classifiers.py 文件源码

def rede_neural(X, y):
    print("Iniciando treinamento da Rede Neural")

    X2 = normalize(X)

    clf = MLPClassifier(hidden_layer_sizes=(100,50), activation='tanh', algorithm='adam', alpha=1e-5,
                        learning_rate='constant',tol=1e-8,learning_rate_init=0.0002,
                        early_stopping=True,validation_fraction=0.2)

    kf = KFold(len(y),n_folds=3)
    i = 0
    for train,test in kf:
        start = time.time()
        i = i + 1
        print("Treinamento",i)

        # dividindo dataset em treino e test
        #X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.4, random_state=1)
        X_train, X_test, y_train, y_test = X2[train], X2[test], y[train], y[test]

        # fit
        clf.fit(X_train, y_train)
        print("score:",clf.score(X_test, y_test),"(",(time.time()-start)/60.0,"minutos )")
    return clf