博客

x_data = [1.0, 2.0, 3.0]
y_data = [2.0, 4.0, 6.0]
 
w = 1.0

def forward(x):
    return x * w
 
def cost(xs, ys):
    cost = 0
    for x, y in zip(xs, ys):
        y_p = forward(x)
        cost += (y_p - y) ** 2
    return cost / len(xs)
    
def gradint(xs, ys):
    grad = 0
    for x, y in zip(xs, ys):
        grad += 2 * x * (x * w - y)
    return grad / len(xs)
for epoch in range(100):
    cost_val = cost(x_data, y_data)
    grad_val = gradint(x_data, y_data)
    w -= 0.01 * grad_val
plt.plot(w_list,mse_list)
plt.ylabel('Loss')
plt.xlabel('w')
plt.show()