Specify multiple loss function for model compilation in Keras

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I want to specify 2 loss functions 1 for the object class which is cross-entropy and the other for the bounding box which is mean squared error. how to specify in model.compile each output with the corresponding loss function?

model = Sequential()

model.add(Dense(128, activation='relu'))
out_last_dense = model.add(Dense(128, activation='relu'))
object_type = model.add(Dense(1, activation='softmax'))(out_last_dense)
object_coordinates = model.add(Dense(4, activation='softmax'))(out_last_dense)

/// here is the problem i want to specify loss function for object type and coordinates
model.compile(loss= keras.losses.categorical_crossentropy,
   optimizer= 'sgd', metrics=['accuracy'])
keras tensorflow deep-learning python
1 Answers

First of all, you can't use Sequential API here since your model has two output layers (i.e. what you have written is all wrong and would raise error). Instead you must use Keras Functional API:

inp = Input(shape=...)
x = Dense(128, activation='relu')(inp)
x = Dense(128, activation='relu')(x)
object_type = Dense(1, activation='sigmoid', name='type')(x)
object_coordinates = Dense(4, activation='linear', name='coord')(x)

Now, you can specify a loss function (as well as metric) for each output layer based on their names given above and using a dictionary:

model.compile(loss={'type': 'binary_crossentropy', 'coord': 'mse'}, 
              optimizer='sgd', metrics={'type': 'accuracy', 'coord': 'mae'})

Further, note that you are using softmax as the activation function and I have changed it to sigomid and linear above. That's because: 1) using softmax on a layer with one unit does not make sense (if there are more than 2 classes then you should use softmax), and 2) the other layer predicts coordinates and therefore using softmax is not suitable at all (unless the problem formulation let you do so).

user_20996070
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