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I would like to predict the relationship between two independent variables and a dependent one (model1):

x1 + x2 = y

Now, x1 is a sequence of vectors (a document composed of a sequence-of-sentences) and x2 is just a binary variable. y is also binary.

In the end I would like to compare this model to another simplified version of the model (model2):

x1 = y

To compare which model is better, I would like to use the F-Score.

I use python Keras to see how well my model1 works:

input  = Input(shape=(sentences_per_doc, maximum_sequence_length))
lstm_out = Bidirectional(LSTM(50, activation='tanh', return_sequences=True))(input  )
sent_dense = Dense(100, activation='relu', name='sent_dense')(lstm_out) 
sent_drop = Dropout(0.5,name='sent_dropout')(sent_dense)
prediction = Dense(1, activation='softmax',name='output')(sent_drop)

But since the Input to my model1 is a vector-sequence, how do I add binary variable x2 to my model to get model2?

I considered adding the binary variable to each sequence-vector (e.g. seq_vec=[1,2,3,4] + binary_x2_i=[1] = new_seq=[1,2,3,4,1]) but I dont know if this makes sense.. cause each sample i then contains binary_x2_i

Pls, add some thougth to this, thanks!

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