Custom loss functions in Keras with penalty depending on the values ​of y_pred and y_true

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I need a custom loss functions in Keras for a regression problem. I have to predict two values ​​(y1, y2) but I want to penalize the error if:

if y1_pred > v1 and y1_true < v1:
or
if y2_pred < v2 and y2_true > v2:

I need something similar to:

if y1_pred > v1 and y1_true < v1:
    p = 1 + (k * (y1_pred-y1_true))
    K.mean(K.square(y1_pred-y1_true) * p)
else:
    K.mean(K.square(y1_pred-y1_true))


if y2_pred < v2 and y2_true > v2:
    p = 1 + (k * (y2_true-y2_pred))
    K.mean(K.square(y2_pred-y2_true) * p)
else:
    K.mean(K.square(y2_pred-y2_true))

v1, v2 and k are constants.

keras tensorflow python
1 Answers

Try tf.where:

import tensorflow as tf

def custom_loss1(v1 = 0.7, v2 = 1, k =0.5):
  def combined_loss(y1_true, y1_pred): 
    return tf.where(tf.logical_and(tf.greater(y1_pred, v1), tf.less(y1_true, v1)),
                    tf.reduce_mean(tf.math.square(y1_pred - y1_true) * (1 + (k * (y1_pred - y1_true)))),
                    tf.reduce_mean(tf.math.square(y1_pred - y1_true)))
  return combined_loss


def custom_loss2(v1 = 0.7, v2 = 1, k =0.5):
  def combined_loss(y2_true, y2_pred):
    return tf.where(tf.logical_and(tf.less(y2_pred, v2), tf.greater(y2_true, v2)),
                    tf.reduce_mean(tf.math.square(y2_pred-y2_true) * (1 + (k * (y2_true - y2_pred)))),
                    tf.reduce_mean(tf.math.square(y2_pred-y2_true)))
  return combined_loss

inputs = tf.keras.layers.Input((5,))
x = tf.keras.layers.Dense(1, activation = 'relu', name='loss1')(inputs)
y = tf.keras.layers.Dense(1, activation = 'tanh', name='loss2')(inputs)

model = tf.keras.Model(inputs, [x, y])
model.compile(optimizer='adam', loss = {'loss1': custom_loss1(), 'loss2': custom_loss2()})
model.fit(tf.random.normal((10, 5)), [tf.random.normal((10, 1)), tf.random.normal((10, 1))], batch_size=2, epochs=5)
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