[실습] Logistic Classfication 구현

x_data = [[1, 2], [2, 3], [3, 1], [4, 3], [5, 3], [6, 2]]
y_data = [[0], [0], [0], [1], [1], [1]]
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') #X의 개수 2, Y의 개수 1
b = tf.Variable(tf.random_normal([1]), name='bias') #bias는 나가는 값(Y)의 개수와 같다

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)

# True(1.0) if hypothesis>0.5 else False(0.0)
predicted = tf.cast(hypothesis > 0.5, dtype=tf.float32)
accuracy  = tf.reduce_mean(tf.cast(tf.equal(predicted, Y), dtype=tf.float32))

Train the model (학습하는 모델)

# 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 - 학습된 모델을 가지고 hypo.. 값, predicted, accuracy를 출력한다. (Hypo.. 값이 0.5보다 작으면 0, 크면 1)
    h, c, a = sess.run([hypothesis, predicted, accuracy],
                        feed_dict={X: x_data, Y: y_data})
    print("\nHypothesis: ", h, "\nCorrect (Y): ", c, "\nAccuracy: ", a)

출력 부분

당뇨병 예측 프로그램

제일 우측 0, 1 - 당뇨병 여부

# Same source after these code
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')

Exercise

• CSV reading using tf.decode_csv
• Try other classification data from Kaggle (https://www.kaggle.com)