convolutional_sparseFiltering.py 文件源码

def cross_validate_Softmax(dataFile, X, Y, pooledFile, imageDim, sgd, save=True, n_folds=5):

    from sklearn.cross_validation import KFold

    m = len(np.squeeze(Y))
    CGrid = [0.1, 0.03, 0.01, 0.003, 0.001, 3e-4, 1e-4, 3e-5, 1e-5]
    kf = KFold(m, n_folds=n_folds)
    mean_FoMs = []
    for C in CGrid:
        fold = 1
        FoMs = []
        for train, test in kf:
            print("[+] training Softmax: LAMBDA : %e, fold : %d" % (C, fold))
            prefix = "cv/cv_fold%d" % fold
            FoM, threshold = train_Softmax(C, dataFile, X[train], Y[train], X[test], Y[test], \
                                             pooledFile, imageDim, sgd, prefix=prefix)
            FoMs.append(FoM)
            fold += 1
        mean_FoMs.append(np.mean(FoMs))

    best_FoM_index = np.argmin(mean_FoMs)
    print("[+] Best performing classifier: C : %lf" % CGrid[best_FoM_index])
    return CGrid[best_FoM_index]