[Python][TensorFlow] 03. 선형회귀 TensorFlow로 구현

TensorFlow(Simple Linear Regression을TensorFlow로 구현)

x_data = [1,2,3,4,5]
y_data = [1,2,3,4,5]

W = tf.Variable(2.9)
b = tf.Variable(0.5)

# hypothesis = W * x + b
hypothesis = W * x_data + b

cost = tf.reduce_mean(tf.square(hypothesis - y_data))

• Cost가 최소가 되는 W, b를 찾는 알고리즘

  • Gradient descent(경사하강알고리즘 or 경사하강법)

    # Learning_rate initialize
    learning_rate = 0.01
    
    # Gradient descent
    with tf.GradientTape() as tape:
        hypothesis = W * x_data + b
        cost = tf.reduce_mean(tf.square(hypothesis - y_data))
    
    W_grad, b_grad = tape.gradient(cost, [W, b])
    
    W.assign_sub(learning_rate * W_grad)
    b.assign_sub(learning_rate * b_grad)
    

  • Gradient descent 전체 코드(경사하강알고리즘 or 경사하강법)

    import tensorflow as tf
    tf.enable_eager_execution()
    
    # Data
    x_data = [1,2,3,4,5]
    y_data = [1,2,3,4,5]
    
    # W, b initialize
    W = tf.Variable(2.9)
    b = tf.Variable(0.5)
    
    learning_rate = 0.01
    
    for i in range(100+1): # W, b update
        # Gradient descent
        with tf.GradientTape() as tape:
        	hypothesis = W * x_data + b
        	cost = tf.reduce_mean(tf.square(hypothesis - y_data))
    
    	W_grad, b_grad = tape.gradient(cost, [W, b])
        W.assign_sub(learning_rate * W_grad)
        b.assign_sub(learning_rate * b_grad)
        if i % 10 == 0:
            print("{:5}|{:10.4f}|{:10.4}|{:10.6f}".format(i, W.numpy(), b.numpy(), cost))