network = models.Sequential()
network.add(layers.Dense(units=64, activation='relu', input_shape=(len(features.columns),)))
network.add(layers.Dense(units=32, activation='relu'))
network.add(layers.Dense(units=1, activation='sigmoid'))

network.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

es = EarlyStopping(monitor='val_loss', mode='min', verbose=0, patience=500)
mc = ModelCheckpoint('data/best_model.h5', monitor='val_loss', mode='min', verbose=2, save_best_only=True)

history = network.fit(train_features, train_target,
            epochs=1000, verbose=0, batch_size=128,
            validation_data=(test_features, test_target), callbacks=[es, mc])

saved_model = load_model('data/best_model.h5')