Deep Learning2019. 11. 29. 19:47

[머신러닝/딥러닝] multi-variable linear regression을 Tensorflow 구현

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※ 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. Hypothesis using matrix

 - H(x1, x2, x3) = x1w1 + x2w2 + x3w3

 -> H(X) = XW

 

2. tensorflow 구현

 2-1. 기존 방법

import tensorflow as tf
tf.set_random_seed(777) # for reproducibility
 
x1_data = [73., 93., 89., 96., 73.]
x2_data = [80., 88., 91., 98., 66.]
x3_data = [75., 93., 90., 100., 70.]
 
y_data = [152., 185., 180., 196., 142.]
 
# placeholders for a tensor that will be always fed.
x1 = tf.placeholder(tf.float32)
x2 = tf.placeholder(tf.float32)
x3 = tf.placeholder(tf.float32)
 
Y = tf.placeholder(tf.float32)
 
w1 = tf.Variable(tf.random_normal([1]), name='weight1')
w2 = tf.Variable(tf.random_normal([1]), name='weight2')
w3 = tf.Variable(tf.random_normal([1]), name='weight3')
b = tf.Variable(tf.random_normal([1]), name='bias')
 
hypothesis = x1 * w1 + x2 * w2 + x3 * w3 + b
 
# cost/loss function
cost = tf.reduce_mean(tf.square(hypothesis - Y))
 
# Minimize. Need a very small learning rate for this data set
optimizer = tf.train.GradientDescentOptimizer(learning_rate=1e-5)
train = optimizer.minimize(cost)
 
# Launch the graph in a session.
sess = tf.Session()
# Initializes global variables in the graph.
sess.run(tf.global_variables_initializer())
 
for step in range(2001):
cost_val, hy_val, _ = sess.run([cost, hypothesis, train],
feed_dict={x1: x1_data, x2: x2_data, x3: x3_data, Y: y_data})
if step % 10 == 0:
print(step, "Cost: ", cost_val, "\nPrediction:\n", hy_val)

 

 2-2. matrix 이용

import tensorflow as tf
tf.set_random_seed(777) # for reproducibility
 
x_data = [[73., 80., 75.],
[93., 88., 93.],
[89., 91., 90.],
[96., 98., 100.],
[73., 66., 70.]]
y_data = [[152.],
[185.],
[180.],
[196.],
[142.]]
 
 
# placeholders for a tensor that will be always fed.
X = tf.placeholder(tf.float32, shape=[None, 3])
Y = tf.placeholder(tf.float32, shape=[None, 1])
 
W = tf.Variable(tf.random_normal([3, 1]), name='weight')
b = tf.Variable(tf.random_normal([1]), name='bias')
 
# Hypothesis
hypothesis = tf.matmul(X, W) + b
 
# Simplified cost/loss function
cost = tf.reduce_mean(tf.square(hypothesis - Y))
 
# Minimize
optimizer = tf.train.GradientDescentOptimizer(learning_rate=1e-5)
train = optimizer.minimize(cost)
 
# Launch the graph in a session.
sess = tf.Session()
# Initializes global variables in the graph.
sess.run(tf.global_variables_initializer())
 
for step in range(2001):
cost_val, hy_val, _ = sess.run(
[cost, hypothesis, train], feed_dict={X: x_data, Y: y_data})
if step % 10 == 0:
print(step, "Cost: ", cost_val, "\nPrediction:\n", hy_val)

 

 

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Posted by CCIBOMB
Deep Learning2019. 11. 21. 18:50

[머신러닝/딥러닝] TensorFlow로 간단한 linear regression 구현

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※ 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://www.holehouse.org/mlcass/ (note)



1. (Linear) Hypothesis and cost function

  * Hypothesis : H(x) = Wx + b

  * Cost function(W,b) = ( H(x) - y ) ^ 2  // How fit the line to our (training) data

  * Goal = Minimize cost

 

  2. How to minimize cost

  * 학습 : W,b 값을 조정하여 cost 값을 최소화 하는 과정

 

  (1) 그래프 생성

import tensorflow as tf
 
# X and Y data
x_train = [1, 2, 3]
y_train = [1, 2, 3]
 
# Try to find values for W and b to compute y_data = x_data * W + b
# We know that W should be 1 and b should be 0
# But let TensorFlow figure it out
W = tf.Variable(tf.random_normal([1]), name="weight")   // Variable은 다른 프로그래밍 언어의 변수와는 달리, Tensorflow가 트레이닝을 위해 사용하는 변수임
b = tf.Variable(tf.random_normal([1]), name="bias")
 
# Our hypothesis XW+b
hypothesis = x_train * W + b
 
# cost/loss function
cost = tf.reduce_mean(tf.square(hypothesis - y_train))

 

   * GradientDescent : 학습

# Minimize
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
train = optimizer.minimize(cost)

 

  (2) 세션 실행 : 데이터 입력 및 그래프 실행

# Launch the graph in a session.
sess = tf.Session()
# Initializes global variables in the graph.
sess.run(tf.global_variables_initializer())
 
# Fit the line
for step in range(2001):
  sess.run(train)
  if step % 20 == 0:
    print(step, sess.run(cost), sess.run(W), sess.run(b))

 

  (3) 그래프 업데이트 및 결과값 반환 : 학습에 의해 cost를 최소화하는 W, b 값 추론

...

(0, 3.5240757, array([2.1286771], dtype=float32), array([-0.8523567], dtype=float32))
(20, 0.19749945, array([1.533928], dtype=float32), array([-1.0505961], dtype=float32))
(40, 0.15214379, array([1.4572546], dtype=float32), array([-1.0239124], dtype=float32))
(60, 0.1379325, array([1.4308538], dtype=float32), array([-0.9779527], dtype=float32))
(80, 0.12527025, array([1.4101374], dtype=float32), array([-0.93219817], dtype=float32))
(100, 0.11377233, array([1.3908179], dtype=float32), array([-0.8884077], dtype=float32))
(120, 0.10332986, array([1.3724468], dtype=float32), array([-0.8466577], dtype=float32))
(140, 0.093845844, array([1.3549428], dtype=float32), array([-0.80686814], dtype=float32))
(160, 0.08523229, array([1.3382617], dtype=float32), array([-0.7689483], dtype=float32))
(180, 0.07740932, array([1.3223647], dtype=float32), array([-0.73281056], dtype=float32))
(200, 0.07030439, array([1.3072149], dtype=float32), array([-0.6983712], dtype=float32))
(220, 0.06385162, array([1.2927768], dtype=float32), array([-0.6655505], dtype=float32))
(240, 0.05799109, array([1.2790174], dtype=float32), array([-0.63427216], dtype=float32))
(260, 0.05266844, array([1.2659047], dtype=float32), array([-0.6044637], dtype=float32))
(280, 0.047834318, array([1.2534081], dtype=float32), array([-0.57605624], dtype=float32))
(300, 0.043443877, array([1.2414987], dtype=float32), array([-0.5489836], dtype=float32))
(320, 0.0394564, array([1.2301493], dtype=float32), array([-0.5231833], dtype=float32))
(340, 0.035834935, array([1.2193329], dtype=float32), array([-0.49859545], dtype=float32))
(360, 0.032545824, array([1.2090251], dtype=float32), array([-0.47516325], dtype=float32))
(380, 0.029558638, array([1.1992016], dtype=float32), array([-0.45283225], dtype=float32))
(400, 0.026845641, array([1.18984], dtype=float32), array([-0.4315508], dtype=float32))
(420, 0.024381675, array([1.1809182], dtype=float32), array([-0.41126958], dtype=float32))
(440, 0.02214382, array([1.1724157], dtype=float32), array([-0.39194146], dtype=float32))
(460, 0.020111356, array([1.1643128], dtype=float32), array([-0.37352163], dtype=float32))
(480, 0.018265454, array([1.1565907], dtype=float32), array([-0.35596743], dtype=float32))
(500, 0.016588978, array([1.1492316], dtype=float32), array([-0.33923826], dtype=float32))
(520, 0.015066384, array([1.1422179], dtype=float32), array([-0.3232953], dtype=float32))
(540, 0.01368351, array([1.1355343], dtype=float32), array([-0.30810148], dtype=float32))
(560, 0.012427575, array([1.1291647], dtype=float32), array([-0.29362184], dtype=float32))
(580, 0.011286932, array([1.1230947], dtype=float32), array([-0.2798227], dtype=float32))
(600, 0.010250964, array([1.1173096], dtype=float32), array([-0.26667204], dtype=float32))
(620, 0.009310094, array([1.1117964], dtype=float32), array([-0.25413945], dtype=float32))
(640, 0.008455581, array([1.1065423], dtype=float32), array([-0.24219586], dtype=float32))
(660, 0.0076795053, array([1.1015354], dtype=float32), array([-0.23081362], dtype=float32))
(680, 0.006974643, array([1.0967635], dtype=float32), array([-0.21996623], dtype=float32))
(700, 0.0063344706, array([1.0922159], dtype=float32), array([-0.20962858], dtype=float32))
(720, 0.0057530706, array([1.0878822], dtype=float32), array([-0.19977672], dtype=float32))
(740, 0.0052250377, array([1.0837522], dtype=float32), array([-0.19038804], dtype=float32))
(760, 0.004745458, array([1.0798159], dtype=float32), array([-0.18144041], dtype=float32))
(780, 0.004309906, array([1.076065], dtype=float32), array([-0.17291337], dtype=float32))
(800, 0.003914324, array([1.0724902], dtype=float32), array([-0.16478711], dtype=float32))
(820, 0.0035550483, array([1.0690835], dtype=float32), array([-0.1570428], dtype=float32))
(840, 0.0032287557, array([1.0658368], dtype=float32), array([-0.14966238], dtype=float32))
(860, 0.0029324207, array([1.0627428], dtype=float32), array([-0.14262886], dtype=float32))
(880, 0.0026632652, array([1.059794], dtype=float32), array([-0.13592596], dtype=float32))
(900, 0.0024188235, array([1.056984], dtype=float32), array([-0.12953788], dtype=float32))
(920, 0.0021968128, array([1.0543059], dtype=float32), array([-0.12345006], dtype=float32))
(940, 0.001995178, array([1.0517538], dtype=float32), array([-0.11764836], dtype=float32))
(960, 0.0018120449, array([1.0493214], dtype=float32), array([-0.11211928], dtype=float32))
(980, 0.0016457299, array([1.0470035], dtype=float32), array([-0.10685005], dtype=float32))
(1000, 0.0014946823, array([1.0447946], dtype=float32), array([-0.10182849], dtype=float32))
(1020, 0.0013574976, array([1.0426894], dtype=float32), array([-0.09704296], dtype=float32))
(1040, 0.001232898, array([1.0406833], dtype=float32), array([-0.09248237], dtype=float32))
(1060, 0.0011197334, array([1.038771], dtype=float32), array([-0.08813594], dtype=float32))
(1080, 0.0010169626, array([1.0369489], dtype=float32), array([-0.08399385], dtype=float32))
(1100, 0.0009236224, array([1.0352125], dtype=float32), array([-0.08004645], dtype=float32))
(1120, 0.0008388485, array([1.0335577], dtype=float32), array([-0.07628451], dtype=float32))
(1140, 0.0007618535, array([1.0319806], dtype=float32), array([-0.07269943], dtype=float32))
(1160, 0.0006919258, array([1.0304775], dtype=float32), array([-0.06928282], dtype=float32))
(1180, 0.00062842044, array([1.0290452], dtype=float32), array([-0.06602671], dtype=float32))
(1200, 0.0005707396, array([1.0276802], dtype=float32), array([-0.06292368], dtype=float32))
(1220, 0.00051835255, array([1.0263793], dtype=float32), array([-0.05996648], dtype=float32))
(1240, 0.00047077626, array([1.0251396], dtype=float32), array([-0.05714824], dtype=float32))
(1260, 0.00042756708, array([1.0239582], dtype=float32), array([-0.0544625], dtype=float32))
(1280, 0.00038832307, array([1.0228322], dtype=float32), array([-0.05190301], dtype=float32))
(1300, 0.00035268333, array([1.0217593], dtype=float32), array([-0.04946378], dtype=float32))
(1320, 0.0003203152, array([1.0207369], dtype=float32), array([-0.04713925], dtype=float32))
(1340, 0.0002909189, array([1.0197623], dtype=float32), array([-0.0449241], dtype=float32))
(1360, 0.00026421514, array([1.0188333], dtype=float32), array([-0.04281275], dtype=float32))
(1380, 0.0002399599, array([1.0179482], dtype=float32), array([-0.04080062], dtype=float32))
(1400, 0.00021793543, array([1.0171047], dtype=float32), array([-0.03888312], dtype=float32))
(1420, 0.00019793434, array([1.0163009], dtype=float32), array([-0.03705578], dtype=float32))
(1440, 0.00017976768, array([1.0155348], dtype=float32), array([-0.03531429], dtype=float32))
(1460, 0.00016326748, array([1.0148047], dtype=float32), array([-0.03365463], dtype=float32))
(1480, 0.00014828023, array([1.0141089], dtype=float32), array([-0.03207294], dtype=float32))
(1500, 0.00013467176, array([1.0134459], dtype=float32), array([-0.03056567], dtype=float32))
(1520, 0.00012231102, array([1.0128139], dtype=float32), array([-0.02912918], dtype=float32))
(1540, 0.0001110848, array([1.0122118], dtype=float32), array([-0.0277602], dtype=float32))
(1560, 0.000100889745, array([1.0116379], dtype=float32), array([-0.02645557], dtype=float32))
(1580, 9.162913e-05, array([1.011091], dtype=float32), array([-0.02521228], dtype=float32))
(1600, 8.322027e-05, array([1.0105698], dtype=float32), array([-0.02402747], dtype=float32))
(1620, 7.5580865e-05, array([1.0100728], dtype=float32), array([-0.02289824], dtype=float32))
(1640, 6.8643785e-05, array([1.0095996], dtype=float32), array([-0.02182201], dtype=float32))
(1660, 6.234206e-05, array([1.0091484], dtype=float32), array([-0.02079643], dtype=float32))
(1680, 5.662038e-05, array([1.0087185], dtype=float32), array([-0.01981908], dtype=float32))
(1700, 5.142322e-05, array([1.0083088], dtype=float32), array([-0.01888768], dtype=float32))
(1720, 4.6704197e-05, array([1.0079182], dtype=float32), array([-0.01800001], dtype=float32))
(1740, 4.2417145e-05, array([1.0075461], dtype=float32), array([-0.01715406], dtype=float32))
(1760, 3.852436e-05, array([1.0071915], dtype=float32), array([-0.01634789], dtype=float32))
(1780, 3.4988276e-05, array([1.0068535], dtype=float32), array([-0.01557961], dtype=float32))
(1800, 3.1776715e-05, array([1.0065314], dtype=float32), array([-0.01484741], dtype=float32))
(1820, 2.8859866e-05, array([1.0062244], dtype=float32), array([-0.0141496], dtype=float32))
(1840, 2.621177e-05, array([1.005932], dtype=float32), array([-0.01348464], dtype=float32))
(1860, 2.380544e-05, array([1.0056531], dtype=float32), array([-0.01285094], dtype=float32))
(1880, 2.1620841e-05, array([1.0053875], dtype=float32), array([-0.012247], dtype=float32))
(1900, 1.9636196e-05, array([1.0051342], dtype=float32), array([-0.01167146], dtype=float32))
(1920, 1.7834054e-05, array([1.004893], dtype=float32), array([-0.01112291], dtype=float32))
(1940, 1.6197106e-05, array([1.0046631], dtype=float32), array([-0.01060018], dtype=float32))
(1960, 1.4711059e-05, array([1.004444], dtype=float32), array([-0.01010205], dtype=float32))
(1980, 1.3360998e-05, array([1.0042351], dtype=float32), array([-0.00962736], dtype=float32))
(2000, 1.21343355e-05, array([1.0040361], dtype=float32), array([-0.00917497], dtype=float32))


  3. How to minimize cost (placeholder 이용) // 에러..

import tensorflow as tf
W = tf.Variable(tf.random_normal([1]), name='weight')
b = tf.Variable(tf.random_normal([1]), name='bias')
X= tf.placeholder(tf.float32, shape=[None])
Y= tf.placeholder(tf.float32, shape=[None])
 
# Our hypothesis XW+b
hypothesis = X * W + b
 
# cost/loss function
cost = tf.reduce_mean(tf.square(hypothesis - Y)
 
# Minimize
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
train = optimizer.minimize(cost)
 
# Launch the graph in a session.
sess = tf.Session()
# Initializes global variables in the graph.
sess.run(tf.global_variables_initializer())
 
# Fit the line with new training data
for step in range(2001):
  cost_val, W_val, b_val, _ = sess.run([cost, W, b, train], feed_dict={X: [1, 2, 3, 4, 5], Y: [2.1, 3.1, 4.1, 5.1, 6.1])
  if step % 20 == 0:
    print(step, cost_val, W_val, b_val)

(0, 1.2035878, array([1.0696986], dtype=float32), array([0.01276637], dtype=float32))
(20, 0.16904518, array([1.2650416], dtype=float32), array([0.13934135], dtype=float32))
(40, 0.14761032, array([1.2485868], dtype=float32), array([0.20250577], dtype=float32))
(60, 0.1289092, array([1.2323107], dtype=float32), array([0.26128453], dtype=float32))
(80, 0.112577364, array([1.2170966], dtype=float32), array([0.3162127], dtype=float32))
(100, 0.09831471, array([1.2028787], dtype=float32), array([0.36754355], dtype=float32))
(120, 0.08585897, array([1.189592], dtype=float32), array([0.41551268], dtype=float32))
(140, 0.07498121, array([1.1771754], dtype=float32), array([0.46034035], dtype=float32))
(160, 0.0654817, array([1.165572], dtype=float32), array([0.5022322], dtype=float32))
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[머신러닝/딥러닝] Tensorflow 설치 및 기본 동작원리

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※ 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

 

Coursera

 

class.coursera.org

  2) http://holehouse.org/mlclass/ (note)

 

Machine Learning - complete course notes

Stanford Machine Learning The following notes represent a complete, stand alone interpretation of Stanford's machine learning course presented by Professor Andrew Ng and originally posted on the ml-class.org website during the fall 2011 semester. The topic

holehouse.org

 

 

1. Tensorflow

 - '데이터 플로우 그래프(data flow graphs)'를 이용한 '수치 계산(numerical computation)'을 위한 오픈소스 라이브러리

 

2. Tensorflow 설치 : pip install --upgrade tensorflow

 

3. Tensorflow 버전확인 : tensorflow.__version__

 

4. Deep Learning 관련 소스코드

 - https://github.com/hunkim/DeepLearningZeroToAll/   (2017년경 다수 commit, 최근에는 활동이 적음)

 

Build software better, together

GitHub is where people build software. More than 40 million people use GitHub to discover, fork, and contribute to over 100 million projects.

github.com

 

5. Hello, Tensorflow

 - Input : 

# Create a constant op

# This op is added as a node to the default graph // "Hello, TensorFlow!"라는 노드 생성

hello = tf.constant("Hello, TensorFlow!")

 

# start a TF session // 다른 프로그래밍 언어와 달리, 명령어 실행을 하려면 Session 생성이 필요

sess = tf.Session()

 

# run the op and get result

print(sess.run(hello))

 

 - Output : 

Hello, TensorFlow!

 

6. 계산 그래프

 - Input : 

# 노드 1, 2, 3 생성

node1 = tf.constant(3.0, tf.float32)

node2 = tf.constant(4.0) // tf.float32 생략

node3 = tf.add(node1, node2)

 

# 계산결과 출력

sess = tf.Session()

print("sess.run(node1, node2): ", sess.run([node1, node2]))

print("sess.run(node3): ", sess.run(node3))

 

 - Output : 

 

7. TensorFlow 메커니즘

 (1) 그래프 생성

 (2) 세션 실행 : 데이터 입력 및 그래프 실행

 (3) 그래프 업데이트 및 결과값 반환

※ 그림출처 : mathwarehouse.com

 

8. Placeholder // 처음에는 값이 없지만 입력 값을 받을 수 있는 노드

 - Input : 

a = tf.placeholder(tf.float32)

b = tf.placeholder(tf.float32)

adder_node = a + b

 

print(sess.run(adder_node, feed_dict={a: 3, b: 4.5}))

print(sess.run(adder_node, feed_dict={a: [1,3], b: [2, 4]}))

 

 - Output : 

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