classifier = Sequential()

classifier.add(Dense(units = 14, kernel_initializer = 'uniform', activation = 'relu', input_dim = 27))

classifier.add(Dense(units = 14, kernel_initializer = 'uniform', activation = 'relu'))

classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))

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

classifier.fit(X_train, y_train, batch_size = 25, epochs = 500)