/* Load libraries */
import numpy as np
from keras import models
from keras import layers
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score
from sklearn.datasets import make_classification

/* Set random seed */
np.random.seed(0)

/* Number of features */
number_of_features = 100

/* Generate features matrix and target vector */
features, target = make_classification(n_samples = 10000,
                                       n_features = number_of_features,
                                       n_informative = 3,
                                       n_redundant = 0,
                                       n_classes = 2,
                                       weights = [.5, .5],
                                       random_state = 0)

/* Create function returning a compiled network */
def create_network():
    
    /* Start neural network */
    network = models.Sequential()

    /* Add fully connected layer with a ReLU activation function */
    network.add(layers.Dense(units=16, activation='relu', input_shape=(number_of_features,)))

    /* Add fully connected layer with a ReLU activation function */
    network.add(layers.Dense(units=16, activation='relu'))

    /* Add fully connected layer with a sigmoid activation function */
    network.add(layers.Dense(units=1, activation='sigmoid'))

    /* Compile neural network */
    network.compile(loss='binary_crossentropy', /* Cross-entropy */
                    optimizer='rmsprop', /* Root Mean Square Propagation */
                    metrics=['accuracy']) /* Accuracy performance metric */
    
    /* Return compiled network */
    return network

/* Wrap Keras model so it can be used by scikit-learn */
neural_network = KerasClassifier(build_fn=create_network, 
                                 epochs=10, 
                                 batch_size=100, 
                                 verbose=0)

/* Evaluate neural network using three-fold cross-validation */
cross_val_score(neural_network, features, target, cv=3)