import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
tf.__version__# 2.3.0당뇨병을 진단해봅시다.
xy = np.loadtxt('./data-03-diabetes.csv', delimiter=',', dtype=np.float32)
x_train = xy[0:-100, 0:-1]
y_train = xy[0:-100, [-1]]
x_test = xy[-100:, 0:-1]
y_test = xy[-100:, [-1]]
print(x_train.shape, y_train.shape)
print(x_test.shape, y_test.shape)#(659, 8) (659, 1)
#(100, 8) (100, 1)print(x_test)
print(y_test)#[[-0.294118 -0.0150754 -0.0491803 -0.333333 -0.550827 0.0134128
# -0.699402 -0.266667 ]
# [ 0.0588235 0.547739 0.278689 -0.393939 -0.763593 -0.0789866
# -0.926558 -0.2 ]
# [-0.294118 0.658291 0.114754 -0.474747 -0.602837 0.00149028
# -0.527754 -0.0666667 ]
# [-0.882353 -0.00502513 -0.0491803 -0.79798 0. -0.242921
# -0.596072 0. ]
# [ 0.176471 -0.316583 0.737705 -0.535354 -0.884161 0.0581222
# -0.823228 -0.133333 ]
#[-0.647059 0.236181 0.639344 -0.292929 -0.432624 0.707899
# -0.315115 -0.966667 ]
#[-0.0588235 -0.0854271 0.344262 0. 0. 0.0611028
# -0.565329 0.566667 ]
#[-0.294118 0.959799 0.147541 0. 0. -0.0789866
# -0.786507 -0.666667 ]
#[ 0.0588235 0.567839 0.409836 0. 0. -0.260805
# -0.870196 0.0666667 ]
#[ 0. -0.0653266 -0.0163934 0. 0. 0.052161
# -0.842015 -0.866667 ]
#[-0.647059 0.21608 -0.147541 0. 0. 0.0730254
# -0.958155 -0.866667 ]
#[-0.764706 0.0150754 -0.0491803 -0.656566 -0.373522 -0.278688
# -0.542272 -0.933333 ]
#[-0.764706 -0.437186 -0.0819672 -0.434343 -0.893617 -0.278688
# -0.783091 -0.966667 ]
#[ 0. 0.628141 0.245902 -0.272727 0. 0.47839
# -0.755764 -0.833333 ]
#[ 0. -0.0452261 0.0491803 -0.212121 -0.751773 0.329359
# -0.754056 -0.966667 ]
#[-0.529412 0.256281 0.311475 0. 0. -0.0372578
# -0.608881 -0.8 ]
#[-0.411765 0.366834 0.344262 0. 0. 0.
# -0.520068 0.6 ]
#[-0.764706 0.296482 0.213115 -0.474747 -0.515366 -0.0104321
# -0.561913 -0.866667 ]
#[-0.647059 0.306533 0.0491803 0. 0. -0.311475
# -0.798463 -0.966667 ]
#[-0.882353 0.0753769 -0.180328 -0.616162 0. -0.156483
# -0.912041 -0.733333 ]
#[-0.882353 0.407035 0.213115 -0.474747 -0.574468 -0.281669
# -0.359522 -0.933333 ]
#[-0.882353 0.447236 0.344262 -0.0707071 -0.574468 0.374069
# -0.780529 -0.166667 ]
#[-0.0588235 0.0753769 0.311475 0. 0. -0.266766
# -0.335611 -0.566667 ]
#[ 0.529412 0.58794 0.868852 0. 0. 0.260805
# -0.847139 -0.233333 ]
#[-0.764706 0.21608 0.147541 -0.353535 -0.775414 0.165425
# -0.309991 -0.933333 ]
#[-0.176471 0.296482 0.114754 -0.010101 -0.704492 0.147541
# -0.691716 -0.266667 ]
#[-0.764706 -0.0954774 -0.0163934 0. 0. -0.299553
# -0.903501 -0.866667 ]
#[-0.176471 0.427136 0.47541 -0.515152 0.134752 -0.0938897
# -0.957301 -0.266667 ]
#[-0.647059 0.698492 0.213115 -0.616162 -0.704492 -0.108793
# -0.837746 -0.666667 ]
#[ 0. -0.00502513 0. 0. 0. -0.254843
# -0.850555 -0.966667 ]
#[-0.529412 0.276382 0.442623 -0.777778 -0.63357 0.028316
# -0.555935 -0.766667 ]
#[-0.529412 0.18593 0.147541 0. 0. 0.326379
# -0.29462 -0.833333 ]
#[-0.764706 0.226131 0.245902 -0.454545 -0.527187 0.0700448
# -0.654142 -0.833333 ]
#[-0.294118 0.256281 0.278689 -0.373737 0. -0.177347
# -0.584116 -0.0666667 ]
#[-0.882353 0.688442 0.442623 -0.414141 0. 0.0432191
# -0.293766 0.0333333 ]
#[-0.764706 0.296482 0. 0. 0. 0.147541
# -0.807003 -0.333333 ]
#[-0.529412 0.105528 0.245902 -0.59596 -0.763593 -0.153502
# -0.965841 -0.8 ]
#[-0.294118 -0.19598 0.311475 -0.272727 0. 0.186289
# -0.915457 -0.766667 ]
#[ 0.176471 0.155779 0. 0. 0. 0.
# -0.843723 -0.7 ]
#[-0.764706 0.276382 -0.245902 -0.575758 -0.208038 0.0253354
# -0.916311 -0.966667 ]
#[ 0.0588235 0.648241 0.278689 0. 0. -0.0223547
# -0.940222 -0.2 ]
#[-0.764706 -0.0653266 0.0491803 -0.353535 -0.621749 0.132638
# -0.491033 -0.933333 ]
#[-0.647059 0.58794 0.0491803 -0.737374 -0.0851064 -0.0700447
# -0.814688 -0.9 ]
#[-0.411765 0.266332 0.278689 -0.454545 -0.947991 -0.117735
# -0.691716 -0.366667 ]
#[ 0.176471 0.296482 0.0163934 -0.272727 0. 0.228018
# -0.690009 -0.433333 ]
#[ 0. 0.346734 -0.0491803 -0.59596 -0.312057 -0.213115
# -0.766012 0. ]
#[-0.647059 0.0251256 0.213115 0. 0. -0.120715
# -0.963279 -0.633333 ]
#[-0.176471 0.879397 -0.180328 -0.333333 -0.0732861 0.0104323
# -0.36123 -0.566667 ]
#[-0.647059 0.738693 0.278689 -0.212121 -0.562648 0.00745157
# -0.238258 -0.666667 ]
#[ 0.176471 -0.0552764 0.180328 -0.636364 0. -0.311475
# -0.558497 0.166667 ]
#[-0.882353 0.0854271 -0.0163934 -0.0707071 -0.579196 0.0581222
# -0.712212 -0.9 ]
#[-0.411765 -0.0251256 0.245902 -0.454545 0. 0.0611028
# -0.743809 0.0333333 ]
#[-0.529412 -0.165829 0.409836 -0.616162 0. -0.126677
# -0.795901 -0.566667 ]
#[-0.882353 0.145729 0.0819672 -0.272727 -0.527187 0.135618
# -0.819812 0. ]
#[-0.882353 0.497487 0.114754 -0.414141 -0.699764 -0.126677
# -0.768574 -0.3 ]
#[-0.411765 0.175879 0.409836 -0.393939 -0.751773 0.165425
# -0.852263 -0.3 ]
#[-0.882353 0.115578 0.540984 0. 0. -0.0223547
# -0.840307 -0.2 ]
#[-0.529412 0.125628 0.278689 -0.191919 0. 0.174367
# -0.865073 -0.433333 ]
#[-0.882353 0.165829 0.278689 -0.414141 -0.574468 0.0760059
# -0.64304 -0.866667 ]
#[ 0. 0.417085 0.377049 -0.474747 0. -0.0342771
# -0.69684 -0.966667 ]
#[-0.764706 0.758794 0.442623 0. 0. -0.317437
# -0.788215 -0.966667 ]
#[-0.764706 -0.0753769 -0.147541 0. 0. -0.102832
# -0.9462 -0.966667 ]
#[-0.647059 0.306533 0.278689 -0.535354 -0.813239 -0.153502
# -0.790777 -0.566667 ]
#[-0.0588235 0.20603 0.409836 0. 0. -0.153502
# -0.845431 -0.966667 ]
#[-0.764706 0.748744 0.442623 -0.252525 -0.716312 0.326379
# -0.514944 -0.9 ]
#[-0.764706 0.0653266 -0.0819672 -0.454545 -0.609929 -0.135618
# -0.702818 -0.966667 ]
#[-0.764706 0.0552764 0.229508 0. 0. -0.305514
# -0.588386 0.0666667 ]
#[-0.529412 -0.0452261 -0.0163934 -0.353535 0. 0.0551417
# -0.824082 -0.766667 ]
#[ 0. 0.266332 0.409836 -0.454545 -0.716312 -0.183308
# -0.626815 0. ]
#[-0.0588235 -0.346734 0.180328 -0.535354 0. -0.0461997
# -0.554227 -0.3 ]
#[-0.764706 -0.00502513 -0.0163934 -0.656566 -0.621749 0.0909091
# -0.679761 0. ]
#[-0.882353 0.0251256 0.213115 0. 0. 0.177347
# -0.816396 -0.3 ]
#[ 0.294118 0.20603 0.311475 -0.252525 -0.64539 0.260805
#-0.396243 -0.1 ]
#[-0.647059 0.0251256 -0.278689 -0.59596 -0.777778 -0.0819672
# -0.725021 -0.833333 ]
#[-0.882353 0.0954774 -0.0491803 -0.636364 -0.725768 -0.150522
# -0.87959 -0.966667 ]
#[ 0.0588235 0.407035 0.540984 0. 0. -0.0253353
# -0.439795 -0.2 ]
# 0.529412 0.537688 0.442623 -0.252525 -0.669031 0.210134
# -0.0640478 -0.4 ]
#[ 0.411765 0.00502513 0.377049 -0.333333 -0.751773 -0.105812
# -0.649872 -0.166667 ]
# [-0.882353 0.477387 0.540984 -0.171717 0. 0.469449
# -0.760888 -0.8 ]
# [-0.882353 -0.18593 0.213115 -0.171717 -0.865248 0.38003
# -0.130658 -0.633333 ]
# [-0.647059 0.879397 0.147541 -0.555556 -0.527187 0.0849479
# -0.71819 -0.5 ]
# [-0.294118 0.628141 0.0163934 0. 0. -0.275708
# -0.914603 -0.0333333 ]
# [-0.529412 0.366834 0.147541 0. 0. -0.0700447
# -0.0572161 -0.966667 ]
# [-0.882353 0.21608 0.278689 -0.212121 -0.825059 0.162444
# -0.843723 -0.766667 ]
# [-0.647059 0.0854271 0.0163934 -0.515152 0. -0.225037
# -0.876174 -0.866667 ]
# [ 0. 0.819095 0.442623 -0.111111 0.205674 0.290611
# -0.877028 -0.833333 ]
# [-0.0588235 0.547739 0.278689 -0.353535 0. -0.0342771
# -0.688301 -0.2 ]
# [-0.882353 0.286432 0.442623 -0.212121 -0.739953 0.0879285
# -0.163962 -0.466667 ]
# [-0.176471 0.376884 0.47541 -0.171717 0. -0.0461997
# -0.732707 -0.4 ]
# [ 0. 0.236181 0.180328 0. 0. 0.0819672
# -0.846285 0.0333333 ]
# [-0.882353 0.0653266 0.245902 0. 0. 0.117735
# -0.898377 -0.833333 ]
# [-0.294118 0.909548 0.508197 0. 0. 0.0581222
# -0.829206 0.5 ]
# [-0.764706 -0.115578 -0.0491803 -0.474747 -0.962175 -0.153502
# -0.412468 -0.966667 ]
# [ 0.0588235 0.708543 0.213115 -0.373737 0. 0.311475
# -0.722459 -0.266667 ]
# [ 0.0588235 -0.105528 0.0163934 0. 0. -0.329359
# -0.945346 -0.6 ]
# [ 0.176471 0.0150754 0.245902 -0.030303 -0.574468 -0.019374
# -0.920581 0.4 ]
# [-0.764706 0.226131 0.147541 -0.454545 0. 0.0968703
# -0.77626 -0.8 ]
# [-0.411765 0.21608 0.180328 -0.535354 -0.735225 -0.219076
# -0.857387 -0.7 ]
# [-0.882353 0.266332 -0.0163934 0. 0. -0.102832
# -0.768574 -0.133333 ]
# [-0.882353 -0.0653266 0.147541 -0.373737 0. -0.0938897
# -0.797609 -0.933333 ]]
#[[1.]
# [1.]
# [1.]
# [1.]
# [1.]
# [1.]
# [1.]
# [0.]
# [0.]
# [1.]
# [0.]
# [1.]
# [1.]
# [0.]
# [1.]
# [0.]
# [1.]
# [1.]
# [1.]
# [1.]
# [1.]
# [0.]
# [1.]
# [0.]
# [1.]
# [0.]
# [1.]
# [0.]
# [0.]
# [1.]
# [1.]
# [1.]
# [1.]
# [0.]
# [0.]
# [1.]
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# [1.]
# [0.]
# [1.]
# [0.]
# [0.]
# [1.]
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# [1.]
# [0.]
# [0.]
# [1.]
# [1.]
# [0.]
# [1.]
# [1.]
# [0.]
# [1.]
# [1.]
# [1.]
# [1.]
# [1.]
# [1.]
# [1.]
# [0.]
# [0.]
# [0.]
# [1.]
# [1.]
# [1.]
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# [1.]
# [1.]
# [0.]
# [0.]
# [1.]
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# [0.]
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# [0.]
# [0.]
# [0.]
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# [0.]
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# [0.]
# [1.]
# [1.]
# [1.]
# [1.]
# [0.]
# [1.]]dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(len(x_train))
``
```python
W = tf.Variable(tf.random.normal([8, 1]), name='weight')
b = tf.Variable(tf.random.normal([1]), name='bias')가설 설정
- 병이 있다 / 없다로 분류
- binary classification으로 진행
def logistic_regression(features):
hypothesis = tf.divide(1., 1. + tf.exp(-(tf.matmul(features, W) + b)))
return hypothesis
print(logistic_regression(x_train))#output
tf.Tensor(
[[0.7636665 ]
[0.80092156]
[0.41501445]
[0.64212984]
[0.08430427]
[0.63381004]
[0.24326754]
[0.3841968 ]
[0.89089465]
[0.82491577]
[0.8023107 ]
[0.4901009 ]
[0.5799539 ]
[0.9825106 ]
[0.4572858 ]
[0.4163838 ]
[0.5812125 ]
[0.18660453]
[0.49397138]
[0.62530833]
[0.73232764]
[0.7523532 ]
[0.6552009 ]
[0.33452913]
[0.68108976]
[0.54304326]
[0.45371786]
[0.7994284 ]
[0.87836325]
[0.5488168 ]
[0.36446616]
[0.72126794]
[0.70075434]
[0.35161632]
[0.48328355]
[0.67992574]
[0.7080763 ]
[0.49282402]
[0.3677201 ]
[0.67686725]
[0.9042856 ]
[0.8459027 ]
[0.5395733 ]
[0.38149193]
[0.58056957]
[0.76291215]
[0.6500029 ]
[0.43171626]
[0.61393666]
[0.3151854 ]
[0.30568883]
[0.7729612 ]
[0.50671214]
[0.8705757 ]
[0.5404438 ]
[0.21647061]
[0.5636013 ]
[0.20140737]
[0.7398153 ]
[0.58383894]
[0.5396699 ]
[0.29455638]
[0.58545834]
[0.78524125]
[0.7630256 ]
[0.88663423]
[0.46257353]
[0.5376614 ]
[0.43710646]
[0.29864013]
[0.58702856]
[0.75715876]
[0.8147204 ]
[0.70629656]
[0.66748947]
[0.7191528 ]
[0.41652465]
[0.784707 ]
[0.6601275 ]
[0.6013106 ]
[0.36445394]
[0.55154824]
[0.8871866 ]
[0.4873026 ]
[0.4423806 ]
[0.44266328]
[0.19323725]
[0.8383805 ]
[0.7044733 ]
[0.6911003 ]
[0.35248557]
[0.7854226 ]
[0.68024784]
[0.5945548 ]
[0.75045216]
[0.31145722]
[0.15239963]
[0.6889401 ]
[0.6916801 ]
[0.5982999 ]
[0.7874695 ]
[0.4930176 ]
[0.6035695 ]
[0.52621114]
[0.93228096]
[0.40088257]
[0.23307744]
[0.3338161 ]
[0.44407237]
[0.75184536]
[0.41691092]
[0.53907806]
[0.28092387]
[0.87783015]
[0.69089836]
[0.36543256]
[0.674538 ]
[0.6241091 ]
[0.15919036]
[0.5720373 ]
[0.4550946 ]
[0.8480272 ]
[0.5062124 ]
[0.15462755]
[0.48597947]
[0.31892073]
[0.7759308 ]
[0.7397651 ]
[0.60515463]
[0.31335363]
[0.4768164 ]
[0.7203091 ]
[0.44128314]
[0.53259516]
[0.333995 ]
[0.18140796]
[0.5336953 ]
[0.6654354 ]
[0.81676185]
[0.84335667]
[0.27014837]
[0.48655856]
[0.5064386 ]
[0.7958667 ]
[0.72779024]
[0.3328443 ]
[0.8311853 ]
[0.836748 ]
[0.47448954]
[0.6056248 ]
[0.45292825]
[0.83604586]
[0.6897159 ]
[0.77199703]
[0.5026937 ]
[0.38735747]
[0.4824938 ]
[0.12066017]
[0.84649557]
[0.38549057]
[0.5493018 ]
[0.8670505 ]
[0.61569124]
[0.55314785]
[0.74935067]
[0.60369956]
[0.5834295 ]
[0.6766917 ]
[0.7095112 ]
[0.40459758]
[0.7286922 ]
[0.24222213]
[0.41957784]
[0.25559497]
[0.6979939 ]
[0.5611082 ]
[0.7786507 ]
[0.6965237 ]
[0.66873777]
[0.23819672]
[0.6364276 ]
[0.4979731 ]
[0.714705 ]
[0.7020187 ]
[0.78507227]
[0.53667647]
[0.2624541 ]
[0.452083 ]
[0.7129255 ]
[0.22131674]
[0.53050655]
[0.41136152]
[0.6669064 ]
[0.53683543]
[0.7375507 ]
[0.34301266]
[0.21388213]
[0.5986855 ]
[0.8330947 ]
[0.80187535]
[0.64903283]
[0.4714769 ]
[0.60809654]
[0.7226655 ]
[0.6793256 ]
[0.9021515 ]
[0.5891988 ]
[0.8016913 ]
[0.75919235]
[0.56440383]
[0.913523 ]
[0.26920265]
[0.4617522 ]
[0.3071325 ]
[0.24110588]
[0.3428931 ]
[0.7469285 ]
[0.6508559 ]
[0.6763496 ]
[0.89952314]
[0.5012992 ]
[0.45024437]
[0.45327234]
[0.48845887]
[0.5373695 ]
[0.54264766]
[0.8453774 ]
[0.30040938]
[0.70696026]
[0.7836389 ]
[0.4648138 ]
[0.6119319 ]
[0.33171308]
[0.6333675 ]
[0.74983406]
[0.7440523 ]
[0.47342327]
[0.8846874 ]
[0.32426697]
[0.46258035]
[0.25525188]
[0.59502435]
[0.8243798 ]
[0.49547455]
[0.90853006]
[0.604871 ]
[0.90686107]
[0.3889982 ]
[0.76802987]
[0.5737063 ]
[0.5890859 ]
[0.45235273]
[0.83422416]
[0.6021486 ]
[0.8165836 ]
[0.665509 ]
[0.5822604 ]
[0.5298171 ]
[0.69548607]
[0.9476272 ]
[0.60625416]
[0.4768089 ]
[0.3412375 ]
[0.6143951 ]
[0.37127158]
[0.6081643 ]
[0.66949815]
[0.46397138]
[0.75950485]
[0.47994053]
[0.4896506 ]
[0.23809849]
[0.6258427 ]
[0.23959407]
[0.7328004 ]
[0.6403352 ]
[0.48757273]
[0.36688936]
[0.6742406 ]
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[0.5771973 ]
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[0.7173807 ]
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[0.76301765]
[0.8754921 ]
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[0.4929719 ]
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[0.7535387 ]
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[0.32570115]
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[0.37332115]
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[0.18112727]
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[0.8256423 ]
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[0.85915315]
[0.5120207 ]
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[0.9156089 ]
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[0.41596973]
[0.31394008]
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[0.62244946]
[0.8170966 ]
[0.7549453 ]
[0.35122392]
[0.9169064 ]
[0.7254303 ]
[0.52748376]
[0.56286156]
[0.7783947 ]
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[0.48204806]
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[0.7844165 ]
[0.65899026]
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[0.83081335]
[0.6027034 ]
[0.48024657]
[0.632341 ]
[0.39004117]
[0.73970485]
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[0.528723 ]
[0.6224033 ]
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[0.33480844]
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[0.347116 ]
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[0.38443407]
[0.51216805]
[0.48601606]
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[0.50945175]
[0.43230698]
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[0.53914785]
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[0.6589845 ]
[0.53246963]
[0.5854794 ]
[0.921835 ]
[0.8908273 ]
[0.37412447]
[0.85578173]
[0.35911554]
[0.5043008 ]
[0.4602722 ]
[0.7887939 ]
[0.71430486]
[0.5824623 ]
[0.47400293]
[0.7626106 ]
[0.39731818]
[0.39681867]
[0.28079882]
[0.53555644]
[0.34190837]
[0.55484104]
[0.40515682]
[0.49421048]
[0.3364206 ]
[0.71804976]
[0.89632165]
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[0.4700739 ]
[0.7745273 ]
[0.16087689]
[0.22672014]
[0.8355446 ]
[0.4568351 ]
[0.50936085]
[0.7896421 ]
[0.6286575 ]
[0.81790966]
[0.6534319 ]
[0.9005289 ]
[0.786376 ]
[0.56117505]
[0.5235736 ]
[0.7055119 ]
[0.36500552]
[0.6618679 ]
[0.42692015]
[0.7948441 ]
[0.25390214]
[0.3877735 ]
[0.49036333]
[0.67437106]
[0.23031549]
[0.79976875]
[0.2996345 ]
[0.9049057 ]
[0.479109 ]
[0.8211558 ]
[0.52805084]
[0.594226 ]
[0.83106095]
[0.5853545 ]
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[0.49695298]
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[0.39794663]
[0.5118717 ]
[0.6532073 ]
[0.7271816 ]
[0.39820278]
[0.66155314]
[0.7709704 ]
[0.3689781 ]
[0.76498526]
[0.3952522 ]
[0.5928684 ]
[0.7847312 ]
[0.755186 ]
[0.44578898]
[0.76340497]
[0.3816837 ]
[0.5955655 ]
[0.67361015]
[0.7707462 ]
[0.8477395 ]
[0.4667048 ]
[0.2416224 ]
[0.56083953]
[0.7360955 ]
[0.3842407 ]
[0.39748034]
[0.7379605 ]
[0.49579504]
[0.32042682]
[0.58163965]
[0.437914 ]
[0.60518587]
[0.7585915 ]
[0.5343307 ]
[0.449775 ]
[0.8288851 ]
[0.6622481 ]
[0.14425522]
[0.36460817]
[0.54965633]
[0.3563462 ]
[0.44539824]
[0.30856517]
[0.5247021 ]
[0.5280862 ]
[0.796762 ]
[0.2778471 ]
[0.57513106]
[0.3974484 ]
[0.6978083 ]
[0.6963511 ]
[0.7044852 ]
[0.2962951 ]
[0.63282883]
[0.49066746]
[0.94800115]
[0.6699201 ]], shape=(659, 1), dtype=float32)Loss Function
- 기존 MSE 대신 Cross Entropy 사용
def loss_fn(hypothesis, labels):
loss = -tf.reduce_mean(labels * tf.math.log(hypothesis) + \
(1 - labels) * tf.math.log(1 - hypothesis))
return loss
optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate=0.005)학습
epochs = 5000
for step in range(epochs):
for features, labels in dataset:
with tf.GradientTape() as tape:
pred = logistic_regression(features)
loss_value = loss_fn(pred, labels)
grads = tape.gradient(loss_value, [W,b])
optimizer.apply_gradients(grads_and_vars=zip(grads,[W,b]))
if step % 100 == 0:
print("Iter: {}, Loss: {:.4f}".format(step, loss_fn(logistic_regression(features),labels)))#Output
Iter: 0, Loss: 0.7648
Iter: 100, Loss: 0.7447
Iter: 200, Loss: 0.7296
Iter: 300, Loss: 0.7179
Iter: 400, Loss: 0.7083
Iter: 500, Loss: 0.7001
Iter: 600, Loss: 0.6929
Iter: 700, Loss: 0.6864
Iter: 800, Loss: 0.6803
Iter: 900, Loss: 0.6746
Iter: 1000, Loss: 0.6692
Iter: 1100, Loss: 0.6640
Iter: 1200, Loss: 0.6591
Iter: 1300, Loss: 0.6543
Iter: 1400, Loss: 0.6497
Iter: 1500, Loss: 0.6453
Iter: 1600, Loss: 0.6410
Iter: 1700, Loss: 0.6368
Iter: 1800, Loss: 0.6328
Iter: 1900, Loss: 0.6289
Iter: 2000, Loss: 0.6251
Iter: 2100, Loss: 0.6214
Iter: 2200, Loss: 0.6179
Iter: 2300, Loss: 0.6144
Iter: 2400, Loss: 0.6111
Iter: 2500, Loss: 0.6079
Iter: 2600, Loss: 0.6048
Iter: 2700, Loss: 0.6017
Iter: 2800, Loss: 0.5988
Iter: 2900, Loss: 0.5959
Iter: 3000, Loss: 0.5932
Iter: 3100, Loss: 0.5905
Iter: 3200, Loss: 0.5879
Iter: 3300, Loss: 0.5854
Iter: 3400, Loss: 0.5829
Iter: 3500, Loss: 0.5806
Iter: 3600, Loss: 0.5783
Iter: 3700, Loss: 0.5760
Iter: 3800, Loss: 0.5739
Iter: 3900, Loss: 0.5718
Iter: 4000, Loss: 0.5697
Iter: 4100, Loss: 0.5677
Iter: 4200, Loss: 0.5658
Iter: 4300, Loss: 0.5639
Iter: 4400, Loss: 0.5621
Iter: 4500, Loss: 0.5604
Iter: 4600, Loss: 0.5586
Iter: 4700, Loss: 0.5570
Iter: 4800, Loss: 0.5553
Iter: 4900, Loss: 0.5538테스트
def accuracy_fn(hypothesis, labels):
predicted = tf.cast(hypothesis > 0.5, dtype=tf.int32)
accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, labels), dtype=tf.float32))
return accuracytest_acc = accuracy_fn(logistic_regression(x_test),y_test)
print("Testset Accuracy: {:.4f}".format(test_acc))#Testset Accuracy: 0.7200
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