# 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