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,