B站--刘二大人《PyTorch深度学习实践》完结合集  03. 梯度下降算法

 PPT 链接：网盘          提取码：cxe4

步骤：1.dataset     2.model     3.training（确定权重）    4.inferring

1.分治法

容易只找到局部最优，错过全局最优

优化问题：找使得目标函数最小的权重组合 

鞍点：梯度为0

2.梯度下降算法

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 = 0for x, y in zip(xs,ys):y_pred = forward(x)cost += (y_pred - y)**2return cost / len(xs)# define the gradient function  gd
def gradient(xs,ys):grad = 0for 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, y_data)grad_val = gradient(x_data, y_data)w-= 0.01 * grad_val  # 0.01 α  learning rate#w=w-α*梯度print('epoch:', epoch, 'w=', w, 'loss=', cost_val)epoch_list.append(epoch)cost_list.append(cost_val)print('predict (after training)', 4, forward(4))
plt.plot(epoch_list,cost_list)
plt.ylabel('cost')
plt.xlabel('epoch')
plt.show() 

import matplotlib.pyplot as pltx_data = [1.0, 2.0, 3.0]
y_data = [2.0, 4.0, 6.0]w = 1.0def forward(x):return x*w# calculate loss function
def loss(x, y):y_pred = forward(x)return (y_pred - y)**2# define the gradient function  sgd
def gradient(x, y):return 2*x*(x*w - y)epoch_list = []
loss_list = []
print('predict (before training)', 4, forward(4))
for epoch in range(100):for x,y in zip(x_data, y_data):grad = gradient(x,y)w = w - 0.01*grad    # update weight by every grad of sample of training setprint("\tgrad:", x, y,grad)l = loss(x,y)print("progress:",epoch,"w=",w,"loss=",l)epoch_list.append(epoch)loss_list.append(l)print('predict (after training)', 4, forward(4))
plt.plot(epoch_list,loss_list)
plt.ylabel('loss')
plt.xlabel('epoch')
plt.show()