05-1. Logistic Regression Classifier

■ sigmoid 함수를 사용한 binary classifier

hypothesis = tf.sigmoid(tf.matmul(X,W)+b)

- hypothesis 에서 tf.matmul(X,W)+b 에 sigmoid 함수를 적용

cost = -tf.reduce_mean(Y*tf.log(hypothesis)+(1-Y)*tf.log(1-hypothesis))

- Cross Entropy 를 코스트 함수로 사용한다.

- 1인 경우 Y*tf.log(hypothesis) 가 0으로 수렴하도록 학습

- 0인 경우 (1-Y)*tf.log(1-hypothesis) 가 0으로 수렴하도록 학습

train = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cost)

- learning_rate 를 0.01 로 학습을 진행함

predicted = tf.cast(hypothesis > 0.5 , dtype=tf.float32)

- 0.5 보다 작으면 0, 0.5보다 크면 1로 predicted 변수를 설정한다

- predicted 데이터 타입을 tf.float32 로 설정

accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted,Y),dtype=tf.float32))

- tf.equal(predicted,Y) 가 같으면 1 다르면 0, 이를 더해서 평균을 낸다.

# Lab05-1 Logistic Regression Classifier
 
import tensorflow as tf
 
tf.set_random_seed(777)  # for reproducibility
 
 
x_data = [[1, 2],
 
          [2, 3],
 
          [3, 1],
 
          [4, 3],
 
          [5, 3],
 
          [6, 2]]
 
y_data = [[0],
 
          [0],
 
          [0],
 
          [1],
 
          [1],
 
          [1]]
 
 
# placeholders for a tensor that will be always fed.
 
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 using sigmoid: tf.div(1., 1. + tf.exp(tf.matmul(X, W)))
 
hypothesis = tf.sigmoid(tf.matmul(X, W) + b)
 
 
# cost/loss function
 
cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) *
 
                       tf.log(1 - hypothesis))
 
 
train = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cost)
 
 
# Accuracy computation
 
# True if hypothesis>0.5 else False
 
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:
 
    # Initialize TensorFlow variables
 
    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)
 
 
    # Accuracy report
 
    h, c, a = sess.run([hypothesis, predicted, accuracy],
 
                       feed_dict={X: x_data, Y: y_data})
 
    print("\nHypothesis: ", h, "\nCorrect (Y): ", c, "\nAccuracy: ", a)