Train at 99% and Validation split accuracy is not more than 70% I am training a model that get accuracy on train set up to 99%
but the validation split test not increase more than 70-72%
this is how my model is configurated:
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=&quot;linear&quot;))#, 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=&quot;linear&quot;))#,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=&quot;mse&quot;,metrics=['accuracy'])
model.fit(X_train,y_train,epochs=8000, batch_size=256, verbose=1, validation_split=0.1, callbacks=callback)

what can be the issue?
This means your model is overfitting the train set. Try simplifying the model by reducing the number of neurons in each layer. That will reduce your train accuracy but may allow your validation accuracy to increase.
You know that you are not overfitting when both train and validation accuracy hover around the same value.