from keras.models import Modelfrom keras.layers import *#inp is a "tensor", that can be passed when calling other layers to produce an output inp = Input((10,)) #supposing you have ten numeric values as input#here, SomeLayer() is defining a layer, #and calling it with (inp) produces the output tensor xx = SomeLayer(blablabla)(inp) x = SomeOtherLayer(blablabla)(x) #here, I just replace x, because this intermediate output is not interesting to keep#here, I want to keep the two different outputs for defining the model#notice that both left and right are called with the same input x, creating a forkout1 = LeftSideLastLayer(balbalba)(x) out2 = RightSideLastLayer(banblabala)(x)#here, you define which path you will follow in the graph you've drawn with layers#notice the two outputs passed in a list, telling the model I want it to have two outputs.model = Model(inp, [out1,out2])model.compile(optimizer = ...., loss = ....) #loss can be one for both sides or a list with different loss functions for out1 and out2model.fit(inputData,[outputYLeft, outputYRight], epochs=..., batch_size=...)
