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잡다한 IT/머신러닝 & 딥러닝

05-2. Logistic regression diabetes

■ Logistic regression 을 파일로 읽어서 처리한다.


xp = np.loadtxt('data-03-diabetes.csv',delimiter=',', dtype=np.float32)

- data-03-diabetes.csv 로 부터 데이터를 읽음


x_data = xy[:,0:-1]

y_data = xy[:,[-1]]

- 데이터 분리


print(x_data.shpae,y_data.shape)

- 데이터가 제대로 분리되었는지 확인


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# Lab 5-2 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.
 
= tf.placeholder(tf.float32, shape=[None, 8])
 
= tf.placeholder(tf.float32, shape=[None, 1])
 
 
= tf.Variable(tf.random_normal([81]), name='weight')
 
= 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)
 
cs

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