最近閑著沒事，想把coursera上斯坦福ML課程里面的練習，用Python來實現(xiàn)一下，一是加深ML的基礎(chǔ)，二是熟悉一下numpy，matplotlib，scipy這些庫。

在EX2中，優(yōu)化theta使用了matlab里面的fminunc函數(shù)，不知道Python里面如何實現(xiàn)。搜索之后，發(fā)現(xiàn)stackflow上有人提到用scipy庫里面的minimize函數(shù)來替代。我嘗試直接調(diào)用我的costfunction和grad，程序報錯，提示(3,)和(100,1)dim維度不等，gradient vector不對之類的，試了N多次后，終于發(fā)現(xiàn)問題何在。。

首先來看看使用np.info(minimize)查看函數(shù)的介紹，傳入的參數(shù)有：

fun : callable The objective function to be minimized. ``fun(x, *args) -> float`` where x is an 1-D array with shape (n,) and `args` is a tuple of the fixed parameters needed to completely specify the function.x0 : ndarray, shape (n,) Initial guess. Array of real elements of size (n,), where ’n’ is the number of independent variables.args : tuple, optional Extra arguments passed to the objective function and its derivatives (`fun`, `jac` and `hess` functions).method : str or callable, optional Type of solver. Should be one of - ’Nelder-Mead’ :ref:`(see here) <optimize.minimize-neldermead>` - ’Powell’ :ref:`(see here) <optimize.minimize-powell>` - ’CG’ :ref:`(see here) <optimize.minimize-cg>` - ’BFGS’ :ref:`(see here) <optimize.minimize-bfgs>` - ’Newton-CG’ :ref:`(see here) <optimize.minimize-newtoncg>` - ’L-BFGS-B’ :ref:`(see here) <optimize.minimize-lbfgsb>` - ’TNC’ :ref:`(see here) <optimize.minimize-tnc>` - ’COBYLA’ :ref:`(see here) <optimize.minimize-cobyla>` - ’SLSQP’ :ref:`(see here) <optimize.minimize-slsqp>` - ’trust-constr’:ref:`(see here) <optimize.minimize-trustconstr>` - ’dogleg’ :ref:`(see here) <optimize.minimize-dogleg>` - ’trust-ncg’ :ref:`(see here) <optimize.minimize-trustncg>` - ’trust-exact’ :ref:`(see here) <optimize.minimize-trustexact>` - ’trust-krylov’ :ref:`(see here) <optimize.minimize-trustkrylov>` - custom - a callable object (added in version 0.14.0), see below for description. If not given, chosen to be one of ``BFGS``, ``L-BFGS-B``, ``SLSQP``, depending if the problem has constraints or bounds.jac : {callable, ’2-point’, ’3-point’, ’cs’, bool}, optional Method for computing the gradient vector. Only for CG, BFGS, Newton-CG, L-BFGS-B, TNC, SLSQP, dogleg, trust-ncg, trust-krylov, trust-exact and trust-constr. If it is a callable, it should be a function that returns the gradient vector: ``jac(x, *args) -> array_like, shape (n,)`` where x is an array with shape (n,) and `args` is a tuple with the fixed parameters. Alternatively, the keywords {’2-point’, ’3-point’, ’cs’} select a finite difference scheme for numerical estimation of the gradient. Options ’3-point’ and ’cs’ are available only to ’trust-constr’. If `jac` is a Boolean and is True, `fun` is assumed to return the gradient along with the objective function. If False, the gradient will be estimated using ’2-point’ finite difference estimation.

需要注意的是fun關(guān)鍵詞參數(shù)里面的函數(shù)，需要把優(yōu)化的theta放在第一個位置，X,y，放到后面。并且，theta在傳入的時候一定要是一個一維shape（n,）的數(shù)組，不然會出錯。

然后jac是梯度，這里的有兩個地方要注意，第一個是傳入的theta依然要是一個一維shape(n,)，第二個是返回的梯度也要是一個一維shape(n,)的數(shù)組。

總之，關(guān)鍵在于傳入的theta一定要是一個1D shape(n,)的，不然就不行。我之前為了方便已經(jīng)把theta塑造成了一個（n,1）的列向量，導致使用minimize時會報錯。所以，學會用help看說明可謂是相當重要啊~

import numpy as npimport pandas as pdimport scipy.optimize as op def LoadData(filename): data=pd.read_csv(filename,header=None) data=np.array(data) return data def ReshapeData(data): m=np.size(data,0) X=data[:,0:2] Y=data[:,2] Y=Y.reshape((m,1)) return X,Y def InitData(X): m,n=X.shape initial_theta = np.zeros(n + 1) VecOnes = np.ones((m, 1)) X = np.column_stack((VecOnes, X)) return X,initial_theta def sigmoid(x): z=1/(1+np.exp(-x)) return z def costFunction(theta,X,Y): m=X.shape[0] J = (-np.dot(Y.T, np.log(sigmoid(X.dot(theta)))) - np.dot((1 - Y).T, np.log(1 - sigmoid(X.dot(theta))))) / m return J def gradient(theta,X,Y): m,n=X.shape theta=theta.reshape((n,1)) grad=np.dot(X.T,sigmoid(X.dot(theta))-Y)/m return grad.flatten() if __name__==’__main__’: data = LoadData(’ex2data1csv.csv’) X, Y = ReshapeData(data) X, initial_theta = InitData(X) result = op.minimize(fun=costFunction, x0=initial_theta, args=(X, Y), method=’TNC’, jac=gradient) print(result)

最后結(jié)果如下，符合MATLAB里面用fminunc優(yōu)化的結(jié)果（fminunc:cost:0.203,theta:-25.161,0.206,0.201）

fun: array([0.2034977]) jac: array([8.95038682e-09, 8.16149951e-08, 4.74505693e-07]) message: ’Local minimum reached (|pg| ~= 0)’ nfev: 36 nit: 17 status: 0 success: True x: array([-25.16131858, 0.20623159, 0.20147149])

此外，由于知道cost在0.203左右，所以我用最笨的梯度下降試了一下，由于后面實在是太慢了，所以設(shè)置while J>0.21，循環(huán)了大概13W次。。可見，使用集成好的優(yōu)化算法是多么重要。。。還有，在以前的理解中，如果一個學習速率不合適，J會一直發(fā)散，但是昨天的實驗發(fā)現(xiàn)，有的速率開始會發(fā)散，后面還是會收斂。

以上這篇基于Python fminunc 的替代方法就是小編分享給大家的全部內(nèi)容了，希望能給大家一個參考，也希望大家多多支持好吧啦網(wǎng)。