def create_model():
    '''Neural network with 3 hidden layers'''
    model = Sequential()
    model.add(Dense(256, input_dim=300, activation='relu', kernel_initializer='normal'))
    model.add(Dropout(0.3))
    model.add(Dense(256, activation='relu', kernel_initializer='normal'))
    model.add(Dropout(0.5))
    model.add(Dense(80, activation='relu', kernel_initializer='normal'))
    model.add(Dense(2, activation="softmax", kernel_initializer='normal'))

    # gradient descent
    sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)

    # configure the learning process of the model
    model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
    return model