PyTorch 深度学习实践 第3讲

 第3讲  梯度下降法 源代码

B站 刘二大人 ，传送门PyTorch 深度学习实践 梯度下降法

深度学习算法中，并没有过多的局部最优点。

import matplotlib.pyplot as plt

# prepare the training set
x_data = [1.0, 2.0, 3.0]
y_data = [2.0, 4.0, 6.0]

# initial guess of weight 
w = 1.0

# define the model linear model y = w*x
def forward(x):
    return x*w

#define the cost function MSE 
def cost(xs, ys):
    cost = 0
    for x, y in zip(xs,ys):
        y_pred = forward(x)
        cost += (y_pred - y)**2
    return cost / len(xs)

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

epoch_list = []
cost_list = []
print('predict (before training)', 4, forward(4))
for epoch in range(100):
    cost_val = cost(x_data,