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<p>我试图用python实现一个非常简单的渐变下降。然而,它看起来像是进入了一个无限循环。你能帮我调试一下吗?在</p>
<pre><code>y = lambda x : x**2
dy_dx = lambda x : 2*x
def gradient_descent(function,derivative,initial_guess):
optimum = initial_guess
while derivative(optimum) != 0:
optimum = optimum - derivative(optimum)
else:
return optimum
gradient_descent(y,dy_dx,5)
</code></pre>
<p>编辑:</p>
<p>现在我有了这个代码,我真的不能理解输出。P、 它可能会冻结你的CPU。在</p>
^{pr2}$
<p>现在,我尝试将其应用于回归问题,但是输出看起来并不正确,如下面的输出所示:</p>
<p><a href="https://i.stack.imgur.com/zd7nv.png" rel="nofollow noreferrer">Output of gradient descent code below</a></p>
<pre><code>import matplotlib.pyplot as plt
def stepGradient(x,y, step):
b_current = 0
m_current = 0
b_gradient = 0
m_gradient = 0
N = int(len(x))
for i in range(0, N):
b_gradient += -(1/N) * (y[i] - ((m_current*x[i]) + b_current))
m_gradient += -(1/N) * x[i] * (y[i] - ((m_current * x[i]) + b_current))
while abs(b_gradient) > 0.01 and abs(m_gradient) > 0.01:
b_current = b_current - (step * b_gradient)
m_current = m_current - (step * m_gradient)
for i in range(0, N):
b_gradient += -(1/N) * (y[i] - ((m_current*x[i]) + b_current))
m_gradient += -(1/N) * x[i] * (y[i] - ((m_current * x[i]) + b_current))
return [b_current, m_current]
x = [1,2, 2,3,4,5,7,8]
y = [1.5,3,1,3,2,5,6,7]
step = 0.00001
(b,m) = stepGradient(x,y,step)
plt.scatter(x,y)
abline_values = [m * i + b for i in x]
plt.plot(x, abline_values, 'b')
plt.show()
</code></pre>
<p>固定:D</p>
<pre><code>import matplotlib.pyplot as plt
def stepGradient(x,y):
step = 0.001
b_current = 0
m_current = 0
b_gradient = 0
m_gradient = 0
N = int(len(x))
for i in range(0, N):
b_gradient += -(1/N) * (y[i] - ((m_current*x[i]) + b_current))
m_gradient += -(1/N) * x[i] * (y[i] - ((m_current * x[i]) + b_current))
while abs(b_gradient) > 0.01 or abs(m_gradient) > 0.01:
b_current = b_current - (step * b_gradient)
m_current = m_current - (step * m_gradient)
b_gradient= 0
m_gradient = 0
for i in range(0, N):
b_gradient += -(1/N) * (y[i] - ((m_current*x[i]) + b_current))
m_gradient += -(1/N) * x[i] * (y[i] - ((m_current * x[i]) + b_current))
return [b_current, m_current]
x = [1,2, 2,3,4,5,7,8,10]
y = [1.5,3,1,3,2,5,6,7,20]
(b,m) = stepGradient(x,y)
plt.scatter(x,y)
abline_values = [m * i + b for i in x]
plt.plot(x, abline_values, 'b')
plt.show()
</code></pre>