数据：x_data = [1.0, 2.0, 3.0]， y_data = [2.0, 4.0, 6.0]
模型选择：y = x * w
代码如下：

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 loss(x, y):
    y_pred = forward(x)
    return (y_pred - y) ** 2

def gradient(x, y):
    return 2 * x * (x * w - y)

print('Predict (before training)', 4, forward(4))

for epoch in range(1000):
    for x, y in zip(x_data, y_data):
        grad = gradient(x, y)
        w -= 0.01 * grad
        print("\tgrad: ", x, y, grad)
        l = loss(x, y)
    print("progress:", epoch, "w=", w, "loss=", l)
print('Predict (after training)', 4, forward(4))

打印结果示例如下：