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CV之IC之AlexNet：基于tensorflow框架采用CNN卷积神经网络算法(改进的AlexNet,训练/评估/推理)实现猫狗分类识别案例应用_alexnet猫狗识别

作者：不正经 | 2024-02-16 11:18:43

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alexnet猫狗识别

CV之IC之AlexNet：基于tensorflow框架采用CNN卷积神经网络算法(改进的AlexNet,训练/评估/推理)实现猫狗分类识别案例应用

目录

基于tensorflow框架采用CNN(改进的AlexNet,训练/评估/推理)卷积神经网络算法实现猫狗图像分类识别

数据集介绍

输出结果

使用model.ckpt-6000模型预测

 预测错误的只有一个案例，如下所示

训练结果

核心代码

基于tensorflow框架采用CNN(改进的AlexNet,训练/评估/推理)卷积神经网络算法实现猫狗图像分类识别

数据集介绍

数据下载：Dogs vs. Cats Redux: Kernels Edition | Kaggle

     train文件夹里有25000张狗和猫的图片。这个文件夹中的每个图像都有标签作为文件名的一部分。测试文件夹包含12500张图片，根据数字id命名。对于测试集中的每个图像，您应该预测图像是一只狗的概率(1 =狗，0 =猫)。

输出结果

使用model.ckpt-6000模型预测

 预测错误的只有一个案例，如下所示

序号使用model.ckpt-4000模型预测使用model.ckpt-6000模型预测使用model.ckpt-8000模型预测使用model.ckpt-10000模型预测使用model.ckpt-12000模型预测
1catcat (1).jpg 猫的概率 0.631cat (1).jpg 狗的概率 0.740cat (1).jpg 狗的概率 0.781cat (1).jpg 狗的概率 0.976cat (1).jpg 狗的概率 0.991
2cat (10).jpg 狗的概率 0.697cat (10).jpg 猫的概率 0.566cat (10).jpg 猫的概率 0.925cat (10).jpg 猫的概率 0.925cat (10).jpg 猫的概率 0.816
3cat (11).jpg 猫的概率 0.927cat (11).jpg 猫的概率 0.988cat (11).jpg 猫的概率 1.000cat (11).jpg 猫的概率 1.000cat (11).jpg 猫的概率 1.000
4cat (12).jpg 狗的概率 0.746cat (12).jpg 狗的概率 0.723cat (12).jpg 狗的概率 0.822cat (12).jpg 狗的概率 0.998cat (12).jpg 狗的概率 1.000
5cat (13).jpg 猫的概率 0.933cat (13).jpg 猫的概率 0.983cat (13).jpg 猫的概率 0.997cat (13).jpg 猫的概率 1.000cat (13).jpg 猫的概率 1.000
6cat (14).jpg 狗的概率 0.657cat (14).jpg 猫的概率 0.597cat (14).jpg 狗的概率 0.758cat (14).jpg 狗的概率 0.695cat (14).jpg 猫的概率 0.544
7cat (15).jpg 狗的概率 0.578cat (15).jpg 狗的概率 0.535cat (15).jpg 狗的概率 0.526cat (15).jpg 狗的概率 0.750cat (15).jpg 狗的概率 0.569
8cat (2).jpg 猫的概率 0.649cat (2).jpg 猫的概率 0.637cat (2).jpg 猫的概率 0.844cat (2).jpg 猫的概率 0.996cat (2).jpg 猫的概率 0.998
9cat (3).jpg 狗的概率 0.668cat (3).jpg 猫的概率 0.538cat (3).jpg 猫的概率 0.710cat (3).jpg 猫的概率 0.968cat (3).jpg 猫的概率 0.995
10cat (4).jpg 狗的概率 0.856cat (4).jpg 狗的概率 0.780cat (4).jpg 狗的概率 0.831cat (4).jpg 狗的概率 0.974cat (4).jpg 狗的概率 0.976
11cat (5).jpg 猫的概率 0.812cat (5).jpg 猫的概率 0.776cat (5).jpg 猫的概率 0.505cat (5).jpg 猫的概率 0.732cat (5).jpg 狗的概率 0.608
12cat (6).jpg 猫的概率 0.524cat (6).jpg 狗的概率 0.661cat (6).jpg 狗的概率 0.748cat (6).jpg 狗的概率 0.970cat (6).jpg 狗的概率 0.987
13cat (7).jpg 狗的概率 0.612cat (7).jpg 猫的概率 0.845cat (7).jpg 猫的概率 0.894cat (7).jpg 猫的概率 0.987cat (7).jpg 猫的概率 0.728
14cat (8).jpg 狗的概率 0.823cat (8).jpg 狗的概率 0.948cat (8).jpg 狗的概率 0.920cat (8).jpg 狗的概率 0.982cat (8).jpg 狗的概率 0.999
15cat (9).jpg 猫的概率 0.697cat (9).jpg 猫的概率 0.704cat (9).jpg 狗的概率 0.819cat (9).jpg 猫的概率 0.930cat (9).jpg 狗的概率 0.718
16dogdog (1).jpg 狗的概率 0.987dog (1).jpg 狗的概率 0.995dog (1).jpg 狗的概率 0.999dog (1).jpg 狗的概率 1.000dog (1).jpg 狗的概率 1.000
17dog (10).jpg 狗的概率 0.628dog (10).jpg 猫的概率 0.629dog (10).jpg 猫的概率 0.994dog (10).jpg 猫的概率 1.000dog (10).jpg 猫的概率 1.000
18dog (11).jpg 狗的概率 0.804dog (11).jpg 狗的概率 0.879dog (11).jpg 狗的概率 0.993dog (11).jpg 狗的概率 1.000dog (11).jpg 狗的概率 1.000
19dog (12).jpg 猫的概率 0.704dog (12).jpg 猫的概率 0.758dog (12).jpg 狗的概率 0.503dog (12).jpg 狗的概率 0.653dog (12).jpg 猫的概率 0.985
20dog (13).jpg 狗的概率 0.987dog (13).jpg 狗的概率 0.997dog (13).jpg 狗的概率 1.000dog (13).jpg 狗的概率 1.000dog (13).jpg 狗的概率 1.000
21dog (14).jpg 狗的概率 0.815dog (14).jpg 狗的概率 0.844dog (14).jpg 狗的概率 0.904dog (14).jpg 狗的概率 0.996dog (14).jpg 狗的概率 0.950
22dog (15).jpg 狗的概率 0.917dog (15).jpg 狗的概率 0.984dog (15).jpg 狗的概率 0.999dog (15).jpg 狗的概率 1.000dog (15).jpg 狗的概率 1.000
23dog (16).jpg 狗的概率 0.883dog (16).jpg 狗的概率 0.931dog (16).jpg 狗的概率 0.830dog (16).jpg 狗的概率 0.975dog (16).jpg 狗的概率 0.983
24dog (2).jpg 狗的概率 0.934dog (2).jpg 狗的概率 0.982dog (2).jpg 狗的概率 0.998dog (2).jpg 狗的概率 1.000dog (2).jpg 狗的概率 1.000
25dog (3).jpg 狗的概率 0.993dog (3).jpg 狗的概率 1.000dog (3).jpg 狗的概率 1.000dog (3).jpg 狗的概率 1.000dog (3).jpg 狗的概率 1.000
26dog (4).jpg 狗的概率 0.693dog (4).jpg 狗的概率 0.754dog (4).jpg 狗的概率 0.976dog (4).jpg 狗的概率 0.515dog (4).jpg 狗的概率 0.995
27dog (5).jpg 狗的概率 0.916dog (5).jpg 狗的概率 0.976dog (5).jpg 狗的概率 0.993dog (5).jpg 狗的概率 0.998dog (5).jpg 狗的概率 1.000
28dog (6).jpg 狗的概率 0.947dog (6).jpg 狗的概率 0.989dog (6).jpg 狗的概率 0.999dog (6).jpg 狗的概率 1.000dog (6).jpg 狗的概率 1.000
29dog (7).jpg 猫的概率 0.526dog (7).jpg 猫的概率 0.685dog (7).jpg 猫的概率 0.961dog (7).jpg 猫的概率 1.000dog (7).jpg 猫的概率 1.000
30dog (8).jpg 狗的概率 0.981dog (8).jpg 狗的概率 0.998dog (8).jpg 狗的概率 1.000dog (8).jpg 狗的概率 1.000dog (8).jpg 狗的概率 1.000
31dog (9).jpg 狗的概率 0.899dog (9).jpg 狗的概率 0.983dog (9).jpg 狗的概率 0.999dog (9).jpg 狗的概率 1.000dog (9).jpg 狗的概率 1.000

训练结果

  1. Step 0, train loss = 0.69, train accuracy = 78.12%
  2. Step 50, train loss = 0.69, train accuracy = 43.75%
  3. Step 100, train loss = 0.70, train accuracy = 46.88%
  4. Step 150, train loss = 0.65, train accuracy = 75.00%
  5. Step 200, train loss = 0.66, train accuracy = 59.38%
  6. Step 250, train loss = 0.66, train accuracy = 62.50%
  7. Step 300, train loss = 0.72, train accuracy = 40.62%
  8. Step 350, train loss = 0.66, train accuracy = 62.50%
  9. Step 400, train loss = 0.58, train accuracy = 68.75%
  10. Step 450, train loss = 0.70, train accuracy = 65.62%
  11. Step 500, train loss = 0.68, train accuracy = 56.25%
  12. Step 550, train loss = 0.51, train accuracy = 81.25%
  13. Step 600, train loss = 0.54, train accuracy = 75.00%
  14. Step 650, train loss = 0.64, train accuracy = 68.75%
  15. Step 700, train loss = 0.69, train accuracy = 53.12%
  16. Step 750, train loss = 0.57, train accuracy = 71.88%
  17. Step 800, train loss = 0.80, train accuracy = 50.00%
  18. Step 850, train loss = 0.62, train accuracy = 59.38%
  19. Step 900, train loss = 0.59, train accuracy = 65.62%
  20. Step 950, train loss = 0.54, train accuracy = 71.88%
  21. Step 1000, train loss = 0.57, train accuracy = 68.75%
  22. Step 1050, train loss = 0.56, train accuracy = 78.12%
  23. Step 1100, train loss = 0.66, train accuracy = 59.38%
  24. Step 1150, train loss = 0.50, train accuracy = 84.38%
  25. Step 1200, train loss = 0.46, train accuracy = 81.25%
  26. Step 1250, train loss = 0.57, train accuracy = 59.38%
  27. Step 1300, train loss = 0.37, train accuracy = 81.25%
  28. Step 1350, train loss = 0.64, train accuracy = 62.50%
  29. Step 1400, train loss = 0.44, train accuracy = 81.25%
  30. Step 1450, train loss = 0.46, train accuracy = 84.38%
  31. Step 1500, train loss = 0.50, train accuracy = 71.88%
  32. Step 1550, train loss = 0.58, train accuracy = 62.50%
  33. Step 1600, train loss = 0.43, train accuracy = 75.00%
  34. Step 1650, train loss = 0.55, train accuracy = 71.88%
  35. Step 1700, train loss = 0.50, train accuracy = 71.88%
  36. Step 1750, train loss = 0.46, train accuracy = 75.00%
  37. Step 1800, train loss = 0.81, train accuracy = 53.12%
  38. Step 1850, train loss = 0.41, train accuracy = 90.62%
  39. Step 1900, train loss = 0.65, train accuracy = 68.75%
  40. Step 1950, train loss = 0.37, train accuracy = 84.38%
  41. Step 2000, train loss = 0.39, train accuracy = 81.25%
  42. Step 2050, train loss = 0.45, train accuracy = 84.38%
  43. Step 2100, train loss = 0.44, train accuracy = 78.12%
  44. Step 2150, train loss = 0.59, train accuracy = 65.62%
  45. Step 2200, train loss = 0.51, train accuracy = 78.12%
  46. Step 2250, train loss = 0.42, train accuracy = 81.25%
  47. Step 2300, train loss = 0.32, train accuracy = 87.50%
  48. Step 2350, train loss = 0.48, train accuracy = 75.00%
  49. Step 2400, train loss = 0.54, train accuracy = 71.88%
  50. Step 2450, train loss = 0.51, train accuracy = 71.88%
  51. Step 2500, train loss = 0.73, train accuracy = 59.38%
  52. Step 2550, train loss = 0.52, train accuracy = 78.12%
  53. Step 2600, train loss = 0.65, train accuracy = 62.50%
  54. Step 2650, train loss = 0.52, train accuracy = 71.88%
  55. Step 2700, train loss = 0.48, train accuracy = 71.88%
  56. Step 2750, train loss = 0.37, train accuracy = 84.38%
  57. Step 2800, train loss = 0.46, train accuracy = 78.12%
  58. Step 2850, train loss = 0.40, train accuracy = 84.38%
  59. Step 2900, train loss = 0.45, train accuracy = 81.25%
  60. Step 2950, train loss = 0.36, train accuracy = 84.38%
  61. Step 3000, train loss = 0.46, train accuracy = 75.00%
  62. Step 3050, train loss = 0.53, train accuracy = 71.88%
  63. Step 3100, train loss = 0.37, train accuracy = 84.38%
  64. Step 3150, train loss = 0.53, train accuracy = 75.00%
  65. Step 3200, train loss = 0.52, train accuracy = 75.00%
  66. Step 3250, train loss = 0.62, train accuracy = 65.62%
  67. Step 3300, train loss = 0.58, train accuracy = 71.88%
  68. Step 3350, train loss = 0.71, train accuracy = 65.62%
  69. Step 3400, train loss = 0.43, train accuracy = 78.12%
  70. Step 3450, train loss = 0.46, train accuracy = 78.12%
  71. Step 3500, train loss = 0.46, train accuracy = 71.88%
  72. Step 3550, train loss = 0.53, train accuracy = 68.75%
  73. Step 3600, train loss = 0.44, train accuracy = 75.00%
  74. Step 3650, train loss = 0.55, train accuracy = 65.62%
  75. Step 3700, train loss = 0.62, train accuracy = 75.00%
  76. Step 3750, train loss = 0.48, train accuracy = 75.00%
  77. Step 3800, train loss = 0.66, train accuracy = 53.12%
  78. Step 3850, train loss = 0.53, train accuracy = 75.00%
  79. Step 3900, train loss = 0.36, train accuracy = 81.25%
  80. Step 3950, train loss = 0.37, train accuracy = 87.50%
  81. Step 4000, train loss = 0.46, train accuracy = 78.12%
  82. Step 4050, train loss = 0.36, train accuracy = 84.38%
  83. Step 4100, train loss = 0.34, train accuracy = 78.12%
  84. Step 4150, train loss = 0.48, train accuracy = 78.12%
  85. Step 4200, train loss = 0.43, train accuracy = 87.50%
  86. Step 4250, train loss = 0.34, train accuracy = 84.38%
  87. Step 4300, train loss = 0.28, train accuracy = 87.50%
  88. Step 4350, train loss = 0.19, train accuracy = 96.88%
  89. Step 4400, train loss = 0.46, train accuracy = 71.88%
  90. Step 4450, train loss = 0.33, train accuracy = 84.38%
  91. Step 4500, train loss = 0.55, train accuracy = 75.00%
  92. Step 4550, train loss = 0.31, train accuracy = 93.75%
  93. Step 4600, train loss = 0.30, train accuracy = 84.38%
  94. Step 4650, train loss = 0.38, train accuracy = 84.38%
  95. Step 4700, train loss = 0.36, train accuracy = 84.38%
  96. Step 4750, train loss = 0.32, train accuracy = 87.50%
  97. Step 4800, train loss = 0.36, train accuracy = 81.25%
  98. Step 4850, train loss = 0.36, train accuracy = 87.50%
  99. Step 4900, train loss = 0.49, train accuracy = 71.88%
  100. Step 4950, train loss = 0.51, train accuracy = 68.75%
  101. Step 5000, train loss = 0.59, train accuracy = 68.75%
  102. Step 5050, train loss = 0.55, train accuracy = 75.00%
  103. Step 5100, train loss = 0.71, train accuracy = 68.75%
  104. Step 5150, train loss = 0.48, train accuracy = 71.88%
  105. Step 5200, train loss = 0.39, train accuracy = 90.62%
  106. Step 5250, train loss = 0.49, train accuracy = 81.25%
  107. Step 5300, train loss = 0.36, train accuracy = 81.25%
  108. Step 5350, train loss = 0.31, train accuracy = 90.62%
  109. Step 5400, train loss = 0.39, train accuracy = 87.50%
  110. Step 5450, train loss = 0.34, train accuracy = 78.12%
  111. Step 5500, train loss = 0.29, train accuracy = 84.38%
  112. Step 5550, train loss = 0.21, train accuracy = 93.75%
  113. Step 5600, train loss = 0.41, train accuracy = 78.12%
  114. Step 5650, train loss = 0.38, train accuracy = 84.38%
  115. Step 5700, train loss = 0.27, train accuracy = 87.50%
  116. Step 5750, train loss = 0.24, train accuracy = 90.62%
  117. Step 5800, train loss = 0.17, train accuracy = 96.88%
  118. Step 5850, train loss = 0.23, train accuracy = 93.75%
  119. Step 5900, train loss = 0.37, train accuracy = 71.88%
  120. Step 5950, train loss = 0.49, train accuracy = 71.88%
  121. Step 6000, train loss = 0.43, train accuracy = 81.25%
  122. Step 6050, train loss = 0.33, train accuracy = 87.50%
  123. Step 6100, train loss = 0.46, train accuracy = 75.00%
  124. Step 6150, train loss = 0.61, train accuracy = 81.25%
  125. Step 6200, train loss = 0.34, train accuracy = 84.38%
  126. Step 6250, train loss = 0.63, train accuracy = 71.88%
  127. Step 6300, train loss = 0.21, train accuracy = 90.62%
  128. Step 6350, train loss = 0.21, train accuracy = 90.62%
  129. Step 6400, train loss = 0.27, train accuracy = 87.50%
  130. Step 6450, train loss = 0.17, train accuracy = 87.50%
  131. Step 6500, train loss = 0.34, train accuracy = 87.50%
  132. Step 6550, train loss = 0.34, train accuracy = 87.50%
  133. Step 6600, train loss = 0.32, train accuracy = 84.38%
  134. Step 6650, train loss = 0.39, train accuracy = 84.38%
  135. Step 6700, train loss = 0.38, train accuracy = 84.38%
  136. Step 6750, train loss = 0.41, train accuracy = 84.38%
  137. Step 6800, train loss = 0.49, train accuracy = 81.25%
  138. Step 6850, train loss = 0.36, train accuracy = 84.38%
  139. Step 6900, train loss = 0.20, train accuracy = 93.75%
  140. Step 6950, train loss = 0.13, train accuracy = 93.75%
  141. Step 7000, train loss = 0.31, train accuracy = 87.50%
  142. Step 7050, train loss = 0.18, train accuracy = 93.75%
  143. Step 7100, train loss = 0.23, train accuracy = 90.62%
  144. Step 7150, train loss = 0.13, train accuracy = 96.88%
  145. Step 7200, train loss = 0.14, train accuracy = 96.88%
  146. Step 7250, train loss = 0.32, train accuracy = 84.38%
  147. Step 7300, train loss = 0.18, train accuracy = 93.75%
  148. Step 7350, train loss = 0.14, train accuracy = 100.00%
  149. Step 7400, train loss = 0.60, train accuracy = 75.00%
  150. Step 7450, train loss = 0.20, train accuracy = 93.75%
  151. Step 7500, train loss = 0.13, train accuracy = 93.75%
  152. Step 7550, train loss = 0.22, train accuracy = 90.62%
  153. Step 7600, train loss = 0.13, train accuracy = 96.88%
  154. Step 7650, train loss = 0.20, train accuracy = 93.75%
  155. Step 7700, train loss = 0.24, train accuracy = 90.62%
  156. Step 7750, train loss = 0.19, train accuracy = 93.75%
  157. Step 7800, train loss = 0.16, train accuracy = 93.75%
  158. Step 7850, train loss = 0.08, train accuracy = 100.00%
  159. Step 7900, train loss = 0.10, train accuracy = 96.88%
  160. Step 7950, train loss = 0.13, train accuracy = 93.75%
  161. Step 8000, train loss = 0.18, train accuracy = 90.62%
  162. Step 8050, train loss = 0.27, train accuracy = 93.75%
  163. Step 8100, train loss = 0.04, train accuracy = 100.00%
  164. Step 8150, train loss = 0.27, train accuracy = 87.50%
  165. Step 8200, train loss = 0.06, train accuracy = 96.88%
  166. Step 8250, train loss = 0.12, train accuracy = 100.00%
  167. Step 8300, train loss = 0.28, train accuracy = 87.50%
  168. Step 8350, train loss = 0.24, train accuracy = 90.62%
  169. Step 8400, train loss = 0.16, train accuracy = 93.75%
  170. Step 8450, train loss = 0.11, train accuracy = 93.75%
  171. Step 8500, train loss = 0.13, train accuracy = 96.88%
  172. Step 8550, train loss = 0.05, train accuracy = 100.00%
  173. Step 8600, train loss = 0.10, train accuracy = 93.75%
  174. Step 8650, train loss = 0.14, train accuracy = 100.00%
  175. Step 8700, train loss = 0.21, train accuracy = 90.62%
  176. Step 8750, train loss = 0.09, train accuracy = 96.88%
  177. Step 8800, train loss = 0.11, train accuracy = 96.88%
  178. Step 8850, train loss = 0.10, train accuracy = 96.88%
  179. Step 8900, train loss = 0.12, train accuracy = 93.75%
  180. Step 8950, train loss = 0.48, train accuracy = 81.25%
  181. Step 9000, train loss = 0.07, train accuracy = 100.00%
  182. Step 9050, train loss = 0.03, train accuracy = 100.00%
  183. Step 9100, train loss = 0.10, train accuracy = 93.75%
  184. Step 9150, train loss = 0.05, train accuracy = 96.88%
  185. Step 9200, train loss = 0.04, train accuracy = 100.00%
  186. Step 9250, train loss = 0.03, train accuracy = 100.00%
  187. Step 9300, train loss = 0.04, train accuracy = 96.88%
  188. Step 9350, train loss = 0.08, train accuracy = 100.00%
  189. Step 9400, train loss = 0.05, train accuracy = 100.00%
  190. Step 9450, train loss = 0.15, train accuracy = 90.62%
  191. Step 9500, train loss = 0.03, train accuracy = 100.00%
  192. Step 9550, train loss = 0.05, train accuracy = 100.00%
  193. Step 9600, train loss = 0.15, train accuracy = 96.88%
  194. Step 9650, train loss = 0.03, train accuracy = 100.00%
  195. Step 9700, train loss = 0.02, train accuracy = 100.00%
  196. Step 9750, train loss = 0.08, train accuracy = 96.88%
  197. Step 9800, train loss = 0.04, train accuracy = 100.00%
  198. Step 9850, train loss = 0.06, train accuracy = 96.88%
  199. Step 9900, train loss = 0.03, train accuracy = 100.00%
  200. Step 9950, train loss = 0.03, train accuracy = 100.00%
  201. Step 10000, train loss = 0.11, train accuracy = 93.75%
  202. Step 10050, train loss = 0.02, train accuracy = 100.00%
  203. Step 10100, train loss = 0.01, train accuracy = 100.00%
  204. Step 10150, train loss = 0.05, train accuracy = 96.88%
  205. Step 10200, train loss = 0.07, train accuracy = 96.88%
  206. Step 10250, train loss = 0.06, train accuracy = 96.88%
  207. Step 10300, train loss = 0.03, train accuracy = 100.00%
  208. Step 10350, train loss = 0.08, train accuracy = 96.88%
  209. Step 10400, train loss = 0.05, train accuracy = 96.88%
  210. Step 10450, train loss = 0.02, train accuracy = 100.00%
  211. Step 10500, train loss = 0.22, train accuracy = 93.75%
  212. Step 10550, train loss = 0.06, train accuracy = 100.00%
  213. Step 10600, train loss = 0.02, train accuracy = 100.00%
  214. Step 10650, train loss = 0.02, train accuracy = 100.00%
  215. Step 10700, train loss = 0.03, train accuracy = 100.00%
  216. Step 10750, train loss = 0.15, train accuracy = 96.88%
  217. Step 10800, train loss = 0.05, train accuracy = 100.00%
  218. Step 10850, train loss = 0.02, train accuracy = 100.00%
  219. Step 10900, train loss = 0.04, train accuracy = 96.88%
  220. Step 10950, train loss = 0.05, train accuracy = 96.88%
  221. Step 11000, train loss = 0.02, train accuracy = 100.00%
  222. Step 11050, train loss = 0.10, train accuracy = 96.88%
  223. Step 11100, train loss = 0.08, train accuracy = 96.88%
  224. Step 11150, train loss = 0.02, train accuracy = 100.00%
  225. Step 11200, train loss = 0.01, train accuracy = 100.00%
  226. Step 11250, train loss = 0.06, train accuracy = 96.88%
  227. Step 11300, train loss = 0.18, train accuracy = 93.75%
  228. Step 11350, train loss = 0.02, train accuracy = 100.00%
  229. Step 11400, train loss = 0.04, train accuracy = 100.00%
  230. Step 11450, train loss = 0.03, train accuracy = 100.00%
  231. Step 11500, train loss = 0.01, train accuracy = 100.00%
  232. Step 11550, train loss = 0.02, train accuracy = 100.00%

核心代码

  1.         weights = tf.get_variable('weights',  
  2.                                   shape=[3, 3, 3, 16],  
  3.                                   dtype=tf.float32,  
  4.                                   initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32))  
  5.         biases = tf.get_variable('biases',  
  6.                                  shape=[16],  
  7.                                  dtype=tf.float32,  
  8.                                  initializer=tf.constant_initializer(0.1))  
  9.         conv = tf.nn.conv2d(images, weights, strides=[1, 1, 1, 1], padding='SAME')  
  10.         pre_activation = tf.nn.bias_add(conv, biases)  
  11.         conv1 = tf.nn.relu(pre_activation, name=scope.name)  
  12.     with tf.variable_scope('pooling1_lrn') as scope:  
  13.             pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pooling1')  
  14.             norm1 = tf.nn.lrn(pool1, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm1')  
  15.             
  16.     with tf.variable_scope('conv2') as scope:  
  17.                 weights = tf.get_variable('weights',  
  18.                                           shape=[3, 3, 16, 16],  
  19.                                           dtype=tf.float32,  
  20.                                           initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32))  
  21.                 biases = tf.get_variable('biases',  
  22.                                          shape=[16],  
  23.                                          dtype=tf.float32,  
  24.                                          initializer=tf.constant_initializer(0.1))  
  25.                 conv = tf.nn.conv2d(norm1, weights, strides=[1, 1, 1, 1], padding='SAME')  
  26.                 pre_activation = tf.nn.bias_add(conv, biases)  
  27.                 conv2 = tf.nn.relu(pre_activation, name='conv2')  
  28.                 
  29.  
  30.     with tf.variable_scope('pooling2_lrn') as scope:  
  31.         norm2 = tf.nn.lrn(conv2, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm2')  
  32.         pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 1, 1, 1], padding='SAME', name='pooling2')  
  33.         
  34.     with tf.variable_scope('local3') as scope:  
  35.         reshape = tf.reshape(pool2, shape=[batch_size, -1])  
  36.         dim = reshape.get_shape()[1].value  
  37.         weights = tf.get_variable('weights',  
  38.                                   shape=[dim, 128],  
  39.                                   dtype=tf.float32,  
  40.                                   initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32))  
  41.         biases = tf.get_variable('biases',  
  42.                                  shape=[128],  
  43.                                  dtype=tf.float32,  
  44.                                  initializer=tf.constant_initializer(0.1))  
  45.     local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)  
  46.   
  47.     # local4  
  48.     with tf.variable_scope('local4') as scope:  
  49.         weights = tf.get_variable('weights',  
  50.                                   shape=[128, 128],  
  51.                                   dtype=tf.float32,  
  52.                                   initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32))  
  53.         biases = tf.get_variable('biases',  
  54.                                  shape=[128],  
  55.                                  dtype=tf.float32,  
  56.                                  initializer=tf.constant_initializer(0.1))  
  57.         local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name='local4')  
  58.   
  59.  
  60.     with tf.variable_scope('softmax_linear') as scope:  
  61.         weights = tf.get_variable('softmax_linear',  
  62.                                   shape=[128, n_classes],  
  63.                                   dtype=tf.float32,  
  64.                                   initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32))  
  65.         biases = tf.get_variable('biases',  
  66.                                  shape=[n_classes],  
  67.                                  dtype=tf.float32,  
  68.                                  initializer=tf.constant_initializer(0.1))  
  69.         softmax_linear = tf.add(tf.matmul(local4, weights), biases, name='softmax_linear')

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