PyTorch 深度学习实践 第2讲/作业（Linear Model）

对一组数据进行预测

 训练损失和MSE均方误差损失

 代码示例 y = w * x

import numpy as np
import matplotlib.pyplot as plt

# 准备数据
x_data = [1.0, 2.0, 3.0]
y_data = [2.0, 4.0, 6.0]

# 定义模型 y = x * w
def forward(x):
    return x * w

def loss(x, y):
    y_pred = forward(x) # y_pred为模型预测值
    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) # 只是为了下面打印y_pred_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()

结果展示

 作业 y = w * x + b

import numpy
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D

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

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

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


w = numpy.arange(0.0, 4.1, 0.1) # 权重W从0.0到4.0间隔0.1取数
b = numpy.arange(-2.0, 2.1, 0.1) # 偏置B从-2.0到2.0间隔0.1取数

[w, b] = numpy.meshgrid(w, b) # [X,Y]=np.meshgrid(x,y) 函数用两个坐标轴上的点在平面上画网格。

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
    
fig = plt.figure()
ax = Axes3D(fig)
ax.set_xlabel("w")
ax.set_ylabel("b")
ax.set_zlabel("loss")
ax.plot_surface(w, b, l_sum/3, rstride=1, cstride=1, cmap=plt.get_cmap('rainbow')) #画曲面图---Axes3D.plot_surface(X, Y, Z)
plt.show()

结果展示 

 reference

《PyTorch深度学习实践》完结合集_哔哩哔哩_bilibili