■ 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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | # 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. 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) | cs |
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