[英]How to calculate cost and theta with fmin_ncg
我正在Coursera上学习Andrew NG课程,我想在python上实现相同的逻辑。 我正在尝试计算成本和θ
scipy.optimize.fmin_ncg
这是一个代码
import numpy as np
from scipy.optimize import fmin_ncg
def sigmoid(z):
return (1 / (1 + np.exp(-z))).reshape(-1, 1)
def compute_cost(theta, X, y):
m = len(y)
hypothesis = sigmoid(np.dot(X, theta))
cost = (1 / m) * np.sum(np.dot(-y.T, (np.log(hypothesis))) - np.dot((1 - y.T), np.log(1 - hypothesis)))
return cost
def compute_gradient(theta, X, y):
m = len(y)
hypothesis = sigmoid(np.dot(X, theta))
gradient = (1 / m) * np.dot(X.T, (hypothesis - y))
return gradient
def main():
data = np.loadtxt("data/data1.txt", delimiter=",") # 100, 3
X = data[:, 0:2]
y = data[:, 2:]
m, n = X.shape
initial_theta = np.zeros((n + 1, 1))
X = np.column_stack((np.ones(m), X))
mr = fmin_ncg(compute_cost, initial_theta, compute_gradient, args=(X, y), full_output=True)
print(mr)
if __name__ == "__main__":
main()
当我尝试运行此程序时,出现如下所示的错误和异常
Traceback (most recent call last):
File "/file/path/without_regression.py", line 78, in <module>
main()
File "/file/path/without_regression.py", line 66, in main
mr = fmin_ncg(compute_cost, initial_theta, compute_gradient, args=(X, y), full_output=True)
File "/usr/local/anaconda3/envs/ml/lib/python3.6/site-packages/scipy/optimize/optimize.py", line 1400, in fmin_ncg
callback=callback, **opts)
File "/usr/local/anaconda3/envs/ml/lib/python3.6/site-packages/scipy/optimize/optimize.py", line 1497, in _minimize_newtoncg
dri0 = numpy.dot(ri, ri)
ValueError: shapes (3,1) and (3,1) not aligned: 1 (dim 1) != 3 (dim 0)
我不明白这个错误。 可能是因为我是初学者,这对我来说并不冗长。
如何使用scipy.optimize.fmin_ncg
或其他任何最小化技术(例如scipy.optimize.minimize(...)
来计算成本和theta?
如评论中所述:
暂时不参考文档,应该始终使用一维数组。
import numpy as np
a = np.random.random(size=(3,1)) # NOT TO USE!
a.shape # (3, 1)
a.ndim # 2
b = np.random.random(size=3) # TO USE!
b.shape # (3,)
b.ndim # 1
这适用于您的x0
(如果不使用python-lists)和渐变。
快速的技巧(=在渐变中降低暗淡程度),例如:
gradient = (1 / m) * np.dot(X.T, (hypothesis - y)).ravel() # .ravel()!
...
initial_theta = np.zeros(n + 1) # drop extra-dim
使代码运行:
Optimization terminated successfully.
Current function value: 0.203498
Iterations: 27
Function evaluations: 71
Gradient evaluations: 229
Hessian evaluations: 0
(array([-25.13045417, 0.20598475, 0.2012217 ]), 0.2034978435366513, 71, 229, 0, 0)
额外:在调试期间,我还检查了梯度本身对数值微分的计算(推荐!),使用x0看起来不错:
from scipy.optimize import check_grad as cg
print(cg(compute_cost, compute_gradient, initial_theta, X, y))
# 1.24034933954e-05
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.