편집 기록

import numpy as np numpy
import matplotlib.pyplot as plt

X = np.random.rand(100) 
Y = 0.2 * X * 0.5       

plt.figure(figsize=(8,6)) 
plt.scatter(X, Y)
plt.show()

def plot_prediction(pred, y): 
    plt.figure(figsize=(8, 6))
    plt.scatter(X, y)
    plt.scatter(X, pred)
    plt.show()

W = np.random.uniform(-1, 1)
b = np.random.uniform(-1, 1)

learning_rate = 0.7

for epoch in range(200):
    Y_Pred = W * X + b
    error = np.abs(Y_Pred - Y).mean()
    if error < 0.001:
        break

    # gradient descent 계산
    w_grad = learning_rate * ((Y_Pred - Y)*X).mean()
    b_grad = learning_rate * (Y_Pred - Y).mean()

    # W, b 값 갱신
    W = W - w_grad
    b = b - b_grad

    if epoch % 10 == 0:
        Y_pred = W * X + b
        plot_prediction(Y_Pred, Y)

import numpy as np numpy import matplotlib.pyplot as plt

X = np.random.rand(100) Y = 0.2 * X * 0.5

plt.figure(figsize=(8,6)) plt.scatter(X, Y) plt.show()

def plot_prediction(pred, y): plt.figure(figsize=(8, 6)) plt.scatter(X, y) plt.scatter(X, pred) plt.show()

W = np.random.uniform(-1, 1)

b = np.random.uniform(-1, 1)

learning_rate = 0.7

for epoch in range(200):

Y_Pred = W * X + b



error = np.abs(Y_Pred - Y).mean()

if error < 0.001:

    break



# gradient descent 계산

w_grad = learning_rate * ((Y_Pred - Y)*X).mean()

b_grad = learning_rate * (Y_Pred - Y).mean()



# W, b 값 갱신

W = W - w_grad

b = b - b_grad



if epoch % 10 == 0:

    Y_pred = W * X + b

    plot_prediction(Y_Pred, Y)