Creating combo loss in tensorflow models for a particular output layer

TensorFlow
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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.

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4

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

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