How to perform multiclass multioutput classification using lstm

Question

I have multiclass multioutput classification (see https://scikit-learn.org/stable/modules/multiclass.html for details). In other words, my dataset looks as follows.

node_name, timeseries_1, timeseries_2, label_1, label_2
node1, [1.2, ...], [1.8, ...], 0, 2
node2, [1.0, ...], [1.1, ...], 1, 1
node3, [1.9, ...], [1.2, ...], 0, 3 
...
...
...

So, my label_1 could be either 0 or 1, whereas my label_2 could be either 0, 1, or 2.

My current code is as follows.

def create_network():
    model = Sequential()
    model.add(LSTM(200, input_shape=(16,2)))
    model.add(Dense(100))
    model.add(Dropout(0.2))
    model.add(Dense(3, activation='softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

    return model

neural_network = KerasClassifier(build_fn=create_network, epochs=100, batch_size=100, verbose=0)

k_fold = StratifiedKFold(n_splits=10, shuffle=True, random_state=0)

scores = cross_validate(neural_network, my_features, label_data_encoded, cv=k_fold, scoring = ('accuracy', 'precision_weighted', 'recall_weighted', 'f1_weighted', 'roc_auc'))

My questions are as follows.

• Since I have two labels (i.e. label_1 and label_2), how to fit these labels to lstm model? Do I have to do something like keras.utils.to_categorical(label_1, 2) and keras.utils.to_categorical(label_2, 3)?
• How to change the model in order to make it suitable for multiclass multioutput classification?

I am happy to provide more details if needed.

Answer 1

If I understand correctly, label_1 is binary, whereas label_2 is a multiclass problem, so we need the model to have two outputs with separate loss functions; binary and categorical crossentropy respectively.

However, Sequential API does not allow multiple input/output.

The Sequential API allows you to create models layer-by-layer for most problems. It is limited in that it does not allow you to create models that share layers or have multiple inputs or outputs.

You can use the functional API to create two output layers, and compile the model with required loss functions.

X=Input(input_shape)
X=Layer(X)
'
'
'
'
out1=Dense(1, activation='sigmoid')(X)
out2=Dense(3, activation='softmax')(X)
model = Model(inputs = input, outputs = [out1,out2])
model.compile(loss = ['binary_crossentropy','categorical_crossentropy'], loss_weights = [l1,l2], ...)

model.fit(input,[label_1, label_2_toCategotical]

The loss that the network will minimize will be the weighted sum of the 2 losses, weighted by l1 and l2.

Hope this helps :)