※ 김성훈 교수님의 [모두를 위한 딥러닝] 강의 정리
- https://www.youtube.com/watch?reload=9&v=BS6O0zOGX4E&feature=youtu.be&list=PLlMkM4tgfjnLSOjrEJN31gZATbcj_MpUm&fbclid=IwAR07UnOxQEOxSKkH6bQ8PzYj2vDop_J0Pbzkg3IVQeQ_zTKcXdNOwaSf_k0
- 참고자료 : Andrew Ng's ML class
1) https://class.coursera.org/ml-003/lecture
2) http://holehouse.org/mlclass/ (note)
1. Linear Regression
2. (binary) classification -> 0, 1 encoding
- Spam E-mail Detection: Spam(0) or Ham(1)
- Facebook feed: show(0) or hide(1)
- Credit Card Fraudulent Transaction detection: legitimate(0) or fraud(1)
3. Logistic Hypothesis
4. Logistic Regression의 새로운 cost 함수
5. cost 함수의 최소화 - Gradient decent algorithm
-> tensorflow
# cost function
cost = -tf.reduce_mean(-tf.reduce_sum(Y*tf.log(hypothesis) + (1-Y)*tf.log(1-hypothesis)))
# Minimize
a = tf.Variable(0.1) # Learnign rate, alpha
optimizer = tf.train.GradientDescentOptimizer(a)
train = optimizer.miminize(cost)
6. Tensorflow로 Logistic (regression) classifier 구현하기
(1) Training Data
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 ])
(2) tensorflow로 hypothesis 구현
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)
(3) tensorflow로 cost/loss function 구현
# cost function
cost = -tf.reduce_mean(-tf.reduce_sum(Y*tf.log(hypothesis) + (1-Y)*tf.log(1-hypothesis)))
(4) tensorflow로 cost 최소화 구현
# Minimize
a = tf.Variable(0.01) # Learnign rate, alpha
optimizer = tf.train.GradientDescentOptimizer(a)
train = optimizer.miminize(cost)
(5) 예측 정확도 계산
# 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))
(6) Training the model
# 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)
(7) 예측 정확도 출력
# Accuracy report
h, c, a = sess.run([hypothesis, predicted, accuracy],
feed_dict = {X: x_data, Y: y_data})
print (" \n Hypothesis: " " \n Correct (Y): " " \n Accuracy: "
(8) 실행결과 : 정확도 100% !!
7. 실제 데이터로 테스트
(1) diabetes.csv
https://github.com/hunkim/DeepLearningZeroToAll/blob/master/data-03-diabetes.csv
hunkim/DeepLearningZeroToAll
TensorFlow Basic Tutorial Labs. Contribute to hunkim/DeepLearningZeroToAll development by creating an account on GitHub.
github.com
(2) tensorflow 구현
# Lab 5 Logistic Regression Classifier
import tensorflow as tf
import numpy as np
tf.set_random_seed(777 ) # for reproducibility
xy = np.loadtxt(' data-03-diabetes.csv' delimiter = ' ,' dtype = np.float32)
x_data = xy[:, 0 :- 1 ]
y_data = xy[:, [- 1 ]]
print (x_data.shape, y_data.shape)
# placeholders for a tensor that will be always fed.
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 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 (" \n Hypothesis: " " \n Correct (Y): " " \n Accuracy: "
(3) 실행결과
step, cost :
(0, 0.82793975)
...
(9000, 0.49424884)
hypothesis :
[0.4434849 ],
...
correct(Y) :
[0.],
...
Accuracy :
0.7628459
