def prepare_model(ninputs=9600,n_feats=47, nclass=5):
    """
    Set up and compile the model architecture (Logistic regression)
    """
    inp1 = Input(shape=(ninputs,))
    inp2 = Input(shape=(n_feats,))
    out_neurons1 = 50
    out_neurons2 = 20
    out_neurons2 = 10
    m1 = Dense(input_dim=ninputs, output_dim=out_neurons1,activation='sigmoid')(inp1)
    m2 = Dense(input_dim=ninputs, output_dim=out_neurons1,activation='softmax')(inp2)
    
    m1 = Dense(input_dim=ninputs, output_dim=out_neurons2,activation='sigmoid')(m1)
    m2 = Dense(input_dim=ninputs, output_dim=out_neurons2,activation='softmax')(m2)
    
    #m1 = Dense(input_dim=ninputs, output_dim=out_neurons2,activation='sigmoid')(m1)
    #m2 = Dense(input_dim=ninputs, output_dim=out_neurons2,activation='softmax')(m2)
    
    m = Merge(mode='concat')([m1,m2])
    
    #mul = Multiply()([m1,m2])
    #add = Abs()([m1,m2])
    #m = Merge(mode='concat')([mul,add])
    
    score = Dense(output_dim=nclass,activation='softmax')(m)
    model = Model([inp1,inp2],score)
    model.compile(loss='categorical_crossentropy', optimizer='adam')
    return model
    '''
    lrmodel = Sequential()
    lrmodel.add(Dense(input_dim=ninputs, output_dim=nclass))
    #lrmodel.add(Activation('softmax'))
    #lrmodel.compile(loss='categorical_crossentropy', optimizer='adam')
    
    #return lrmodel
    
    model_feat = Sequential()
    model_feat.add(Dense(input_dim=27, output_dim=nclass))
    merge_model = Sequential()
    merge_model.add(Merge([lrmodel, model_feat], mode='concat'))
    merge_model.add(Dense(output_dim=nclass))
    merge_model.add(Activation('softmax'))
    merge_model.compile(loss='categorical_crossentropy', optimizer='adam')
    return merge_model'''
    
    '''lrmodel.add(Dense(input_dim=ninputs, output_dim=1000,activation = 'relu'))
    lrmodel.add(Dropout(0.5))
    lrmodel.add(Dense(output_dim=500,activation = 'relu'))
    lrmodel.add(Dropout(0.5))
    lrmodel.add(Dense(output_dim=nclass))'''
    #return merge_model

    Set up and compile the model architecture (Logistic regression)
    
fake news challenge (FNC-1)/eval_sick.py