[Tensorflow] Binary Classfication

ex)

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Credit Card Fraudulent Trasaction detection : legitimate / fraud

 

Linear Regression 문제점

합격임에도 불구하고 불합격 판정을 줄 수 있다.

 

- We know Y is 0 or 1

- H(x) = Wx + b

but Hypothesis can give vaules large than 1 or less than 0 (너무 큰 값을 받아들이지 못함)

 

Solution!

Used sigmoid Func.

 

Cost Func. 문제점

기존의 func는 변경된 H(x) 함수에 적용할 수 없음

 

Solution!

연립방정식으로 두가지 경우를 생각 (y=0, y=1)

 

import tensorflow as tf
 
x_data = [[1,2], [2,3], [3,1], [4,3], [5,3], [6,2]]
y_data = [[0], [0], [0], [1], [1], [1]]
 
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 Func.
hypothesis = tf.sigmoid(tf.matmul(X, W) + b)
 
# Cost Func.
cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) * tf.log(1 - hypothesis))
 
# Minimize Func.
train = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cost)
 
predicted = tf.cast(hypothesis > 0.5, dtype = tf.float32)
accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, Y), dtype = tf.float32))
 
 
# Launch Graph
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
 
    for step in range(10001):
        cost_val, _ = sess.run([cost, train], feed_dict={X: x_data, Y: y_data})
        if step % 200 == 0:
            print(step, cost_val)
 
 
    h, c, a = sess.run([hypothesis, predicted, accuracy], feed_dict = {X: x_data, Y: y_data})
    print("\nHypthesis: ", h, "\nCorrect (Y) ", c, "\Accuracy: ", a)

 https://github.com/BadSchool/Study/blob/master/tensorflow/tf_5-2.py

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