数据挖掘 - 以 99% 训练且验证拆分准确率不超过 70% - 吾爱随笔录

我正在训练一个模型，该模型在训练设置上的准确率高达 99%
，但验证拆分测试增加不超过 70-72%

这就是我的模型的配置方式：

model = Sequential()
#model.add(TimeDistributed(Dense(64, activation='linear')))
#model.add(GRU(512, return_sequences=True, activation='linear',))
model.add(LSTM(128, return_sequences=True, activation='linear'))
model.add(Conv1D(64, 64, strides=1, padding='same'))
model.add(Dense(128, activation="linear"))#, kernel_regularizer=regularizers.l1_l2(l1=1e-4, l2=1e-6)
model.add(Dropout(0.2))


#model.add(Conv1D(16, 16, strides=1, padding='same'))
#model.add(TimeDistributed(Dense(32, activation='linear')))
#model.add(GRU(512, return_sequences=True, activation='linear'))
model.add(LSTM(128, return_sequences=True, activation='linear'))
model.add(Conv1D(64, 64, strides=1, padding='same'))
model.add(Dense(128, activation="linear"))#,kernel_regularizer=regularizers.l1_l2(l1=1e-4, l2=1e-6)
model.add(Dropout(0.2))
#model.add(BatchNormalization())

model.add(Dense(1,activation='linear'))

model.compile(optimizer='adamax',loss="mse",metrics=['accuracy'])
model.fit(X_train,y_train,epochs=8000, batch_size=256, verbose=1, validation_split=0.1, callbacks=callback)

可能是什么问题？