Using Tensorflow Huber loss in Keras

Question

I am trying to use huber loss in a keras model (writing DQN), but I am getting bad result, I think I am something doing wrong. My is code is below.

model = Sequential()
model.add(Dense(output_dim=64, activation='relu', input_dim=state_dim))
model.add(Dense(output_dim=number_of_actions, activation='linear'))
loss = tf.losses.huber_loss(delta=1.0)
model.compile(loss=loss, opt='sgd')
return model

Answer 1

You can wrap Tensorflow's tf.losses.huber_loss in a custom Keras loss function and then pass it to your model.

The reason for the wrapper is that Keras will only pass y_true, y_pred to the loss function, and you likely want to also use some of the many parameters to tf.losses.huber_loss. So, you'll need some kind of closure like:

def get_huber_loss_fn(**huber_loss_kwargs):

    def custom_huber_loss(y_true, y_pred):
        return tf.losses.huber_loss(y_true, y_pred, **huber_loss_kwargs)

    return custom_huber_loss

# Later...
model.compile(
    loss=get_huber_loss_fn(delta=0.1)
    ...
)