Predicting Multiple Outputs one after another in Tensorflow-CodePudding

I want to create a model which can predict two outputs. I did some research and I found that there's a

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

CodePudding user response：

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

CodePudding user response：

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.