RNN : understanfingConcatenating layers

Question

I am trying to understanding concatenating of layers in tensorflow keras. Below I have drew what I think is the concatenation of 2 RNN layers [ Spare for picture clarity] and the output

Here I am trying to concatenate two RNN layers. One layer has longitudinal data[ integer valued ] of patients in some time sequence and other layer has again details of same patients of other time sequence with categorical input.

I don't want these two different time sequences to be mixed up since it is medical data. So I am trying this. But before that I want to be sure if what I have drawn is what concatenating of two layers means. Below is my code. It appears to work well but I want to confirm if my what i drew and what is implemented are correct .

#create simpleRNN with one sequence of input

first_input = Input(shape=(4, 7),dtype='float32')
simpleRNN1 = layers.SimpleRNN(units=25,bias_initializer= initializers.RandomNormal(stddev=0.0001),
                           activation="relu",kernel_initializer= "random_uniform")(first_input)

#another layer of RNN
second_input = Input(shape=(16,1),dtype='float32')
simpleRNN2 = layers.SimpleRNN(units=25,bias_initializer= initializers.RandomNormal(stddev=0.0001),
                           activation="relu",kernel_initializer= "random_uniform")(second_input)

#concatenate two layers,stack dense layer on top
concat_lay = tf.keras.layers.Concatenate()([simpleRNN1, simpleRNN2]) 
dens_lay = layers.Dense(64, activation='relu')(concat_lay)
dens_lay = layers.Dense(32, activation='relu')(dens_lay)
dens_lay = layers.Dense(1, activation='sigmoid')(dens_lay)

model = tf.keras.Model(inputs=[first_input, second_input], outputs= [dens_lay])
model.compile(loss='binary_crossentropy', optimizer='adam',metrics=["accuracy"],lr=0.001)
model.summary()

Answer 1

Concatenation means 'chaining together' or 'unification' here, making a union of two enities.

i think your problem is addressed in https://datascience.stackexchange.com/questions/29634/how-to-combine-categorical-and-continuous-input-features-for-neural-network-trai (How to combine categorical and continuous input features for neural network training)

If you have biomedical data, i.e. ECG, as the continuous data and diagnoses as the categorical data i would consider ensemble learning as the best ansatz.

What is the best solution here depends on the details of your problem ...

Building an ensembleof two neural nets is described in https://machinelearningmastery.com/ensemble-methods-for-deep-learning-neural-networks/