python - SimpleRNN的多维输入结果效率不高
问题描述
我正在制作超级简单的 RNN 模型
它需要明显有规律的时间序列数据
([[1,2,3], [3, 4, 5],[5,6,7], [7, 8, 9],[9,10,11], [11, 12, 13],[13,14,15], [15, 16, 17],[17,18,19], [19, 20, 21],[21, 22, 23],[23, 24, 25],[25, 26, 27],[27, 28, 29],[29, 30, 31]])
并且需要前 10 个数组数据来预测下一个数据，但结果很糟糕
[[ 1. 2. 3. ]
[ 3. 4. 5. ]
[ 5. 6. 7. ]
[ 7. 8. 9. ]
[ 9. 10. 11. ]
[11. 12. 13. ]
[13. 14. 15. ]
[15. 16. 17. ]
[17. 18. 19. ]
[19. 20. 21. ]
[16.58571815 14.85821152 14.95420837] # predict from here below
[16.53819847 13.39703369 13.26765823]
[16.53710938 13.11023235 13.01197338]
[16.53708267 13.06925201 12.98667526]
[16.53708267 13.06376362 12.98433018]
[16.53708267 13.06303596 12.98411465]
[16.53708267 13.06293964 12.98409462]
[16.53708267 13.06292725 12.98409176]
[16.53708267 13.06292534 12.98409271]
[16.53708267 13.06292534 12.98409271]]
为什么结果这么差？？？
在我的假设中，它应该很容易解决，非常简单的数字测验。
有什么好的方法来改进这个模型或我的代码有问题吗？
import tensorflow
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import SimpleRNN
import numpy as np
from tensorflow.keras.optimizers import SGD
ori_data = np.array([[1,2,3], [3, 4, 5],[5,6,7], [7, 8, 9],[9,10,11], [11, 12, 13],[13,14,15], [15, 16, 17],[17,18,19], [19, 20, 21],[21, 22, 23],
[23, 24, 25],[25, 26, 27],[27, 28, 29],[29, 30, 31]])
x = np.array(ori_data[:-1])
y = np.array(ori_data[1:])
print(x.shape) #(10,3)
print(y.shape) #(10,3)
x_train = np.array(x).reshape(14, 3, 1)
y_train = np.array(y).reshape(14, 3, 1)
print(x_train.shape)
print(y_train.shape)
NUM_DIM = 20
NUM_RNN = 10
epoch = 100
model = Sequential()
model.add(SimpleRNN(NUM_DIM, input_shape=(NUM_RNN, 1), return_sequences=True))
model.add(Dense(1, activation="linear"))
model.compile(loss="mean_squared_error", optimizer="sgd")
model.summary()
history = model.fit(x_train, y_train, epochs=50, batch_size=12)
# get first 10 set
x_test = ori_data[0:NUM_RNN,]
NUM_DATA = 10
for i in range(NUM_DATA):
y_pred = model.predict(x_test[-NUM_RNN:].reshape(NUM_RNN, 3, 1))
res = y_pred[NUM_RNN-1][:,0].reshape(1,3)
x_test = np.concatenate((x_test,res))
print(x_test)
更新
我尝试增加训练数据并按照评论更改优化器。
使用 1000 个训练数据并根据最新的 100 个数据进行预测
k = []
for i in range(1000):
k = np.append(k, np.array([i * 2, i * 2 + 1,i* 2 + 2]))
ori_data = k.reshape(1000,3)
x = np.array(ori_data[:-1])
y = np.array(ori_data[1:])
x_train = np.array(x).reshape(999, 3, 1)
y_train = np.array(y).reshape(999, 3, 1)
NUM_DIM = 20
NUM_RNN = 100
epoch = 100
model = Sequential()
model.add(SimpleRNN(NUM_DIM, input_shape=(NUM_RNN, 1), return_sequences=True))
model.add(Dense(1, activation="linear"))
model.compile(loss='mean_squared_error', optimizer=Adam(lr=0.01, beta_1=0.9, beta_2=0.999))
model.summary()
history = model.fit(x_train, y_train, epochs=50, batch_size=12)
# get first set
x_test = ori_data[0:NUM_RNN,]
NUM_DATA = 100
for i in range(NUM_DATA):
y_pred = model.predict(x_test[-NUM_RNN:].reshape(NUM_RNN, 3, 1))
res = y_pred[NUM_RNN-1][:,0].reshape(1,3)
x_test = np.concatenate((x_test,res))
print(x_test)
然而结果并没有太大变化。
我有两个想法。
simpleRNN不适合这个目的吗？
或者
我的多维模型是错误的。
[[ 0. 1. 2. ]
[ 2. 3. 4. ]
[ 4. 5. 6. ]
[ 6. 7. 8. ]
[ 8. 9. 10. ]
[ 10. 11. 12. ]
[ 12. 13. 14. ]
[ 14. 15. 16. ]
[ 16. 17. 18. ]
[ 18. 19. 20. ]
[ 20. 21. 22. ]
[ 22. 23. 24. ]
[ 24. 25. 26. ]
[ 26. 27. 28. ]
[ 28. 29. 30. ]
[ 30. 31. 32. ]
[ 32. 33. 34. ]
[ 34. 35. 36. ]
[ 36. 37. 38. ]
[ 38. 39. 40. ]
[ 40. 41. 42. ]
[ 42. 43. 44. ]
[ 44. 45. 46. ]
[ 46. 47. 48. ]
[ 48. 49. 50. ]
[ 50. 51. 52. ]
[ 52. 53. 54. ]
[ 54. 55. 56. ]
[ 56. 57. 58. ]
[ 58. 59. 60. ]
[ 60. 61. 62. ]
[ 62. 63. 64. ]
[ 64. 65. 66. ]
[ 66. 67. 68. ]
[ 68. 69. 70. ]
[ 70. 71. 72. ]
[ 72. 73. 74. ]
[ 74. 75. 76. ]
[ 76. 77. 78. ]
[ 78. 79. 80. ]
[ 80. 81. 82. ]
[ 82. 83. 84. ]
[ 84. 85. 86. ]
[ 86. 87. 88. ]
[ 88. 89. 90. ]
[ 90. 91. 92. ]
[ 92. 93. 94. ]
[ 94. 95. 96. ]
[ 96. 97. 98. ]
[ 98. 99. 100. ]
[100. 101. 102. ]
[102. 103. 104. ]
[104. 105. 106. ]
[106. 107. 108. ]
[108. 109. 110. ]
[110. 111. 112. ]
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[114. 115. 116. ]
[116. 117. 118. ]
[118. 119. 120. ]
[120. 121. 122. ]
[122. 123. 124. ]
[124. 125. 126. ]
[126. 127. 128. ]
[128. 129. 130. ]
[130. 131. 132. ]
[132. 133. 134. ]
[134. 135. 136. ]
[136. 137. 138. ]
[138. 139. 140. ]
[140. 141. 142. ]
[142. 143. 144. ]
[144. 145. 146. ]
[146. 147. 148. ]
[148. 149. 150. ]
[150. 151. 152. ]
[152. 153. 154. ]
[154. 155. 156. ]
[156. 157. 158. ]
[158. 159. 160. ]
[160. 161. 162. ]
[162. 163. 164. ]
[164. 165. 166. ]
[166. 167. 168. ]
[168. 169. 170. ]
[170. 171. 172. ]
[172. 173. 174. ]
[174. 175. 176. ]
[176. 177. 178. ]
[178. 179. 180. ]
[180. 181. 182. ]
[182. 183. 184. ]
[184. 185. 186. ]
[186. 187. 188. ]
[188. 189. 190. ]
[190. 191. 192. ]
[192. 193. 194. ]
[194. 195. 196. ]
[196. 197. 198. ]
[198. 199. 200. ]
[406.66326904 264.12637329 234.36053467] # predict from here
[478.32727051 264.20413208 264.20501709]
[497.1206665 264.20425415 269.84753418]
[518.88244629 264.20721436 285.3477478 ]
[553.58422852 264.41403198 332.9395752 ]
[605.4630127 275.01095581 335.0244751 ]
[657.63604736 320.15002441 335.02252197]
[686.33428955 336.98327637 335.01068115]
[690.27746582 340.27520752 335.00494385]
[690.53912354 341.20166016 335.00292969]
[690.55493164 341.4654541 335.00231934]
[690.5559082 341.54095459 335.00213623]
[690.55596924 341.56268311 335.0020752 ]
[690.55596924 341.56890869 335.0020752 ]
[690.55596924 341.57073975 335.0020752 ]
[690.55596924 341.57122803 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]
[690.55596924 341.57141113 335.0020752 ]]
训练日志
Epoch 1/50
WARNING:tensorflow:Model was constructed with shape (None, 100, 1) for input Tensor("simple_rnn_input:0", shape=(None, 100, 1), dtype=float32), but it was called on an input with incompatible shape (None, 3, 1).
WARNING:tensorflow:Model was constructed with shape (None, 100, 1) for input Tensor("simple_rnn_input:0", shape=(None, 100, 1), dtype=float32), but it was called on an input with incompatible shape (None, 3, 1).
84/84 [==============================] - 0s 981us/step - loss: 1320584.7500
Epoch 2/50
84/84 [==============================] - 0s 875us/step - loss: 1286373.3750
Epoch 3/50
84/84 [==============================] - 0s 919us/step - loss: 1253214.8750
Epoch 4/50
84/84 [==============================] - 0s 863us/step - loss: 1220912.0000
Epoch 5/50
84/84 [==============================] - 0s 880us/step - loss: 1189455.5000
Epoch 6/50
84/84 [==============================] - 0s 1ms/step - loss: 1158770.0000
Epoch 7/50
84/84 [==============================] - 0s 872us/step - loss: 1128863.6250
Epoch 8/50
84/84 [==============================] - 0s 854us/step - loss: 1099460.8750
Epoch 9/50
84/84 [==============================] - 0s 878us/step - loss: 1070951.0000
Epoch 10/50
84/84 [==============================] - 0s 860us/step - loss: 1043161.9375
Epoch 11/50
84/84 [==============================] - 0s 867us/step - loss: 1016199.6250
Epoch 12/50
84/84 [==============================] - 0s 869us/step - loss: 989827.5000
Epoch 13/50
84/84 [==============================] - 0s 871us/step - loss: 964060.3125
Epoch 14/50
84/84 [==============================] - 0s 884us/step - loss: 939177.3125
Epoch 15/50
84/84 [==============================] - 0s 865us/step - loss: 914734.3750
Epoch 16/50
84/84 [==============================] - 0s 964us/step - loss: 891065.8750
Epoch 17/50
84/84 [==============================] - 0s 866us/step - loss: 867806.8750
Epoch 18/50
84/84 [==============================] - 0s 898us/step - loss: 845191.5000
Epoch 19/50
84/84 [==============================] - 0s 952us/step - loss: 822929.1875
Epoch 20/50
84/84 [==============================] - 0s 890us/step - loss: 801307.5000
Epoch 21/50
84/84 [==============================] - 0s 883us/step - loss: 780187.2500
Epoch 22/50
84/84 [==============================] - 0s 877us/step - loss: 759871.3125
Epoch 23/50
84/84 [==============================] - 0s 862us/step - loss: 739762.5625
Epoch 24/50
84/84 [==============================] - 0s 862us/step - loss: 719785.1250
Epoch 25/50
84/84 [==============================] - 0s 867us/step - loss: 701341.1875
Epoch 26/50
84/84 [==============================] - 0s 870us/step - loss: 682664.1250
Epoch 27/50
84/84 [==============================] - 0s 896us/step - loss: 666033.3125
Epoch 28/50
84/84 [==============================] - 0s 882us/step - loss: 646248.3750
Epoch 29/50
84/84 [==============================] - 0s 874us/step - loss: 628936.5625
Epoch 30/50
84/84 [==============================] - 0s 895us/step - loss: 611742.2500
Epoch 31/50
84/84 [==============================] - 0s 932us/step - loss: 594522.4375
Epoch 32/50
84/84 [==============================] - 0s 972us/step - loss: 577997.2500
Epoch 33/50
84/84 [==============================] - 0s 920us/step - loss: 563100.8125
Epoch 34/50
84/84 [==============================] - 0s 875us/step - loss: 547811.2500
Epoch 35/50
84/84 [==============================] - 0s 870us/step - loss: 531739.3125
Epoch 36/50
84/84 [==============================] - 0s 869us/step - loss: 517295.8125
Epoch 37/50
84/84 [==============================] - 0s 905us/step - loss: 506299.7188
Epoch 38/50
84/84 [==============================] - 0s 880us/step - loss: 498595.6562
Epoch 39/50
84/84 [==============================] - 0s 890us/step - loss: 531498.5000
Epoch 40/50
84/84 [==============================] - 0s 869us/step - loss: 516119.3750
Epoch 41/50
84/84 [==============================] - 0s 870us/step - loss: 502201.9688
Epoch 42/50
84/84 [==============================] - 0s 872us/step - loss: 487284.6250
Epoch 43/50
84/84 [==============================] - 0s 886us/step - loss: 472474.1875
Epoch 44/50
84/84 [==============================] - 0s 879us/step - loss: 458271.5938
Epoch 45/50
84/84 [==============================] - 0s 946us/step - loss: 444749.9375
Epoch 46/50
84/84 [==============================] - 0s 927us/step - loss: 431224.7812
Epoch 47/50
84/84 [==============================] - 0s 856us/step - loss: 417638.5625
Epoch 48/50
84/84 [==============================] - 0s 861us/step - loss: 406279.1562
Epoch 49/50
84/84 [==============================] - 0s 867us/step - loss: 394453.1562
Epoch 50/50
84/84 [==============================] - 0s 890us/step - loss: 384451.8750
更新
我使用 LSTM 而不是 simpleRNN
#model.add(SimpleRNN(NUM_DIM, input_shape=(NUM_RNN, 1), return_sequences=True))
model.add(LSTM(NUM_DIM, activation=None, input_shape=(NUM_RNN, 1), return_sequences=True))
结果大大改善。
也许 simpleRNN 不适合这个目的，我猜。
[[ 0. 1. 2. ]
[ 2. 3. 4. ]
[ 4. 5. 6. ]
[ 6. 7. 8. ]
[ 8. 9. 10. ]
[ 10. 11. 12. ]
[ 12. 13. 14. ]
[ 14. 15. 16. ]
[ 16. 17. 18. ]
[ 18. 19. 20. ]
[ 20. 21. 22. ]
[ 22. 23. 24. ]
[ 24. 25. 26. ]
[ 26. 27. 28. ]
[ 28. 29. 30. ]
[ 30. 31. 32. ]
[ 32. 33. 34. ]
[ 34. 35. 36. ]
[ 36. 37. 38. ]
[ 38. 39. 40. ]
[ 39.9719429 41.04755783 42.01205063] # predict from here below
[ 41.96513367 43.08579636 44.03870392]
[ 43.98114395 45.11688995 46.06534576]
[ 46.02096558 47.14200974 48.0843277 ]
[ 48.08501816 49.16280365 50.09306335]
[ 50.17318726 51.18208694 52.09262466]
[ 52.28491592 53.20392227 54.08646393]
[ 54.41921997 55.23324966 56.0792923 ]
[ 56.5747757 57.27529907 58.0763855 ]
[ 58.75000763 59.3350029 60.08308411]
[ 60.94314575 61.41643524 62.104496 ]
[ 63.15228653 63.52256393 64.1452179 ]
[ 65.37547302 65.65501404 66.20915985]
[ 67.61073303 67.81411743 68.29927826]
[ 69.85613251 69.9990921 70.41743469]
[ 72.10982513 72.20815277 72.56438446]
[ 74.37003326 74.4388504 74.73970795]
[ 76.63514709 76.68830872 76.94197845]
[ 78.90366364 78.95341492 79.16893768]
[ 81.17423248 81.23103333 81.41772461]
[ 83.44564819 83.51813507 83.68505859]
[ 85.71686554 85.81192017 85.96752167]
[ 87.98696136 88.10987854 88.26168823]
[ 90.25514221 90.40979767 90.56433105]
[ 92.52074432 92.70979309 92.87243652]
[ 94.78321838 95.00827789 95.18333435]
[ 97.042099 97.30395508 97.49468994]
[ 99.29704285 99.59580231 99.80455017]
[101.54774475 101.88302612 102.11128998]
[103.79402924 104.16498566 104.41358948]
[106.03572083 106.44125366 106.71047211]
[108.2727356 108.71152496 109.00115204]
[110.5050354 110.97562408 111.28508759]
[112.73260498 113.23347473 113.56192017]
[114.95548248 115.48503876 115.8314209 ]
[117.17371368 117.73036957 118.09352112]
[119.38736725 119.96955872 120.34823608]
[121.59655762 122.20272827 122.59564209]
[123.80136871 124.43003845 124.83587646]
[126.00195312 126.6516571 127.06913757]
[128.19842529 128.86779785 129.2956543 ]
[130.39091492 131.0786438 131.51568604]
[132.57955933 133.28440857 133.72949219]
[134.76451111 135.48529053 135.93736267]
[136.94590759 137.68148804 138.13954163]
[139.12390137 139.87322998 140.33633423]
[141.2986145 142.06069946 142.5280304 ]
[143.47021484 144.24411011 144.71487427]
[145.63882446 146.42364502 146.89710999]
[147.80458069 148.5994873 149.07501221]
[149.96762085 150.77185059 151.24880981]
[152.1280365 152.94088745 153.41877747]
[154.28598022 155.10676575 155.58509827]
[156.44155884 157.26963806 157.74801636]
[158.59490967 159.42967224 159.90769958]
[160.74610901 161.58702087 162.06439209]
[162.89524841 163.74182129 164.2182312 ]
[165.04244995 165.8941803 166.36941528]
[167.18782043 168.04425049 168.51806641]
[169.33140564 170.19215393 170.66436768]
[171.4733429 172.33796692 172.8085022 ]
[173.61366272 174.4818573 174.95051575]
[175.75247192 176.62388611 177.09059143]
[177.88986206 178.76416016 179.2288208 ]
[180.02584839 180.90278625 181.3653717 ]
[182.16053772 183.03982544 183.5002594 ]
[184.29397583 185.17538452 185.63366699]
[186.42622375 187.30953979 187.765625 ]
[188.55734253 189.44233704 189.89625549]
[190.68737793 191.57385254 192.02560425]
[192.81639099 193.7041626 194.15379333]
[194.94444275 195.83332825 196.28085327]
[197.07154846 197.96142578 198.40689087]
[199.19773865 200.08851624 200.53193665]
[201.3230896 202.21459961 202.65603638]
[203.44761658 204.33973694 204.77923584]
[205.5713501 206.46398926 206.90161133]
[207.6943512 208.5874176 209.02322388]
[209.81660461 210.71002197 211.144104 ]
[211.93817139 212.83190918 213.26425171]
[214.05905151 214.95300293 215.3837738 ]
[216.17930603 217.07339478 217.50263977]
[218.29896545 219.19311523 219.62091064]
[220.41802979 221.31221008 221.73861694]
[222.53651428 223.43067932 223.85575867]
[224.65444946 225.54858398 225.97236633]
[226.77186584 227.66589355 228.08853149]
[228.88874817 229.78268433 230.2041626 ]
[231.00515747 231.89897156 232.31942749]
[233.12104797 234.01473999 234.43423462]
[235.23651123 236.13000488 236.5486145 ]
[237.35151672 238.24479675 238.66256714]
[239.46606445 240.35916138 240.77612305]
[241.5802002 242.47306824 242.88931274]
[243.69392395 244.58657837 245.00213623]
[245.80723572 246.699646 247.11462402]
[247.92015076 248.81234741 249.22677612]
[250.03268433 250.9246521 251.33860779]
[252.14483643 253.03659058 253.45010376]
[254.25662231 255.14816284 255.56130981]]
解决方案
我使用LTSM而不是simpleRNN.
问题已经解决了。
文章已更新。
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