深度学习之梯度下降算法

梯度下降算法

数学公式

这里案例是用梯度下降算法，来计算 y = w * x
先计算出梯度，再进行梯度的更新

import numpy as np
import matplotlib.pyplot as plt

x_data = [1.0, 2.0, 3.0, 4.0]
y_data = [2.0, 4.0, 6.0, 8.0]

mse_list= []
w_list = []

w = 1.0 #注意：这里设初始权重为1.0
def forward(x):
    return w*x
def const(xs, ys):
    const = 0
    for x, y in zip(xs, ys):
        y_pred = forward(x)
        const += (y_pred - y)**2
    return const/ len(xs)

def gradient(xs, ys):
    grad  = 0
    for x, y in zip(xs, ys):
        grad += 2 * x * (w * x - y)
    return grad / len(xs)

print(