import torch 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] # our model for the forward pass def forward(x): return x * w # loss function 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) l=loss(x_val,y_val) l_sum+=l print("\t",x_val,y_val,y_pred_val,l) 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()
Lec2: Linear model
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转载自blog.csdn.net/zhuoyuezai/article/details/80379784
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