PyTorch 深度学习实践第二讲（线性模型）

写在前面：B站 刘二大人 ，传送门 ​​PyTorch深度学习实践——线性模型​​​​

学习步骤

• 准备数据集
• 模型的选择和设计
• 训练的过程（确定权重）
• 推理和测试工作（infering）

 Training loss

备注：详见刘二大人所举例子，针对一个样本

图形绘制

import numpy as np
import matplotlib.pyplot as plt
 
x_data = [1.0, 2.0, 3.0]
y_data = [2.0, 4.0, 6.0]
 
 
def forward(x):
    return x*w
 
 
def loss(x, y):
    y_pred = forward(x)
    return (y_pred - y)**2
 
 
# 穷举法
w_list = []
mse_list = []
for w in np.arange(0.0, 4.1, 0.1):
    print("w=", w)
    l_sum = 0
    for x_val, y_val in zip(x_data, y_data):
        y_pred_val = forward(x_val)
        loss_val = loss(x_val, y_val)
        l_sum += loss_val
        print('\t', x_val, y_val, y_pred_val, loss_val)
    print('MSE=', l_sum/3)
    w_list.append(w)
    mse_list.append(l_sum/3)
    
plt.plot(w_list,mse_list)
plt.ylabel('Loss')
plt.xlabel('w')
plt.show()

课后作业

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from pylab import mpl   #解决中文符号问题
mpl.rcParams["font.sans-serif"] = ["SimHei"]
fig = plt.figure(figsize = (12,8))
ax = Axes3D(fig)

#这里假设函数为y = 3x + 2
x_data = [1.0,2.0,3.0]
y_data = [5.0,8.0,11.0]

def forward(x):
    return x * w + b

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

mse_list = []
W = np.arange(0.0,4.1,0.1)
B = np.arange(0.0,4.1,0.1)
[w,b] = np.meshgrid(W,B)

l_sum = 0
for x_val, y_val in zip(x_data, y_data):
    y_pred_val = forward(x_val)
    print(y_pred_val)
    loss_val = loss(x_val, y_val)
    l_sum += loss_val

surf = ax.plot_surface(w,b,l_sum/3,rstride=1,cstride=1,cmap=plt.get_cmap('rainbow'))
#设置标题
plt.title("3D图")
fig.colorbar(surf,shrink = 0.5,aspect = 5)
plt.show()