How to pass 2 features to LSTM , one of them is one-hot-encoded with Keras? I have a very simple LSTM model 

model = Sequential()
model.add(LSTM(64, input_shape=(seq_length, X_train.shape[2]) , return_sequences=True))
model.add(Dense(y_cat_train.shape[2], activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_cat_train, epochs=100, batch_size=10, verbose=2)


The input X_train has 2 feature , one is categorical (values 1-4) and the other is numeric (values 1-100). There are 4 classes in y_test that I one-hot-encoded with keras's to_categorical . 


Should I encode the categorical input feature as well ? If I do , how can I pass it along with the other feature ? (e.g. now a timestep looks like this for example: [1,44])
Later , I would like to take make a sampling , meaning I need to take the predicted y_hat&lt;t-1&gt; and pass it as x&lt;t&gt; . I will have to pass the second numeric feature (1-100) along with it. How can it be done ?


EDIT : note that I do not want my numeric feature to become categorical since there is importance to the values (meaning 2&lt;10&lt;90 etc)
(1) Yes, it is a common practice to encode the categorical feature by one-hot encoding, for example, encode [1,44] as [1,0,0,0,44], encode [2,44] as [0,1,0,0,44], etc.

(2) Same as (1), just concatenate the one-hot encoded categorical feature and the numerical feature(s).