Creating combo loss in tensorflow models for a particular output layer

TensorFlow
,
1

I have a CNN model with a single output neuron consisting of sigmoid activation, hence its value is in between 0 and 1. I wanted to calculate a combination of loss for this particular output neuron.

I was using Mean Absolute Error and Mean Squared Error for the same, and creating a loss like this:


loss = tf.keras.losses.MeanAbsoluteError() + tf.keras.losses.MeanSquaredError()

Now, due to some issue, the tensorflow framework is not supporting loss function like this. Here is the error:


Traceback (most recent call last):
  File "run_kfold.py", line 189, in <module>
    loss = tf.keras.losses.MeanAbsoluteError() + tf.keras.losses.MeanSquaredError()
TypeError: unsupported operand type(s) for +: 'MeanAbsoluteError' and 'MeanSquaredError'

Can anyone suggest how to calculate combo loss for a certain output layer. This will help to create multiple weighted losses in combination, like this:


l_1 = 0.6
l_2 = 0.4
loss = l_1 * tf.keras.losses.MeanAbsoluteError() + l_2 *tf.keras.losses.MeanSquaredError()

I can then pass this loss variable to the model.compile() function


model.compile(optimizer=opt, 
                  loss=loss,
                  metrics = ['accuracy', sensitivity, specificity, tf.keras.metrics.RootMeanSquaredError(name='rmse')]
                )
3

Try this

def custom_loss(y_true, y_pred):

   l_1 = 0.6
   l_2 = 0.4

  return tf.math.add(
      tf.keras.metrics.mean_absolute_error(y_true, y_pred) * l_1, 
      tf.keras.metrics.mean_squared_error(y_true, y_pred) * l_2
  )

model.compile(loss=custom_loss)

Here is a similar end-to-end example.

1 Like
4

@innat it works like a charm… Thanks a lot!

1 Like