test.py

import numpy
import pandas
from keras.layers import Dense
from keras.models import Sequential
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score


# define base model
def baseline_model():
    # create model
    model = Sequential()
    model.add(Dense(13, input_dim=13, kernel_initializer='normal', activation='relu'))
    model.add(Dense(1, kernel_initializer='normal'))
    # Compile model
    model.compile(loss='mean_squared_error', optimizer='adam')
    return model


if __name__ == '__main__':
    # load dataset
    dataframe = pandas.read_csv("housing.csv", delim_whitespace=True, header=None)
    dataset = dataframe.values
    # split into input (X) and output (Y) variables
    X = dataset[:, 0:13]
    Y = dataset[:, 13]

    # fix random seed for reproducibility
    seed = 7
    numpy.random.seed(seed)
    # evaluate model with standardized dataset
    estimator = KerasRegressor(build_fn=baseline_model, epochs=100, batch_size=5, verbose=0)
    kfold = KFold(n_splits=10, random_state=seed)
    results = cross_val_score(estimator, X, Y, cv=kfold)

    print("Results: %.2f (%.2f) MSE" % (results.mean(), results.std()))