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.
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= = 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!