Deep Learning2019. 11. 21. 19:53

[머신러닝/딥러닝] Linear Regression의 cost 최소화의 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 and Cost

 - 기존 Hypothesis and Cost

 

 - 단순화한 Hypothesis and Cost

 

2. Gradient descent algorithm

 - cost 최소화 등 많은 최소화 문제 해결에 활용되는 알고리즘

 - cost(W,b)의 경우, cost를 최소화하는 W, b 값을 찾아냄

 - 미분을 하여 기울기가 최소가 되는 점이 cost가 최소가 되는 점

 - linear regression을 사용할 때, cost function이 convex function(볼록함수)이 되는지 확인해야 함

   -> 그래야 정확한 값 획득 가능

 

3. Linear Regression의 cost 최소화의 Tensorflow 구현

import tensorflow as tf
import matplotlib.pyplot as plt // "pip install matplotlib" 명령어 실행을 통해 사전 설치 필요 (그래프 그려주는 라이브러리)
 
X = [1, 2, 3]
Y = [1, 2, 3]
 
W = tf.placeholder(tf.float32)
 
# Our hypothesis for linear model X * W
hypothesis = X * W
 
# cost/loss function
cost = tf.reduce_mean(tf.square(hypothesis - Y))
 
# Variables for plotting cost function
W_history = []
cost_history = []
 
# Launch the graph in a session.
with tf.Session() as sess:
for i in range(-30, 50):
curr_W = i * 0.1
curr_cost = sess.run(cost, feed_dict={W: curr_W})
 
W_history.append(curr_W)
cost_history.append(curr_cost)
 
# Show the cost function
plt.plot(W_history, cost_history)
plt.show()

 

4. Gradient descent algorithm 적용

 (1) 미분을 이용한 알고리즘 수동 구현

# Minimize: Gradient Descent using derivative: W -= learning_rate * derivative
learning_rate = 0.1 // 알파 값 
gradient = tf.reduce_mean((W * X - Y) * X)
descent = W - learning_rate * gradient
update = W.assign(descent)

 

  - full code

import tensorflow as tf
 
tf.set_random_seed(777) # for reproducibility
 
x_data = [1, 2, 3]
y_data = [1, 2, 3]
 
# Try to find values for W and b to compute y_data = W * x_data
# We know that W should be 1
# But let's use TensorFlow to figure it out
W = tf.Variable(tf.random_normal([1]), name="weight")
 
X = tf.placeholder(tf.float32)
Y = tf.placeholder(tf.float32)
 
# Our hypothesis for linear model X * W
hypothesis = X * W
 
# cost/loss function
cost = tf.reduce_mean(tf.square(hypothesis - Y))
 
# Minimize: Gradient Descent using derivative: W -= learning_rate * derivative
learning_rate = 0.1
gradient = tf.reduce_mean((W * X - Y) * X)
descent = W - learning_rate * gradient
update = W.assign(descent)
 
# Launch the graph in a session.
with tf.Session() as sess:
# Initializes global variables in the graph.
sess.run(tf.global_variables_initializer())
 
for step in range(21):
_, cost_val, W_val = sess.run(
[update, cost, W], feed_dict={X: x_data, Y: y_data}
)
print(step, cost_val, W_val)

 

 (2) 텐서플로우의 Optimizer를 이용한 구현 (굳이 우리가 직접 미분하지 않아도 됨)

#Minimize: Gradient Descent Magic
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1)
train = optimizer.minimize(cost)

 

  - full code


import tensorflow as tf
tf.set_random_seed(777) # for reproducibility

# tf Graph Input
X = [1, 2, 3]
Y = [1, 2, 3]
W = tf.Variable(tf.random_normal([1], name="weight"))

# Linear model
hypothesis = X * W

# cost/loss function
cost = tf.reduce_mean(tf.square(hypothesis - Y))

# Minimize: Gradient Descent Optimizer
train = tf.train.GradientDescentOptimizer(learning_rate=0.1).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(101):
    _, W_val = sess.run([train, W])
    print(step, W_val)

 

 

  - W 초기값을 5로 주었을 때,

import tensorflow as tf
 
# tf Graph Input
X = [1, 2, 3]
Y = [1, 2, 3]
 
# Set wrong model weights
W = tf.Variable(5.0)
 
# Linear model
hypothesis = X * W
 
# cost/loss function
cost = tf.reduce_mean(tf.square(hypothesis - Y))
 
# Minimize: Gradient Descent Optimizer
train = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)
 
# Launch the graph in a session.
with tf.Session() as sess:
# Initializes global variables in the graph.
sess.run(tf.global_variables_initializer())
 
for step in range(101):
_, W_val = sess.run([train, W])
print(step, W_val)

 

5. (Optional) Tensoflow의 Gradient 계산 및 적용

import tensorflow as tf
 
# tf Graph Input
X = [1, 2, 3]
Y = [1, 2, 3]
 
# Set wrong model weights
W = tf.Variable(5.)
 
# Linear model
hypothesis = X * W
 
# Manual gradient
gradient = tf.reduce_mean((W * X - Y) * X) * 2
 
# cost/loss function
cost = tf.reduce_mean(tf.square(hypothesis - Y))
 
# Minimize: Gradient Descent Optimizer
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
 
# Get gradients
gvs = optimizer.compute_gradients(cost) // optimizer의 gradients 계산
 
# Optional: modify gradient if necessary
# gvs = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gvs]
 
# Apply gradients
apply_gradients = optimizer.apply_gradients(gvs) // optimizer의 gradients 적용
 
# Launch the graph in a session.
with tf.Session() as sess:
# Initializes global variables in the graph.
sess.run(tf.global_variables_initializer())
 
for step in range(101):
gradient_val, gvs_val, _ = sess.run([gradient, gvs, apply_gradients])
print(step, gradient_val, gvs_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

...

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(800, 0.003914324, array([1.0724902], dtype=float32), array([-0.16478711], dtype=float32))
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(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))
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(1080, 0.0010169626, array([1.0369489], dtype=float32), array([-0.08399385], dtype=float32))
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(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))
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(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))
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(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))
(180, 0.05718561, array([1.1547288], dtype=float32), array([0.54138047], dtype=float32))
(200, 0.049940635, array([1.1445953], dtype=float32), array([0.5779649], dtype=float32))
(220, 0.043613486, array([1.1351256], dtype=float32), array([0.6121535], dtype=float32))
(240, 0.038087945, array([1.1262761], dtype=float32), array([0.64410305], dtype=float32))
(260, 0.033262506, array([1.1180062], dtype=float32), array([0.6739601], dtype=float32))
(280, 0.029048424, array([1.1102779], dtype=float32), array([0.7018617], dtype=float32))
(300, 0.025368208, array([1.1030556], dtype=float32), array([0.7279361], dtype=float32))
(320, 0.022154227, array([1.0963064], dtype=float32), array([0.7523028], dtype=float32))
(340, 0.019347461, array([1.0899993], dtype=float32), array([0.7750737], dtype=float32))
(360, 0.016896311, array([1.0841053], dtype=float32), array([0.7963533], dtype=float32))
(380, 0.014755693, array([1.0785972], dtype=float32), array([0.8162392], dtype=float32))
(400, 0.012886246, array([1.0734499], dtype=float32), array([0.83482295], dtype=float32))
(420, 0.011253643, array([1.0686395], dtype=float32), array([0.85218966], dtype=float32))
(440, 0.009827888, array([1.0641443], dtype=float32), array([0.868419], dtype=float32))
(460, 0.008582776, array([1.0599433], dtype=float32), array([0.88358533], dtype=float32))
(480, 0.0074953884, array([1.0560175], dtype=float32), array([0.89775866], dtype=float32))
(500, 0.006545782, array([1.0523489], dtype=float32), array([0.9110037], dtype=float32))
(520, 0.005716468, array([1.0489205], dtype=float32), array([0.9233812], dtype=float32))
(540, 0.0049922303, array([1.0457168], dtype=float32), array([0.93494815], dtype=float32))
(560, 0.004359761, array([1.0427227], dtype=float32), array([0.94575745], dtype=float32))
(580, 0.0038074062, array([1.0399247], dtype=float32), array([0.95585895], dtype=float32))
(600, 0.0033250246, array([1.0373099], dtype=float32), array([0.96529907], dtype=float32))
(620, 0.0029037776, array([1.0348666], dtype=float32), array([0.9741207], dtype=float32))
(640, 0.0025359015, array([1.0325832], dtype=float32), array([0.9823645], dtype=float32))
(660, 0.002214623, array([1.0304493], dtype=float32), array([0.99006844], dtype=float32))
(680, 0.0019340345, array([1.028455], dtype=float32), array([0.99726814], dtype=float32))
(700, 0.00168901, array([1.0265915], dtype=float32), array([1.0039961], dtype=float32))
(720, 0.0014750187, array([1.02485], dtype=float32), array([1.0102835], dtype=float32))
(740, 0.0012881459, array([1.0232226], dtype=float32), array([1.0161589], dtype=float32))
(760, 0.0011249502, array([1.0217017], dtype=float32), array([1.0216497], dtype=float32))
(780, 0.0009824366, array([1.0202806], dtype=float32), array([1.026781], dtype=float32))
(800, 0.00085795636, array([1.0189523], dtype=float32), array([1.0315762], dtype=float32))
(820, 0.00074926845, array([1.017711], dtype=float32), array([1.0360574], dtype=float32))
(840, 0.0006543383, array([1.0165511], dtype=float32), array([1.0402449], dtype=float32))
(860, 0.00057143776, array([1.0154672], dtype=float32), array([1.0441583], dtype=float32))
(880, 0.00049904286, array([1.0144542], dtype=float32), array([1.0478154], dtype=float32))
(900, 0.0004358191, array([1.0135076], dtype=float32), array([1.0512332], dtype=float32))
(920, 0.00038059853, array([1.0126231], dtype=float32), array([1.0544269], dtype=float32))
(940, 0.00033238466, array([1.0117964], dtype=float32), array([1.0574113], dtype=float32))
(960, 0.0002902703, array([1.0110238], dtype=float32), array([1.0602009], dtype=float32))
(980, 0.00025349384, array([1.0103018], dtype=float32), array([1.0628073], dtype=float32))
(1000, 0.00022137808, array([1.009627], dtype=float32), array([1.0652432], dtype=float32))
(1020, 0.00019332914, array([1.0089965], dtype=float32), array([1.0675194], dtype=float32))
(1040, 0.00016882908, array([1.0084072], dtype=float32), array([1.0696473], dtype=float32))
(1060, 0.00014743926, array([1.0078566], dtype=float32), array([1.0716351], dtype=float32))
(1080, 0.00012875989, array([1.007342], dtype=float32), array([1.0734928], dtype=float32))
(1100, 0.00011244613, array([1.0068612], dtype=float32), array([1.0752288], dtype=float32))
(1120, 9.8200355e-05, array([1.0064118], dtype=float32), array([1.0768511], dtype=float32))
(1140, 8.5755724e-05, array([1.0059919], dtype=float32), array([1.0783674], dtype=float32))
(1160, 7.489431e-05, array([1.0055996], dtype=float32), array([1.0797837], dtype=float32))
(1180, 6.5406595e-05, array([1.0052328], dtype=float32), array([1.0811077], dtype=float32))
(1200, 5.7120622e-05, array([1.0048901], dtype=float32), array([1.0823449], dtype=float32))
(1220, 4.9882394e-05, array([1.0045699], dtype=float32), array([1.0835012], dtype=float32))
(1240, 4.3564207e-05, array([1.0042707], dtype=float32), array([1.0845816], dtype=float32))
(1260, 3.804614e-05, array([1.003991], dtype=float32), array([1.0855912], dtype=float32))
(1280, 3.3225275e-05, array([1.0037296], dtype=float32), array([1.0865349], dtype=float32))
(1300, 2.901571e-05, array([1.0034853], dtype=float32), array([1.0874166], dtype=float32))
(1320, 2.5340463e-05, array([1.003257], dtype=float32), array([1.0882409], dtype=float32))
(1340, 2.2129901e-05, array([1.0030438], dtype=float32), array([1.089011], dtype=float32))
(1360, 1.9328054e-05, array([1.0028446], dtype=float32), array([1.0897301], dtype=float32))
(1380, 1.6878726e-05, array([1.0026582], dtype=float32), array([1.0904027], dtype=float32))
(1400, 1.4740454e-05, array([1.0024842], dtype=float32), array([1.0910312], dtype=float32))
(1420, 1.2873619e-05, array([1.0023215], dtype=float32), array([1.0916185], dtype=float32))
(1440, 1.1241735e-05, array([1.0021695], dtype=float32), array([1.0921675], dtype=float32))
(1460, 9.818069e-06, array([1.0020274], dtype=float32), array([1.0926803], dtype=float32))
(1480, 8.574677e-06, array([1.0018947], dtype=float32), array([1.0931597], dtype=float32))
(1500, 7.4886166e-06, array([1.0017706], dtype=float32), array([1.0936075], dtype=float32))
(1520, 6.539272e-06, array([1.0016547], dtype=float32), array([1.0940262], dtype=float32))
(1540, 5.711003e-06, array([1.0015464], dtype=float32), array([1.0944173], dtype=float32))
(1560, 4.9874334e-06, array([1.001445], dtype=float32), array([1.094783], dtype=float32))
(1580, 4.3559958e-06, array([1.0013504], dtype=float32), array([1.0951246], dtype=float32))
(1600, 3.804345e-06, array([1.0012621], dtype=float32), array([1.0954438], dtype=float32))
(1620, 3.322312e-06, array([1.0011792], dtype=float32), array([1.0957422], dtype=float32))
(1640, 2.9007756e-06, array([1.0011021], dtype=float32), array([1.0960212], dtype=float32))
(1660, 2.5334934e-06, array([1.00103], dtype=float32), array([1.0962818], dtype=float32))
(1680, 2.2123513e-06, array([1.0009624], dtype=float32), array([1.0965253], dtype=float32))
(1700, 1.9319202e-06, array([1.0008993], dtype=float32), array([1.096753], dtype=float32))
(1720, 1.6872369e-06, array([1.0008405], dtype=float32), array([1.0969656], dtype=float32))
(1740, 1.4738443e-06, array([1.0007855], dtype=float32), array([1.0971642], dtype=float32))
(1760, 1.2871467e-06, array([1.0007341], dtype=float32), array([1.0973498], dtype=float32))
(1780, 1.12424e-06, array([1.0006859], dtype=float32), array([1.0975232], dtype=float32))
(1800, 9.815564e-07, array([1.0006411], dtype=float32), array([1.0976855], dtype=float32))
(1820, 8.573661e-07, array([1.0005993], dtype=float32), array([1.0978369], dtype=float32))
(1840, 7.4871434e-07, array([1.00056], dtype=float32), array([1.0979784], dtype=float32))
(1860, 6.5427787e-07, array([1.0005234], dtype=float32), array([1.0981107], dtype=float32))
(1880, 5.712507e-07, array([1.0004891], dtype=float32), array([1.0982342], dtype=float32))
(1900, 4.989224e-07, array([1.0004572], dtype=float32), array([1.0983498], dtype=float32))
(1920, 4.358085e-07, array([1.0004272], dtype=float32), array([1.0984578], dtype=float32))
(1940, 3.8070743e-07, array([1.0003992], dtype=float32), array([1.0985587], dtype=float32))
(1960, 3.3239553e-07, array([1.000373], dtype=float32), array([1.098653], dtype=float32))
(1980, 2.9042917e-07, array([1.0003488], dtype=float32), array([1.0987409], dtype=float32))
(2000, 2.5372992e-07, array([1.000326], dtype=float32), array([1.0988233], 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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