分类中输入对输出的影响[已结束]

利用MLP神经网络设计了一个分类器。网络输入具有2000个特征,网络输出为6类。

我的代码:

train_X, test_X, train_Y, test_Y = train_test_split(X, Y, test_size=0.2, random_state=np.random.seed(7), shuffle=True)

train_X = np.reshape(train_X, (train_X.shape[0], 1, train_X.shape[1]))
test_X = np.reshape(test_X, (test_X.shape[0], 1, test_X.shape[1]))

model = Sequential()
model.add(LSTM(100, return_sequences=False, input_shape=(train_X.shape[1], train_X.shape[2])))
model.add(Dense(train_Y.shape[1], activation='softmax'))
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])

model.fit(train_X, train_Y, validation_split=.20,
                        epochs=1000, batch_size=50)

问题: 我怎样才能找出网络的哪些输入对每个类的分类影响最大?

有Python的示例吗?