[머신러닝 - Tensorflow] Lec11-TensorFlow로 Logistic Classification의 구현하기(new)

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[머신러닝 - Tensorflow] Lec11-TensorFlow로 Logistic Classification의 구현하기(new)

감자형 2018. 3. 17. 02:49

1. Tensorflow 로 Logistic Classfication의 구현하기(new)

cost를 작게하는 W를 구하는것이 이번 장의 핵심!

가장 작은 W를 구하는 방법은? 위의 2차 함수에서 어떤 주어진 점에서 기울기를 구해서 기울기의 반대방향으로 움직이면 된다. 여기서 a(알파)는 그만큼 움직이는 수

2. Training Data

x_data는 2개 , y_data는 1개를 나타내는것을 볼 수 있는데, 여기서 tf.placeholder를 사용하여 float32타입으로 하나의 노드를 생성시키고, shape값을 지정하는데 이것은 N개의 개수로 놓을때, None의 속성값을 주고, 2개의 데이터를 나타내므로 shape = [None,2] 이런식으로 나타낼 수 있다.

3. Code 설명

1. x,y로 노드를 생성한다. (Shape=None,2 / Shape = None,1

2. Variable을 사용하여 weight값과 bias값을 지정한다

3. H(x)식 hypothesis를 간단히 구할 수 있는 메소드는 tf.sigmoid를 사용하여 간단히 구할 수 있다.

4. tf.reduce_mean 메소드를 사용하여 시그마 값을 구할 수 있다. 이 시그마값을 구한 후 cost(W)값을 구할 수 있다.

5. 이제 위에서 구한 값들을 이용하여 tf.train 에 있는 메소드 GradientDescentOptimizer를 사용하여 W:값을 구한다. predicted 값과 ,accurac값을 적절한 형변환 한다.

이제 이것들을 활용하여 train model에다가 training을 한다.

* train the model

1. 세션을 생성시킨후 variables들을 모두 초기화 시켜준다

2. 10001 Range 까지 반복문 실행후 train그래프를 실행시키게 되면서 x_data와 y_data를 던져 주게 된다.

3. 200번 마다 step과 cost_val값을 출력 시켜 주게 된다.

4. 이제 세션들을 모두 run 시키면서 hypothesis, predicted , accuracy값들을 실행시키게 되어 그 값들을 h,c,a에 넣어 print로 출력하여 확인해볼 수 있다.

4. 전체 코드

5. 응용 하기(Classifying diabetes)

당뇨병 기초 데이터를 통해 tensorflow를 활용하여 결과값을 확인해보자.

예제 1)

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)

예제2)

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("\nHypothesis: ", h, "\nCorrect (Y): ", c, "\nAccuracy: ", a)

6. 실행 결과1

(0, 1.5079803)

(200, 0.3676095)

(400, 0.3527939)

(600, 0.340542)

(800, 0.32940093)

(1000, 0.3191423)

(1200, 0.309602)

(1400, 0.30066207)

(1600, 0.2922383)

(1800, 0.28426877)

(2000, 0.2767068)

(2200, 0.2695162)

(2400, 0.2626675)

(2600, 0.25613597)

(2800, 0.24990028)

(3000, 0.24394138)

(3200, 0.23824249)

(3400, 0.23278804)

(3600, 0.22756386)

(3800, 0.22255677)

(4000, 0.21775454)

(4200, 0.21314593)

(4400, 0.20872037)

(4600, 0.20446783)

(4800, 0.20037915)

(5000, 0.19644572)

(5200, 0.19265936)

(5400, 0.18901253)

(5600, 0.18549804)

(5800, 0.1821093)

(6000, 0.17883994)

(6200, 0.17568415)

(6400, 0.1726364)

(6600, 0.16969144)

(6800, 0.16684441)

(7000, 0.16409071)

(7200, 0.16142601)

(7400, 0.15884613)

(7600, 0.15634735)

(7800, 0.1539259)

(8000, 0.15157852)

(8200, 0.14930183)

(8400, 0.14709276)

(8600, 0.14494851)

(8800, 0.1428663)

(9000, 0.14084356)

(9200, 0.1388777)

(9400, 0.1369666)

(9600, 0.13510786)

(9800, 0.13329959)

(10000, 0.1315396)

('\nHypothesis: ', array([[0.02356477],

[0.14780492],

[0.268441 ],

[0.798558 ],

[0.94988024],

[0.98368007]], dtype=float32), '\nCorrect (Y): ', array([[0.],

[0.],

[1.],

[1.]], dtype=float32), '\nAccuracy: ', 1.0)

실행결과 2(당뇨병 표본)

(0, 0.82793975)

(200, 0.7551809)

(400, 0.72635543)

(600, 0.70517904)

(800, 0.6866307)

(1000, 0.669853)

(1200, 0.654603)

(1400, 0.6407365)

(1600, 0.6281296)

(1800, 0.61666805)

(2000, 0.60624564)

(2200, 0.59676415)

(2400, 0.58813345)

(2600, 0.58027065)

(2800, 0.5731005)

(3000, 0.5665548)

(3200, 0.5605713)

(3400, 0.5550946)

(3600, 0.5500747)

(3800, 0.5454661)

(4000, 0.54122907)

(4200, 0.53732735)

(4400, 0.5337287)

(4600, 0.53040445)

(4800, 0.52732897)

(5000, 0.5244792)

(5200, 0.5218346)

(5400, 0.519377)

(5600, 0.51708966)

(5800, 0.514958)

(6000, 0.5129687)

(6200, 0.5111098)

(6400, 0.5093705)

(6600, 0.5077413)

(6800, 0.50621307)

(7000, 0.5047781)

(7200, 0.5034291)

(7400, 0.50215966)

(7600, 0.5009635)

(7800, 0.4998357)

(8000, 0.49877086)

(8200, 0.49776477)

(8400, 0.49681306)

(8600, 0.49591216)

(8800, 0.49505854)

(9000, 0.49424884)

(9200, 0.49348038)

(9400, 0.49275038)

(9600, 0.49205622)

(9800, 0.49139577)

(10000, 0.49076685)

('\nHypothesis: ', array([[0.44348487],

[0.9153647 ],

[0.22591162],

[0.93583125],

[0.33763626],

[0.70926887],

[0.9440914 ],

[0.63417906],

[0.25953037],

[0.46434346],

[0.64745134],

[0.2013701 ],

[0.25898227],

[0.3507237 ],

[0.7484501 ],

[0.48230037],

[0.70017725],

[0.9126371 ],

[0.81194925],

[0.56007695],

[0.64738876],

[0.10781654],

[0.6211922 ],

[0.6812047 ],

[0.3879278 ],

[0.9218674 ],

[0.5113428 ],

[0.57813394],

[0.7277054 ],

[0.42962766],

[0.947626 ],

[0.78322077],

[0.57234144],

[0.82043636],

[0.3719366 ],

[0.65550166],

[0.8322083 ],

[0.5858857 ],

[0.4828183 ],

[0.37765244],

[0.7715164 ],

[0.15435624],

[0.41105628],

[0.09908313],

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