Get classes for multi-output model in Keras

Question

I am utilising Keras 2+8 Functional API to both solve classification and regression problem at the same time. I do not know how to assign labels from classification output as I get probabilities. There is no predict_classes for Functional API. I would welcome suggestions.

def run (X_train):
    _input = keras.layers.Input(shape=(1024,))

    hidden1=Dense(500, activation = 'elu')(_input)

    hidden2=Dense(300, activation = 'elu')(hidden1)

    classification = keras.layers.Dense(1, activation="sigmoid", name="classification")(hidden2)

    regression = keras.layers.Dense(1, activation="linear", name="regression")(hidden2)

    multi_model = keras.Model(inputs=[_input], outputs=[classification, regression])
    
    multi_model.compile(loss={'classification': 'binary_crossentropy','regression': 'mse'},
                        optimizer='Nadam',
                        metrics={'classification':'AUC', 'regression': 'mse'})
   
    multi_model.fit([X_train, X_train],
                    [y_train_C, y_train_R],
                    validation_split=0.2,
                    callbacks=callbacks,
                    batch_size=128,
                    epochs=500,
                    verbose=0)
    return multi_model

This is how I predict with trained model:

prediction = fcfp4.predict([X_test,X_test])

I have tried using argmax, but it provides me with only 0 values (should be either 0 or 1). According to evaluate I should get very good classification prediction:

fcfp4.evaluate([X_test,X_test], [y_test_C, y_test_R])
1/1 [==============================] - 0s 998us/step - loss: 2.0826 - classification_loss: 0.0845 - regression_loss: 1.9981 - classification_auc_55: 1.0000 - regression_mean_squared_error: 1.9981

I am expecting such array:

array([0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0])

But getting only 0's

Answer 1

to obtain the prediction from sigmoid you simply have to consider as class 1 if pred is above 0.5 otherwise is 0. here the full example

n_sample = 100
features = 1024
X_train = np.random.uniform(0,1, (n_sample,features))
y_train_R = np.random.uniform(0,1, n_sample)
y_train_C = np.random.randint(0,2, n_sample)

def run(X_train, y_train_C, y_train_R):
    
    _input = keras.layers.Input(shape=(features,))

    hidden1 = keras.layers.Dense(500, activation = 'elu')(_input)

    hidden2 = keras.layers.Dense(300, activation = 'elu')(hidden1)

    classification = keras.layers.Dense(1, activation="sigmoid", name="classification")(hidden2)

    regression = keras.layers.Dense(1, activation="linear", name="regression")(hidden2)

    multi_model = keras.Model(inputs=[_input], outputs=[classification, regression])
    
    multi_model.compile(loss={'classification': 'binary_crossentropy','regression': 'mse'},
                        optimizer='Nadam',
                        metrics={'classification':'AUC', 'regression': 'mse'})
   
    multi_model.fit(X_train,
                    [y_train_C, y_train_R],
                    validation_split=0.2,
                    batch_size=128,
                    epochs=5,
                    verbose=1)
    
    return multi_model

multi_model = run(X_train, y_train_C, y_train_R)
prediction_class, prediction_reg = multi_model.predict(X_train)
prediction_class = (prediction_class>0.5).ravel()+0