kfold_verification.py

# MLP for Pima Indians Dataset with 10-fold cross validation
from keras.models import Sequential
from keras.layers import Dense
from sklearn.model_selection import StratifiedKFold
import numpy

# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load pima indians dataset
FILENAME = '../data/pima-indians-diabetes.csv'

dataset = numpy.loadtxt(FILENAME, delimiter=',')

# split into input (X) and output (Y) variables
X = dataset[:, 0:8]
Y = dataset[:, 8]
# define 10-fold cross validation test harness
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)
cvscores = []
for train, test in kfold.split(X, Y):
    # create model
    model = Sequential()
    model.add(Dense(12, input_dim=8, kernel_initializer='uniform', activation='relu'))
    model.add(Dense(8, kernel_initializer='uniform', activation='relu'))
    model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))
    # Compile model
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])  # Fit the model
    model.fit(X[train], Y[train], nb_epoch=150, batch_size=10, verbose=0)
    # evaluate the model
    scores = model.evaluate(X[test], Y[test], verbose=0)
    print("%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
    cvscores.append(scores[1] * 100)
    print("%.2f%% (+/- %.2f%%)" % (numpy.mean(cvscores), numpy.std(cvscores)))