我试图在 Keras 中实现一个回归模型，但无法弄清楚如何计算我的模型的分数，即它在我的数据集上的表现如何。

import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.cross_validation import cross_val_score, KFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline


## Load the dataset
dataframe = pd.read_csv("housing.csv", delim_whitespace=True,header=None)
dataset = dataframe.values
X_train = dataset[:400,0:13]
Y_train = dataset[:400,13]
X_test = dataset[401:,0:13]
Y_test = dataset[401:,13]

##define base model
def base_model():
     model = Sequential()
     model.add(Dense(14, input_dim=13, init='normal', activation='relu'))
     model.add(Dense(7, init='normal', activation='relu'))
     model.add(Dense(1, init='normal'))
     model.compile(loss='mean_squared_error', optimizer = 'adam')
     return model

seed = 7
np.random.seed(seed)

scale = StandardScaler()
X_train = scale.fit_transform(X_train)
X_test = scale.fit_transform(X_test)

clf = KerasRegressor(build_fn=base_model, nb_epoch=100, batch_size=5,verbose=0)

clf.fit(X_test,Y_test)
res = clf.predict(X_test)

## line below throws an error
clf.score(Y_test,res)

请告诉我如何获得我的模型的分数以及我在上面的代码中犯了什么错误。