数据集分别利用CNN_Init、ST_CNN算法(CNN+SpatialTransformer)实现多分类预测

数据特征工程

1. Train samples: (50000, 60, 60, 1)
2. Validation samples: (10000, 60, 60, 1)
3. Test samples: (10000, 60, 60, 1)
4. Input shape: (60, 60, 1)

 

T1、CNN_Init start

输出结果

1. T1、CNN_Init start!
2. _________________________________________________________________
3. Layer (type)                 Output Shape              Param #   
4. =================================================================
5. conv2d_1 (Conv2D)            (None, 58, 58, 32)        320
6. _________________________________________________________________
7. conv2d_2 (Conv2D)            (None, 56, 56, 64)        18496
8. _________________________________________________________________
9. max_pooling2d_1 (MaxPooling2 (None, 28, 28, 64)        0
10. _________________________________________________________________
11. conv2d_3 (Conv2D)            (None, 26, 26, 64)        36928
12. _________________________________________________________________
13. max_pooling2d_2 (MaxPooling2 (None, 13, 13, 64)        0
14. _________________________________________________________________
15. conv2d_4 (Conv2D)            (None, 11, 11, 64)        36928
16. _________________________________________________________________
17. dropout_1 (Dropout)          (None, 11, 11, 64)        0
18. _________________________________________________________________
19. flatten_1 (Flatten)          (None, 7744)              0
20. _________________________________________________________________
21. dense_1 (Dense)              (None, 128)               991360
22. _________________________________________________________________
23. dropout_2 (Dropout)          (None, 128)               0
24. _________________________________________________________________
25. dense_2 (Dense)              (None, 10)                1290
26. =================================================================
27. Total params: 1,085,322
28. Trainable params: 1,085,322
29. Non-trainable params: 0
30. _________________________________________________________________
31. None
32. Train on 50000 samples, validate on 10000 samples
33. Epoch 1/30

核心代码

1. #(1)、定义模型结构
2.     model = Sequential()
3.     model.add(Conv2D(32, kernel_size=(3, 3),
4.                      activation='relu',
5.                      input_shape=input_shape))
6.     model.add(Conv2D(64, (3, 3), activation='relu'))
7.     model.add(MaxPooling2D(pool_size=(2, 2)))
8.     model.add(Conv2D(64, kernel_size=(3, 3),
9.                      activation='relu'))
10.     model.add(MaxPooling2D(pool_size=(2, 2)))
11.     model.add(Conv2D(64, (3, 3), activation='relu'))
12.     model.add(Dropout(0.25))
13.     model.add(Flatten())
14.     model.add(Dense(128, activation='relu'))
15.     model.add(Dropout(0.5))
16.     model.add(Dense(nb_classes, activation='softmax'))

 

 

 

 

 

T2、ST_CNN start

1. _________________________________________________________________
2. Layer (type)                 Output Shape              Param #   
3. =================================================================
4. conv2d_1 (Conv2D)            (None, 56, 56, 32)        832
5. _________________________________________________________________
6. activation_1 (Activation)    (None, 56, 56, 32)        0
7. _________________________________________________________________
8. max_pooling2d_1 (MaxPooling2 (None, 28, 28, 32)        0
9. _________________________________________________________________
10. conv2d_2 (Conv2D)            (None, 24, 24, 64)        51264
11. _________________________________________________________________
12. activation_2 (Activation)    (None, 24, 24, 64)        0
13. _________________________________________________________________
14. conv2d_3 (Conv2D)            (None, 22, 22, 64)        36928
15. _________________________________________________________________
16. activation_3 (Activation)    (None, 22, 22, 64)        0
17. _________________________________________________________________
18. max_pooling2d_2 (MaxPooling2 (None, 11, 11, 64)        0
19. _________________________________________________________________
20. flatten_1 (Flatten)          (None, 7744)              0
21. _________________________________________________________________
22. dense_1 (Dense)              (None, 50)                387250
23. _________________________________________________________________
24. activation_4 (Activation)    (None, 50)                0
25. _________________________________________________________________
26. dense_2 (Dense)              (None, 6)                 306
27. =================================================================
28. Total params: 476,580
29. Trainable params: 476,580
30. Non-trainable params: 0
31. _________________________________________________________________
32. None
33. _________________________________________________________________
34. Layer (type)                 Output Shape              Param #   
35. =================================================================
36. spatial_transformer_1 (Spati (None, 30, 30, 1)         476580
37. _________________________________________________________________
38. conv2d_4 (Conv2D)            (None, 28, 28, 32)        320
39. _________________________________________________________________
40. dropout_1 (Dropout)          (None, 28, 28, 32)        0
41. _________________________________________________________________
42. conv2d_5 (Conv2D)            (None, 26, 26, 64)        18496
43. _________________________________________________________________
44. dropout_2 (Dropout)          (None, 26, 26, 64)        0
45. _________________________________________________________________
46. max_pooling2d_3 (MaxPooling2 (None, 13, 13, 64)        0
47. _________________________________________________________________
48. conv2d_6 (Conv2D)            (None, 11, 11, 64)        36928
49. _________________________________________________________________
50. dropout_3 (Dropout)          (None, 11, 11, 64)        0
51. _________________________________________________________________
52. max_pooling2d_4 (MaxPooling2 (None, 5, 5, 64)          0
53. _________________________________________________________________
54. flatten_2 (Flatten)          (None, 1600)              0
55. _________________________________________________________________
56. dense_3 (Dense)              (None, 256)               409856
57. _________________________________________________________________
58. dropout_4 (Dropout)          (None, 256)               0
59. _________________________________________________________________
60. activation_5 (Activation)    (None, 256)               0
61. _________________________________________________________________
62. dense_4 (Dense)              (None, 10)                2570
63. _________________________________________________________________
64. activation_6 (Activation)    (None, 10)                0
65. =================================================================
66. Total params: 944,750
67. Trainable params: 944,750
68. Non-trainable params: 0
69. _________________________________________________________________
70. None
71. Train on 50000 samples, validate on 10000 samples
72. Epoch 1/30
73.  - 974s - loss: 2.0926 - categorical_accuracy: 0.2345 - val_loss: 1.6258 - val_categorical_accuracy: 0.5949
74. Epoch 2/30
75.  - 1007s - loss: 1.0926 - categorical_accuracy: 0.6387 - val_loss: 0.7963 - val_categorical_accuracy: 0.8433
76. Epoch 3/30
77.  - 844s - loss: 0.6038 - categorical_accuracy: 0.8118 - val_loss: 0.4906 - val_categorical_accuracy: 0.8977
78. Epoch 4/30
79.  - 851s - loss: 0.4351 - categorical_accuracy: 0.8648 - val_loss: 0.3909 - val_categorical_accuracy: 0.9160
80. Epoch 5/30
81.  - 864s - loss: 0.3483 - categorical_accuracy: 0.8914 - val_loss: 0.3046 - val_categorical_accuracy: 0.9367
82. Epoch 6/30
83.  - 872s - loss: 0.3158 - categorical_accuracy: 0.9027 - val_loss: 0.2826 - val_categorical_accuracy: 0.9349
84. Epoch 7/30
85.  - 861s - loss: 0.2772 - categorical_accuracy: 0.9136 - val_loss: 0.3244 - val_categorical_accuracy: 0.9243
86. Epoch 8/30
87.  - 862s - loss: 0.2414 - categorical_accuracy: 0.9251 - val_loss: 0.2228 - val_categorical_accuracy: 0.9600
88. Epoch 9/30
89.  - 858s - loss: 0.2278 - categorical_accuracy: 0.9287 - val_loss: 0.2305 - val_categorical_accuracy: 0.9556
90. Epoch 10/30
91.  - 860s - loss: 0.2150 - categorical_accuracy: 0.9328 - val_loss: 0.2119 - val_categorical_accuracy: 0.9600
92. Epoch 11/30
93.  - 862s - loss: 0.2130 - categorical_accuracy: 0.9334 - val_loss: 0.1949 - val_categorical_accuracy: 0.9583
94. Epoch 12/30
95.  - 855s - loss: 0.1917 - categorical_accuracy: 0.9410 - val_loss: 0.1841 - val_categorical_accuracy: 0.9595
96. Epoch 13/30
97.  - 857s - loss: 0.1891 - categorical_accuracy: 0.9414 - val_loss: 0.2455 - val_categorical_accuracy: 0.9613
98. Epoch 14/30
99.  - 862s - loss: 0.1865 - categorical_accuracy: 0.9423 - val_loss: 0.2044 - val_categorical_accuracy: 0.9629
100. Epoch 15/30
101.  - 863s - loss: 0.1789 - categorical_accuracy: 0.9446 - val_loss: 0.2147 - val_categorical_accuracy: 0.9647
102. Epoch 16/30
103.  - 855s - loss: 0.1708 - categorical_accuracy: 0.9460 - val_loss: 0.1748 - val_categorical_accuracy: 0.9692
104. Epoch 17/30
105.  - 859s - loss: 0.1615 - categorical_accuracy: 0.9509 - val_loss: 0.1870 - val_categorical_accuracy: 0.9707
106. Epoch 18/30
107.  - 862s - loss: 0.1538 - categorical_accuracy: 0.9514 - val_loss: 0.1906 - val_categorical_accuracy: 0.9689
108. Epoch 19/30
109.  - 866s - loss: 0.1494 - categorical_accuracy: 0.9537 - val_loss: 0.1596 - val_categorical_accuracy: 0.9728
110. Epoch 20/30
111.  - 864s - loss: 0.1490 - categorical_accuracy: 0.9537 - val_loss: 0.1821 - val_categorical_accuracy: 0.9692
112. Epoch 21/30
113.  - 860s - loss: 0.1517 - categorical_accuracy: 0.9524 - val_loss: 0.1579 - val_categorical_accuracy: 0.9701
114. Epoch 22/30
115.  - 859s - loss: 0.1506 - categorical_accuracy: 0.9539 - val_loss: 0.1595 - val_categorical_accuracy: 0.9712
116. Epoch 23/30
117.  - 859s - loss: 0.1407 - categorical_accuracy: 0.9567 - val_loss: 0.1590 - val_categorical_accuracy: 0.9712
118. Epoch 24/30
119.  - 856s - loss: 0.1361 - categorical_accuracy: 0.9569 - val_loss: 0.2160 - val_categorical_accuracy: 0.9723
120. Epoch 25/30
121.  - 856s - loss: 0.1348 - categorical_accuracy: 0.9583 - val_loss: 0.1678 - val_categorical_accuracy: 0.9741
122. Epoch 26/30
123.  - 856s - loss: 0.1298 - categorical_accuracy: 0.9596 - val_loss: 0.1820 - val_categorical_accuracy: 0.9707
124. Epoch 27/30
125.  - 856s - loss: 0.1317 - categorical_accuracy: 0.9597 - val_loss: 0.1998 - val_categorical_accuracy: 0.9738
126. Epoch 28/30
127.  - 855s - loss: 0.1325 - categorical_accuracy: 0.9594 - val_loss: 0.1991 - val_categorical_accuracy: 0.9674
128. Epoch 29/30
129.  - 856s - loss: 0.1230 - categorical_accuracy: 0.9621 - val_loss: 0.1848 - val_categorical_accuracy: 0.9720
130. Epoch 30/30
131.  - 856s - loss: 0.1246 - categorical_accuracy: 0.9611 - val_loss: 0.1754 - val_categorical_accuracy: 0.9755
132. 
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446.

核心代码

1. #(2)、建立ST定位网络：尝试更多的conv层，并分别在X轴和y轴上做最大池化
2. # localization net. TODO: try more conv layers, and do max pooling on X- and Y-axes respectively
3.     locnet = Sequential()
4. # locnet.add(MaxPooling2D(pool_size=(2,2), input_shape=input_shape))
5. # locnet.add(Convolution2D(32, (5, 5)))
6.     locnet.add(Convolution2D(32, (5, 5), input_shape=input_shape))
7.     locnet.add(Activation('relu'))
8. # locnet.add(Dropout(0.2)) # 0.2
9.     locnet.add(MaxPooling2D(pool_size=(2,2)))
10.     locnet.add(Convolution2D(64, (5, 5)))
11.     locnet.add(Activation('relu'))
12. # locnet.add(Dropout(0.2)) # 0.3
13.     locnet.add(Convolution2D(64, (3, 3)))
14.     locnet.add(Activation('relu'))
15.     locnet.add(MaxPooling2D(pool_size=(2,2)))
16. 
17.     locnet.add(Flatten())
18.     locnet.add(Dense(50))
19.     locnet.add(Activation('relu'))
20.     locnet.add(Dense(6, weights=weights))
21. print(locnet.summary())
22. 
23. 
24. #(3)、建立CNN网络
25.     model = Sequential()
26.     model.add(SpatialTransformer(localization_net=locnet,
27.                                  output_size=(30,30), input_shape=input_shape))
28. # model.add(Convolution2D(32, (3, 3), padding='same'))
29. # model.add(Activation('relu'))
30. # model.add(MaxPooling2D(pool_size=(2, 2)))
31. # model.add(Convolution2D(64, (3, 3)))
32. # model.add(Activation('relu'))
33. # model.add(MaxPooling2D(pool_size=(2, 2)))
34. # model.add(Dropout(0.5)) # 0.25
35. 
36. # E: removed first 3 dropout layers
37.     model.add(Conv2D(32, kernel_size=(3, 3), activation='relu'))
38.     model.add(Dropout(0.5)) # 0.5
39.     model.add(Conv2D(64, (3, 3), activation='relu'))
40.     model.add(Dropout(0.5)) # 0.5
41.     model.add(MaxPooling2D(pool_size=(2, 2)))
42.     model.add(Conv2D(64, kernel_size=(3, 3),
43.                      activation='relu'))
44.     model.add(Dropout(0.5)) # 0.5
45.     model.add(MaxPooling2D(pool_size=(2, 2)))
46. # model.add(Conv2D(64, (3, 3), activation='relu'))
47. # model.add(Dropout(0.5))
48.     model.add(Flatten())
49.     model.add(Dense(256)) # 256
50.     model.add(Dropout(0.5)) # 0.5
51.     model.add(Activation('relu'))
52.     model.add(Dense(nb_classes))
53.     model.add(Activation('softmax'))


文章标签：
机器学习/深度学习
算法
关键词：
CNN算法
算法分类
算法图片
图片算法
CNN数据集
一个处女座的程序猿
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