神经网络学习入门:从冷热学习到梯度下降
1. 冷热学习
冷热学习是一种较为简单的学习形式。以下是在 Jupyter 笔记本中执行的代码,该代码尝试正确预测 0.8:
weight = 0.5
input = 0.5
goal_prediction = 0.8
step_amount = 0.001
for iteration in range(1101):
prediction = input * weight
error = (prediction - goal_prediction) ** 2
print("Error:" + str(error) + " Prediction:" + str(prediction))
up_prediction = input * (weight + step_amount)
up_error = (goal_prediction - up_prediction) ** 2
down_prediction = input * (weight - step_amount)
down_error = (goal_prediction - down_prediction) ** 2
if(down_error < up_error):
weight = weight - step_amount
if(down_error > up_error):
weight = weight + step_amount
这段代码的逻辑是:先进行一次预测,然后分别增
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