In [11]:
import tensorflow as tf
import numpy as np
In [12]:
xy = np.loadtxt('./dataset/pass.csv', delimiter=',', dtype=np.float32)
In [13]:
x = xy[:,0:-1]
y = xy[:,[-1]]
In [14]:
print(x.shape, y.shape)
(6, 2) (6, 1)
In [15]:
X = tf.placeholder(tf.float32, shape=[None, 2])
Y = tf.placeholder(tf.float32, shape=[None, 1])
W = tf.Variable(tf.random_normal([2,1]), name='weight')
b = tf.Variable(tf.random_normal([1]), name='bias')
hypothesis = tf.sigmoid(tf.matmul(X, W) + b)
# sigmoid fnc. 의 cost fnc.
cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) *
tf.log(1-hypothesis))
train = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cost)
# tf.cast : 부동소수점을 버린다.(1,0으로 표현한다)
predicted = tf.cast(hypothesis > 0.5, dtype=tf.float32)
# tf.equal을 통해 predicted와 Y를 비교한다.
accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, Y), dtype=tf.float32))
In [16]:
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for step in range(1001):
cost_val, _ = sess.run([cost, train], feed_dict={X:x, Y:y})
if step % 100 == 0:
print(step, cost_val)
h, c, a = sess.run([hypothesis, predicted, accuracy]
, feed_dict={X:x, Y:y})
print("\nhypothesis:",h,'\npredicted:',c, '\naccuracy:',a)
0 1.9660257
hypothesis: [[0.25650516]
[0.11951312]
[0.13775477]
[0.04179943]
[0.02413322]
[0.01980096]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.26602328]
[0.12856945]
[0.14799857]
[0.04728255]
[0.02797878]
[0.02323315]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.27564895]
[0.13809416]
[0.15875396]
[0.05337775]
[0.03236889]
[0.02720201]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.2853612 ]
[0.14807808]
[0.1700089 ]
[0.06012776]
[0.03736173]
[0.03177408]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.29513744]
[0.15850693]
[0.18174523]
[0.06757224]
[0.04301649]
[0.03701904]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.30495352]
[0.16936034]
[0.1939384 ]
[0.07574609]
[0.04939191]
[0.04300839]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.31478405]
[0.18061203]
[0.20655733]
[0.08467826]
[0.05654448]
[0.0498135 ]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.3246026 ]
[0.19222957]
[0.21956435]
[0.09438908]
[0.06452614]
[0.0575032 ]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.3343817 ]
[0.20417449]
[0.2329154 ]
[0.10488904]
[0.07338184]
[0.0661412 ]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.34409335]
[0.21640274]
[0.24656054]
[0.11617684]
[0.08314693]
[0.07578258]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.3537094 ]
[0.22886509]
[0.26044458]
[0.12823829]
[0.09384435]
[0.08647058]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.3632017 ]
[0.24150783]
[0.27450794]
[0.14104491]
[0.10548226]
[0.09823301]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.37254268]
[0.2542737 ]
[0.2886877 ]
[0.15455356]
[0.11805151]
[0.11107907]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.38170567]
[0.2671033 ]
[0.302919 ]
[0.1687069 ]
[0.13152467]
[0.12499662]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.39066544]
[0.279936 ]
[0.3171363 ]
[0.18343392]
[0.14585468]
[0.13995047]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.39939845]
[0.29271105]
[0.3312747 ]
[0.19865161]
[0.16097547]
[0.15588155]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.4078834 ]
[0.30536956]
[0.34527168]
[0.21426743]
[0.17680311]
[0.17270757]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.41610143]
[0.31785527]
[0.3590681 ]
[0.2301816 ]
[0.19323799]
[0.19032511]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.42403638]
[0.33011568]
[0.37260956]
[0.2462905 ]
[0.21016818]
[0.20861268]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.43167514]
[0.34210318]
[0.38584712]
[0.26248968]
[0.22747311]
[0.22743508]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.4390074 ]
[0.35377574]
[0.3987383 ]
[0.27867696]
[0.24502786]
[0.24664855]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.44602603]
[0.3650974 ]
[0.4112473 ]
[0.29475525]
[0.2627071 ]
[0.26610586]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.45272672]
[0.37603852]
[0.4233452 ]
[0.31063485]
[0.28038937]
[0.2856615 ]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.459108 ]
[0.38657564]
[0.43500996]
[0.3262351 ]
[0.29796025]
[0.30517638]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.46517092]
[0.39669162]
[0.44622597]
[0.34148586]
[0.31531537]
[0.32452166]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.47091874]
[0.40637496]
[0.45698372]
[0.35632783]
[0.33236188]
[0.34358165]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.4763568 ]
[0.4156194 ]
[0.4672792 ]
[0.3707128 ]
[0.34902015]
[0.36225566]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.48149207]
[0.42442346]
[0.47711313]
[0.38460332]
[0.36522394]
[0.380459 ]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.48633292]
[0.4327897 ]
[0.48649043]
[0.39797208]
[0.38092026]
[0.39812312]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.4908888 ]
[0.4407242 ]
[0.49541938]
[0.41080076]
[0.3960688 ]
[0.415195 ]]
predicted: [[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
accuracy: 0.5
hypothesis: [[0.49516997]
[0.44823575]
[0.5039111 ]
[0.4230793 ]
[0.4106408 ]
[0.4316359 ]]
predicted: [[0.]
[0.]
[1.]
[0.]
[0.]
[0.]]
accuracy: 0.33333334
hypothesis: [[0.49918726]
[0.45533556]
[0.5119788 ]
[0.43480465]
[0.42461795]
[0.4474203 ]]
predicted: [[0.]
[0.]
[1.]
[0.]
[0.]
[0.]]
accuracy: 0.33333334
hypothesis: [[0.50295186]
[0.46203646]
[0.51963735]
[0.4459796 ]
[0.4379911 ]
[0.46253383]]
predicted: [[1.]
[0.]
[1.]
[0.]
[0.]
[0.]]
accuracy: 0.16666667
hypothesis: [[0.5064751 ]
[0.4683527 ]
[0.5269028 ]
[0.45661187]
[0.45075867]
[0.47697213]]
predicted: [[1.]
[0.]
[1.]
[0.]
[0.]
[0.]]
accuracy: 0.16666667
hypothesis: [[0.50976837]
[0.47429928]
[0.5337918 ]
[0.4667131 ]
[0.46292555]
[0.49073866]]
predicted: [[1.]
[0.]
[1.]
[0.]
[0.]
[0.]]
accuracy: 0.16666667
hypothesis: [[0.512843 ]
[0.47989187]
[0.5403215 ]
[0.47629791]
[0.47450185]
[0.50384355]]
predicted: [[1.]
[0.]
[1.]
[0.]
[0.]
[1.]]
accuracy: 0.33333334
hypothesis: [[0.51571 ]
[0.48514634]
[0.54650927]
[0.4853833 ]
[0.48550174]
[0.5163023 ]]
predicted: [[1.]
[0.]
[1.]
[0.]
[0.]
[1.]]
accuracy: 0.33333334
hypothesis: [[0.5183801 ]
[0.49007863]
[0.552372 ]
[0.49398777]
[0.49594265]
[0.52813405]]
predicted: [[1.]
[0.]
[1.]
[0.]
[0.]
[1.]]
accuracy: 0.33333334
hypothesis: [[0.52086383]
[0.49470443]
[0.55792654]
[0.5021309 ]
[0.50584406]
[0.5393612 ]]
predicted: [[1.]
[0.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.6666667
hypothesis: [[0.5231712 ]
[0.49903923]
[0.5631893 ]
[0.50983304]
[0.51522714]
[0.5500078 ]]
predicted: [[1.]
[0.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.6666667
hypothesis: [[0.52531177]
[0.5030981 ]
[0.56817615]
[0.5171146 ]
[0.52411413]
[0.56009936]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5272948 ]
[0.5068955 ]
[0.5729023 ]
[0.52399594]
[0.5325276 ]
[0.569662 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.529129 ]
[0.51044554]
[0.57738227]
[0.5304973 ]
[0.5404904 ]
[0.578722 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.53082275]
[0.51376164]
[0.58163005]
[0.5366383 ]
[0.5480251 ]
[0.5873055 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.53238386]
[0.51685655]
[0.5856588 ]
[0.54243815]
[0.5551539 ]
[0.59543806]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.53381985]
[0.51974255]
[0.58948106]
[0.5479152 ]
[0.56189847]
[0.6031445 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5351378 ]
[0.5224312 ]
[0.59310865]
[0.5530871 ]
[0.56827956]
[0.6104491 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.53634423]
[0.52493346]
[0.59655285]
[0.5579709 ]
[0.57431734]
[0.6173746 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.53744555]
[0.52725977]
[0.59982413]
[0.5625825 ]
[0.58003104]
[0.62394315]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5384476 ]
[0.5294199 ]
[0.6029325 ]
[0.56693727]
[0.58543885]
[0.63017565]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.539356 ]
[0.5314232 ]
[0.6058873 ]
[0.57104975]
[0.5905583 ]
[0.63609207]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.540176 ]
[0.5332784 ]
[0.6086973 ]
[0.57493365]
[0.5954059 ]
[0.64171094]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.54091245]
[0.53499377]
[0.61137074]
[0.5786019 ]
[0.59999716]
[0.6470502 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5415701 ]
[0.536577 ]
[0.61391544]
[0.5820668 ]
[0.6043468 ]
[0.6521264 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.54215336]
[0.5380356 ]
[0.61633873]
[0.5853397 ]
[0.6084689 ]
[0.65695524]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.54266626]
[0.53937626]
[0.6186474 ]
[0.58843166]
[0.6123765 ]
[0.6615515 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5431127 ]
[0.5406056 ]
[0.6208478 ]
[0.5913528 ]
[0.61608183]
[0.6659289 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5434963 ]
[0.5417298 ]
[0.6229462 ]
[0.59411275]
[0.6195966 ]
[0.6701005 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5438206 ]
[0.54275453]
[0.624948 ]
[0.5967206 ]
[0.6229317 ]
[0.67407846]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5440888 ]
[0.5436852 ]
[0.6268588 ]
[0.59918475]
[0.6260974 ]
[0.67787415]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5443039]
[0.544527 ]
[0.6286834]
[0.6015133]
[0.6291032]
[0.6814982]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5444688 ]
[0.5452847 ]
[0.6304265 ]
[0.60371375]
[0.6319583 ]
[0.6849607 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5445863 ]
[0.54596287]
[0.6320928 ]
[0.60579324]
[0.6346711 ]
[0.68827105]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.54465884]
[0.5465658 ]
[0.63368607]
[0.60775834]
[0.63724965]
[0.6914378 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5446889 ]
[0.54709744]
[0.6352105 ]
[0.6096153 ]
[0.6397014 ]
[0.6944694 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5446787]
[0.5475617]
[0.6366697]
[0.6113701]
[0.6420334]
[0.6973734]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5446304 ]
[0.5479621 ]
[0.63806707]
[0.6130282 ]
[0.6442523 ]
[0.70015705]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5445461 ]
[0.5483021 ]
[0.63940597]
[0.6145949 ]
[0.64636433]
[0.7028271 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.54442775]
[0.5485849 ]
[0.6406894 ]
[0.61607504]
[0.6483753 ]
[0.70539004]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.544277 ]
[0.54881346]
[0.6419202 ]
[0.6174733 ]
[0.6502907 ]
[0.7078516 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5440957 ]
[0.54899067]
[0.6431012 ]
[0.618794 ]
[0.65211576]
[0.7102175 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5438854 ]
[0.54911923]
[0.644235 ]
[0.6200414 ]
[0.6538553 ]
[0.71249294]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5436477 ]
[0.54920167]
[0.645324 ]
[0.6212191 ]
[0.655514 ]
[0.7146828 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.543384 ]
[0.5492404 ]
[0.64637035]
[0.622331 ]
[0.6570961 ]
[0.7167918 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5430957 ]
[0.5492377 ]
[0.6473764 ]
[0.6233804 ]
[0.6586056 ]
[0.71882415]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.54278404]
[0.5491958 ]
[0.64834404]
[0.6243707 ]
[0.66004646]
[0.72078407]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5424503 ]
[0.54911673]
[0.64927536]
[0.6253048 ]
[0.66142225]
[0.72267526]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.54209566]
[0.54900235]
[0.65017205]
[0.6261858 ]
[0.6627363 ]
[0.72450143]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5417212 ]
[0.54885453]
[0.65103585]
[0.62701625]
[0.66399187]
[0.72626597]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.541328 ]
[0.548675 ]
[0.65186846]
[0.6277989 ]
[0.6651921 ]
[0.72797203]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.54091704]
[0.54846543]
[0.65267134]
[0.62853605]
[0.6663397 ]
[0.7296226 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.54048926]
[0.5482273 ]
[0.65344596]
[0.62923014]
[0.6674375 ]
[0.73122054]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.54004556]
[0.54796225]
[0.6541937 ]
[0.6298833 ]
[0.66848797]
[0.7327686 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5395868 ]
[0.54767156]
[0.6549159 ]
[0.63049763]
[0.66949344]
[0.7342692 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5391137 ]
[0.5473566 ]
[0.65561384]
[0.6310751 ]
[0.67045635]
[0.73572475]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.53862715]
[0.54701865]
[0.65628856]
[0.63161755]
[0.6713788 ]
[0.73713756]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5381278 ]
[0.5466589 ]
[0.6569413 ]
[0.6321268 ]
[0.67226285]
[0.7385096 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5376164 ]
[0.5462784 ]
[0.657573 ]
[0.6326044 ]
[0.67311037]
[0.739843 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5370935 ]
[0.5458784 ]
[0.65818477]
[0.63305205]
[0.6739232 ]
[0.7411397 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5365599 ]
[0.5454598 ]
[0.6587774 ]
[0.63347113]
[0.6747031 ]
[0.74240124]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.536016 ]
[0.5450237 ]
[0.65935194]
[0.6338632 ]
[0.67545176]
[0.7436296 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5354625 ]
[0.54457086]
[0.6599092 ]
[0.63422954]
[0.6761706 ]
[0.7448262 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.53489983]
[0.5441023 ]
[0.6604499 ]
[0.63457143]
[0.6768611 ]
[0.74599254]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.53432864]
[0.5436188 ]
[0.660975 ]
[0.63489 ]
[0.6775248 ]
[0.74713016]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5337493 ]
[0.54312116]
[0.661485 ]
[0.63518655]
[0.67816293]
[0.7482403 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5331623 ]
[0.54261017]
[0.6619808 ]
[0.63546216]
[0.67877674]
[0.7493243 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.53256804]
[0.54208654]
[0.6624629 ]
[0.63571775]
[0.67936736]
[0.7503834 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.53196704]
[0.54155093]
[0.662932 ]
[0.63595444]
[0.67993605]
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predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5313596 ]
[0.541004 ]
[0.6633886 ]
[0.636173 ]
[0.68048376]
[0.7524312 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.53074616]
[0.5404464 ]
[0.66383344]
[0.63637453]
[0.68101156]
[0.7534221 ]]
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[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
100 0.62392217
hypothesis: [[0.53012705]
[0.5398787 ]
[0.6642669 ]
[0.6365597 ]
[0.6815204 ]
[0.7543922 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.52950263]
[0.5393015 ]
[0.66468954]
[0.6367295 ]
[0.6820112 ]
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predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5288732 ]
[0.5387153 ]
[0.6651018 ]
[0.63688445]
[0.6824848 ]
[0.7562741 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5282391]
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[0.6655043]
[0.6370254]
[0.6829421]
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predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5276006 ]
[0.53751796]
[0.66589725]
[0.63715315]
[0.6833839 ]
[0.7580836 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.526958 ]
[0.53690773]
[0.6662813 ]
[0.6372682 ]
[0.68381083]
[0.7589632 ]]
predicted: [[1.]
[1.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.5
hypothesis: [[0.5263115 ]
[0.53629047]
[0.6666566 ]
[0.63737124]
[0.68422353]
[0.759827 ]]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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hypothesis: [[0.52566147]
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[0.6670237 ]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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hypothesis: [[0.52500814]
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[0.66738296]
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[0.68500954]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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hypothesis: [[0.52435166]
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[0.637614 ]
[0.68538386]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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[0.668079 ]
[0.6376747 ]
[0.68574667]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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hypothesis: [[0.5230303 ]
[0.5331117 ]
[0.66841656]
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[0.6860984 ]
[0.7639311 ]]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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hypothesis: [[0.5223658 ]
[0.53246003]
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[0.68643963]
[0.764713 ]]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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hypothesis: [[0.521699 ]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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[0.6378286 ]
[0.68709236]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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[0.6874048 ]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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[0.6877086 ]
[0.7677279 ]]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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[0.6880042 ]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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[0.67060894]
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[0.6882918 ]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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hypothesis: [[0.5115293 ]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]]
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hypothesis: [[0.50606275]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]]
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[1.]
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[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]]
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[1.]
[1.]
[1.]
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[1.]]
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[1.]
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[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]]
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[1.]
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[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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[1.]
[1.]
[1.]
[1.]
[1.]]
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predicted: [[0.]
[1.]
[1.]
[1.]
[1.]
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[0.9271068 ]]
predicted: [[0.]
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[1.]
[1.]
[1.]
[1.]]
accuracy: 0.8333333
hypothesis: [[0.2443052 ]
[0.27064323]
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predicted: [[0.]
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[1.]]
accuracy: 0.8333333
hypothesis: [[0.244167 ]
[0.27054033]
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[0.9272012 ]]
predicted: [[0.]
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[1.]
[1.]]
accuracy: 0.8333333
hypothesis: [[0.24402893]
[0.2704376 ]
[0.7183924 ]
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[0.7708517 ]
[0.92724824]]
predicted: [[0.]
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hypothesis: [[0.24389115]
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[0.61727715]
[0.77091277]
[0.9272951 ]]
predicted: [[0.]
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[0.61729074]
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predicted: [[0.]
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[0.6173043 ]
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predicted: [[0.]
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predicted: [[0.]
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[1.]
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hypothesis: [[0.24334192]
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[0.77115667]
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hypothesis: [[0.24320507]
[0.26982546]
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[0.6173454 ]
[0.77121735]
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predicted: [[0.]
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predicted: [[0.]
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accuracy: 0.8333333
hypothesis: [[0.24265978]
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hypothesis: [[0.24252397]
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accuracy: 0.8333333
hypothesis: [[0.24238831]
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[0.6174282 ]
[0.77158093]
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predicted: [[0.]
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accuracy: 0.8333333
hypothesis: [[0.24225295]
[0.26912045]
[0.7180983 ]
[0.6174421 ]
[0.77164143]
[0.9278499 ]]
predicted: [[0.]
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accuracy: 0.8333333
hypothesis: [[0.2421177 ]
[0.26902044]
[0.71807504]
[0.617456 ]
[0.77170175]
[0.9278954 ]]
predicted: [[0.]
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[1.]
[1.]]
accuracy: 0.8333333
hypothesis: [[0.24198267]
[0.26892072]
[0.7180517 ]
[0.61746997]
[0.771762 ]
[0.9279409 ]]
predicted: [[0.]
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[1.]
[1.]
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[1.]]
accuracy: 0.8333333
hypothesis: [[0.24184781]
[0.26882115]
[0.7180283 ]
[0.6174839 ]
[0.7718223 ]
[0.92798626]]
predicted: [[0.]
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[1.]
[1.]]
accuracy: 0.8333333
hypothesis: [[0.24171314]
[0.26872176]
[0.7180047 ]
[0.6174979 ]
[0.7718825 ]
[0.92803156]]
predicted: [[0.]
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[1.]
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[1.]
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accuracy: 0.8333333
hypothesis: [[0.24157873]
[0.26862264]
[0.7179811 ]
[0.617512 ]
[0.7719426 ]
[0.92807674]]
predicted: [[0.]
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[1.]
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accuracy: 0.8333333
hypothesis: [[0.24144447]
[0.2685237 ]
[0.7179574 ]
[0.6175261 ]
[0.77200276]
[0.9281218 ]]
predicted: [[0.]
[0.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.8333333
hypothesis: [[0.24131039]
[0.26842496]
[0.71793365]
[0.61754024]
[0.7720628 ]
[0.9281668 ]]
predicted: [[0.]
[0.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.8333333
hypothesis: [[0.24117652]
[0.26832634]
[0.7179098 ]
[0.6175543 ]
[0.7721227 ]
[0.9282117 ]]
predicted: [[0.]
[0.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.8333333
hypothesis: [[0.24104282]
[0.268228 ]
[0.71788585]
[0.6175685 ]
[0.7721825 ]
[0.92825645]]
predicted: [[0.]
[0.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.8333333
hypothesis: [[0.24090937]
[0.26812977]
[0.7178618 ]
[0.6175827 ]
[0.7722424 ]
[0.92830116]]
predicted: [[0.]
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accuracy: 0.8333333
hypothesis: [[0.24077603]
[0.26803178]
[0.71783763]
[0.61759686]
[0.77230227]
[0.92834574]]
predicted: [[0.]
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accuracy: 0.8333333
hypothesis: [[0.24064296]
[0.267934 ]
[0.71781343]
[0.61761117]
[0.77236205]
[0.9283902 ]]
predicted: [[0.]
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accuracy: 0.8333333
hypothesis: [[0.24051002]
[0.2678364 ]
[0.7177892 ]
[0.6176254 ]
[0.7724217 ]
[0.9284346 ]]
predicted: [[0.]
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accuracy: 0.8333333
hypothesis: [[0.2403773 ]
[0.26773894]
[0.71776474]
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[0.7724813 ]
[0.9284789 ]]
predicted: [[0.]
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accuracy: 0.8333333
hypothesis: [[0.24024478]
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[0.6176541 ]
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[0.92852306]]
predicted: [[0.]
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accuracy: 0.8333333
hypothesis: [[0.24011245]
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[0.6176684 ]
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[0.9285672 ]]
predicted: [[0.]
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accuracy: 0.8333333
hypothesis: [[0.23998028]
[0.26744783]
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[0.6176828 ]
[0.7726599 ]
[0.9286113 ]]
predicted: [[0.]
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accuracy: 0.8333333
hypothesis: [[0.23984832]
[0.26735115]
[0.7176664 ]
[0.61769724]
[0.7727193 ]
[0.92865515]]
predicted: [[0.]
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accuracy: 0.8333333
hypothesis: [[0.23971656]
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[0.71764153]
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predicted: [[0.]
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accuracy: 0.8333333
hypothesis: [[0.23958492]
[0.26715833]
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accuracy: 0.8333333
hypothesis: [[0.23945355]
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accuracy: 0.8333333
hypothesis: [[0.23932233]
[0.26696628]
[0.7175665 ]
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[0.92882985]]
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accuracy: 0.8333333
hypothesis: [[0.2391913 ]
[0.26687056]
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[0.61776966]
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accuracy: 0.8333333
hypothesis: [[0.23906043]
[0.26677498]
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accuracy: 0.8333333
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hypothesis: [[0.23866901]
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accuracy: 0.8333333
hypothesis: [[0.23853892]
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predicted: [[0.]
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accuracy: 0.8333333
hypothesis: [[0.23840898]
[0.26629996]
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accuracy: 0.8333333
hypothesis: [[0.23827925]
[0.26620543]
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accuracy: 0.8333333
hypothesis: [[0.2381497 ]
[0.2661112 ]
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[0.7734868 ]
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predicted: [[0.]
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[1.]]
accuracy: 0.8333333
hypothesis: [[0.23802036]
[0.26601714]
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[0.6179017 ]
[0.7735454 ]
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predicted: [[0.]
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[1.]
[1.]]
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hypothesis: [[0.23789114]
[0.26592326]
[0.717285 ]
[0.6179165 ]
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predicted: [[0.]
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[1.]]
accuracy: 0.8333333
hypothesis: [[0.23776215]
[0.26582947]
[0.71725893]
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[1.]
[1.]
[1.]]
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hypothesis: [[0.23763332]
[0.26573595]
[0.7172327 ]
[0.61794615]
[0.773721 ]
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predicted: [[0.]
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[1.]
[1.]]
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hypothesis: [[0.23750466]
[0.26564255]
[0.7172064 ]
[0.61796105]
[0.77377945]
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[0.]
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[1.]
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hypothesis: [[0.23737621]
[0.26554942]
[0.71718 ]
[0.61797595]
[0.77383786]
[0.9294717 ]]
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[0.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.8333333
hypothesis: [[0.23724794]
[0.26545638]
[0.71715367]
[0.61799085]
[0.77389616]
[0.9295137 ]]
predicted: [[0.]
[0.]
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[1.]
[1.]
[1.]]
accuracy: 0.8333333
hypothesis: [[0.23711982]
[0.26536354]
[0.71712714]
[0.6180059 ]
[0.7739545 ]
[0.9295557 ]]
predicted: [[0.]
[0.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.8333333
hypothesis: [[0.23699185]
[0.26527092]
[0.71710056]
[0.61802083]
[0.7740127 ]
[0.9295975 ]]
predicted: [[0.]
[0.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.8333333
hypothesis: [[0.23686418]
[0.26517844]
[0.7170739 ]
[0.61803585]
[0.77407086]
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predicted: [[0.]
[0.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.8333333
hypothesis: [[0.2367366 ]
[0.2650861 ]
[0.7170471 ]
[0.61805093]
[0.77412903]
[0.92968106]]
predicted: [[0.]
[0.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.8333333
hypothesis: [[0.23660925]
[0.26499403]
[0.7170203 ]
[0.61806595]
[0.7741871 ]
[0.9297226 ]]
predicted: [[0.]
[0.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.8333333
1000 0.44170213
hypothesis: [[0.236482 ]
[0.2649021 ]
[0.71699333]
[0.61808103]
[0.7742451 ]
[0.9297641 ]]
predicted: [[0.]
[0.]
[1.]
[1.]
[1.]
[1.]]
accuracy: 0.8333333
In [17]:
# 실습
xy = np.loadtxt('./dataset/data-03-diabetes.csv', delimiter=','
, dtype=np.float32)
x = xy[:,0:-1]
y =xy[:,[-1]]
print(x.shape, y.shape)
(759, 8) (759, 1)
In [18]:
X = tf.placeholder(tf.float32, shape=[None, 8])
Y = tf.placeholder(tf.float32, shape=[None, 1])
W = tf.Variable(tf.random_normal([8,1]), name='weight')
b = tf.Variable(tf.random_normal([1]), name='bias')
hypothesis = tf.sigmoid(tf.matmul(X, W) + b)
cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) * tf.log(1 - hypothesis))
train = tf.train.GradientDescentOptimizer(learning_rate = 0.01).minimize(cost)
predicted = tf.cast(hypothesis > 0.5, dtype=tf.float32)
accuracy = tf.cast(tf.equal(predicted, Y), dtype=tf.float32)
In [22]:
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
feed = {X:x, Y:y}
for step in range(1001):
sess.run(train, feed_dict = feed)
if step % 200 == 0:
print(step, sess.run(cost, feed_dict=feed))
h, c, a = sess.run([hypothesis, predicted, accuracy], feed_dict=feed)
hca = [h, c, a]
0 1.0444983
200 0.780901
400 0.72328484
600 0.69887966
800 0.6810927
1000 0.6654639
In [ ]:
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