《PyTorch深度学习实践》完结合集_哔哩哔哩 (゜-゜)つロ 干杯~-bilibili
原视频下方评论置顶有课件
对于以上一组数据x,y,为了预测接下来的x对应的y,我们选择一个模型。这里选择线性(Linear Model)模型
为了简化,下面的例子去掉了b.
为了评价模型,引入MSE
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) * (y_pred - y)
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)
#画图
plt.plot(w_list, mse_list)
plt.ylabel('loss')
plt.xlabel('w')
plt.show()