# fix random seed for reproducing same output
seed = 10
numpy.random.seed(seed)
# load pima indians dataset
dataset = numpy.loadtxt("pima-indians-diabetes.csv", 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_folds=10, shuffle=True, random_state=seed)
cvscores = []
for train, test in kfold.test_folds(X, Y):
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# Compile Model
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# Fit the model
model.fit(X[train], Y[train], epochs=150, batch_size=10, verbose=0)
# Evaluate the model
