import pandas as pd
import numpy as np
import tensorflow as tf
dataframe = pd.read_csv("data.csv")
dataframe = dataframe.drop(["id"], axis = 1)
train = dataframe[1:250]
test = dataframe[251:569]
dataY = train["diagnosis"]
for key,value in dataY.iteritems():
if value == "M":
dataY[key] = 1
if value == "B":
dataY[key] = 0
dataY = np.asarray(dataY)
dataX = np.asarray(train.drop(["diagnosis"], axis = 1))
trainRows = len(train)
trainColumns = len(dataframe.columns)-1
inputX = tf.placeholder(tf.float32, [trainRows, trainColumns])
inputY = tf.placeholder(tf.float32, [trainRows])
W = tf.Variable(tf.zeros([trainColumns, trainRows]))
b = tf.Variable(tf.zeros([trainRows]))
Y_compare = tf.nn.softmax(tf.matmul(inputX, W)+b)
cross_entropy = -tf.reduce_sum(inputY * tf.log(Y_compare))
optimizer = tf.train.GradientDescentOptimizer(.001)
trainer = optimizer.minimize(cross_entropy)
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
for step in range(1000):
sess.run(trainer, feed_dict={inputX: dataX, inputY: dataY})
print(sess.run(cross_entropy, feed_dict={inputX: dataX, inputY: dataY}))
sess.close()
当我运行这段代码时，它只为每一步打印nan。我尝试更改错误函数，但没有任何区别。我很确定问题出在错误函数或优化器上。有什么问题的想法吗？
有关乳腺癌诊断的数据：
这是输入X：
[[ 20.57 17.77 132.9 1326. ]
[ 19.69 21.25 130. 1203. ]
[ 11.42 20.38 77.58 386.1 ]
[ 20.29 14.34 135.1 1297. ]
[ 12.45 15.7 82.57 477.1 ]
[ 18.25 19.98 119.6 1040. ]
[ 13.71 20.83 90.2 577.9 ]
[ 13. 21.82 87.5 519.8 ]
[ 12.46 24.04 83.97 475.9 ]
[ 16.02 23.24 102.7 797.8 ]
[ 15.78 17.89 103.6 781. ]
[ 19.17 24.8 132.4 1123. ]
[ 15.85 23.95 103.7 782.7 ]
[ 13.73 22.61 93.6 578.3 ]
[ 14.54 27.54 96.73 658.8 ]
[ 14.68 20.13 94.74 684.5 ]
[ 16.13 20.68 108.1 798.8 ]
[ 19.81 22.15 130. 1260. ]
[ 13.54 14.36 87.46 566.3 ]
[ 13.08 15.71 85.63 520. ]
[ 9.504 12.44 60.34 273.9 ]
[ 15.34 14.26 102.5 704.4 ]
[ 21.16 23.04 137.2 1404. ]
[ 16.65 21.38 110. 904.6 ]
[ 17.14 16.4 116. 912.7 ]
[ 14.58 21.53 97.41 644.8 ]
[ 18.61 20.25 122.1 1094. ]
[ 15.3 25.27 102.4 732.4 ]
[ 17.57 15.05 115. 955.1 ]
[ 18.63 25.11 124.8 1088. ]
[ 11.84 18.7 77.93 440.6 ]
[ 17.02 23.98 112.8 899.3 ]
[ 19.27 26.47 127.9 1162. ]
[ 16.13 17.88 107. 807.2 ]
[ 16.74 21.59 110.1 869.5 ]
[ 14.25 21.72 93.63 633. ]
[ 13.03 18.42 82.61 523.8 ]
[ 14.99 25.2 95.54 698.8 ]
[ 13.48 20.82 88.4 559.2 ]
[ 13.44 21.58 86.18 563. ]
[ 10.95 21.35 71.9 371.1 ]
[ 19.07 24.81 128.3 1104. ]
[ 13.28 20.28 87.32 545.2 ]
[ 13.17 21.81 85.42 531.5 ]
[ 18.65 17.6 123.7 1076. ]
[ 8.196 16.84 51.71 201.9 ]
[ 13.17 18.66 85.98 534.6 ]
[ 12.05 14.63 78.04 449.3 ]
[ 13.49 22.3 86.91 561. ]
[ 11.76 21.6 74.72 427.9 ]
[ 13.64 16.34 87.21 571.8 ]
[ 11.94 18.24 75.71 437.6 ]
[ 18.22 18.7 120.3 1033. ]
[ 15.1 22.02 97.26 712.8 ]
[ 11.52 18.75 73.34 409. ]
[ 19.21 18.57 125.5 1152. ]
[ 14.71 21.59 95.55 656.9 ]
[ 13.05 19.31 82.61 527.2 ]
[ 8.618 11.79 54.34 224.5 ]
[ 10.17 14.88 64.55 311.9 ]
[ 8.598 20.98 54.66 221.8 ]
[ 14.25 22.15 96.42 645.7 ]
[ 9.173 13.86 59.2 260.9 ]
[ 12.68 23.84 82.69 499. ]
[ 14.78 23.94 97.4 668.3 ]
[ 9.465 21.01 60.11 269.4 ]
[ 11.31 19.04 71.8 394.1 ]
[ 9.029 17.33 58.79 250.5 ]
[ 12.78 16.49 81.37 502.5 ]
[ 18.94 21.31 123.6 1130. ]
[ 8.888 14.64 58.79 244. ]
[ 17.2 24.52 114.2 929.4 ]
[ 13.8 15.79 90.43 584.1 ]
[ 12.31 16.52 79.19 470.9 ]
[ 16.07 19.65 104.1 817.7 ]
[ 13.53 10.94 87.91 559.2 ]
[ 18.05 16.15 120.2 1006. ]
[ 20.18 23.97 143.7 1245. ]
[ 12.86 18. 83.19 506.3 ]
[ 11.45 20.97 73.81 401.5 ]
[ 13.34 15.86 86.49 520. ]
[ 25.22 24.91 171.5 1878. ]
[ 19.1 26.29 129.1 1132. ]
[ 12. 15.65 76.95 443.3 ]
[ 18.46 18.52 121.1 1075. ]
[ 14.48 21.46 94.25 648.2 ]
[ 19.02 24.59 122. 1076. ]
[ 12.36 21.8 79.78 466.1 ]
[ 14.64 15.24 95.77 651.9 ]
[ 14.62 24.02 94.57 662.7 ]
[ 15.37 22.76 100.2 728.2 ]
[ 13.27 14.76 84.74 551.7 ]
[ 13.45 18.3 86.6 555.1 ]
[ 15.06 19.83 100.3 705.6 ]
[ 20.26 23.03 132.4 1264. ]
[ 12.18 17.84 77.79 451.1 ]
[ 9.787 19.94 62.11 294.5 ]
[ 11.6 12.84 74.34 412.6 ]
[ 14.42 19.77 94.48 642.5 ]
[ 13.61 24.98 88.05 582.7 ]
[ 6.981 13.43 43.79 143.5 ]
[ 12.18 20.52 77.22 458.7 ]
[ 9.876 19.4 63.95 298.3 ]
[ 10.49 19.29 67.41 336.1 ]
[ 13.11 15.56 87.21 530.2 ]
[ 11.64 18.33 75.17 412.5 ]
[ 12.36 18.54 79.01 466.7 ]
[ 22.27 19.67 152.8 1509. ]
[ 11.34 21.26 72.48 396.5 ]
[ 9.777 16.99 62.5 290.2 ]
[ 12.63 20.76 82.15 480.4 ]
[ 14.26 19.65 97.83 629.9 ]
[ 10.51 20.19 68.64 334.2 ]
[ 8.726 15.83 55.84 230.9 ]
[ 11.93 21.53 76.53 438.6 ]
[ 8.95 15.76 58.74 245.2 ]
[ 14.87 16.67 98.64 682.5 ]
[ 15.78 22.91 105.7 782.6 ]
[ 17.95 20.01 114.2 982. ]
[ 11.41 10.82 73.34 403.3 ]
[ 18.66 17.12 121.4 1077. ]
[ 24.25 20.2 166.2 1761. ]
[ 14.5 10.89 94.28 640.7 ]
[ 13.37 16.39 86.1 553.5 ]
[ 13.85 17.21 88.44 588.7 ]
[ 13.61 24.69 87.76 572.6 ]
[ 19. 18.91 123.4 1138. ]
[ 15.1 16.39 99.58 674.5 ]
[ 19.79 25.12 130.4 1192. ]
[ 12.19 13.29 79.08 455.8 ]
[ 15.46 19.48 101.7 748.9 ]
[ 16.16 21.54 106.2 809.8 ]
[ 15.71 13.93 102. 761.7 ]
[ 18.45 21.91 120.2 1075. ]
[ 12.77 22.47 81.72 506.3 ]
[ 11.71 16.67 74.72 423.6 ]
[ 11.43 15.39 73.06 399.8 ]
[ 14.95 17.57 96.85 678.1 ]
[ 11.28 13.39 73. 384.8 ]
[ 9.738 11.97 61.24 288.5 ]
[ 16.11 18.05 105.1 813. ]
[ 11.43 17.31 73.66 398. ]
[ 12.9 15.92 83.74 512.2 ]
[ 10.75 14.97 68.26 355.3 ]
[ 11.9 14.65 78.11 432.8 ]
[ 11.8 16.58 78.99 432. ]
[ 14.95 18.77 97.84 689.5 ]
[ 14.44 15.18 93.97 640.1 ]
[ 13.74 17.91 88.12 585. ]
[ 13. 20.78 83.51 519.4 ]
[ 8.219 20.7 53.27 203.9 ]
[ 9.731 15.34 63.78 300.2 ]
[ 11.15 13.08 70.87 381.9 ]
[ 13.15 15.34 85.31 538.9 ]
[ 12.25 17.94 78.27 460.3 ]
[ 17.68 20.74 117.4 963.7 ]
[ 16.84 19.46 108.4 880.2 ]
[ 12.06 12.74 76.84 448.6 ]
[ 10.9 12.96 68.69 366.8 ]
[ 11.75 20.18 76.1 419.8 ]
[ 19.19 15.94 126.3 1157. ]
[ 19.59 18.15 130.7 1214. ]
[ 12.34 22.22 79.85 464.5 ]
[ 23.27 22.04 152.1 1686. ]
[ 14.97 19.76 95.5 690.2 ]
[ 10.8 9.71 68.77 357.6 ]
[ 16.78 18.8 109.3 886.3 ]
[ 17.47 24.68 116.1 984.6 ]
[ 14.97 16.95 96.22 685.9 ]
[ 12.32 12.39 78.85 464.1 ]
[ 13.43 19.63 85.84 565.4 ]
[ 15.46 11.89 102.5 736.9 ]
[ 11.08 14.71 70.21 372.7 ]
[ 10.66 15.15 67.49 349.6 ]
[ 8.671 14.45 54.42 227.2 ]
[ 9.904 18.06 64.6 302.4 ]
[ 16.46 20.11 109.3 832.9 ]
[ 13.01 22.22 82.01 526.4 ]
[ 12.81 13.06 81.29 508.8 ]
[ 27.22 21.87 182.1 2250. ]
[ 21.09 26.57 142.7 1311. ]
[ 15.7 20.31 101.2 766.6 ]
[ 11.41 14.92 73.53 402. ]
[ 15.28 22.41 98.92 710.6 ]
[ 10.08 15.11 63.76 317.5 ]
[ 18.31 18.58 118.6 1041. ]
[ 11.71 17.19 74.68 420.3 ]
[ 11.81 17.39 75.27 428.9 ]
[ 12.3 15.9 78.83 463.7 ]
[ 14.22 23.12 94.37 609.9 ]
[ 12.77 21.41 82.02 507.4 ]
[ 9.72 18.22 60.73 288.1 ]
[ 12.34 26.86 81.15 477.4 ]
[ 14.86 23.21 100.4 671.4 ]
[ 12.91 16.33 82.53 516.4 ]
[ 13.77 22.29 90.63 588.9 ]
[ 18.08 21.84 117.4 1024. ]
[ 19.18 22.49 127.5 1148. ]
[ 14.45 20.22 94.49 642.7 ]
[ 12.23 19.56 78.54 461. ]
[ 17.54 19.32 115.1 951.6 ]
[ 23.29 26.67 158.9 1685. ]
[ 13.81 23.75 91.56 597.8 ]
[ 12.47 18.6 81.09 481.9 ]
[ 15.12 16.68 98.78 716.6 ]
[ 9.876 17.27 62.92 295.4 ]
[ 17.01 20.26 109.7 904.3 ]
[ 13.11 22.54 87.02 529.4 ]
[ 15.27 12.91 98.17 725.5 ]
[ 20.58 22.14 134.7 1290. ]
[ 11.84 18.94 75.51 428. ]
[ 28.11 18.47 188.5 2499. ]
[ 17.42 25.56 114.5 948. ]
[ 14.19 23.81 92.87 610.7 ]
[ 13.86 16.93 90.96 578.9 ]
[ 11.89 18.35 77.32 432.2 ]
[ 10.2 17.48 65.05 321.2 ]
[ 19.8 21.56 129.7 1230. ]
[ 19.53 32.47 128. 1223. ]
[ 13.65 13.16 87.88 568.9 ]
[ 13.56 13.9 88.59 561.3 ]
[ 10.18 17.53 65.12 313.1 ]
[ 15.75 20.25 102.6 761.3 ]
[ 13.27 17.02 84.55 546.4 ]
[ 14.34 13.47 92.51 641.2 ]
[ 10.44 15.46 66.62 329.6 ]
[ 15. 15.51 97.45 684.5 ]
[ 12.62 23.97 81.35 496.4 ]
[ 12.83 22.33 85.26 503.2 ]
[ 17.05 19.08 113.4 895. ]
[ 11.32 27.08 71.76 395.7 ]
[ 11.22 33.81 70.79 386.8 ]
[ 20.51 27.81 134.4 1319. ]
[ 9.567 15.91 60.21 279.6 ]
[ 14.03 21.25 89.79 603.4 ]
[ 23.21 26.97 153.5 1670. ]
[ 20.48 21.46 132.5 1306. ]
[ 14.22 27.85 92.55 623.9 ]
[ 17.46 39.28 113.4 920.6 ]
[ 13.64 15.6 87.38 575.3 ]
[ 12.42 15.04 78.61 476.5 ]
[ 11.3 18.19 73.93 389.4 ]
[ 13.75 23.77 88.54 590. ]
[ 19.4 23.5 129.1 1155. ]
[ 10.48 19.86 66.72 337.7 ]
[ 13.2 17.43 84.13 541.6 ]
[ 12.89 14.11 84.95 512.2 ]
[ 10.65 25.22 68.01 347. ]
[ 11.52 14.93 73.87 406.3 ]]
这是输入Y：
[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0
1 1 1 1 1 1 1 1 0 1 0 0 0 0 0 1 1 0 1 1 0 0 0 0 1 0 1 1 0 0 0 0 1 0 1 1 0
1 0 1 1 0 0 0 1 1 0 1 1 1 0 0 0 1 0 0 1 1 0 0 0 1 1 0 0 0 0 1 0 0 1 0 0 0
0 0 0 0 0 1 1 1 0 1 1 0 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 0 0 1 0 0 0 0 1 0 0
0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 1 0 0 1 1 0 0 1 1 0 0 0 0 1 0 0 1 1 1 0 1 0
1 0 0 0 1 0 0 1 1 0 1 1 1 1 0 1 1 1 0 1 0 1 0 0 1 0 1 1 1 1 0 0 1 1 0 0 0
1 0 0 0 0 0 1 1 0 0 1 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0 0]