Can we use multiple loss functions in same layer?

Can we use mulitple loss function in this architecture: I have two different type of loss functions and want to use it on last layer [Output] loss functions :

• binary_crossentropy
• custom loss function

Can we do that?

yes you can... you simply have to repeat 2 times the model output in the model definition. you can also merge your loss in a different way using loss_weights params (default is [1,1] for two losses). Below an example in a dummy regression problem. https://colab.research.google.com/drive/1SVHC6RuHgNNe5Qj6IOtmBD5geAJ-G9-v?usp=sharing

def rmse(y_true, y_pred):

    error = y_true-y_pred

    return K.sqrt(K.mean(K.square(error)))


X1 = np.random.uniform(0,1, (1000,10))
X2 = np.random.uniform(0,1, (1000,10))
y = np.random.uniform(0,1, 1000)

inp1 = Input((10,))
inp2 = Input((10,))
x = Concatenate()([inp1,inp2])
x = Dense(32, activation='relu')(x)
out = Dense(1)(x)

m = Model([inp1,inp2], [out,out])
m.compile(loss=[rmse,'mse'], optimizer='adam') # , loss_weights=[0.3, 0.7]
history = m.fit([X1,X2], [y,y], epochs=10, verbose=2)