y=x*w
loss= (y_pred-y)^2
通過窮舉法來窮舉w,繪圖可以檢視到使Loss最小的w為2
(該方法提前知道w在0-4.1之間)
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)
plt.plot(w_list, mse_list)
plt.ylabel('Loss')
plt.xlabel('w')
plt.show()
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