MLlab05-1.py

import tensorflow as tf
import numpy as np
tf.set_random_seed(777)
xy = np.loadtxt('data-03-diabetes.csv',delimiter=',', dtype=np.float32)
x_data = xy[:,0:-1]
y_data = xy[:,[-1]]

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 = tf.sigmoid(tf.matmul(X, W) + b)
cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) * tf.log(1 - hypothesis))

train = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cost)

predicted = tf.cast(hypothesis > 0.5, dtype=tf.float32)
accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, Y), dtype=tf.float32))

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    feed = {X : x_data, Y : y_data}

    for step in range(10001):
        sess.run(train, feed_dict=feed)
        if step % 200 == 0:
            print(step, sess.run(cost, feed_dict=feed))
    h, c, a = sess.run([hypothesis, predicted, accuracy], feed_dict=feed)
    print("\nHypothesis : ", h, "\nCorrect (Y) : ", c, "\nAccuracy : ", a)