Predicting Multiple Outputs one after another in Tensorflow

Question

I want to create a model which can predict two outputs. I did some research and I found that there's a way to do it by creating two branches (for predicting two outputs) using functional API in Tensorflow Keras but I have a another approach in my mind which looks like this :

i.e. given a input, first I want to predict output1 and then based on that I want to predict output2.

So how can this can be done in Tensorflow ? Please let me know how the training will be done as well i.e. how I'll be to pass labels for each output1 and output2 and then calculate the loss as well. Thank you

Answer 1

You can do it with functional API of tensorflow. I write it in some sort of pseudo code:

Inputs = your_input
x = hidden_layers()(Inputs)
Output1 = Dense()(x)
x = hidden_layers()(Output1)
Output2 = Dense()(x)

So you can separate it to two models if it is what you desired:

model1 = tf.keras.models.Model(inputs=[Input], outputs=[Output1])
model2 = tf.keras.models.Model(inputs=[Input], outputs=[Output2])

Or have everything in one model:

model = tf.keras.models.Model(inputs=[Input], outputs=[Output2])
Output1_pred = model.get_layer('Output1').output

UPDATE:
In order to training model with two outputs, you can separate model to two parts and train each part separately as follow:

model1 = tf.keras.models.Model(inputs=[Input], outputs=[Output1])
model2 = tf.keras.models.Model(inputs=[model1.get_layer('Output1').output], outputs=[Output2])
model1.cmpile(...)
model1.fit(...)

for layer in model1.layers:
    layer.trainable = False

model2.compile(...)
model2.fit(...)

Answer 2

You can actually modify the great answer by @Mohammad to compose a unique model with two outputs.

Inputs = your_input
x = hidden_layers()(Inputs)
Output1 = Dense()(x)
x = hidden_layers()(Output1)
Output2 = Dense()(x)

model = tf.keras.models.Model(inputs=[Inputs], outputs=[Output1, Output2])
model.compile(loss=[loss_1, loss_2], loss_weights=[0.5, 0.5], optimizer=sgd, metrics=['accuracy'])

of course you can change weights, optimiser and metric according to your case.

Then the model has to be trained on data like (X, y1, y2) where (y1, y2) are output1 and output2 labels respectively.