Feed input to a keras model only to be accessed by a custom loss function

Question

Is there a way to pass a feature to a keras model as an input only to be accessed by a custom loss function without affecting the model as an input feature? I only need the feature to calculate the loss, not to feed-forward through the hidden layers in the network. (Basically what I want is to feed the feature in as an input and extract it as it is as an output along with y_pred to be accessed in the loss function). A worked example would be much appreciated.

Answer 1

If you are writing your custom loss, you could use pass the feature as an input, and then using a Lambda layer, you can make it bypass the network and directly concatenate at the end. Something like the following -

from tensorflow.keras import layers, Model, utils

inp = layers.Input((11,))
x = layers.Lambda(lambda x: x[:,:-1])(inp)
o2 = layers.Lambda(lambda x: x[:,-1:])(inp)
x = layers.Dense(20)(x)
x = layers.Dense(20)(x)
o1 = layers.Dense(1)(x)
out = layers.concatenate([o1, o2])

model = Model(inp, out)


def custom_loss(outputs, actuals):
    ...

utils.plot_model(model, show_shapes=True, show_layer_names=False)

Here the first 10 features are the ones you want to pass via the network, and the last feature is the one you just want as is, for the custom loss. The final output is going to just be a concatenation of your expected output for the first 10 features via the network + the untouched feature.

If you want to know how to write a custom loss, please check this excellent SO post that explains it.